Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

Fecal Microbial Dysbiosis Is Associated with Colorectal Cancer Risk in a Korean Population: Jeongseon Kim, Madhawa Gunathilake, Hyun Yang Yeo, Jae Hwan Oh, Byung Chang Kim, Nayoung Han, Bun Kim, Hyojin Pyun, Mi Young Lim, Young-Do Nam, Hee Jin Chang; Cancer Res Treat. 2025;57(1):198-211. Published online July 26, 2024; DOI: https://doi.org/10.4143/crt.2024.382

Abstract PDF Supplementary Material PubReader ePub

Purpose
The association between the fecal microbiota and colorectal cancer (CRC) risk has been suggested in epidemiologic studies. However, data from large-scale population-based studies are lacking.
Materials and Methods
In this case-control study, we recruited 283 CRC patients from the Center for Colorectal Cancer, National Cancer Center Hospital, Korea to perform 16S rRNA gene sequencing of fecal samples. A total of 283 age- and sex-matched healthy participants were selected from 890 cohort of healthy Koreans that are publicly available (PRJEB33905). The microbial dysbiosis index (MDI) was calculated based on the differentially abundant species. The association between MDI and CRC risk was observed using conditional logistic regression. Sparse Canonical Correlation Analysis was performed to integrate species data with microbial pathways obtained by PICRUSt2.
Results
There is a significant divergence of the microbial composition between CRC patients and controls (permutational multivariate analysis of variance p=0.001). Those who were in third tertile of the MDI showed a significantly increased risk of CRC in the total population (odds ratio [OR], 6.93; 95% confidence interval [CI], 3.98 to 12.06; p-trend < 0.001) compared to those in the lowest tertile. Similar results were found for men (OR, 6.28; 95% CI, 3.04 to 12.98; p-trend < 0.001) and women (OR, 7.39; 95% CI, 3.10 to 17.63; p-trend < 0.001). Bacteroides coprocola and Bacteroides plebeius species and 12 metabolic pathways were interrelated in healthy controls that explain 91% covariation across samples.
Conclusion
Dysbiosis in the fecal microbiota may be associated with an increased risk of CRC. Due to the potentially modifiable nature of the gut microbiota, our findings may have implications for CRC prevention among Koreans.

Citations

Citations to this article as recorded by

Quantification of Naturally Occurring Prebiotics in Selected Foods
Arianna Natale, Federica Fiori, Federica Turati, Carlo La Vecchia, Maria Parpinel, Marta Rossi
Nutrients.2025; 17(4): 683. CrossRef

2,156 View
200 Download
1 Web of Science
1 Crossref

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

Abstract PDF Supplementary Material PubReader ePub

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; 50(2): 569. 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
Vincenza Granata, Roberta Fusco, Chiara Risi, Alessandro Ottaiano, Antonio Avallone, Alfonso De Stefano, Robert Grimm, Roberta Grassi, Luca Brunese, Francesco Izzo, Antonella Petrillo
Cancers.2020; 12(9): 2420. CrossRef
CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning
Víctor González-Castro, Eva Cernadas, Emilio Huelga, Manuel Fernández-Delgado, Jacobo Porto, José Ramón Antunez, Miguel Souto-Bayarri
Applied Sciences.2020; 10(18): 6214. CrossRef
Cancer Genotypes Prediction and Associations Analysis From Imaging Phenotypes: A Survey on Radiogenomics
Yao Wang, Yan Wang, Chunjie Guo, Xuping Xie, Sen Liang, Ruochi Zhang, Wei Pang, Lan Huang
Biomarkers in Medicine.2020; 14(12): 1151. CrossRef
Diagnostic performance of multiparametric MRI parameters for Gleason score and cellularity metrics of prostate cancer in different zones: a quantitative comparison
J. Gao, Q. Zhang, C. Zhang, M. Chen, D. Li, Y. Fu, X. Lv, B. Zhang, H. Guo
Clinical Radiology.2019; 74(11): 895.e17. CrossRef
Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach
Yang Zhang, Chaoyue Chen, Yangfan Cheng, Yuen Teng, Wen Guo, Hui Xu, Xuejin Ou, Jian Wang, Hui Li, Xuelei Ma, Jianguo Xu
Frontiers in Oncology.2019;[Epub] CrossRef
The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study
Chaoyue Chen, Xinyi Guo, Jian Wang, Wen Guo, Xuelei Ma, Jianguo Xu
Frontiers in Oncology.2019;[Epub] CrossRef

