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Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer
Ji Eun Oh, Min Ju Kim, Joohyung Lee, Bo Yun Hur, Bun Kim, Dae Yong Kim, Ji Yeon Baek, Hee Jin Chang, Sung Chan Park, Jae Hwan Oh, Sun Ah Cho, Dae Kyung Sohn
Cancer Res Treat. 2020;52(1):51-59.   Published online May 7, 2019
DOI: https://doi.org/10.4143/crt.2019.050
AbstractAbstract PDFSupplementary MaterialPubReaderePub
Purpose
Mutation of the Kirsten Ras (KRAS) oncogene is present in 30%-40% of colorectal cancers and has prognostic significance in rectal cancer. In this study, we examined the ability of radiomics features extracted from T2-weighted magnetic resonance (MR) images to differentiate between tumors with mutant KRAS and wild-type KRAS.
Materials and Methods
Sixty patients with primary rectal cancer (25 with mutant KRAS, 35 with wild-type KRAS) were retrospectively enrolled. Texture analysis was performed in all regions of interest on MR images, which were manually segmented by two independent radiologists. We identified potentially useful imaging features using the two-tailed t test and used them to build a discriminant model with a decision tree to estimate whether KRAS mutation had occurred.
Results
Three radiomic features were significantly associated with KRASmutational status (p < 0.05). The mean (and standard deviation) skewness with gradient filter value was significantly higher in the mutant KRAS group than in the wild-type group (2.04±0.94 vs. 1.59±0.69). Higher standard deviations for medium texture (SSF3 and SSF4) were able to differentiate mutant KRAS (139.81±44.19 and 267.12±89.75, respectively) and wild-type KRAS (114.55±29.30 and 224.78±62.20). The final decision tree comprised three decision nodes and four terminal nodes, two of which designated KRAS mutation. The sensitivity, specificity, and accuracy of the decision tree was 84%, 80%, and 81.7%, respectively.
Conclusion
Using MR-based texture analysis, we identified three imaging features that could differentiate mutant from wild-type KRAS. T2-weighted images could be used to predict KRAS mutation status preoperatively in patients with rectal cancer.

Citations

Citations to this article as recorded by  
  • A multicenter study: predicting KRAS mutation and prognosis in colorectal cancer through a CT-based radiomics nomogram
    Manman Li, Yiwen Yuan, Hui Zhou, Feng Feng, Guodong Xu
    Abdominal Radiology.2024; 49(6): 1816.     CrossRef
  • SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer
    Yulan Ma, Yuzhu Guo, Weigang Cui, Jingyu Liu, Yang Li, Yingsen Wang, Yan Qiang
    Computers in Biology and Medicine.2024; 173: 108293.     CrossRef
  • CHNet: A multi-task global–local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer
    Meiling Cai, Lin Zhao, Yan Qiang, Long Wang, Juanjuan Zhao
    Artificial Intelligence in Medicine.2024; 155: 102931.     CrossRef
  • Assessment of prognostic indicators and KRAS mutations in rectal cancer using a fractional-order calculus MR diffusion model: whole tumor histogram analysis
    Mi Zhou, Hongyun Huang, Deying Bao, Meining Chen, Fulin Lu
    Abdominal Radiology.2024;[Epub]     CrossRef
  • The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer
    Sebastian Curcean, Andra Curcean, Daniela Martin, Zsolt Fekete, Alexandru Irimie, Alina-Simona Muntean, Cosmin Caraiani
    Cancers.2024; 16(17): 3111.     CrossRef
  • A segmentation-based sequence residual attention model for KRAS gene mutation status prediction in colorectal cancer
    Lin Zhao, Kai Song, Yulan Ma, Meiling Cai, Yan Qiang, Jingyu Sun, Juanjuan Zhao
    Applied Intelligence.2023; 53(9): 10232.     CrossRef
  • Rectal MRI Interpretation After Neoadjuvant Therapy
    Natally Horvat, Maria El Homsi, Joao Miranda, Yousef Mazaheri, Marc J. Gollub, Viktoriya Paroder
    Journal of Magnetic Resonance Imaging.2023; 57(2): 353.     CrossRef
  • Association between Dynamic Contrast-Enhanced MRI Parameters and Prognostic Factors in Patients with Primary Rectal Cancer
    Hye Ri Kim, Seung Ho Kim, Kyung Han Nam
    Current Oncology.2023; 30(2): 2543.     CrossRef
  • Virtual biopsy in abdominal pathology: where do we stand?
