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Original Article What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?
Soo Hee Kim, MD1,a, Hee Jin Chang, MD, PhD1,2,, Dae Yong Kim, MD, PhD2, Ji Won Park, MD, PhD3, Ji Yeon Baek, MD, PhD2, Sun Young Kim, MD, PhD2, Sung Chan Park, MD, MS2, Jae Hwan Oh, MD, PhD2, Ami Yu, PhD4, Byung-Ho Nam, PhD4
Cancer Research and Treatment : Official Journal of Korean Cancer Association 2016;48(3):998-1009.
DOI: https://doi.org/10.4143/crt.2015.254
Published online: October 22, 2015

1Department of Pathology, Research Institute and Hospital, National Cancer Center, Goyang, Korea

2Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Korea

3Division of Colorectal Surgery, Department of Surgery, Seoul National University, Seoul, Korea

4Biometric Research Branch, Research Institute and Hospital, National Cancer Center, Goyang, Korea

Correspondence: Hee Jin Chang, MD, PhD  Department of Pathology, Research Institute and Hospital, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang 10408, Korea 
Tel: 82-31-920-1741 Fax: 82-31-920-1369 E-mail: heejincmd@yahoo.com
aPresent address: Anatomic Pathology Reference Lab, Seegene Medical Foundation, Seoul, Korea
• Received: July 14, 2015   • Accepted: September 20, 2015

Copyright © 2016 by the Korean Cancer Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Purpose
    Tumor regression grade (TRG) is predictive of therapeutic response in rectal cancer patients after chemoradiotherapy (CRT) followed by curative resection. However, various TRG systems have been suggested, with subjective categorization, resulting in interobserver variability. This study compared the prognostic validity of four different TRG systems in order to identify the most ideal TRG system.
  • Materials and Methods
    This study included 933 patients who underwent preoperative CRT and curative resection. Primary tumors alone were graded according to the American Joint Committee on Cancer (AJCC), Dworak, and Ryan TRG systems, and both primary tumors and regional lymph nodes were graded according to a modified Dworak TRG system. The ability of each TRG system to predict recurrence-free survival (RFS) and overall survival (OS) was analyzed using chi-square and C statistics.
  • Results
    All four TRG systems were significantly predictive of both RFS and OS (p < 0.001 each), however none was a better predictor of prognosis than ypStage. Among the four TRGs, the mDworak TRG system was a better predictor of RFS and OS than the AJCC, Dworak, and Ryan TRG systems, and both the chi-square and C statistics were higher for the former, although the differences were not statistically significant. The combination of ypStage and the modified Dworak TRG better predicted RFS and OS than ypStage alone.
  • Conclusion
    The modified Dworak TRG system for evaluation of entire tumors including regional lymph nodes is a better predictor of survival than current TRG systems for evaluation of the primary tumor alone.
Pre-operative chemoradiation therapy (CRT), followed by curative resection, has become the standard treatment for patients with locally advanced rectal cancer [1]. Accurate determination of tumor regression grade (TRG), ypT, ypN, and ypStage in the rectum after CRT is important for both pathologists and patients. TRG reflects therapeutic response, and ypT, ypN, and ypStage have been shown to predict prognosis [2-4]. The anatomical criteria in TNM staging are relatively objective and reproducible. Although the definition of regional lymph node (LN) metastasis (including pericolorectal tumor nodules) has been modified several times [5-7], ypN remains a major prognostic factor in these patients [8,9]. Various grading systems have been proposed for TRG, however, resulting in interobserver variability in grading [10]. The most widely used TRG systems are those of Ryan et al. [11], Dworak et al. [12], and Mandard [13]. The Mandard and Dworak TRG systems are classified according to five-point grades based on residual tumor and fibrosis [12,13], whereas the Ryan TRG system, with three-point grading, is a type of modified Mandard TRG system [11]. The 2010 American Joint Committee on Cancer (AJCC) TRG system is a modification of the Ryan TRG system based on the volume of residual primary tumor cells [5]. Details of each of these TRG systems are shown in Table 1.
The current TRG systems evaluate only the primary tumor with no consideration of regional LN status. To determine the most clinically-valid TRG system predictive of prognosis and therapeutic response, we retrospectively compared the prognostic significance of current TRG systems that evaluate the primary tumor alone with that of a newly developed modified Dworak (mDworak) TRG system that evaluates both the primary tumor and regional LNs.
1. Patients
This study enrolled 1,063 patients with primary rectal cancer who had undergone preoperative CRT at the National Cancer Center, Korea, between January 1, 2002 and December 30, 2011. All of the patients had biopsy-proven carcinoma of the middle or lower rectum (within 9 cm of the anal verge) and were classified as cT3 or cT4 on magnetic resonance imaging (MRI), with or without transrectal ultrasonography. Of these 1,063 patients, 130 were excluded, including 71 who refused surgery, 15 who were transferred to other hospitals, and 44 who underwent local excision because of the presence of comorbidities or inoperable status (initial clinical stage IV). The remaining 933 patients were treated with neoadjuvant CRT, followed by curative resection. Neoadjuvant CRT consisted of preoperative radiotherapy (total dose, 45 Gy) applied over 5-6 weeks to the pelvis, with a boost to the rectum, resulting in a total of 50.4 Gy in 28 fractions. Concomitant chemotherapy was initiated on the first day of radiotherapy, and administered intravenously or orally during the 6 weeks of radiotherapy. Multiple chemotherapeutic regimens were employed, with 536 patients (57.5%) treated with 5-fluorouracil (5-FU) and leucovorin; 255 (27.3%) with capecitabine, with or without irinotecan; 117 (12.5%) with tegafur-uracil; and 25 (2.7%) with cetuximab, irinotecan, and capecitabine. Radical surgery, including total mesorectal excision, was performed 4-6 weeks after completion of CRT. Of the 944 patients, 809 (86.7%) subsequently received adjuvant chemotherapy, consisting of fluoropyrimidine (5-FU/leucovorin, capecitabine, or tegafur-uracil/leucovorin; n=747) or combination therapy (5-FU/leucovorin/oxaliplatin, capecitabine/oxaliplatin, S-1/oxaliplatin, or 5-FU/leucovorin/irinotecan; n=62). This study was approved by the Institutional Review Board of the National Cancer Center, Korea, and each patient provided written informed consent prior to preoperative CRT.
2. Pathological examination
Each tumor was classified using World Health Organization (WHO) criteria [14] and initially staged using the TNM system of the AJCC, sixth edition [6]. TRGs for both primary tumors and regional LNs were initially determined using our mDworak TRG system. The original Dworak TRG system evaluates the primary tumor only [12], whereas our mDworak TRG system evaluates the entire lesion including the rectum and underlying mesorectum. The resected rectum was embedded in full thickness for evaluation of the circumferential resection margin and total mesorectal excision status. Sections from the rectal wall often include the regional LNs and perirectal tumor deposits. TRG determination may be ambiguous in patients having predominant residual cancer cells in the mesorectum as a separate nodular form (regressed mesorectal tumor [ypT3] vs. perirectal tumor deposits [ypN1c]). Thus, we estimated TRG for the primary tumor and regional LNs, including perirectal tumor deposits, as a whole. The mDworak TRG system was graded as follows: TRG 4, or complete regression, defined as no residual tumor cells in the primary tumor and regional LNs (ypT0N0); TRG 3, or near complete regression, defined as one or two microscopic foci (each < 0.5 cm in diameter) of residual tumor cells or groups in the primary tumor and regional LNs; TRG 2, or moderate regression, defined as dominant fibroinflammatory changes with vasculopathy encompassing more than 50% of the entire tumor, including the tumor, regional LN metastases, and perirectal tumor deposits; TRG 1, or minimal regression, defined as a dominant tumor mass encompassing more than 50% of the primary tumor and/or regional LN metastases. All tumors were reviewed by two pathologists (S.H.K. and H.J.C.) to determine tumor deposit status (ypN1c); this allowed restaging of the tumors according to the seventh edition of the AJCC [5]. TRGs of the primary tumors were also re-assessed using the Dworak, Ryan, and AJCC TRG systems [5,11,12]. Since the Mandard and Dworak TRG systems have similar grading criteria, with the only difference being the reverse order of TRG number (Table 1), tumor assessment using the Mandard TRG system was not performed. Of the 933 patients, 55 (5.89%) changed their TRGs by using Dworak TRG instead of the mDworak TRG system. Among the 55 patients, six were found to be ypT0N+ (ypT0N1), with these patients classified as having complete regression according to the AJCC and Dworak TRG systems. Patient distribution according to each TRG system is shown in Table 2.
3. Follow-up
Patients were followed-up for local recurrence and distant metastasis every 3 months for the first 2 postoperative years, then every 6-12 months thereafter. Follow-up included physical examinations, measurements of serum carcinoembryonic antigen concentration, chest X-ray, and abdominal ultrasound or computed tomography (CT).
4. Statistical analysis
Pearson’s chi-square test or Fisher exact test was used for comparison of between-group differences in recurrence or survival rate predicted by clinicopathological parameters. Overall survival (OS) was defined as the time from diagnosis to death. Relapse-free survival (RFS) was defined as the time from operation to any type of recurrence, as evidenced by CT, MRI, or histology. RFS and OS curves were plotted using the Kaplan-Meier method and compared using the log-rank test. Prognostic factors were evaluated using Cox regression models. The predictive abilities of the mDworak, AJCC, original Dworak (Dworak), and Ryan TRG systems, and ypStage for RFS and OS were evaluated by chi-square and C statistics, the latter being a concordance measure analogous to the receiver operating characteristic curve area for the logistic model. The value indicates the probability that a model produces a higher risk for those who do than do not develop an event [15]. Higher chi-square and C-statistic values indicate better predictive capabilities. Models that combined ypStage with each TRG were also examined. Interobserver variation of mDworak TRG systems was also analyzed by kappa value. A p-value of < 0.05 was considered statistically significant. Statistical analyses were performed using IBM SPSS ver. 20 software for Windows (IBM Co., Armonk, NY), and the chi-square and C statistics were calculated using Stata software (Stata Corp., College Station, TX).
1. Patients
Of the 933 patients, 188 (20.1%) experienced tumor recurrence over a median follow-up period of 53.7 months (range, 0 to 126 months). Locoregional recurrence occurred in 101 patients (10.8%), distant metastasis in 171 (18.3%), of whom 84 (9%) had both locoregional recurrence and distant metastasis. The 5-year RFS and OS rates were 77.4% and 62.8%, respectively. The number of patients in each TRG classification is shown in Table 2. Over 50% of patients graded using the mDworak, Ryan, Dworak and AJCC TRGs were grade 2. The clinicopathologic characteristics of the included patients are shown in Table 3.
2. Survival analysis (RFS and OS)
In Kaplan-Meier univariate analysis, ypN, ypT, and ypStage; all four TRG systems (mDworak, AJCC, Dworak, and Ryan); histological grade; venous, lymphatic, and perineural invasion; and circumferential resection margin showed significant association with both RFS and OS (p < 0.01 each) (Table 3). The RFS and OS of patients classified as original or mDworak grades 3 and 4, and those classified as AJCC TRG0 and 1, did not differ significantly (Table 3, Figs. 1 and 2). The RFS and OS of ypStage 0 and I patients also did not differ significantly (p > 0.1 each) (Table 3). RFS was significantly lower in the ypT0N+ (ypT0N1) than in other early stage groups (p < 0.001) (Fig. 1). The 5-year RFS rate of the ypT0N+ group was comparable to that of the ypstage III group (55.6% vs. 58%, respectively) (Table 3). Two of six ypT0N+ patients developed recurrences at 20 and 51 months postoperatively, and both were classified as ypT0N1a. However, no ypT0N+ patient died during the follow-up period.
Using multivariate analysis, we performed an analysis to determine whether each TRG system, as well as ypStage, histological grade, perineural invasion, and circumferential resection margin, were prognostic of RFS and OS. In multivariate analysis, only ypStage, perineural invasion, and circumferential resection margin were independently prognostic for RFS and OS (Table 4).
3. Ability of the four TRG systems and ypStage to predict RFS and OS
The chi-square and C statistics of a model using ypStage for prediction of RFS and OS were significantly higher than those of the models using the TRG systems, indicating that ypStage was a better predictor of RFS and OS than the TRG systems (Table 5). Among the four TRGs, the mDworak TRG system was a better predictor of RFS and OS than the AJCC, Dworak, and Ryan TRG systems, and both the chi-square and C statistics were higher for the former, although the differences were not statistically significant (Table 5). However, the combination of ypStage and the mDworak TRG system showed significantly better chi-square and C statistics for both RFS and OS than ypStage alone (Table 5). A combination of ypStage and the AJCC TRG system showed increased chi-square and C statistics for RFS and OS compared with ypStage alone; however, this model did not distinguish among the hazard ratios of groups (p > 0.05 for AJCC TRG), indicating that this model was inadequate for prognosis.
An ideal TRG system should consistently measure therapeutic response and predict patient outcomes. However, previous studies on the prognostic significance of current TRG systems have yielded variable results, owing to the use of different grading systems, different endpoints for pathological complete response, different TRG components, and/or ambiguous grading criteria [10,16-18]. In addition, classification according to TRG systems showed a very low concordance rate among experienced gastrointestinal pathologists, even when using the same TRG system, indicating poor reproducibility of these systems [10]. Current TRG systems have two major limitations: the subjectivity of the grading criteria and the range of tumors being evaluated (i.e., the primary tumor alone or the primary tumor and regional LNs).
The endpoint of pathologic complete response has been defined as ypT0N0 [19,20]; however, current TRG systems evaluate only the primary tumor [5,11,12]. Even though regional LN status after CRT (ypN) is the most important prognostic factor, current TRG systems do not consider regional LN metastasis. Thus, the TRG systems may be inaccurate in predicting prognosis, particularly in ypT0N+ patients. Even though ypT0N+ patients have residual tumors, they would be classified as having achieved complete response using the current TRG systems. RFS and OS rates were significantly lower in ypT0N+ patients than in ypT0N0 patients [21]. This study therefore compared the predictive abilities of four TRG systems: the Dworak, Ryan, AJCC, and mDworak TRG systems. Although all were predictive of OS and RFS, the mDworak TRG system, which assesses both the primary tumor and regional LNs, was superior to the other TRG systems, which assess the primary tumor alone. However, none of the four TRG systems was superior in predictive ability to ypStage, but the mDworak TRG system was found to complement the predictive power of ypStage, further suggesting that consideration of regional LN status could enhance the prognostic ability of TRG systems that evaluate the primary tumor alone.
Another limitation of current TRG systems is that grading is imprecise and the criteria, particularly for near complete regression, may be very subjective. For example, Dworak TRG 3 was originally defined as ‘very few (difficult to find microscopically) tumor cells in fibrotic tissue with or without mucous substance’ [12]; however, this criterion was modified to ‘regression of > 50% of the tumor mass’ [22,23]. The latter criterion was actually for ‘good regression’ of the five-point TRG system proposed by Rodel et al. [18]. Similarly, Mandard TRG 2 was originally defined as ‘the presence of rare residual cancer cells scattered throughout areas of fibrosis’ [13], but has been modified to ‘single cells, or small groups of cancer cells’ in the Ryan TRG system [11]. The meaning of ‘small groups of cancer cells’ was further modified from near complete regression to moderate regression in the AJCC TRG system [5]. To overcome the subjectivity of these criteria, it may be necessary to quantify the estimated volume of residual tumor cells, perhaps by assessing the modified rectal cancer regression grade (m-RCRG). The criteria for m-RCRG are grade 1 (complete or near-complete regression), defined as no tumor epithelium and scattered foci of malignant epithelium comprising < 5% of the overall area of abnormality; grade 2 (moderate regression), defined as malignant epithelium comprising 5%-50% of the overall area of abnormality; and grade 3 (minimal regression), defined as malignant epithelium comprising > 50% of the area of abnormality [10]. The m-RCRG is a quantified version of the Ryan TRG, except that one of the criteria of m-RCRG grade 1, ‘< 5% of the residual tumor lesion,’ may not match ‘near-complete regression’ of large tumors. The total residual tumor cell volume may vary by tumor size or number of sections. Therefore, ‘nearcomplete regression’ (grade 3) of the mDworak TRG system was defined as one or two microscopic foci (< 0.5 cm in diameter) of residual tumor cells or groups of tumor cells in the primary tumor and regional LNs. The criteria for moderate regression could consequently be determined by the criteria for near-complete and minimal regression. The mDworak TRG system defined the upper limit of moderate regression as 50% of residual tumor cell volume within the regressed tumor lesion.
A major limitation of this study was the heterogeneity in chemotherapeutic regimens. Use of different combination regimens may have affected therapeutic responses, the results of TRGs, and patient prognosis [19,24]. However, this limitation may not have had a significant impact in comparative analysis of TRG systems. The other limitation was that we did not compare interobserver variability among the various TRG systems. However, in random analysis of 5% of our cases (47 cases) for interobserver variability, kappa value for mDworal TRG between two pathologists (S.H.K. and H.J.C.) was 0.936 (data not shown). This kappa value is much higher than those reported in the previous study [10], and the reason why may be due to the differences in numbers of observers (2 vs. 17), and due to microscopic examination of entire lesions instead of one representative digitalized image. In addition the grading criteria of the mDworak TRG system could be relatively objective.
Despite multiple trials of various TRG systems, none was found to be a better predictor of prognosis than ypStage. Pathologic staging after neoadjuvant therapy is more objective and more predictive of prognosis and therapeutic responses (for complete response vs. partial response). Pathologic evaluation of surgically resected specimens after neoadjuvant therapy is an extra-burden for pathologists, since meticulous examination is necessary for the accurate evaluation of pathologic stage and therapeutic responses [25]. Thus, application of a clinically valid TRG system is necessary. Our results showed that the mDworak TRG system may complement ypStage, with their combination better predictive of RFS and OS than ypStage alone.
In conclusion, an ideal TRG system should reflect the therapeutic responses of both the primary tumor and regional LNs, and the criteria should not be subjective. Our mDworak TRG system may be an example of an ideal TRG system, enabling better prediction of survival, either alone or in combination with ypStage.

