Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

Clinical Significance of Combining Preoperative and Postoperative Albumin-Bilirubin Score in Colorectal Cancer: Doyoun Kim, Jae-Hoon Lee, Eun-Suk Cho, Su-Jin Shin, Hye Sun Lee, Hwa-Hee Koh, Kang Young Lee, Jeonghyun Kang; Cancer Res Treat. 2023;55(4):1261-1269. Published online April 17, 2023; DOI: https://doi.org/10.4143/crt.2022.1444

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Purpose
Albumin-bilirubin (ALBI) score is a well-known prognostic factor for various diseases, including colorectal cancer (CRC). However, little is known about the significance of postoperative ALBI score changes in patients with CRC.
Materials and Methods
A total of 723 patients who underwent surgery were enrolled. Preoperative ALBI (ALBI-pre) and postoperative ALBI (ALBI-post) scores were divided into low and high score groups. ALBI-trend was defined as a combination of four groups comprising the low and high ALBI-pre and ALBI-post score groups. Kaplan-Meier survival curves were used to compare the overall survival (OS) between the different ALBI groups. The Cox proportional hazards model was used to examine the independent relevant factors of OS. Stratification performance was compared between the different ALBI groupings using Harrell’s concordance index (C-index).
Results
ALBI-pre, ALBI-post, and ALBI-trend score groups were significant prognostic factors of OS in the univariable analysis. However, multivariable analysis showed that ALBI-trend was an independent prognostic factor while ALBI-pre and ALBI-post were not. The C-index of ALBI-trend (0.622; 95% confidence interval [CI], 0.587 to 0.655) was higher than that of ALBI-pre (0.589; 95% CI, 0.557 to 0.621; bootstrap mean difference, 0.033; 95% CI, 0.013 to 0.057) and ALBI-post (0.575; 95% CI, 0.545 to 0.605; bootstrap mean difference, 0.047; 95% CI, 0.024 to 0.074).
Conclusion
Combining ALBI-pre and ALBI-post scores is an independent prognostic factor of OS and shows superior predictive power compared to ALBI-pre or ALBI-post alone in patients with CRC.

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Comparing laboratory toxicity of selective intra-arterial radionuclide therapy for primary and metastatic liver tumors: resin versus glass microspheres
Başak Soydaş-Turan, M. Fani Bozkurt, Gonca Eldem, Bora Peynircioglu, Omer Ugur, Bilge Volkan-Salanci
Annals of Nuclear Medicine.2025; 39(4): 373. CrossRef
Improvement of Hypoalbuminemia and Hepatic Reserve after Stent Placement for Postsurgical Portal Vein Stenosis
Naoya Kinota, Daisuke Abo, Ryo Morita, Koji Yamasaki, Takaaki Fujii, Daisuke Kato, Tasuku Kimura, Yusuke Sakuhara, Kazufumi Okada, Isao Yokota, Tatsuya Orimo, Tatsuhiko Kakisaka, Toru Nakamura, Satoshi Hirano, Kazuyuki Minowa, Kohsuke Kudo
Journal of Vascular and Interventional Radiology.2025; 36(4): 616. CrossRef
Gastrointestinal tumors of the small bowel: prognostic roles of tumor stage and inflammatory markers
Mehmet Torun, Sevil Özkan, Deniz Kol Özbay, Erkan Özkan
Anatolian Current Medical Journal.2025; 7(2): 164. CrossRef
Assessment of the albumin-bilirubin score in breast cancer patients with liver metastasis after surgery
Li Chen, Chunlei Tan, Qingwen Li, Zhibo Ma, Meng Wu, Xiaosheng Tan, Tiangen Wu, Jinwen Liu, Jing Wang
Heliyon.2023; 9(11): e21772. CrossRef

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Association of Body Mass Index with Survival in Asian Patients with Colorectal Cancer: Sangwon Lee, Dong Hee Lee, Jae-Hoon Lee, Su-Jin Shin, Hye Sun Lee, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Jeonghyun Kang; Cancer Res Treat. 2022;54(3):860-872. Published online October 15, 2021; DOI: https://doi.org/10.4143/crt.2021.656

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Purpose
The clinical significance of body mass index (BMI) on long-term outcomes has not been extensively investigated in Asian patients with colorectal cancer (CRC). This study aims to describe the association between BMI and survival, plus providing BMI cut-off value for predicting prognosis in CRC patients.
Materials and Methods
A total of 1,182 patients who had undergone surgery for stage I-III CRC from June 2004 to February 2014 were included. BMI was categorized into four groups based on the recommendation for Asian ethnicity. The optimal BMI cut-off value was determined to maximize overall survival (OS) difference.
Results
In multivariable analysis, underweight BMI was significantly associated with poor OS (hazard ratio [HR], 2.38; 95% confidence interval [CI], 1.55 to 3.71; p < 0.001) and obese BMI was associated with better OS (HR, 0.72; 95% CI, 0.53 to 0.97; p=0.036) compared with the normal BMI. Overweight and obese BMI were associated with better recurrence-free survival (HR, 0.64; 95% CI, 0.42 to 0.99; p=0.046 and HR, 0.58; 95% CI, 0.38 to 0.89; p=0.014, respectively) compared with the normal BMI group. BMI cutoff value was 20.44 kg/m². Adding the BMI cutoff value to cancer staging could increase discriminatory performance in terms of integrated area under the curve and Harrell’s concordance index.
Conclusion
Compared to normal BMI, underweight BMI was associated with poor survival whereas obese BMI was associated with better survival. BMI cut-off value of 20.44 kg/m² is a useful discriminator in Asian patients with CRC.