    Arianna Defeudis, Jovana Panic, Giulia Nicoletti, Simone Mazzetti, Valentina Giannini, Daniele Regge
    BJR|Open.2023;[Epub]     CrossRef
  • Repeatability of radiomics studies in colorectal cancer: a systematic review
    Ying Liu, Xiaoqin Wei, Xu Feng, Yan Liu, Guiling Feng, Yong Du
    BMC Gastroenterology.2023;[Epub]     CrossRef
  • The Role of Radiomics in Rectal Cancer
    Joao Miranda, Natally Horvat, Jose A. B. Araujo-Filho, Kamila S. Albuquerque, Charlotte Charbel, Bruno M. C. Trindade, Daniel L. Cardoso, Lucas de Padua Gomes de Farias, Jayasree Chakraborty, Cesar Higa Nomura
    Journal of Gastrointestinal Cancer.2023; 54(4): 1158.     CrossRef
  • Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions
    Niall J. O’Sullivan, Michael E. Kelly
    Current Oncology.2023; 30(5): 4936.     CrossRef
  • Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics
    Yuntai Cao, Jing Zhang, Lele Huang, Zhiyong Zhao, Guojin Zhang, Jialiang Ren, Hailong Li, Hongqian Zhang, Bin Guo, Zhan Wang, Yue Xing, Junlin Zhou
    Japanese Journal of Radiology.2023; 41(11): 1236.     CrossRef
  • Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review
    Giuseppe Di Costanzo, Raffaele Ascione, Andrea Ponsiglione, Anna Giacoma Tucci, Serena Dell’Aversana, Francesca Iasiello, Enrico Cavaglià
    Exploration of Targeted Anti-tumor Therapy.2023; : 406.     CrossRef
  • Investigating the Feasibility of Predicting KRAS Status, Tumor Staging, and Extramural Venous Invasion in Colorectal Cancer Using Inter-Platform Magnetic Resonance Imaging Radiomic Features
    Mohammed S. Alshuhri, Abdulaziz Alduhyyim, Haitham Al-Mubarak, Ahmad A. Alhulail, Othman I. Alomair, Yahia Madkhali, Rakan A. Alghuraybi, Abdullah M. Alotaibi, Abdullalh G. M. Alqahtani
    Diagnostics.2023; 13(23): 3541.     CrossRef
  • Radiogenomics: Contemporary Applications in the Management of Rectal Cancer
    Niall J. O’Sullivan, Hugo C. Temperley, Michelle T. Horan, Alison Corr, Brian J. Mehigan, John O. Larkin, Paul H. McCormick, Dara O. Kavanagh, James F. M. Meaney, Michael E. Kelly
    Cancers.2023; 15(24): 5816.     CrossRef
  • KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients
    Yirong Xiang, Shuai Li, Maxiaowei Song, Hongzhi Wang, Ke Hu, Fengwei Wang, Zhi Wang, Zhiyong Niu, Jin Liu, Yong Cai, Yongheng Li, Xianggao Zhu, Jianhao Geng, Yangzi Zhang, Huajing Teng, Weihu Wang
    BMC Medical Imaging.2023;[Epub]     CrossRef
  • Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer
    Kai Song, Zijuan Zhao, Jiawen Wang, Yan Qiang, Juanjuan Zhao, Muhammad Bilal Zia
    International Journal of Machine Learning and Cybernetics.2022; 13(5): 1283.     CrossRef
  • A multitask dual‐stream attention network for the identification of KRAS mutation in colorectal cancer
    Kai Song, Zijuan Zhao, Yulan Ma, JiaWen Wang, Wei Wu, Yan Qiang, Juanjuan Zhao, Suman Chaudhary
    Medical Physics.2022; 49(1): 254.     CrossRef
  • Multi-Omic Approaches in Colorectal Cancer beyond Genomic Data
    Emilia Sardo, Stefania Napolitano, Carminia Maria Della Corte, Davide Ciardiello, Antonio Raucci, Gianluca Arrichiello, Teresa Troiani, Fortunato Ciardiello, Erika Martinelli, Giulia Martini
    Journal of Personalized Medicine.2022; 12(2): 128.     CrossRef
  • The application of radiomics in predicting gene mutations in cancer
    Yana Qi, Tingting Zhao, Mingyong Han
    European Radiology.2022; 32(6): 4014.     CrossRef