Conflict of interest relevant to this article was not reported

Acknowledgements
This study was supported by the Converging Research Center Program funded by the Ministry of Science, ICT and Future Planning, Republic of Korea (Project No. 2013K000 271).
The authors thank Mr. Dong-Su Jang, Research Assistant, Department of Anatomy, Yonsei University College of Medicine, Seoul, Korea, for his help with the figures. We also thank Ms. Kyung-Min Kang, Biometric Research Branch, Research Institute and Hospital, National Cancer Center, Goyang, Korea, for her help with the statistical analysis.
Fig. 1.
Relapse-free survival of 933 rectal cancer patients treated with pre-operative chemoradiotherapy followed by surgical resection, according to tumor regression grades (TRG) according to the modified Dworak (mDworak) system (A), which assesses both the primary tumor and regional lymph nodes, the American Joint Committee on Cancer (AJCC) system (B), which assesses the primary tumor alone, and ypStage (C).
crt-2015-254f1.gif
Fig. 2.
Overall survival of 933 rectal cancer patients treated with pre-operative chemoradiotherapy followed by surgical resection, according to tumor regression grades (TRG) according to the modified Dworak (mDworak) system (A), which assesses both the primary tumor and regional lymph nodes, the American Joint Committee on Cancer (AJCC) system (B), which assesses the primary tumor alone, and ypStage (C).
crt-2015-254f2.gif
Table 1.
Tumor regression grade (TRG) systems
Dworak Mandard Ryan AJCC Modified Dworak (pT+pN)a)
Complete regression No tumor cells (TRG 4) No residual cancer cells (TRG 1) No viable cancer cells, or single cells, or small groups of cancer cells (TRG 1) No viable cancer cells (TRG 0) No tumor cells (TRG 4)
Near complete regression Very few tumor cells (TRG 3) Rare residual cancer cells (TRG 2) - Single or small groups of tumor cells (TRG 1: moderate response) Very few tumor cells (one or two microscopic foci of < 0.5 cm in diameter) (TRG 3)
Moderate regression Dominantly fibrotic changes with few tumor cells or groups (TRG 2) Predominant fibrosis with increased number of residual cancer cells (TRG 3) Residual cancer outgrown by fibrosis (TRG 2) Residual cancer outgrown by fibrosis (TRG 2: minimal response) Dominantly fibrotic changes with few tumor cells or groups (TRG 2)
Minimal regression Dominant tumor mass with obvious fibrosis (TRG 1) Residual cancer outgrowing fibrosis (TRG 4) Significant fibrosis outgrown by cancer, or no fibrosis with extensive residual cancer (TRG 3) Minimal or no tumor cells killed (TRG 3: poor response) Dominant tumor cell mass (> 50%) with obvious fibrosis or no regression (TRG 1)
No regression No regression (TRG 0) No regressive change (TRG 5) - - -

AJCC, American Joint Committee on Cancer.

a) Modified Dworak TRG was used to evaluate both the primary tumor and regional lymph nodes as a whole.