12,155 View
403 Download
55 Web of Science
54 Crossref

Nomogram Development and External Validation for Predicting the Risk of Lymph Node Metastasis in T1 Colorectal Cancer: Jung Ryul Oh, Boram Park, Seongdae Lee, Kyung Su Han, Eui-Gon Youk, Doo-Han Lee, Do-Sun Kim, Doo-Seok Lee, Chang Won Hong, Byung Chang Kim, Bun Kim, Min Jung Kim, Sung Chan Park, Dae Kyung Sohn, Hee Jin Chang, Jae Hwan Oh; Cancer Res Treat. 2019;51(4):1275-1284. Published online January 17, 2019; DOI: https://doi.org/10.4143/crt.2018.569

Abstract PDF Supplementary Material PubReader ePub

Purpose
Predicting lymph node metastasis (LNM) risk is crucial in determining further treatment strategies following endoscopic resection of T1 colorectal cancer (CRC). This study aimed to establish a new prediction model for the risk of LNM in T1 CRC patients.
Materials and Methods
The development set included 833 patients with T1 CRC who had undergone endoscopic (n=154) or surgical (n=679) resection at the National Cancer Center. The validation set included 722 T1 CRC patients who had undergone endoscopic (n=249) or surgical (n=473) resection at Daehang Hospital. A logistic regression model was used to construct the prediction model. To assess the performance of prediction model, discrimination was evaluated using the receiver operating characteristic (ROC) curves with area under the ROC curve (AUC), and calibration was assessed using the Hosmer-Lemeshow (HL) goodness-of-fit test.
Results
Five independent risk factors were determined in the multivariable model, including vascular invasion, high-grade histology, submucosal invasion, budding, and background adenoma. In final prediction model, the performance of the model was good that the AUC was 0.812 (95% confidence interval [CI], 0.770 to 0.855) and the HL chi-squared test statistic was 1.266 (p=0.737). In external validation, the performance was still good that the AUC was 0.771 (95% CI, 0.708 to 0.834) and the p-value of the HL chi-squared test was 0.040. We constructed the nomogram with the final prediction model.
Conclusion
We presented an externally validated new prediction model for LNM risk in T1 CRC patients, guiding decision making in determining whether additional surgery is required after endoscopic resection of T1 CRC.