  • The prognostic effect of PNN in digestive tract cancers and its correlation with the tumor immune landscape in colon adenocarcinoma
    Hui Zhang, Ming Jin, Meng Ye, Yanping Bei, Shaohui Yang, Kaitai Liu
    Journal of Clinical Laboratory Analysis.2022;[Epub]     CrossRef
  • 18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy
    Giulia Capelli, Cristina Campi, Quoc Riccardo Bao, Francesco Morra, Carmelo Lacognata, Pietro Zucchetta, Diego Cecchin, Salvatore Pucciarelli, Gaya Spolverato, Filippo Crimì
    Nuclear Medicine Communications.2022; 43(7): 815.     CrossRef
  • Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer
    Kaiming Xue, Lin Liu, Yunxia Liu, Yan Guo, Yuhang Zhu, Mengchao Zhang
    La radiologia medica.2022; 127(7): 702.     CrossRef
  • Heteronemin and Tetrac Induce Anti-Proliferation by Blocking EGFR-Mediated Signaling in Colorectal Cancer Cells
    Sukanya Unson, Tung-Cheng Chang, Yung-Ning Yang, Shwu-Huey Wang, Chi-Hung Huang, Dana R. Crawford, Haw-Ming Huang, Zi-Lin Li, Hung-Yun Lin, Jacqueline Whang-Peng, Kuan Wang, Paul J. Davis, Wen-Shan Li
    Marine Drugs.2022; 20(8): 482.     CrossRef
  • Association between Texture Analysis Parameters and Molecular Biologic KRAS Mutation in Non-Mucinous Rectal Cancer
    Sung Jae Jo, Seung Ho Kim, Sang Joon Park, Yedaun Lee, Jung Hee Son
    Journal of the Korean Society of Radiology.2021; 82(2): 406.     CrossRef
  • Actualización de la recomendación para la determinación de biomarcadores en el carcinoma colorrectal. Consenso Nacional de la Sociedad Española de Oncología Médica y de la Sociedad Española de Anatomía Patológica
    Samuel Navarro, Miriam Cuatrecasas, Javier Hernández-Losa, Stefania Landolfi, Eva Musulén, Santiago Ramón y Cajal, Rocío García-Carbonero, Jesús García-Foncillas, Pedro Pérez-Segura, Ramón Salazar, Ruth Vera, Pilar García-Alfonso
    Revista Española de Patología.2021; 54(1): 41.     CrossRef
  • Texture analysis using T1-weighted images for muscles in Charcot-Marie-Tooth disease patients and volunteers
    Ji Hyun Lee, Young Cheol Yoon, Hyun Su Kim, Jae-Hun Kim, Byung-Ok Choi
    European Radiology.2021; 31(5): 3508.     CrossRef
  • Radiomics signature of brain metastasis: prediction of EGFR mutation status
    Guangyu Wang, Bomin Wang, Zhou Wang, Wenchao Li, Jianjun Xiu, Zhi Liu, Mingyong Han
    European Radiology.2021; 31(7): 4538.     CrossRef
  • Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice
    Francesca Coppola, Valentina Giannini, Michela Gabelloni, Jovana Panic, Arianna Defeudis, Silvia Lo Monaco, Arrigo Cattabriga, Maria Adriana Cocozza, Luigi Vincenzo Pastore, Michela Polici, Damiano Caruso, Andrea Laghi, Daniele Regge, Emanuele Neri, Rita
    Diagnostics.2021; 11(5): 756.     CrossRef
  • MRI Radiomics Signature as a Potential Biomarker for Predicting KRAS Status in Locally Advanced Rectal Cancer Patients
    ZhiYuan Zhang, LiJun Shen, Yan Wang, Jiazhou Wang, Hui Zhang, Fan Xia, JueFeng Wan, Zhen Zhang
    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics
    Jing Gao, Xiahan Chen, Xudong Li, Fei Miao, Weihuan Fang, Biao Li, Xiaohua Qian, Xiaozhu Lin
    Frontiers in Oncology.2021;[Epub]     CrossRef
  • Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer
    Zhuokai Zhuang, Zongchao Liu, Juan Li, Xiaolin Wang, Peiyi Xie, Fei Xiong, Jiancong Hu, Xiaochun Meng, Meijin Huang, Yanhong Deng, Ping Lan, Huichuan Yu, Yanxin Luo
    Journal of Translational Medicine.2021;[Epub]     CrossRef
  • Systematic Review on the Association of Radiomics with Tumor Biological Endpoints