Table 2.
Distribution of case numbers according to four different TRG systems
TRG system Distribution
Modified Dworak TRG AJCC TRG Ryan TRG Dworak TRG
Grade 0 - 135 (14.5)a) - 0
Grade 1 162 (17.3) 140 (15.0) 275 (29.5) 113 (12.1)
Grade 2 526 (56.4) 546 (58.5) 546 (56.1) 575 (61.6)
Grade 3 116 (12.4) 112 (12.0) 112 (11.5) 110 (11.8)
Grade 4 129 (13.8) - - 135 (14.5)

Values are presented as number (%). The modified Dworak system assessed the primary tumor and regional lymph nodes, whereas the American Joint Committee on Cancer (AJCC), Ryan, and Dworak systems assessed the primary tumor alone. TRG, tumor regression grade.

a) Including six patients classified as ypT0N1.

Table 3.
Parameters used in Kaplan-Meier survival analysis
Parameter No. of cases 5-Year RFS (%) p-value 5-Year OS (%) p-value
Sex
 Male 635 76.4 0.434 98.4 0.271
 Female 298 78.7 95.3
Age (yr)a)
 < 60 530 77.2 0.887 86.2 0.057
 ≥ 60 403 77.6 97.1
Distance from AV (cm)
 < 5 318 71.3 0.002 81.6 0.15
 ≥ 5 615 80.8 85.9
Histological typeb)
 Adenocarcinoma 768 75.5 < 0.001 83.1 < 0.001
 Other typec) 31 60.9 57.5
Histological gradeb)
 Low 757 76 < 0.001 83.6 < 0.001
 High 42 56.1 54.5
ypT
 ypT0 134 92 < 0.001 97.6 < 0.001
 ypTis 12 91.7 87.5
 ypT1 51 97.8 97.3
 ypT2 222 88.7 95.4
 ypT3 468 66.9 73.9
 ypT4 46 55.1 73.6
ypN
 ypN0 612 87 < 0.001 92.1 < 0.001
 ypN1a 83 68.1 82.4
 ypN1b 103 64.6 77.3
 ypN1c 33 59 65.1
 ypN2a 61 45.4 60.4
 ypN2b 41 22.4 29.7
ypStage
 ypT0N1 6 55.6 < 0.001 100 < 0.001
 0 140 93.1 97.7
 I 228 91.6 96
 II 244 78.5 83.6
 III 315 58 68.9
 IV 0 0 0
Circumferential RM
 Negative 847 80 < 0.001 87.8 < 0.001
 Positive 86 48.8 52
mDworak TRGd)
 1 (minimal) 162 56 < 0.001 64.9 < 0.001
 2 (moderate) 526 76.2 83.7
 3 (near complete) 116 91.1 95.8
 4 (compete) 129 92.5 97.5
AJCC TRG
 0 (complete) 135 90.9 < 0.001 97.6 < 0.001
 1 (moderate) 140 89.7 93
 2 (minimal) 546 73.9 82.2
 3 (poor) 112 57.8 62.4
Dworak TRG
 ≤ 1 (minimal) 113 57.1 < 0.001 62.6 < 0.001
 2 (moderate) 575 74.3 82.3
 3 (near complete) 110 93 95.6
 4 (complete) 135 90.9 97.6
Ryan TRG
 1 (good) 275 90.3 < 0.001 95.2 < 0.001
 2 (moderate) 546 73.9 82.2
 3 (poor) 112 57.8 62.4
TME
 Complete 664 79.2 0.072 85.7 0.275
 Near-complete 236 73 81
 Incomplete 33 64.1 71.3
Lymphatic invasion
 Present 256 58.4 < 0.001 63.9 < 0.001
 Absent 677 83.8 90.9
Perineural invasion
 Present 215 79.8 < 0.001 62.1 < 0.001
 Absent 718 84.7 89.9
Venous invasion
 Present 185 57.6 < 0.001 62.1 < 0.001
 Absent 748 96.7 88.7

RFS, recurrence-free survival; OS, overall survival; AV, anal verge; RM, resection margin; mDwork, modified Dwork; TRG, tumor regression grade; AJCC, American Joint Committee on Cancer; TME, total mesorectal excision.

a) Median 59 years (range, 22 to 87 years),

b) No residual tumors were noted in 134 cases (14.4%); these are excluded,

c) 23 mucinous adenocarcinomas, six signet ring cell carcinomas, two adenosquamous carcinomas,

d) Dworak TRG assessing primary tumor and regional lymph nodes as a whole.

Table 4.
Multivariate analysis of factors influencing RFS and OS
Factor RFS
OS
Hazard ratio (95% CI) p-value Hazard ratio (95% CI) p-value
ypStage
 0 & I 1.000 < 0.001 1.000 < 0.001
 II 2.057 (1.253-3.379) 2.668 (1.449-4.913)
 III 4.514 (2.888-7.055) 4.747 (2.686-8.389)
Perineural invasion
 Absent 1.000 < 0.001 1.000 < 0.001
 Present 2.440 (1.802-3.304) 2.161 (1.504-3.105)
Circumferential resection margin
 Negative 1.000 0.010 1.000 < 0.001
 Positive 1.656 (1.128-2.430) 2.942 (1.979-4.375)

RFS, recurrence-free survival; OS, overall survival; CI, confidence interval.

Table 5.
Univariate Cox’s proportional hazards models and model validation of RFS and OS
Model RFS
OS
HR (95% CI) p-value χ2 Harrell’s Ca) HR (95% CI) p-value χ2 Harrell’s Cb)
Modified Dworak TRG 68.92 0.6492 58.06 0.6783
 1 1.000 1.000
 2 0.450 (0.336-0.603) < 0.001 0.426 (0.297-0.610) < 0.001
 3 0.178 (0.099-0.322) < 0.001 0.106 (0.042-0.267) < 0.001
 4 0.172 (0.097-0.305) < 0.001 0.132 (0.060-0.292) < 0.001
AJCC TRG 59.58 0.6359 53.95 0.6718
 0 1.000 1.000
 1 1.035 (0.523-2.048) 0.922 1.243 (0.463-3.337) 0.666
 2 2.662 (1.587-4.464) < 0.001 3.696 (1.710-7.986) 0.001
 3 5.553 (3.146-9.803) < 0.001 9.036 (4.004-20.394) < 0.001
Dworak TRG 61.85 0.6374 55.52 0.6711
 ≤ 1 1.000 1.000
 2 0.460 (0.332-0.637) < 0.001 0.403 (0.272-0.599) < 0.001
 3 0.149 (0.077-0.287) < 0.001 0.098 (0.038-0.250) < 0.001
 4 0.177 (0.101-0.312) < 0.001 0.111 (0.049-0.251) < 0.001
Ryan TRG 59.57 0.6356 53.76 0.6700
 1 1.000 1.000
 2 2.615 (1.791-3.820) < 0.001 3.289 (1.929-5.610) < 0.001
 3 5.457 (3.492-8.527) < 0.001 8.043 (4.437-14.580) < 0.001
ypStage 119.46 0.7046 82.46 0.7175
 ≤ I 1.000 1.000
 II 2.892 (1.885-4.436) < 0.001 3.959 (2.191-7.154) < 0.001
 IIIc) 6.328 (4.339-9.229) < 0.001 7.864 (4.608-13.422) < 0.001
ypStage 133.35 0.7248 97.33 0.7482
 ≤ I 1.000 1.000
 II 2.613 (1.532-4.456) < 0.001 3.297 (1.551-7.007) 0.002
 IIIc) 5.404 (3.286-8.886) < 0.001 6.206 (3.040-12.669) < 0.001
Modified Dworak TRG
 1 1.000 1.000
 2 0.612 (0.455-0.824) 0.001 0.57 (0.395-0.822) 0.003
 3 0.411 (0.22-0.769) 0.005 0.25 (0.097-0.649) 0.004
 4 0.702 (0.333-1.480) 0.352 0.639 (0.223-1.827) 0.403
ypStage 130.76 0.7208 96.72 0.7439
 ≤ I 1.000 1.000
 II 2.600 (1.567-4.315) < 0.001 3.141 (1.569-6.285) 0.001
 IIIc) 5.439 (3.438-8.606) < 0.001 5.949 (3.146-11.249) < 0.001
AJCC TRG
 0 1.000 1.000
 1 0.710 (0.352-1.435) 0.340 0.766 (0.276-2.216) 0.608
 2 0.983 (0.532-1.816) 0.955 1.203 (0.490-2.956) 0.687
 3 1.654 (0.848-3.226) 0.140 2.464 (0.957-6.347) 0.062

RFS, recurrence-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; TRG, tumor regression grade; AJCC, American Joint Committee on Cancer.

a) Differences between C-statistics for RFS: modified Dworak (mDworak) TRG vs. AJCC TRG, p=0.091; mDworak TRG vs. Dworak TRG, p=0.118; mDworak TRG vs. Ryan TRG, p=0.110; AJCC TRG vs. Dworak TRG, p=0.794; AJCC TRG vs. Ryan TRG, p=0.893; Dworak TRG vs. Ryan TRG, p=0.750; ypStage vs. mDworak TRG, p < 0.001; ypStage+mDworak TRG vs. ypStage, p < 0.001; ypStage+mDworak TRG vs. ypStage+AJCC TRG, p=0.251,

b) Differences between C-statistics for OS: mDworak TRG vs. AJCC TRG, p=0.542; mDworak TRG vs. Dworak TRG, p=0.407; mDworak TRG vs. Ryan TRG, p=0.444; AJCC TRG vs. Dworak TRG, p=0.925; AJCC TRG vs. Ryan TRG, p=0.475; Dworak TRG vs. Ryan TRG, p=0.878; ypStage vs. mDworak TRG, p=0.043; ypStage+mDworak TRG vs. ypStage, p < 0.001; ypStage+mDworak TRG vs. ypStage+AJCC TRG, p=0.582,

c) Including six patients classified as ypT0N1.