Citations

Citations to this article as recorded by

Risk stratification scores for lymph node metastases in T1 colorectal cancer—A systematic review
Rakesh Quinn, Giuleta Jamsari, Ewan MacDermid
Colorectal Disease.2025;[Epub] CrossRef
The critical role of tumor size in predicting lymph node metastasis in early-stage colorectal cancer
Attila Ulkucu, Metincan Erkaya, Ekin Inal, Emre Gorgun
The American Journal of Surgery.2025; 241: 116152. CrossRef
Pitfalls during histological assessment in locally resected pT1 colorectal cancer
Emma J Norton, Adrian C Bateman
Histopathology.2025;[Epub] CrossRef
Artificial intelligence-based model to predict recurrence after local excision in T1 rectal cancer
Jiarui Su, Zhiyuan Liu, Haiming Li, Li Kang, Kaihong Huang, Jiawei Wu, Han Huang, Fei Ling, Xueqing Yao, Chengzhi Huang
European Journal of Surgical Oncology.2025; 51(6): 109717. CrossRef
Prediction of lymph node metastasis in stage I–III colon cancer patients younger than 40 years
Wei-Hao Zhang, Meng-Di Huang, Yan-Ling Tu, Kun-Zhai Huang, Chao-Jun Wang, Zhao-Hui Liu, Rui-Sheng Ke
Clinical and Translational Oncology.2025;[Epub] CrossRef
Risk assessment in pT1 colorectal cancer
Emma Jane Norton, Adrian C Bateman
Journal of Clinical Pathology.2024; 77(4): 225. CrossRef
Curative criteria for endoscopic treatment of colorectal cancer
Lucille Quénéhervé, Mathieu Pioche, Jérémie Jacques
Best Practice & Research Clinical Gastroenterology.2024; 68: 101883. CrossRef
Commentary: An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer
Katsuro Ichimasa, Shin-ei Kudo, Khay Guan Yeoh
Frontiers in Oncology.2024;[Epub] CrossRef
Management after non-curative endoscopic resection of T1 rectal cancer
Hao Dang, Daan A. Verhoeven, Jurjen J. Boonstra, Monique E. van Leerdam
Best Practice & Research Clinical Gastroenterology.2024; 68: 101895. CrossRef
A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer
Qian Li, Rui Hong, Ping Zhang, Liting Hou, Hailun Bao, Lin Bai, Jian Zhao
Clinical & Experimental Metastasis.2024; 41(5): 639. CrossRef
A new clinical model for predicting lymph node metastasis in T1 colorectal cancer
Kai Wang, Hui He, Yanyun Lin, Yanhong Zhang, Junguo Chen, Jiancong Hu, Xiaosheng He
International Journal of Colorectal Disease.2024;[Epub] CrossRef
Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions
Xu Song, Jun Li, Jiang Zhu, Yun-Fei Kong, Yu-Hang Zhou, Zi-Kun Wang, Jin Zhang
Frontiers in Oncology.2024;[Epub] CrossRef
Approaches and considerations in the endoscopic treatment of T1 colorectal cancer
Yunho Jung
The Korean Journal of Internal Medicine.2024; 39(4): 563. CrossRef
A Nomogram for Predicting Unplanned Intraoperative Hypothermia in Patients With Colorectal Cancer Undergoing Laparoscopic Colorectal Procedures
Lupei Yan, Jingxing Tan, Hao Chen, Han Xiao, Yi Zhang, Qin Yao, Yuerong Li
AORN Journal.2023;[Epub] CrossRef
A proposal for grading the risk of lymph node metastasis after endoscopic resection of T1 colorectal cancer
Zhenghua Piao, Rong Ge, Chunnian Wang
International Journal of Colorectal Disease.2023;[Epub] CrossRef
Nomogram as a novel predictive tool for lymph node metastasis in T1 colorectal cancer treated with endoscopic resection: a nationwide, multicenter study
Yoshiki Kajiwara, Shiro Oka, Shinji Tanaka, Takahiro Nakamura, Shoichi Saito, Yosuke Fukunaga, Manabu Takamatsu, Hiroshi Kawachi, Kinichi Hotta, Hiroaki Ikematsu, Motohiro Kojima, Yutaka Saito, Masayoshi Yamada, Yukihide Kanemitsu, Shigeki Sekine, Shinji
Gastrointestinal Endoscopy.2023; 97(6): 1119. CrossRef
CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer