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Body mass index, weight change, and cancer prognosis: a meta-analysis and systematic review of 73 cohort studies
H. Wen, G. Deng, X. Shi, Z. Liu, A. Lin, Q. Cheng, J. Zhang, P. Luo
ESMO Open.2024; 9(3): 102241. CrossRef
Trends in Anticoagulant Utilization and Clinical Outcomes for Cancer-Associated Thrombosis: A Multicenter Cohort Study in Thailand's Upper-Middle–Income Country Setting
Kirati Kengkla, Surakit Nathisuwan, Warunsuda Sripakdee, Pirun Saelue, Kwanruethai Sengnoo, Aumkhae Sookprasert, Suphat Subongkot
JCO Global Oncology.2024;[Epub] CrossRef
Post‐diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta‐analysis
Nerea Becerra‐Tomás, Georgios Markozannes, Margarita Cariolou, Katia Balducci, Rita Vieira, Sonia Kiss, Dagfinn Aune, Darren C. Greenwood, Laure Dossus, Ellen Copson, Andrew G. Renehan, Martijn Bours, Wendy Demark‐Wahnefried, Melissa M. Hudson, Anne M. Ma
International Journal of Cancer.2024; 155(3): 400. CrossRef
Low muscle mass-to-fat ratio is an independent factor that predicts worse overall survival and complications in patients with colon cancer: a retrospective single-center cohort study
Jiabao Tang, Jingwen Xu, Xiaohua Li, Chun Cao
Annals of Surgical Treatment and Research.2024; 107(2): 68. CrossRef
Comment on “Dense Tumor‐Infiltrating Lymphocytes (TILs) in Liver Metastasis From Colorectal Cancer Are Related to Improved Overall Survival”
Fuji Lai, Sheng Li, Zhonglei Shen
Journal of Surgical Oncology.2024;[Epub] CrossRef
Obesity, the Adipose Organ and Cancer in Humans: Association or Causation?
Elisabetta Trevellin, Silvia Bettini, Anna Pilatone, Roberto Vettor, Gabriella Milan
Biomedicines.2023; 11(5): 1319. CrossRef
Higher body mass index was associated with better prognosis in diabetic patients with stage II colorectal cancer
Xiao-Yu Liu, Bing Kang, Yu-Xi Cheng, Chao Yuan, Wei Tao, Bin Zhang, Zheng-Qiang Wei, Dong Peng
BMC Cancer.2022;[Epub] CrossRef
Preoperative carcinoembryonic antigen to body mass index ratio contributes to prognosis prediction in colorectal cancer
Jia Xiang, Mengyao Ding, Jixing Lin, Tianhui Xue, Qianwen Ye, Bing Yan
Oncology Letters.2022;[Epub] CrossRef

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Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer: Mohamed Hosny Osman, Reham Hosny Mohamed, Hossam Mohamed Sarhan, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Jeonghyun Kang; Cancer Res Treat. 2022;54(2):517-524. Published online June 15, 2021; DOI: https://doi.org/10.4143/crt.2021.206

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Purpose
Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.
Materials and Methods
A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values.
Results
Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com).
Conclusion
ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC.

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Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms
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Catarina Sousa Santos, Mário Amorim-Lopes
BMC Medical Research Methodology.2025;[Epub] CrossRef
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SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients
Ping Yang, Hang Qiu, Xulin Yang, Liya Wang, Xiaodong Wang
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The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction
Chenghao Lu, Lu Liu, Minyue Yin, Jiaxi Lin, Shiqi Zhu, Jingwen Gao, Shuting Qu, Guoting Xu, Lihe Liu, Jinzhou Zhu, Chunfang Xu
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洪铭崔
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Gastrointestinal Cancer

LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer: Jeonghyun Kang, Yoon Jung Choi, Im-kyung Kim, Hye Sun Lee, Hogeun Kim, Seung Hyuk Baik, Nam Kyu Kim, Kang Young Lee; Cancer Res Treat. 2021;53(3):773-783. Published online December 29, 2020; DOI: https://doi.org/10.4143/crt.2020.974

Abstract PDF Supplementary Material PubReader ePub

Purpose
The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning–based approach has not been widely studied.
Materials and Methods
Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.
Results
LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.
Conclusion
Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.

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In Vitro Adenosine Triphosphate-Based Chemotherapy Response Assay as a Predictor of Clinical Response to Fluorouracil-Based Adjuvant Chemotherapy in Stage II Colorectal Cancer: Hye Youn Kwon, Im-kyung Kim, Jeonghyun Kang, Seung-Kook Sohn, Kang Young Lee; Cancer Res Treat. 2016;48(3):970-977. Published online October 22, 2015; DOI: https://doi.org/10.4143/crt.2015.140

Abstract PDF PubReader ePub

Purpose
We evaluated the usefulness of the in vitro adenosine triphosphate-based chemotherapy response assay (ATP-CRA) for prediction of clinical response to fluorouracil-based adjuvant chemotherapy in stage II colorectal cancer. Materials and Methods Tumor specimens of 86 patients with pathologically confirmed stage II colorectal adenocarcinoma were tested for chemosensitivity to fluorouracil. Chemosensitivity was determined by cell death rate (CDR) of drug-exposed cells, calculated by comparing the intracellular ATP level with that of untreated controls. Results Among the 86 enrolled patients who underwent radical surgery followed by fluorouracilbased adjuvant chemotherapy, recurrence was found in 11 patients (12.7%). The CDR ≥ 20% group was associated with better disease-free survival than the CDR < 20% group (89.4% vs. 70.1%, p=0.027). Multivariate analysis showed that CDR < 20% and T4 stage were poor prognostic factors for disease-free survival after fluorouracil-based adjuvant chemotherapy. Conclusion In stage II colorectal cancer, the in vitro ATP-CRA may be useful in identifying patients likely to benefit from fluorouracil-based adjuvant chemotherapy.

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