  • 1. Quah HM, Chou JF, Gonen M, Shia J, Schrag D, Saltz LB, et al. Pathologic stage is most prognostic of disease-free survival in locally advanced rectal cancer patients after preoperative chemoradiation. Cancer. 2008;113:57–64. ArticlePubMed
  • 2. Hermanek P, Merkel S, Hohenberger W. Prognosis of rectal carcinoma after multimodal treatment: ypTNM classification and tumor regression grading are essential. Anticancer Res. 2013;33:559–66. PubMed
  • 3. Kim DW, Kim DY, Kim TH, Jung KH, Chang HJ, Sohn DK, et al. Is T classification still correlated with lymph node status after preoperative chemoradiotherapy for rectal cancer? Cancer. 2006;106:1694–700. ArticlePubMed
  • 4. Vecchio FM, Valentini V, Minsky BD, Padula GD, Venkatraman ES, Balducci M, et al. The relationship of pathologic tumor regression grade (TRG) and outcomes after preoperative therapy in rectal cancer. Int J Radiat Oncol Biol Phys. 2005;62:752–60. ArticlePubMed
  • 5. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A 3rd. AJCC cancer staging manual. 7th ed. New York: Springer-Verlag; 2010.
  • 6. Greene FL, Page DL, Fleming ID, Fritz AG, Balch CM, Haller DG, et al. AJCC cancer staging manual. 6th ed. New York: Springer-Verlag; 2002.
  • 7. Fleming ID, Cooper JS, Henson DE, Hutter RV, Kennedy BJ, Murphy GP, et al. AJCC cancer staging manual. 5th ed. Philadelphia, PA: Lippincott-Raven Publishers; 1997.
  • 8. Lee SD, Kim TH, Kim DY, Baek JY, Kim SY, Chang HJ, et al. Lymph node ratio is an independent prognostic factor in patients with rectal cancer treated with preoperative chemoradiotherapy and curative resection. Eur J Surg Oncol. 2012;38:478–83. ArticlePubMed
  • 9. Tsai CJ, Crane CH, Skibber JM, Rodriguez-Bigas MA, Chang GJ, Feig BW, et al. Number of lymph nodes examined and prognosis among pathologically lymph node-negative patients after preoperative chemoradiation therapy for rectal adenocarcinoma. Cancer. 2011;117:3713–22. ArticlePubMedPMC
  • 10. Chetty R, Gill P, Govender D, Bateman A, Chang HJ, Deshpande V, et al. International study group on rectal cancer regression grading: interobserver variability with commonly used regression grading systems. Hum Pathol. 2012;43:1917–23. ArticlePubMed
  • 11. Ryan R, Gibbons D, Hyland JM, Treanor D, White A, Mulcahy HE, et al. Pathological response following long-course neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Histopathology. 2005;47:141–6. ArticlePubMed
  • 12. Dworak O, Keilholz L, Hoffmann A. Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis. 1997;12:19–23. ArticlePubMed
  • 13. Mandard AM, Dalibard F, Mandard JC, Marnay J, Henry-Amar M, Petiot JF, et al. Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma: clinicopathologic correlations. Cancer. 1994;73:2680–6. ArticlePubMed
  • 14. Bosman FT, Carneiro F, Hruban RH. WHO classification of tumours of the digestive system. 4th ed. Lyon: IARC Press; 2010.
  • 15. Balakrishnan N, Rao CR. Handbook of statistics: advances in survival analysis. Burlington, MA: Elsevier; 2004.
  • 16. Rullier A, Laurent C, Capdepont M, Vendrely V, Bioulac-Sage P, Rullier E. Impact of tumor response on survival after radiochemotherapy in locally advanced rectal carcinoma. Am J Surg Pathol. 2010;34:562–8. ArticlePubMed
  • 17. Suarez J, Vera R, Balen E, Gomez M, Arias F, Lera JM, et al. Pathologic response assessed by Mandard grade is a better prognostic factor than down staging for disease-free survival after preoperative radiochemotherapy for advanced rectal cancer. Colorectal Dis. 2008;10:563–8. ArticlePubMed
  • 18. Rodel C, Martus P, Papadoupolos T, Fuzesi L, Klimpfinger M, Fietkau R, et al. Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer. J Clin Oncol. 2005;23:8688–96. ArticlePubMed
  • 19. Rodel C, Liersch T, Becker H, Fietkau R, Hohenberger W, Hothorn T, et al. Preoperative chemoradiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial. Lancet Oncol. 2012;13:679–87. ArticlePubMed
  • 20. Maas M, Nelemans PJ, Valentini V, Crane CH, Capirci C, Rodel C, et al. Adjuvant chemotherapy in rectal cancer: defining subgroups who may benefit after neoadjuvant chemoradiation and resection: a pooled analysis of 3,313 patients. Int J Cancer. 2015;137:212–20. ArticlePubMedPMC
  • 21. Yeo SG, Kim DY, Kim TH, Chang HJ, Oh JH, Park W, et al. Pathologic complete response of primary tumor following preoperative chemoradiotherapy for locally advanced rectal cancer: long-term outcomes and prognostic significance of pathologic nodal status (KROG 09-01). Ann Surg. 2010;252:998–1004. ArticlePubMed
  • 22. Huebner M, Wolff BG, Smyrk TC, Aakre J, Larson DW. Partial pathologic response and nodal status as most significant prognostic factors for advanced rectal cancer treated with preoperative chemoradiotherapy. World J Surg. 2012;36:675–83. ArticlePubMed
  • 23. Trakarnsanga A, Gonen M, Shia J, Nash GM, Temple LK, Guillem JG, et al. Comparison of tumor regression grade systems for locally advanced rectal cancer after multimodality treatment. J Natl Cancer Inst. 2014;106:dju248ArticlePubMedPMC
  • 24. Hong YS, Nam BH, Kim KP, Kim JE, Park SJ, Park YS, et al. Oxaliplatin, fluorouracil, and leucovorin versus fluorouracil and leucovorin as adjuvant chemotherapy for locally advanced rectal cancer after preoperative chemoradiotherapy (ADORE): an open-label, multicentre, phase 2, randomised controlled trial. Lancet Oncol. 2014;15:1245–53. ArticlePubMed
  • 25. Park SY, Chang HJ, Kim DY, Jung KH, Kim SY, Park JW, et al. Is step section necessary for determination of complete pathological response in rectal cancer patients treated with preoperative chemoradiotherapy? Histopathology. 2011;59:650–9. ArticlePubMed