Suyun Li, Zhenhui Li, Li Wang, Mimi Wu, Xiaobo Chen, Chutong He, Yao Xu, Mengyi Dong, Yanting Liang, Xin Chen, Zaiyi Liu
European Radiology.2023; 33(10): 6861. CrossRef
Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography
Chao-Tao Tang, Jun Li, Peng Wang, You-Xiang Chen, Chun-Yan Zeng
Surgical Endoscopy.2023; 37(10): 7444. CrossRef
Lymph Node Molecular Analysis with OSNA Enables the Identification of pT1 CRC Patients at Risk of Recurrence: A Multicentre Study
Karmele Saez de Gordoa, Maria Teresa Rodrigo-Calvo, Ivan Archilla, Sandra Lopez-Prades, Alba Diaz, Jordi Tarragona, Isidro Machado, Juan Ruiz Martín, Diana Zaffalon, Maria Daca-Alvarez, Maria Pellisé, Jordi Camps, Miriam Cuatrecasas
Cancers.2023; 15(22): 5481. CrossRef
Prediction of disease recurrence or residual disease after primary endoscopic resection of pT1 colorectal cancer—results from a large nationwide Danish study
Ilze Ose, Katarina Levic, Lau Caspar Thygesen, Orhan Bulut, Thue Bisgaard, Ismail Gögenur, Tine Plato Kuhlmann
International Journal of Colorectal Disease.2023;[Epub] CrossRef
Endoscopic resection alone as a potential treatment method for low-risk deep invasive T1 colorectal cancer
Yuta Kouyama, Shin-ei Kudo, Katsuro Ichimasa, Shingo Matsudaira, Yushi Ogawa, Kenichi Mochizuki, Yuki Takashina, Yuta Sato, Tatsuya Sakurai, Yasuharu Maeda, Hiroki Nakamura, Masashi Misawa, Yuichi Mori, Toyoki Kudo, Takemasa Hayashi, Kunihiko Wakamura, Te
iGIE.2023; 2(4): 503. CrossRef
A Retrospective Multicenter Study of Risk Factors, Stratification, and Prognosis of Lymph Node Metastasis in T1 and T2 Colorectal Cancer
Eui Myung Kim, Il Tae Son, Byung Chun Kim, Jun Ho Park, Byung Mo Kang, Jong Wan Kim
Journal of Clinical Medicine.2023; 12(24): 7744. CrossRef
Risk and Time Pattern of Recurrences After Local Endoscopic Resection of T1 Colorectal Cancer: A Meta-analysis
Hao Dang, Nik Dekkers, Saskia le Cessie, Jeanin E. van Hooft, Monique E. van Leerdam, Philip P. Oldenburg, Louis Flothuis, Jan W. Schoones, Alexandra M.J. Langers, James C.H. Hardwick, Jolein van der Kraan, Jurjen J. Boonstra
Clinical Gastroenterology and Hepatology.2022; 20(2): e298. CrossRef
Lymphatic Node Metastasis Risk Scoring System: A Novel Instrument for Predicting Lymph Node Metastasis After Thymic Epithelial Tumor Resection
Xinxin Cheng, Yaxin Lu, Sai Chen, Weilin Yang, Bo Xu, Jianyong Zou, Zhenguang Chen
Annals of Surgical Oncology.2022; 29(1): 598. CrossRef
Current problems and perspectives of pathological risk factors for lymph node metastasis in T1 colorectal cancer: Systematic review
Katsuro Ichimasa, Shin‐ei Kudo, Hideyuki Miyachi, Yuta Kouyama, Kenichi Mochizuki, Yuki Takashina, Yasuharu Maeda, Yuichi Mori, Toyoki Kudo, Yuki Miyata, Yoshika Akimoto, Yuki Kataoka, Takafumi Kubota, Tetsuo Nemoto, Fumio Ishida, Masashi Misawa
Digestive Endoscopy.2022; 34(5): 901. CrossRef
Tumor Location as a Prognostic Factor in T1 Colorectal Cancer
Katsuro Ichimasa, Shin-ei Kudo, Yuta Kouyama, Kenichi Mochizuki, Yuki Takashina, Masashi Misawa, Yuichi Mori, Takemasa Hayashi, Kunihiko Wakamura, Hideyuki Miyachi
Journal of the Anus, Rectum and Colon.2022; 6(1): 9. CrossRef
Identification of Predictive Factors for Lymph Node Metastasis in pT1 Stage Colorectal Cancer Patients: A Retrospective Analysis Based on the Population Database
Jiawei Song, Huanhuan Yin, Yong Zhu, Shengqi Fei
Pathology and Oncology Research.2022;[Epub] CrossRef
Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer
Zitao Liu, Chao Huang, Huakai Tian, Yu Liu, Yongshan Huang, Zhengming Zhu
Frontiers in Surgery.2022;[Epub] CrossRef