    Agustina La Greca Saint-Esteven, Diem Vuong, Fabienne Tschanz, Janita E. van Timmeren, Riccardo Dal Bello, Verena Waller, Martin Pruschy, Matthias Guckenberger, Stephanie Tanadini-Lang
    Cancers.2021; 13(12): 3015.     CrossRef
  • Emerging applications of radiomics in rectal cancer: State of the art and future perspectives
    Min Hou, Ji-Hong Sun
    World Journal of Gastroenterology.2021; 27(25): 3802.     CrossRef
  • Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRI
    Yulan Ma, Jiawen Wang, Kai Song, Yan Qiang, Xiong Jiao, Juanjuan Zhao
    Computer Methods and Programs in Biomedicine.2021; 209: 106311.     CrossRef
  • Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma
    Zhi-Hua Lu, Kai-Jian Xia, Heng Jiang, Jian-Long Jiang, Mei Wu
    World Journal of Clinical Cases.2021; 9(24): 6987.     CrossRef
  • 2. Radiomics of MRI
    Koji Sakai
    Japanese Journal of Radiological Technology.2021; 77(8): 866.     CrossRef
  • Radiomics and machine learning applications in rectal cancer: Current update and future perspectives
    Arnaldo Stanzione, Francesco Verde, Valeria Romeo, Francesca Boccadifuoco, Pier Paolo Mainenti, Simone Maurea
    World Journal of Gastroenterology.2021; 27(32): 5306.     CrossRef
  • Role of MRI‑based radiomics in locally advanced rectal cancer (Review)
    Siyu Zhang, Mingrong Yu, Dan Chen, Peidong Li, Bin Tang, Jie Li
    Oncology Reports.2021;[Epub]     CrossRef
  • Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer
    Yanfen Cui, Huanhuan Liu, Jialiang Ren, Xiaosong Du, Lei Xin, Dandan Li, Xiaotang Yang, Dengbin Wang
    European Radiology.2020; 30(4): 1948.     CrossRef
  • Multi-branch cross attention model for prediction of KRAS mutation in rectal cancer with t2-weighted MRI
    JiaWen Wang, YanFen Cui, GuoHua Shi, JuanJuan Zhao, XiaoTang Yang, Yan Qiang, QianQian Du, Yue Ma, Ntikurako Guy-Fernand Kazihise
    Applied Intelligence.2020; 50(8): 2352.     CrossRef
  • Development and validation of a radiomics model based on T2WI images for preoperative prediction of microsatellite instability status in rectal cancer
    Zixing Huang, Wei Zhang, Du He, Xing Cui, Song Tian, Hongkun Yin, Bin Song
    Medicine.2020; 99(10): e19428.     CrossRef
  • PET/MRI Radiomics in Rectal Cancer: a Pilot Study on the Correlation Between PET- and MRI-Derived Image Features with a Clinical Interpretation
    Barbara Juarez Amorim, Angel Torrado-Carvajal, Shadi A Esfahani, Sara S Marcos, Mark Vangel, Dan Stein, David Groshar, Onofrio A Catalano
    Molecular Imaging and Biology.2020; 22(5): 1438.     CrossRef
  • MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC)
    Filippo Crimì, Giulia Capelli, Gaya Spolverato, Quoc Riccardo Bao, Anna Florio, Sebastiano Milite Rossi, Diego Cecchin, Laura Albertoni, Cristina Campi, Salvatore Pucciarelli, Roberto Stramare
    La radiologia medica.2020; 125(12): 1216.     CrossRef
  • Update of the recommendations for the determination of biomarkers in colorectal carcinoma: National Consensus of the Spanish Society of Medical Oncology and the Spanish Society of Pathology
    P. García-Alfonso, R. García-Carbonero, J. García-Foncillas, P. Pérez-Segura, R. Salazar, R. Vera, S. Ramón y Cajal, J. Hernández-Losa, S. Landolfi, E. Musulén, M. Cuatrecasas, S. Navarro
    Clinical and Translational Oncology.2020; 22(11): 1976.     CrossRef
  • MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment
    Yang Zhang, Chaoyue Chen, Yangfan Cheng, Danni Cheng, Fumin Zhao, Jianguo Xu