Figure & Data

REFERENCES

    Citations

    Citations to this article as recorded by  
    • Prognostic Value of Tumor Regression Grade Combined with Pathological Lymph Node Status in Initially Node-Positive Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy
      Dakui Luo, Yajie Chen, Zhouyu Luo, Huangbo Gong, Qingguo Li, Xinxiang Li
      Journal of Investigative Surgery.2025;[Epub]     CrossRef
    • Interpretation of Complete Tumor Response on MRI Following Chemoradiotherapy of Rectal Cancer: Inter-Reader Agreement and Associated Factors in Multi-Center Clinical Practice
      Hae Young Kim, Seung Hyun Cho, Jong Keon Jang, Bohyun Kim, Chul-min Lee, Joon Seok Lim, Sung Kyoung Moon, Soon Nam Oh, Nieun Seo, Seong Ho Park
      Korean Journal of Radiology.2024; 25(4): 351.     CrossRef
    • Evaluating complete response prediction rates in locally advanced rectal cancer with different radiomics segmentation approaches
      Gizem Kaval, Merve Gulbiz Dagoglu Kartal, Sena Azamat, Eda Cingoz, Gokhan Ertas, Sule Karaman, Basak Kurtuldu, Metin Keskin, Neslihan Berker, Senem Karabulut, Ethem Nezih Oral, Nergiz Dagoglu Sakin
      Pathology and Oncology Research.2024;[Epub]     CrossRef
    • Regressionsgrading gastrointestinaler Tumoren
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      Die Gastroenterologie.2024; 19(3): 234.     CrossRef
    • Rectal Cancer Survival for Residual Carcinoma In Situ Vs. Pathologic Complete Response After Neoadjuvant Therapy
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      Diseases of the Colon & Rectum.2024;[Epub]     CrossRef
    • Predictive value of flexible proctosigmoidoscopy and laboratory findings for complete clinical responses after neoadjuvant chemoradiotherapy in patients with locally advanced primary rectal cancer: a retrospective cohort study
      Alireza Hadizadeh, Hamed Kazemi-Khaledi, Mohammad-Sadegh Fazeli, Seyed-Mohsen Ahmadi-Tafti, Amir Keshvari, Reza Akbari-Asbagh, Mohammad-Reza Keramati, Alireza Kazemeini, Amir-Reza Fazeli, Behnam Behboudi, Mohammadamin Parsaei
      International Journal of Colorectal Disease.2024;[Epub]     CrossRef
    • Are the tumor microenvironment characteristics of pretreatment biopsy specimens of colorectal cancer really effectively predict the efficacy of neoadjuvant therapy: A retrospective multicenter study
      Bingbing Li, Longjiao Chen, Yichun Huang, Meng Wu, Weilan Fang, Xin Zou, Yihong Zheng, Qiuxiang Xiao
      Medicine.2024; 103(35): e39429.     CrossRef
    • Outcomes of rectal cancer treatment in rural Australia and New Zealand: analysis of the bowel cancer outcomes registry
      Ishmam Murshed, Tessa L. Dinger, Duveke P. E. de Gaay Fortman, Luke Traeger, Sergei Bedrikovetski, Andrew Hunter, Hidde M. Kroon, Tarik Sammour
      ANZ Journal of Surgery.2024; 94(10): 1823.     CrossRef
    • Relationship Between Neoadjuvant Chemoradiotherapy Response and Mesorectum Volume in Rectum Cancer
      Ramazan Saygın Kerimoğlu, Ebru Esen, Mustafa Saraçoğlu, İbrahim Babalıoğlu, Bekir Turgut, İlknur Küçükosmanoğlu, Osman Doğru
      Acta Haematologica Oncologica Turcica.2024; : 44.     CrossRef
    • Molecular Mechanism of Radioresponsiveness in Colorectal Cancer: A Systematic Review
      Matthew Lau, Md Islam Khan, Helen Law
      Genes.2024; 15(10): 1257.     CrossRef
    • Tumor Immune Microenvironment Biomarkers for Recurrence Prediction in Locally Advanced Rectal Cancer Patients after Neoadjuvant Chemoradiotherapy
      Jun-Eul Hwang, Sung-Sun Kim, Hyun-Jin Bang, Hyeon-Jong Kim, Hyun-Jeong Shim, Woo-Kyun Bae, Ik-Joo Chung, Eun-Gene Sun, Taebum Lee, Chan-Young Ock, Jeong-Seok Nam, Sang-Hee Cho
      Cancers.2024; 16(19): 3353.     CrossRef
    • Neoadjuvant chemotherapy in locally advanced colon cancer: A systematic review with proportional meta-analysis
      K. van den Berg, I.E.G. van Hellemond, J.M.W.E. Willems, J.W.A. Burger, H.J.T. Rutten, G.J. Creemers
      European Journal of Surgical Oncology.2024; : 109560.     CrossRef
    • A Novel Engineered AAV-Based Neoantigen Vaccine in Combination with Radiotherapy Eradicates Tumors
      Kevin Chih-Yang Huang, Chia-Ying Lai, Wei-Ze Hung, Hsin-Yu Chang, Pei-Chun Lin, Shu-Fen Chiang, Tao-Wei Ke, Ji-An Liang, An-Cheng Shiau, Pei-Chen Yang, William Tzu-Liang Chen, K.S. Clifford Chao
      Cancer Immunology Research.2023; 11(1): 123.     CrossRef
    • Does Pathological Complete Response after Neoadjuvant Therapy Influence Postoperative Morbidity in Rectal Cancer after Transanal Total Mesorectal Excision?
      Martin Svoboda, Vladimír Procházka, Tomáš Grolich, Tomáš Pavlík, Monika Mazalová, Zdeněk Kala
      Journal of Gastrointestinal Cancer.2023; 54(2): 528.     CrossRef
    • MRI‐Based Artificial Intelligence in Rectal Cancer
      Chinting Wong, Yu Fu, Mingyang Li, Shengnan Mu, Xiaotong Chu, Jiahui Fu, Chenghe Lin, Huimao Zhang
      Journal of Magnetic Resonance Imaging.2023; 57(1): 45.     CrossRef
    • Pilot proteomic study of locally advanced rectal cancer before and after neoadjuvant chemoradiotherapy indicates high metabolic activity in non‐responders' tumor tissue
      Tamara Babic, Vasiliki Lygirou, Jovana Rosic, Marko Miladinov, Aleksandra Djikic Rom, Eirini Baira, Rafael Stroggilos, Eftychia Pappa, Jerome Zoidakis, Zoran Krivokapic, Aleksandra Nikolic
      PROTEOMICS – Clinical Applications.2023;[Epub]     CrossRef
    • Comparison of the diagnostic performance of changes in signal intensity and volume from multiparametric MRI for assessing response of rectal cancer to neoadjuvant chemoradiotherapy
      Zhengwu Tan, Lan Cheng, Lingling Xie, Lan Zhang, Zhenyu Lin, Ping Han, Xin Li
      Asia-Pacific Journal of Clinical Oncology.2023; 19(3): 327.     CrossRef
    • Carbonic Anhydrase IX Expression and Treatment Response Measured in Rectal Adenocarcinoma Following Neoadjuvant Chemo-Radiotherapy
      Emese Sarolta Bádon, Lívia Beke, Attila Mokánszki, Csilla András, Gábor Méhes
      International Journal of Molecular Sciences.2023; 24(3): 2581.     CrossRef
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      Chonnam Medical Journal.2023; 59(1): 76.     CrossRef
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      Topias Karjula, Niko Kemi, Anne Niskakangas, Olli Mustonen, Iiris Puro, Vesa-Matti Pohjanen, Teijo Kuopio, Hanna Elomaa, Maarit Ahtiainen, Jukka-Pekka Mecklin, Toni T. Seppälä, Erkki-Ville Wirta, Eero Sihvo, Juha P. Väyrynen, Fredrik Yannopoulos, Olli Hel
      European Journal of Surgical Oncology.2023; 49(7): 1298.     CrossRef
    • An investigation into tumor regression grade as a parameter for locally advanced rectal cancer and 5-year overall survival rate
      Supparerk Laohawiriyakamol, Wongsakorn Chaochankit, Worawit Wanichsuwan, Kanet Kanjanapradit, Teeranan Laohawiriyakamol
      Annals of Coloproctology.2023; 39(1): 59.     CrossRef
    • Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer
      Anqi Wang, Jieli Zhou, Gang Wang, Beibei Zhang, Hongyi Xin, Haiyang Zhou
      Asian Journal of Surgery.2023; 46(9): 3568.     CrossRef
    • Neoadjuvant chemoradiotherapy versus neoadjuvant chemotherapy alone for patients with locally advanced rectal cancer: a propensity-score-matched analysis combined with SEER validation
      Jingjing Wu, Mingzhe Huang, Yuanhui Wu, Yisong Hong, Linbin Cai, Rongzhao He, Yanxin Luo, Puning Wang, Meijin Huang, Jinxin Lin
      Journal of Cancer Research and Clinical Oncology.2023; 149(11): 8897.     CrossRef
    • A multi-class classification algorithm based on hematoxylin-eosin staining for neoadjuvant therapy in rectal cancer: a retrospective study
      Yihan Wu, Xiaohua Liu, Fang Liu, Yi Li, Xiaomin Xiong, Hao Sun, Bo Lin, Yu Li, Bo Xu
      PeerJ.2023; 11: e15408.     CrossRef
    • Early Age of Onset Is an Independent Predictor for a Worse Response to Neoadjuvant Therapies in Sporadic Rectal Cancer Patients
      Caterina Foppa, Annalisa Maroli, Antonio Luberto, Carlotta La Raja, Paola Spaggiari, Cristiana Bonifacio, Stefano De Zanet, Marco Montorsi, Salvatore Piscuoglio, Luigi Maria Terracciano, Armando Santoro, Antonino Spinelli
      Cancers.2023; 15(14): 3750.     CrossRef
    • Metformin increases pathological responses to rectal cancers with neoadjuvant chemoradiotherapy: a systematic review and meta-analysis
      I-Li Lai, Jeng-Fu You, Wen-Sy Tsai, Yu-Jen Hsu, Yih-Jong Chern, Ming-Ying Wu
      World Journal of Surgical Oncology.2023;[Epub]     CrossRef
    • Risk factors for lateral pelvic lymph node metastasis in patients with lower rectal cancer: a systematic review and meta-analysis
      De-xing Zeng, Zhou Yang, Ling Tan, Meng-ni Ran, Zi-lin Liu, Jiang-wei Xiao
      Frontiers in Oncology.2023;[Epub]     CrossRef
    • Clinical predictors of rectal cancer response after neo-adjuvant (Chemo)Radiotherapy in Australia and New Zealand: Analysis of the Bi-National Colorectal Cancer Audit (BCCA)
      Jianliang Liu, Justin Y.T. Lee, Sergei Bedrikovetski, Luke Traeger, James W. Moore, Joanne L. Perry, Hidde M. Kroon, Tarik Sammour
      European Journal of Surgical Oncology.2023; 49(11): 107070.     CrossRef
    • Pancreatic ductal adenocarcinoma complete regression after preoperative chemotherapy: Surgical results in a small series
      Domenico Pinelli, Andrea Micalef, Barbara Merelli, Rosangela Trezzi, Annalisa Amaduzzi, Stefano Agnesi, Michela Guizzetti, Stefania Camagni, Veronica Fedele, Michele Colledan
      Cancer Treatment and Research Communications.2023; 37: 100770.     CrossRef
    • The ypT may better predict the efficacy of neoadjuvant chemoradiotherapy than tumor regression grade in locally advanced rectal cancer patients diagnosed ypT1-4N0
      Yujun Cui, Xinzhi Liu, Shuai Li, Hongzhi Wang, Yirong Xiang, Yangzi Zhang, Maxiaowei Song, Jianhao Geng, Zhiyan Liu, Huajing Teng, Xianggao Zhu, Yong Cai, Yongheng Li, Weihu Wang
      Clinical and Translational Oncology.2023; 26(4): 1012.     CrossRef
    • Grading der Tumorregression gastrointestinaler Karzinome nach neoadjuvanter Therapie
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      Der Pathologe.2022; 43(1): 51.     CrossRef
    • Deeper sections reveal residual tumor cells in rectal cancer specimens diagnosed with pathological complete response following neoadjuvant treatment
      Lasse Slumstrup, Susanne Eiholm, Astrid Louise Bjørn Bennedsen, Dea Natalie Munch Jepsen, Ismail Gögenur, Anne-Marie Kanstrup Fiehn
      Virchows Archiv.2022; 480(5): 1041.     CrossRef
    • MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy
      Jaeseung Shin, Nieun Seo, Song-Ee Baek, Nak-Hoon Son, Joon Seok Lim, Nam Kyu Kim, Woong Sub Koom, Sungwon Kim
      Radiology.2022; 303(2): 351.     CrossRef
    • Antitumor Effect and Induced Immune Response Following Exposure of Hexaminolevulinate and Blue Light in Combination with Checkpoint Inhibitor in an Orthotopic Model of Rat Bladder Cancer
      Laureline Lamy, Jacques Thomas, Agnès Leroux, Jean-François Bisson, Kari Myren, Aslak Godal, Gry Stensrud, Lina Bezdetnaya
      Biomedicines.2022; 10(3): 548.     CrossRef
    • High Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio and Low Lymphocyte Levels Are Correlated With Worse Pathological Complete Response Rates
      Serdar Karakaya, İbrahim Karadağ, Mehmet Emin Yılmaz, Ömür Berna Çakmak Öksüzoğlu
      Cureus.2022;[Epub]     CrossRef
    • Development of a method for digital assessment of tumor regression grade in patients with rectal cancer following neoadjuvant therapy
      Dea Natalie Munch Jepsen, Henrik Høeg, Jeppe Thagaard, Julie Sparholt Walbech, Ismail Gögenur, Anne-Marie Kanstrup Fiehn
      Journal of Pathology Informatics.2022; 13: 100152.     CrossRef
    • The reduction of 18F-FDG uptake ability of tumor tissue after neoadjuvant chemoradiotherapy in locally advanced rectal cancer can effectively reflect the degree of tumor regression
      Fengpeng Wu, Xiaoxiao Zhang, Congrong Yang, Kanghua Wang, Linlin Xiao, Chaoxi Zhou, Xinming Zhao, Guiying Wang
      Frontiers in Oncology.2022;[Epub]     CrossRef
    • Reduced field-of-view versus full field-of-view diffusion-weighted imaging for the evaluation of complete response to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer
      Siwon Jang, Jeong Min Lee, Jeong Hee Yoon, Jae Seok Bae
      Abdominal Radiology.2021; 46(4): 1468.     CrossRef
    • Impact of KRAS status on tumor response and survival after neoadjuvant treatment of locally advanced rectal cancer
      Peige Zhou, Paolo Goffredo, Timothy Ginader, Dakota Thompson, Jennifer Hrabe, Irena Gribovskaja‐Rupp, Muneera Kapadia, Imran Hassan
      Journal of Surgical Oncology.2021; 123(1): 278.     CrossRef
    • Clinical predictors of response to chemoradiotherapy for rectal cancer as an aid to organ preservation
      Jesse Fischer, Tim W. Eglinton, Frank A. Frizelle
      ANZ Journal of Surgery.2021; 91(6): 1190.     CrossRef
    • Can the mesorectal fat tissue volume be used as a predictive factor in foreseeing the response to neoadjuvant chemoradiotherapy in rectum cancer? A CT-based preliminary study
      Okan Dilek, Huseyin Akkaya, Cenk Parlatan, Tolga Koseci, Zeynel Abidin Tas, Gökhan Soker, Bozkurt Gulek
      Abdominal Radiology.2021; 46(6): 2415.     CrossRef
    • Pathological response post neoadjuvant therapy for locally advanced rectal cancer is an independent predictor of survival
      Jason On, Joanna Shim, Craig Mackay, Graeme Murray, Leslie Samuel, Craig Parnaby, George Ramsay
      Colorectal Disease.2021; 23(6): 1326.     CrossRef
    • Complete pathological response in rectal cancer utilising novel treatment strategies for neo-adjuvant therapy: A systematic review
      K. Wilson, M. Flood, V. Narasimhan, T. Pham, S. Warrier, R. Ramsay, M. Michael, A. Heriot
      European Journal of Surgical Oncology.2021; 47(8): 1862.     CrossRef
    • Contemporary snapshot of tumor regression grade (TRG) distribution in locally advanced rectal cancer: a cross sectional multicentric experience
      Paola Germani, Francesca Di Candido, Daniel Léonard, Dajana Cuicchi, Ugo Elmore, Marco Ettore Allaix, Vittoria Pia Barbieri, Laura D’Allens, Seraina Faes, Marika Milani, Damiano Caputo, Carmen Martinez, Jan Grosek, Valerio Caracino, Niki Christou, Sapho X
      Updates in Surgery.2021; 73(5): 1795.     CrossRef
    • The value of the tumour-stroma ratio for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer: a case control study
      Yanting Liang, Yaxi Zhu, Huan Lin, Shenyan Zhang, Suyun Li, Yanqi Huang, Chen Liu, Jinrong Qu, Changhong Liang, Ke Zhao, Zhenhui Li, Zaiyi Liu
      BMC Cancer.2021;[Epub]     CrossRef
    • Can histologic features predict neoadjuvant therapy response in rectal adenocarcinoma?
      Yuho Ono, Justin M.M. Cates, Raul S. Gonzalez
      Pathology - Research and Practice.2021; 226: 153608.     CrossRef
    • A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation
      Zhengfa Xue, Shuxin Yang, Yun Luo, Hao Cai, Ming He, Youping Ding, Lei Lei, Wei Peng, Guini Hong, You Guo
      Frontiers in Medicine.2021;[Epub]     CrossRef
    • Validation of Gene Expression-Based Predictive Biomarkers for Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
      Tomoyuki Momma, Hirokazu Okayama, Yasuyuki Kanke, Satoshi Fukai, Hisashi Onozawa, Shotaro Fujita, Wataru Sakamoto, Motonobu Saito, Shinji Ohki, Koji Kono
      Cancers.2021; 13(18): 4642.     CrossRef
    • Varying practices in tumor regression grading of gastrointestinal carcinomas after neoadjuvant therapy: results of an international survey
      Maria Westerhoff, Marek Osecky, Rupert Langer
      Modern Pathology.2020; 33(4): 676.     CrossRef
    • Importance of the neoadjuvant rectal (NAR) score to the outcome of neoadjuvant chemotherapy alone for locally advanced rectal cancer
      Toshiki Mukai, Keisuke Uehara, Toshisada Aiba, Atsushi Ogura, Toyonori Tsuzuki, Aya Tanaka, Masanori Sando, Noriyuki Ohara, Yusuke Sato, Norifumi Hattori, Goro Nakayama, Yasuhiro Kodera, Masato Nagino
      Surgery Today.2020; 50(8): 912.     CrossRef
    • The Effect of Lymph Nodes’ Histologic Response on Survival Outcomes in Moroccan Patients with Rectal Cancer
      Ihsane El Otmani, Fatima El Agy, Mohammed El Abkari, Karim Ibn Majdoub Hassani, Khalid Mazaz, El Bachir Benjelloun, Khalid Ait Taleb, Touria Bouhafa, Zineb Benbrahim, Sidi Adil Ibrahimi, Laila Chbani
      International Journal of Surgical Oncology.2020; 2020: 1.     CrossRef
    • Prognostic Value of Tumor Regression Grade Based on Ryan Score in Squamous Cell Carcinoma and Adenocarcinoma of Esophagus
      Flávio Roberto Takeda, Francisco Tustumi, Carlos de Almeida Obregon, Gustavo Gonçalves Yogolare, Yasmin Peres Navarro, Vanderlei Segatelli, Rubens Antonio Aissar Sallum, Ulysses Ribeiro Junior, Ivan Cecconello
      Annals of Surgical Oncology.2020; 27(4): 1241.     CrossRef
    • Standardized Pathology Report for Colorectal Cancer, 2nd Edition
      Baek-hui Kim, Joon Mee Kim, Gyeong Hoon Kang, Hee Jin Chang, Dong Wook Kang, Jung Ho Kim, Jeong Mo Bae, An Na Seo, Ho Sung Park, Yun Kyung Kang, Kyung-Hwa Lee, Mee Yon Cho, In-Gu Do, Hye Seung Lee, Hee Kyung Chang, Do Youn Park, Hyo Jeong Kang, Jin Hee So
      Journal of Pathology and Translational Medicine.2020; 54(1): 1.     CrossRef
    • Genomic variation as a marker of response to neoadjuvant therapy in locally advanced rectal cancer
      Jason K. Douglas, Rose E. Callahan, Zachary A. Hothem, Craig S. Cousineau, Samer Kawak, Bryan J. Thibodeau, Shelli Bergeron, Wei Li, Claire E. Peeples, Harry J. Wasvary
      Molecular & Cellular Oncology.2020; 7(3): 1716618.     CrossRef
    • 53BP1 expression and immunoscore are associated with the efficacy of neoadjuvant chemoradiotherapy for rectal cancer
      Ai Huang, Yong Xiao, Chunfen Peng, Tao Liu, Zhenyu Lin, Qin Yang, Tao Zhang, Jun Liu, Hong Ma
      Strahlentherapie und Onkologie.2020; 196(5): 465.     CrossRef
    • Predictors of pathological response and clinical outcome following chemoradiation for locally advanced rectal cancer — a systematic review
      Erica Amaral, Maria Bernardes, Sara Ribeiro, Beatriz Rosa, Ana Pereira, Sandra F. Martins
      Journal of Coloproctology.2020; 40(03): 278.     CrossRef
    • The effect of radiotherapy on rectal cancer: a histopathological appraisal and prognostic indicators
      Mohammad AlQudah, Emil Salmo, Najib Haboubi
      Radiation Oncology Journal.2020; 38(2): 77.     CrossRef
    • Short-course versus long-course neoadjuvant chemoradiotherapy in patients with rectal cancer: preliminary results of a randomized controlled trial
      Mahdi Aghili, Nastaran Khalili, Neda Khalili, Mohammad Babaei, Farshid Farhan, Peiman Haddad, Samaneh Salarvand, Amir Keshvari, Mohammad Sadegh Fazeli, Negin Mohammadi, Reza Ghalehtaki
      Radiation Oncology Journal.2020; 38(2): 119.     CrossRef
    • Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study
      Bianca Petresc, Andrei Lebovici, Cosmin Caraiani, Diana Sorina Feier, Florin Graur, Mircea Marian Buruian
      Cancers.2020; 12(7): 1894.     CrossRef
    • Preoperative Fibrinogen-Albumin Ratio Index (FARI) is a Reliable Prognosis and Chemoradiotherapy Sensitivity Predictor in Locally Advanced Rectal Cancer Patients Undergoing Radical Surgery Following Neoadjuvant Chemoradiotherapy