Deep Submucosal Invasion Is Not an Independent Risk Factor for Lymph Node Metastasis in T1 Colorectal Cancer: A Meta-Analysis
Liselotte W. Zwager, Barbara A.J. Bastiaansen, Nahid S.M. Montazeri, Roel Hompes, Valeria Barresi, Katsuro Ichimasa, Hiroshi Kawachi, Isidro Machado, Tadahiko Masaki, Weiqi Sheng, Shinji Tanaka, Kazutomo Togashi, Chihiro Yasue, Paul Fockens, Leon M.G. Moo
Gastroenterology.2022; 163(1): 174. CrossRef
Composite scoring system and optimal tumor budding cut-off number for estimating lymph node metastasis in submucosal colorectal cancer
Jeong-ki Kim, Ye-Young Rhee, Jeong Mo Bae, Jung Ho Kim, Seong-Joon Koh, Hyun Jung Lee, Jong Pil Im, Min Jung Kim, Seung-Bum Ryoo, Seung-Yong Jeong, Kyu Joo Park, Ji Won Park, Gyeong Hoon Kang
BMC Cancer.2022;[Epub] CrossRef
Lymph node metastasis in T1 colorectal cancer with the only high-risk histology of submucosal invasion depth ≥ 1000 μm
Yusuke Yamaoka, Akio Shiomi, Hiroyasu Kagawa, Hitoshi Hino, Shoichi Manabe, Kai Chen, Kenji Nanishi, Akifumi Notsu
International Journal of Colorectal Disease.2022; 37(11): 2387. CrossRef
Subpopulation analysis of survival in high-risk T1 colorectal cancer: surgery versus endoscopic resection only
Ryun Kyong Ha, Boram Park, Kyung Su Han, Dae Kyung Sohn, Chang Won Hong, Byung Chang Kim, Bun Kim, Sung Chan Park, Hee Jin Chang, Jae Hwan Oh
Gastrointestinal Endoscopy.2022; 96(6): 1036. CrossRef
Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning
Morten Hartwig, Karoline Bendix Bräuner, Rasmus Vogelsang, Ismail Gögenur
International Journal of Colorectal Disease.2022; 37(12): 2517. CrossRef
Preoperative Predictors of Lymph Node Metastasis in Colon Cancer
Yansong Xu, Yi Chen, Chenyan Long, Huage Zhong, Fangfang Liang, Ling-xu Huang, Chuanyi Wei, Shaolong Lu, Weizhong Tang
Frontiers in Oncology.2021;[Epub] CrossRef
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 Research and Treatment.2021; 53(3): 773. CrossRef
Risk Stratification of T1 Colorectal Cancer Metastasis to Lymph Nodes: Current Status and Perspective
Katsuro Ichimasa, Shin-ei Kudo, Hideyuki Miyachi, Yuta Kouyama, Masashi Misawa, Yuichi Mori
Gut and Liver.2021; 15(6): 818. CrossRef
Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
Jinlian Jin, Haiyan Zhou, Shulin Sun, Zhe Tian, Haibing Ren, Jinwu Feng
Cancer Management and Research.2021; Volume 13: 8967. CrossRef
Risk factors and predictors of lymph nodes metastasis and distant metastasis in newly diagnosed T1 colorectal cancer
Kaibo Guo, Yuqian Feng, Li Yuan, Harpreet S. Wasan, Leitao Sun, Minhe Shen, Shanming Ruan
Cancer Medicine.2020; 9(14): 5095. CrossRef
Development and external validation of a predictive scoring system associated with metastasis of T1‐2 colorectal tumors to lymph nodes
Shaobo Mo, Zheng Zhou, Weixing Dai, Wenqiang Xiang, Lingyu Han, Long Zhang, Renjie Wang, Sanjun Cai, Qingguo Li, Guoxiang Cai
Clinical and Translational Medicine.2020; 10(1): 275. CrossRef
Inverse Association of Age with Risk of Lymph Node Metastasis in Superficial Colorectal Cancer: A Large Population-Based Study
Qing-Wei Zhang, Long-Ci Sun, Chao-Tao Tang, Qian Liang, Yang-Yang Zhou, Hui-Min Chen, Yun-Jie Gao, Zhi-Zheng Ge
The Oncologist.2020; 25(6): e920. CrossRef
T1 colorectal cancers: The impact of screening programs on a curable disease
Adriana Vaz Safatle-Ribeiro
Digestive and Liver Disease.2020; 52(8): 918. CrossRef
Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies
Wei Xu, Yazhou He, Yuming Wang, Xue Li, Jane Young, John P. A. Ioannidis, Malcolm G. Dunlop, Evropi Theodoratou
BMC Medicine.2020;[Epub] CrossRef

10,597 View
387 Download
44 Web of Science
42 Crossref

First
Prev
Page of 1
Next
Last

Search