    Contrast Media & Molecular Imaging.2020; 2020: 1.     CrossRef
  • Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning
    Yu Li, Aydin Eresen, Junjie Shangguan, Jia Yang, Al B. Benson, Vahid Yaghmai, Zhuoli Zhang
    Journal of Cancer Research and Clinical Oncology.2020; 146(12): 3165.     CrossRef
  • Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis
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    Cancers.2020; 12(9): 2420.     CrossRef
  • CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning
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    Applied Sciences.2020; 10(18): 6214.     CrossRef
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    Biomarkers in Medicine.2020; 14(12): 1151.     CrossRef
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    Clinical Radiology.2019; 74(11): 895.e17.     CrossRef
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  • The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study
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    Frontiers in Oncology.2019;[Epub]     CrossRef
  • 11,624 View
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Redefining the Positive Circumferential Resection Margin by Incorporating Preoperative Chemoradiotherapy Treatment Response in Locally Advanced Rectal Cancer: A Multicenter Validation Study
Joo Ho Lee, Eui Kyu Chie, Seung-Yong Jeong, Tae-You Kim, Dae Yong Kim, Tae Hyun Kim, Sun Young Kim, Ji Yeon Baek, Hee Jin Chang, Min Ju Kim, Sung Chan Park, Jae Hwan Oh, Sung Hwan Kim, Jong Hoon Lee, Doo Ho Choi, Hee Chul Park, Sung-Bum Kang, Jae-Sung Kim
Cancer Res Treat. 2018;50(2):506-517.   Published online May 24, 2017
DOI: https://doi.org/10.4143/crt.2016.607
AbstractAbstract PDFPubReaderePub
Purpose
This study was conducted to validate the prognostic influence of treatment response among patients with positive circumferential resection margin for locally advanced rectal cancer.
Materials and Methods
Clinical data of 197 patientswith positive circumferentialresection margin defined as ≤ 2 mm after preoperative chemoradiotherapy followed by total mesorectal excision between 2004 and 2009were collected forthis multicenter validation study. All patients underwent median 50.4Gy radiationwith concurrentfluoropyrimidine based chemotherapy. Treatmentresponse was dichotomized to good response, including treatmentresponse of grade 2 or 3, and poor response, including grade 0 or 1.
Results
After 52 months median follow-up, 5-year overall survival (OS) for good responders and poor responders was 79.1% and 48.4%, respectively (p < 0.001). In multivariate analysis, circumferential resection margin involvement and treatment response were a prognosticator for OS and locoregional recurrence-free survival. In subgroup analysis, good responders with close margin showed significantly better survival outcomes for survival. Good responders with involved margin and poor responders with close margin shared similar results, whereas poorresponderswith involved margin hadworst survival (5-year OS, 81.2%, 57.0%, 50.0%, and 32.4%, respectively; p < 0.001).
Conclusion
Among patients with positive circumferential resection margin after preoperative chemoradiotherapy, survival of the good responders was significantly better than poor responders. Subgroup analysis revealed that definition of positive circumferential resection margin may be individualized as involvement for good responders, whereas ≤ 2 mm for poor responders.