      Siyi Lu, Zhenzhen Liu, Xin Zhou, Bingyan Wang, Fei Li, Yanpeng Ma, Wendong Wang, Junren Ma, Yuxia Wang, Hao Wang, Wei Fu
      Cancer Management and Research.2020; Volume 12: 8555.     CrossRef
    • Staging of Locally Advanced Rectal Cancer Beyond TME
      Deborah S. Keller
      Clinics in Colon and Rectal Surgery.2020; 33(05): 258.     CrossRef
    • Tumor Regression in Lymph Node Metastases of Esophageal Adenocarcinomas after Neoadjuvant Therapy
      Marek Osecky, Dino Kröll, Marcus Feith, Daniel Reim, Bastian Dislich, Karen Becker, Rupert Langer
      Gastrointestinal Disorders.2020; 2(4): 397.     CrossRef
    • Prognostic value of changes in serum carcinoembryonic antigen levels for preoperative chemoradiotherapy response in locally advanced rectal cancer
      Chinock Cheong, Jun Sang Shin, Kwang Wook Suh
      World Journal of Gastroenterology.2020; 26(44): 7022.     CrossRef
    • MicroRNA Profiling in Oesophageal Adenocarcinoma Cell Lines and Patient Serum Samples Reveals a Role for miR-451a in Radiation Resistance
      Frederike Butz, Ann-Kathrin Eichelmann, George C. Mayne, Tingting Wang, Isabell Bastian, Karen Chiam, Shashikanth Marri, Pamela J. Sykes, Bas P. Wijnhoven, Eelke Toxopeus, Michael Z. Michael, Christos S. Karapetis, Richard Hummel, David I. Watson, Damian
      International Journal of Molecular Sciences.2020; 21(23): 8898.     CrossRef
    • Targeting Histone Chaperone FACT Complex Overcomes 5-Fluorouracil Resistance in Colon Cancer
      Heyu Song, Jiping Zeng, Shrabasti Roychoudhury, Pranjal Biswas, Bhopal Mohapatra, Sutapa Ray, Kayvon Dowlatshahi, Jing Wang, Vimla Band, Geoffrey Talmon, Kishor K. Bhakat
      Molecular Cancer Therapeutics.2020; 19(1): 258.     CrossRef
    • Tumor regression grade as a clinically useful outcome predictor in patients with rectal cancer after preoperative chemoradiotherapy
      Jung Wook Huh, Hee Cheol Kim, Seok Hyung Kim, Yoon Ah Park, Yong Beom Cho, Seong Hyeon Yun, Woo Yong Lee, Hee Chul Park, Doo Ho Choi, Joon Oh Park, Young Suk Park, Ho-Kyung Chun
      Surgery.2019; 165(3): 579.     CrossRef
    • Tumour regression after radiotherapy for rectal cancer – Results from the randomised Stockholm III trial
      Johan Erlandsson, Ester Lörinc, Madelene Ahlberg, David Pettersson, Torbjörn Holm, Bengt Glimelius, Anna Martling
      Radiotherapy and Oncology.2019; 135: 178.     CrossRef
    • Differences in prognostic relevance of rectal magnetic resonance imaging findings before and after neoadjuvant chemoradiotherapy
      Kwang-Seop Song, Dong Woon Lee, Bun Kim, Bo Yun Hur, Min Jung Kim, Min Ju Kim, Chang Won Hong, Sung Chan Park, Hyoung-Chul Park, Dae Kyung Sohn, Byung Chang Kim, Kyung Su Han, Jae Hwan Oh
      Scientific Reports.2019;[Epub]     CrossRef
    • A prospective feasibility study evaluating the role of multimodality imaging and liquid biopsy for response assessment in locally advanced rectal carcinoma
      Zahra Kassam, Kyle Burgers, Joanna C. Walsh, Ting-Yim Lee, Hon S. Leong, Barbara Fisher
      Abdominal Radiology.2019; 44(11): 3641.     CrossRef
    • Pretreatment Blood Biomarkers Predict Pathologic Responses to Neo-Crt in Patients with Locally Advanced Rectal Cancer
      Aijie Li, Kewen He, Dong Guo, Chao Liu, Duoying Wang, Xiangkui Mu, Jinming Yu
      Future Oncology.2019; 15(28): 3233.     CrossRef
    • Pathological Tumor Regression Grade Classifications in Gastrointestinal Cancers: Role on Patients’ Prognosis
      Giuseppe N. Fanelli, Fotios Loupakis, Elizabeth Smyth, Marco Scarpa, Sara Lonardi, Salvatore Pucciarelli, Giada Munari, Massimo Rugge, Nicola Valeri, Matteo Fassan
      International Journal of Surgical Pathology.2019; 27(8): 816.     CrossRef
    • Translational Potential of MicroRNAs for Preoperative Staging and Prediction of Chemoradiotherapy Response in Rectal Cancer
      Tana Machackova, Vladimir Prochazka, Zdenek Kala, Ondrej Slaby
      Cancers.2019; 11(10): 1545.     CrossRef
    • Association of tumor differentiation and prognosis in patients with rectal cancer undergoing neoadjuvant chemoradiation therapy
      Qunsheng Huang, Huabo Qin, Jian Xiao, Xiaosheng He, Minghao Xie, Xin He, Qiuqiong Yao, Ping Lan, Lei Lian
      Gastroenterology Report.2019; 7(4): 283.     CrossRef
    • Histopathological factors predicting response to neoadjuvant therapy in gastric carcinoma
      M. L. Sánchez de Molina, C. Díaz del Arco, P. Vorwald, D. García-Olmo, L. Estrada, M. J. Fernández-Aceñero
      Clinical and Translational Oncology.2018; 20(2): 253.     CrossRef
    • Tumor regression grading of gastrointestinal cancers after neoadjuvant therapy
      Rupert Langer, Karen Becker
      Virchows Archiv.2018; 472(2): 175.     CrossRef
    • Neoadjuvant radiotherapy combined with capecitabine and sorafenib in patients with advanced KRAS -mutated rectal cancer: A phase I/II trial (SAKK 41/08)
      Roger von Moos, Dieter Koeberle, Sabina Schacher, Stefanie Hayoz, Ralph C. Winterhalder, Arnaud Roth, György Bodoky, Panagiotis Samaras, Martin D. Berger, Daniel Rauch, Piercarlo Saletti, Ludwig Plasswilm, Daniel Zwahlen, Urs R. Meier, Pu Yan, Paola Izzo,
      European Journal of Cancer.2018; 89: 82.     CrossRef
    • A qualitative signature for predicting pathological response to neoadjuvant chemoradiation in locally advanced rectal cancers
      You Guo, Weizhong Jiang, Lu Ao, Kai Song, Huxing Chen, Qingzhou Guan, Qiao Gao, Jun Cheng, Huaping Liu, Xianlong Wang, Guoxian Guan, Zheng Guo
      Radiotherapy and Oncology.2018; 129(1): 149.     CrossRef
    • Prediction of lateral pelvic lymph node metastasis in patients with locally advanced rectal cancer with preoperative chemoradiotherapy: Focus on MR imaging findings
      Min Ju Kim, Bo Yun Hur, Eun Sun Lee, Boram Park, Jungnam Joo, Min Jung Kim, Sung Chan Park, Ji Yeon Baek, Hee Jin Chang, Dae Yong Kim, Jae Hwan Oh, Ju-Seog Lee
      PLOS ONE.2018; 13(4): e0195815.     CrossRef
    • Magnetic resonance tumour regression grade and pathological correlates in patients with rectal cancer
      J K Jang, J L Lee, S H Park, H J Park, I J Park, J H Kim, S H Choi, J Kim, C S Yu, J C Kim
      British Journal of Surgery.2018; 105(12): 1671.     CrossRef
    • Impact of Tumor Regression Grade as a Major Prognostic Factor in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Proposal for a Modified Staging System
      Changhoon Song, Joo-Hyun Chung, Sung-Bum Kang, Duck-Woo Kim, Heung-Kwon Oh, Hye Seung Lee, Jin Won Kim, Keun-Wook Lee, Jee Hyun Kim, Jae-Sung Kim
      Cancers.2018; 10(9): 319.     CrossRef
    • Prognostic Value of Sterilized Lymph Nodes After Preoperative Chemoradiotherapy for Patients with ypN0 Rectal Cancer
      Karina Vychnevskaia, Frederic Dumont, Julie Agostini, Catherine Julié, Peggy Dartigues, Thierry Lazure, Valérie Boige, Diane Goéré, Antoine Brouquet, Christophe Penna, Frédérique Peschaud, Stéphane Benoist
      Annals of Surgical Oncology.2017; 24(5): 1304.     CrossRef
    • Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer
      Delphine Dayde, Ichidai Tanaka, Rekha Jain, Mei Tai, Ayumu Taguchi
      International Journal of Molecular Sciences.2017; 18(3): 573.     CrossRef
    • Histological grading based on poorly differentiated clusters is predictive of tumour response and clinical outcome in rectal carcinoma treated with neoadjuvant chemoradiotherapy
      Luca Reggiani Bonetti, Simona Lionti, Federica Domati, Giuliana Pagliani, Elisabetta Mattioli, Valeria Barresi
      Histopathology.2017; 71(3): 393.     CrossRef
    • Applicability of American Joint Committee on Cancer and College of American Pathologists Regression Grading System in Rectal Cancer
      Tarkan Jäger, Daniel Neureiter, Romana Urbas, Eckhard Klieser, Wolfgang Hitzl, Klaus Emmanuel, Adam Dinnewitzer
      Diseases of the Colon & Rectum.2017; 60(8): 815.     CrossRef
    • Endoscopic assessment of tumor regression after preoperative chemoradiotherapy as a prognostic marker in locally advanced rectal cancer
      Dae Kyung Sohn, Kyung Su Han, Byung Chang Kim, Chang Won Hong, Hee Jin Chang, Ji Yeon Baek, Min Ju Kim, Sung Chan Park, Jae Hwan Oh, Dae Yong Kim
      Surgical Oncology.2017; 26(4): 453.     CrossRef
    • Do pathological variables have prognostic significance in rectal adenocarcinoma treated with neoadjuvant chemoradiotherapy and surgery?
      Luca Reggiani Bonetti, Simona Lionti, Federica Domati, Valeria Barresi
      World Journal of Gastroenterology.2017; 23(8): 1412.     CrossRef
    • Identification of protein clusters predictive of tumor response in rectal cancer patients receiving neoadjuvant chemo-radiotherapy
      Ombretta Repetto, Valli De Re, Antonino De Paoli, Claudio Belluco, Lara Alessandrini, Vincenzo Canzonieri, Renato Cannizzaro
      Oncotarget.2017; 8(17): 28328.     CrossRef
    • Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers
      You Guo, Jun Cheng, Lu Ao, Xiangyu Li, Qingzhou Guan, Juan Zhang, Haidan Yan, Hao Cai, Qiao Gao, Weizhong Jiang, Zheng Guo
      Scientific Reports.2016;[Epub]     CrossRef
    • Analysis of long-term outcomes and application of the tumor regression grading system in the therapeutic assessment of resectable limited-disease small cell lung cancer
      Shuonan Xu, Jianfei Zhu, Yawei Dou, Wei Tian, Yun Dai, Xianghui Luo, Hongtao Wang
      Oncology and Translational Medicine.2016; 2(5): 227.     CrossRef

