ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gastrointestinal Cancers: Colorectal Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1573431
This article is part of the Research TopicProgressive Role of Artificial Intelligence in Treatment Decision - Making in the Field of Medical OncologyView all 8 articles
A Predictive Model to Identify Optimal Candidates for Surgery among Patients with Metastatic Colorectal Cancer
Provisionally accepted
- 1Shandong University, Jinan, China
- 2Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
- 3Qilu Hospital, Shandong University, Jinan, Shandong Province, China
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Purpose: To improve clinical decision-making, we developed a predictive model to identify metastatic colorectal cancer (mCRC) patients who might benefit from primary tumor resection (PTR).We extracted clinical data of stage IV CRC patients between 2010 and 2019 from the Surveillance, Epidemiology, and End Results database. Propensity score matching (PSM) was used to balance confounding factors by categorizing patients into surgery and non-surgery groups. To identify independent predictors of cancer-specific survival (CSS), we used multivariate Cox regression analysis. We further sorted patients who underwent surgery into benefit and non-benefit groups based on the median CSS of the non-surgery group; subsequently, we split the groups into training and test sets at a ratio of 6:4. To construct predictive models, we used the Boruta selection method to further filter variables, focusing on whether patients benefited from the surgery, based on key predictive factors. Results: We identified 23,649 mCRC patients, of whom 80. 97% (19,148) underwent PTR. After PSM, compared to no surgical intervention, surgical intervention was independently associated with a extended median CSS [median: 22 vs. 12 months; HR: 2.323, P < 0.001]. Among the nine machine learning models, the Categorical Boosting model performed the best but was still slightly inferior to traditional logistic regression.The traditional logistic regression model showed good discriminative ability in both the training (area under the curve [AUC]: 0.727 [0.699-0.756]) and test (AUC: 0.741 [0.706-0.776]) sets.We achieved a predictive model which could identify optimal candidates for PTR among mCRC patients with high accuracy.
Keywords: primary tumor resection, machine learning, Metastatic colorectal cancer, Cancerspecific survival, predictive model
Received: 09 Feb 2025; Accepted: 20 May 2025.
Copyright: © 2025 Zhang, Jing, Wu, Tao and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dandan Lu, Qilu Hospital, Shandong University, Jinan, 250012, Shandong Province, China
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