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Original Articles
- Gastrointestinal cancer
- 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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Abstract
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Supplementary Material
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ePub - 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. -
Citations
Citations to this article as recorded by
- Impact of overweight and obesity on gastric and colorectal cancer incidence in the older adults: a nationwide cohort study
Jinju Choi, Cheol Min Shin, Kyungdo Han, Jin-Hyung Jung, Se Yun Kim, Hyemin Jo, Ho-Kyoung Lee, Eun Hyo Jin, Seung Joo Kang, Joo Hyun Lim, Dong Ho Lee
BMC Geriatrics.2026;[Epub] CrossRef - Survival Outcomes of Patients With Colorectal Cancer After Primary Surgery: A Population‐Based Study in Hainan, China
Le Du, Luyao Sun, Dongsheng Ge, Mingfa Wang, Benguo Yu, Wenjun Yang, Wenlong Yang, Ruyuan An, Junchao Li, Jike Zhu, Zaipan Wang, Ning Liu, Wenting Cao
Cancer Medicine.2026;[Epub] 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.2025; 131(4): 566. CrossRef - 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 - 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
- Impact of overweight and obesity on gastric and colorectal cancer incidence in the older adults: a nationwide cohort study
- 11,707 View
- 161 Download
- 12 Web of Science
- 10 Crossref
- 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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Abstract
PDFSupplementary MaterialPubReaderePub
- 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. -
Citations
Citations to this article as recorded by- Explainable AI for colorectal cancer mortality and risk factor prediction in Korea: A nationwide cancer cohort study
Sang Won Park, Na Young Yeo, Tae-Hoon Kim, Myoung Nam Lim, Inhyeok Yim, Oh Beom Kwon, Seung-Joo Nam, Hui-Young Lee, Woo Jin Kim
International Journal of Medical Informatics.2026; 205: 106125. CrossRef - A clinical visualization model for predicting distant metastasis and prognostic outcomes in mucinous adenocarcinoma of the appendix: a SEER database study
Xiaoming Xu, Hualei Chen, Jingzhi Wang
Expert Review of Anticancer Therapy.2026; 26(1): 131. CrossRef - Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam
Advanced Intelligent Systems.2026;[Epub] CrossRef - Artificial Intelligence Remote Patient Monitoring for Predicting Overall Survival for Patients Undergoing Radical Cystectomy for Bladder Cancer: Exploratory Analysis of the Prospective Trial
Yansong Liu, Pramit Khetrapal, Ronnie Strafford, Adamos Hadjivasilou, Nikhil Vasdev, Philip Charlesworth, Muhamaad Shamim Khan, Ahmed Abdulaal, Zhaoyan Dong, James W F Catto, Yukun Zhou, John D Kelly, Ivana Drobnjak
JMIR AI.2026; 5: e68994. CrossRef - A Hybrid Deep and Machine Learning Framework with Feature Selection for Automated Classification of Acute Lymphoblastic Leukemia
Husne Farah, Fahmida Islam, Shuvo Biswas, Mohammad Shorif Uddin
Journal of Computer and Communications.2026; 14(03): 278. CrossRef - AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review
Bhagvat J Maheta, Rachel L Ross, Isabella Raspi, Christina Keny, Marti N Okech, Selen Bozkurt, Karleen F Giannitrapani
JMIR Perioperative Medicine.2026; 9: e89936. CrossRef - Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms
Erfan Ayubi, Sajjad Farashi, Leili Tapak, Saeid Afshar
Heliyon.2025; 11(1): e41443. CrossRef - Predicting Factors Affecting Survival Rate in Patients Undergoing On‐Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review
Alireza Jafarkhani, Behzad Imani, Soheila Saeedi, Amir Shams
Health Science Reports.2025;[Epub] CrossRef - Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review
Catarina Sousa Santos, Mário Amorim-Lopes
BMC Medical Research Methodology.2025;[Epub] CrossRef - Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up
Ionut Negoi
World Journal of Gastroenterology.2025;[Epub] CrossRef - Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model
Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang
BMC Cancer.2025;[Epub] CrossRef - Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study
Yongjie Zhou, Jinhong Zhao, Fei Zou, Yongming Tan, Wei Zeng, Jiahui Jiang, Jiale Hu, Qiao Zeng, Lianggeng Gong, Lan Liu, Linhua Zhong
Computer Methods and Programs in Biomedicine.2025; 269: 108874. CrossRef - Leveraging automated machine learning to predict colon cancer prognosis from clinical features and risk groups: a retrospective cohort study
Jakob Woerner, Yonghyun Nam, Sang-Hyuk Jung, Manu Shivakumar, Matthew Lee, Eun Kyung Choe, Min Jung Kim, Rumi Shin, Seung-Bum Ryoo, Seung-Yong Jeong, Kyu Joo Park, Sung Chan Park, Dae Kyung Sohn, Jae Hwan Oh, Dokyoon Kim, Ji Won Park
European Journal of Surgical Oncology.2025; 51(9): 110194. CrossRef - Integrating inflammation, nutrition, and artificial intelligence: A new era in colorectal cancer prognostic scoring
Semra Demirli Atici, Aras Emre Canda, Mustafa Cem Terzi
World Journal of Gastroenterology.2025;[Epub] CrossRef - Artificial intelligence for predicting long-term outcomes in patients with colorectal cancer (a systematic review and meta-analysis)
R. Sh. Abdulaeva, V. I. Pavlova, T. G. Gevorkyan, Y. V. Belenkaya, M. Sh. Manukyan, S. S. Gordeev
Koloproktologia.2025; 24(4): 125. CrossRef - New perspectives on cancer clinical research in the era of big data and machine learning
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Surgical Oncology.2024; 52: 102009. CrossRef - SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients
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Computer Methods and Programs in Biomedicine.2024; 249: 108159. CrossRef - The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction
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Frontiers in Medicine.2024;[Epub] CrossRef - Machine learning for predicting colon cancer recurrence
Erkan Kayikcioglu, Arif Hakan Onder, Burcu Bacak, Tekin Ahmet Serel
Surgical Oncology.2024; 54: 102079. CrossRef - Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters
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Frontiers in Oncology.2024;[Epub] CrossRef - Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters
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Frontiers in Oral Health.2024;[Epub] CrossRef - Enhanced prediction of preoperative mesenteric lymph node metastasis in colorectal cancer using machine learning with CT‐based data
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MedComm – Future Medicine.2024;[Epub] CrossRef - An interactive web-based tool for predicting and exploring brain cancer survivability
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Healthcare Analytics.2023; 3: 100132. CrossRef - Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury
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PLOS ONE.2023; 18(1): e0278562. CrossRef - Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit
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洪铭 崔
Advances in Clinical Medicine.2023; 13(03): 3942. CrossRef - Clinicopathologic and prognostic factors of patients with T3/T4 colorectal signet ring cell carcinoma: a population-based study
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Journal of Cancer Research and Clinical Oncology.2023; 149(12): 9747. CrossRef - Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer
Chunxia Guo, Jun Pan, Shan Tian, Yuanjun Gao
Journal of International Medical Research.2023;[Epub] CrossRef
- Explainable AI for colorectal cancer mortality and risk factor prediction in Korea: A nationwide cancer cohort study
- 15,448 View
- 382 Download
- 24 Web of Science
- 28 Crossref
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