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      What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?
      Cancer Res Treat. 2016;48(3):998-1009.   Published online October 22, 2015
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    What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?
    Image Image
    Fig. 1. Relapse-free survival of 933 rectal cancer patients treated with pre-operative chemoradiotherapy followed by surgical resection, according to tumor regression grades (TRG) according to the modified Dworak (mDworak) system (A), which assesses both the primary tumor and regional lymph nodes, the American Joint Committee on Cancer (AJCC) system (B), which assesses the primary tumor alone, and ypStage (C).
    Fig. 2. Overall survival of 933 rectal cancer patients treated with pre-operative chemoradiotherapy followed by surgical resection, according to tumor regression grades (TRG) according to the modified Dworak (mDworak) system (A), which assesses both the primary tumor and regional lymph nodes, the American Joint Committee on Cancer (AJCC) system (B), which assesses the primary tumor alone, and ypStage (C).
    What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?
    Dworak Mandard Ryan AJCC Modified Dworak (pT+pN)a)
    Complete regression No tumor cells (TRG 4) No residual cancer cells (TRG 1) No viable cancer cells, or single cells, or small groups of cancer cells (TRG 1) No viable cancer cells (TRG 0) No tumor cells (TRG 4)
    Near complete regression Very few tumor cells (TRG 3) Rare residual cancer cells (TRG 2) - Single or small groups of tumor cells (TRG 1: moderate response) Very few tumor cells (one or two microscopic foci of < 0.5 cm in diameter) (TRG 3)
    Moderate regression Dominantly fibrotic changes with few tumor cells or groups (TRG 2) Predominant fibrosis with increased number of residual cancer cells (TRG 3) Residual cancer outgrown by fibrosis (TRG 2) Residual cancer outgrown by fibrosis (TRG 2: minimal response) Dominantly fibrotic changes with few tumor cells or groups (TRG 2)
    Minimal regression Dominant tumor mass with obvious fibrosis (TRG 1) Residual cancer outgrowing fibrosis (TRG 4) Significant fibrosis outgrown by cancer, or no fibrosis with extensive residual cancer (TRG 3) Minimal or no tumor cells killed (TRG 3: poor response) Dominant tumor cell mass (> 50%) with obvious fibrosis or no regression (TRG 1)
    No regression No regression (TRG 0) No regressive change (TRG 5) - - -
    TRG system Distribution
    Modified Dworak TRG AJCC TRG Ryan TRG Dworak TRG
    Grade 0 - 135 (14.5)a) - 0
    Grade 1 162 (17.3) 140 (15.0) 275 (29.5) 113 (12.1)
    Grade 2 526 (56.4) 546 (58.5) 546 (56.1) 575 (61.6)
    Grade 3 116 (12.4) 112 (12.0) 112 (11.5) 110 (11.8)
    Grade 4 129 (13.8) - - 135 (14.5)
    Parameter No. of cases 5-Year RFS (%) p-value 5-Year OS (%) p-value
    Sex
     Male 635 76.4 0.434 98.4 0.271
     Female 298 78.7 95.3
    Age (yr)a)
     < 60 530 77.2 0.887 86.2 0.057
     ≥ 60 403 77.6 97.1
    Distance from AV (cm)
     < 5 318 71.3 0.002 81.6 0.15
     ≥ 5 615 80.8 85.9
    Histological typeb)
     Adenocarcinoma 768 75.5 < 0.001 83.1 < 0.001
     Other typec) 31 60.9 57.5
    Histological gradeb)
     Low 757 76 < 0.001 83.6 < 0.001
     High 42 56.1 54.5
    ypT
     ypT0 134 92 < 0.001 97.6 < 0.001
     ypTis 12 91.7 87.5
     ypT1 51 97.8 97.3
     ypT2 222 88.7 95.4
     ypT3 468 66.9 73.9
     ypT4 46 55.1 73.6
    ypN
     ypN0 612 87 < 0.001 92.1 < 0.001
     ypN1a 83 68.1 82.4
     ypN1b 103 64.6 77.3
     ypN1c 33 59 65.1
     ypN2a 61 45.4 60.4
     ypN2b 41 22.4 29.7
    ypStage
     ypT0N1 6 55.6 < 0.001 100 < 0.001
     0 140 93.1 97.7
     I 228 91.6 96
     II 244 78.5 83.6
     III 315 58 68.9
     IV 0 0 0
    Circumferential RM
     Negative 847 80 < 0.001 87.8 < 0.001
     Positive 86 48.8 52
    mDworak TRGd)
     1 (minimal) 162 56 < 0.001 64.9 < 0.001
     2 (moderate) 526 76.2 83.7
     3 (near complete) 116 91.1 95.8
     4 (compete) 129 92.5 97.5
    AJCC TRG
     0 (complete) 135 90.9 < 0.001 97.6 < 0.001
     1 (moderate) 140 89.7 93
     2 (minimal) 546 73.9 82.2
     3 (poor) 112 57.8 62.4
    Dworak TRG
     ≤ 1 (minimal) 113 57.1 < 0.001 62.6 < 0.001
     2 (moderate) 575 74.3 82.3
     3 (near complete) 110 93 95.6
     4 (complete) 135 90.9 97.6
    Ryan TRG
     1 (good) 275 90.3 < 0.001 95.2 < 0.001
     2 (moderate) 546 73.9 82.2
     3 (poor) 112 57.8 62.4
    TME
     Complete 664 79.2 0.072 85.7 0.275
     Near-complete 236 73 81
     Incomplete 33 64.1 71.3
    Lymphatic invasion
     Present 256 58.4 < 0.001 63.9 < 0.001
     Absent 677 83.8 90.9
    Perineural invasion
     Present 215 79.8 < 0.001 62.1 < 0.001
     Absent 718 84.7 89.9
    Venous invasion
     Present 185 57.6 < 0.001 62.1 < 0.001
     Absent 748 96.7 88.7
    Factor RFS
    OS
    Hazard ratio (95% CI) p-value Hazard ratio (95% CI) p-value
    ypStage
     0 & I 1.000 < 0.001 1.000 < 0.001
     II 2.057 (1.253-3.379) 2.668 (1.449-4.913)
     III 4.514 (2.888-7.055) 4.747 (2.686-8.389)
    Perineural invasion
     Absent 1.000 < 0.001 1.000 < 0.001
     Present 2.440 (1.802-3.304) 2.161 (1.504-3.105)
    Circumferential resection margin
     Negative 1.000 0.010 1.000 < 0.001
     Positive 1.656 (1.128-2.430) 2.942 (1.979-4.375)
    Model RFS
    OS
    HR (95% CI) p-value χ2 Harrell’s Ca) HR (95% CI) p-value χ2 Harrell’s Cb)
    Modified Dworak TRG 68.92 0.6492 58.06 0.6783
     1 1.000 1.000
     2 0.450 (0.336-0.603) < 0.001 0.426 (0.297-0.610) < 0.001
     3 0.178 (0.099-0.322) < 0.001 0.106 (0.042-0.267) < 0.001
     4 0.172 (0.097-0.305) < 0.001 0.132 (0.060-0.292) < 0.001
    AJCC TRG 59.58 0.6359 53.95 0.6718
     0 1.000 1.000
     1 1.035 (0.523-2.048) 0.922 1.243 (0.463-3.337) 0.666
     2 2.662 (1.587-4.464) < 0.001 3.696 (1.710-7.986) 0.001
     3 5.553 (3.146-9.803) < 0.001 9.036 (4.004-20.394) < 0.001
    Dworak TRG 61.85 0.6374 55.52 0.6711
     ≤ 1 1.000 1.000
     2 0.460 (0.332-0.637) < 0.001 0.403 (0.272-0.599) < 0.001
     3 0.149 (0.077-0.287) < 0.001 0.098 (0.038-0.250) < 0.001
     4 0.177 (0.101-0.312) < 0.001 0.111 (0.049-0.251) < 0.001
    Ryan TRG 59.57 0.6356 53.76 0.6700
     1 1.000 1.000
     2 2.615 (1.791-3.820) < 0.001 3.289 (1.929-5.610) < 0.001
     3 5.457 (3.492-8.527) < 0.001 8.043 (4.437-14.580) < 0.001
    ypStage 119.46 0.7046 82.46 0.7175
     ≤ I 1.000 1.000
     II 2.892 (1.885-4.436) < 0.001 3.959 (2.191-7.154) < 0.001
     IIIc) 6.328 (4.339-9.229) < 0.001 7.864 (4.608-13.422) < 0.001
    ypStage 133.35 0.7248 97.33 0.7482
     ≤ I 1.000 1.000
     II 2.613 (1.532-4.456) < 0.001 3.297 (1.551-7.007) 0.002
     IIIc) 5.404 (3.286-8.886) < 0.001 6.206 (3.040-12.669) < 0.001
    Modified Dworak TRG
     1 1.000 1.000
     2 0.612 (0.455-0.824) 0.001 0.57 (0.395-0.822) 0.003
     3 0.411 (0.22-0.769) 0.005 0.25 (0.097-0.649) 0.004
     4 0.702 (0.333-1.480) 0.352 0.639 (0.223-1.827) 0.403
    ypStage 130.76 0.7208 96.72 0.7439
     ≤ I 1.000 1.000
     II 2.600 (1.567-4.315) < 0.001 3.141 (1.569-6.285) 0.001
     IIIc) 5.439 (3.438-8.606) < 0.001 5.949 (3.146-11.249) < 0.001
    AJCC TRG
     0 1.000 1.000
     1 0.710 (0.352-1.435) 0.340 0.766 (0.276-2.216) 0.608
     2 0.983 (0.532-1.816) 0.955 1.203 (0.490-2.956) 0.687
     3 1.654 (0.848-3.226) 0.140 2.464 (0.957-6.347) 0.062
    Table 1. Tumor regression grade (TRG) systems