Citations

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  • Tailored Strategy for Locally Advanced Rectal Carcinoma (GRECCAR 4): Long-term Results From a Multicenter, Randomized, Open-Label, Phase II Trial
    Philippe Rouanet, Eric Rullier, Bernard Lelong, Philippe Maingon, Jean-Jacques Tuech, Denis Pezet, Florence Castan, Stephanie Nougaret
    Diseases of the Colon & Rectum.2022; 65(8): 986.     CrossRef
  • Colorectal Cancer Surgery Quality in Manitoba: A Population-Based Descriptive Analysis
    Iresha Ratnayake, Jason Park, Natalie Biswanger, Allison Feely, Grace Musto, Kathleen Decker
    Current Oncology.2021; 28(3): 2239.     CrossRef
  • 11,109 View
  • 266 Download
  • 4 Web of Science
  • 2 Crossref
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Endoscopic Criteria for Evaluating Tumor Stage after Preoperative Chemoradiation Therapy in Locally Advanced Rectal Cancer
Kyung Su Han, Dae Kyung Sohn, Dae Yong Kim, Byung Chang Kim, Chang Won Hong, Hee Jin Chang, Sun Young Kim, Ji Yeon Baek, Sung Chan Park, Min Ju Kim, Jae Hwan Oh
Cancer Res Treat. 2016;48(2):567-573.   Published online September 22, 2015
DOI: https://doi.org/10.4143/crt.2015.195
AbstractAbstract PDFPubReaderePub
Purpose
Local excision may be an another option for selected patients with markedly down-staged rectal cancer after preoperative chemoradiation therapy (CRT), and proper evaluation of post-CRT tumor stage (ypT) is essential prior to local excision of these tumors. This study was designed to determine the correlations between endoscopic findings and ypT of rectal cancer.
Materials and Methods
In this study, 481 patients with locally advanced rectal cancer who underwent preoperative CRT followed by surgical resection between 2004 and 2013 at a single institution were evaluated retrospectively. Pathological good response (p-GR) was defined as ypT ≤ 1, and pathological minimal or no response (p-MR) as ypT ≥ 2. The patients were randomly classified according to two groups, a testing (n=193) and a validation (n=288) group. Endoscopic criteria were determined from endoscopic findings and ypT in the testing group and used in classifying patients in the validation group as achieving or not achieving p-GR.
Results
Based on findings in the testing group, the endoscopic criteria for p-GR included scarring, telangiectasia, and erythema, whereas criteria for p-MR included nodules, ulcers, strictures, and remnant tumors. In the validation group, the kappa statistic was 0.965 (p < 0.001), and the sensitivity, specificity, positive predictive value, and negative predictive value were 0.362, 0.963, 0.654, and 0.885, respectively.
Conclusion
The endoscopic criteria presented are easily applicable for evaluation of ypT after preoperative CRT for rectal cancer. These criteria may be used for selection of patients for local excision of down-staged rectal tumors, because patients with p-MR could be easily ruled out.

Citations

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  • The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer
    Bin Liu, Chuan Sun, Xinyu Zhao, Lingyu Liu, Shuang Liu, Haichuan Ma
    Technology and Health Care.2024; 32(2): 615.     CrossRef
  • Feasibility of endoscopic techniques in assessing the radicality of chemoradiotherapy in patients with rectal cancer
    A. A. Salimova, M. V. Makarova, M. Yu. Kurdanova, Yu. P. Kuvshinov, I. A. Karasev, T. S. Davydkina
    Surgery and Oncology.2024; 14(2): 48.     CrossRef
  • Clinical predictors of pathological good response in locally advanced rectal cancer
    Kongfeng Shao, Rong Zheng, Anchuan Li, Xiaobo Li, Benhua Xu
    Radiation Oncology.2021;[Epub]     CrossRef
  • A prospective clinical study assessing the presence of exfoliated cancer cells and rectal washout including tumors in patients who receive neoadjuvant chemoradiotherapy for rectal cancer
    Kazutake Okada, Sotaro Sadahiro, Yutaro Kamei, Lin Fung Chan, Takashi Ogimi, Hiroshi Miyakita, Gota Saito, Akira Tanaka, Toshiyuki Suzuki
    Surgery Today.2020; 50(4): 352.     CrossRef
  • Oncologic Risk of Rectal Preservation Against Medical Advice After Chemoradiotherapy for Rectal Cancer: A Multicenter Comparative Cross‐Sectional Study with Rectal Preservation as Supported by Surgeon
    Kwang‐Seop Song, Sung Chan Park, Dae Kyung Sohn, Jae Hwan Oh, Min Jung Kim, Ji Won Park, Seung‐Bum Ryoo, Seung‐Yong Jeong, Kyu Joo Park, Heung‐Kwon Oh, Duck‐Woo Kim, Sung‐Bum Kang
    World Journal of Surgery.2019; 43(12): 3216.     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
  • Advances in organ preserving strategies in rectal cancer patients
    Rutger CH. Stijns, Mike-Stephen R. Tromp, Niek Hugen, Johannes HW de Wilt
    European Journal of Surgical Oncology.2017;[Epub]     CrossRef
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