    AJCC, American Joint Committee on Cancer.

    Modified Dworak TRG was used to evaluate both the primary tumor and regional lymph nodes as a whole.

    Table 2. Distribution of case numbers according to four different TRG systems

    Values are presented as number (%). The modified Dworak system assessed the primary tumor and regional lymph nodes, whereas the American Joint Committee on Cancer (AJCC), Ryan, and Dworak systems assessed the primary tumor alone. TRG, tumor regression grade.

    Including six patients classified as ypT0N1.

    Table 3. Parameters used in Kaplan-Meier survival analysis

    RFS, recurrence-free survival; OS, overall survival; AV, anal verge; RM, resection margin; mDwork, modified Dwork; TRG, tumor regression grade; AJCC, American Joint Committee on Cancer; TME, total mesorectal excision.

    Median 59 years (range, 22 to 87 years),

    No residual tumors were noted in 134 cases (14.4%); these are excluded,

    23 mucinous adenocarcinomas, six signet ring cell carcinomas, two adenosquamous carcinomas,

    Dworak TRG assessing primary tumor and regional lymph nodes as a whole.

    Table 4. Multivariate analysis of factors influencing RFS and OS

    RFS, recurrence-free survival; OS, overall survival; CI, confidence interval.

    Table 5. Univariate Cox’s proportional hazards models and model validation of RFS and OS

    RFS, recurrence-free survival; OS, overall survival; HR, hazard ratio; CI, confidence interval; TRG, tumor regression grade; AJCC, American Joint Committee on Cancer.

    Differences between C-statistics for RFS: modified Dworak (mDworak) TRG vs. AJCC TRG, p=0.091; mDworak TRG vs. Dworak TRG, p=0.118; mDworak TRG vs. Ryan TRG, p=0.110; AJCC TRG vs. Dworak TRG, p=0.794; AJCC TRG vs. Ryan TRG, p=0.893; Dworak TRG vs. Ryan TRG, p=0.750; ypStage vs. mDworak TRG, p < 0.001; ypStage+mDworak TRG vs. ypStage, p < 0.001; ypStage+mDworak TRG vs. ypStage+AJCC TRG, p=0.251,

    Differences between C-statistics for OS: mDworak TRG vs. AJCC TRG, p=0.542; mDworak TRG vs. Dworak TRG, p=0.407; mDworak TRG vs. Ryan TRG, p=0.444; AJCC TRG vs. Dworak TRG, p=0.925; AJCC TRG vs. Ryan TRG, p=0.475; Dworak TRG vs. Ryan TRG, p=0.878; ypStage vs. mDworak TRG, p=0.043; ypStage+mDworak TRG vs. ypStage, p < 0.001; ypStage+mDworak TRG vs. ypStage+AJCC TRG, p=0.582,

    Including six patients classified as ypT0N1.


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