An interpretable machine learning model for predicting distant organ metastasis after radical resection of colorectal cancer

This study established a high-accuracy and interpretable ML model capable of effectively predicting the 5-year cumulative risk of distant organ metastasis after radical colorectal cancer surgery, while further external validation is necessary to confirm its clinical utility.

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Publication Details

Journal
Frontiers in Oncology
Published
2026-02-24
DOI
https://doi.org/10.3389/fonc.2026.1764032
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

An interpretable machine learning model for predicting distant organ metastasis after radical resection of colorectal cancer

Huang Guoliang, Lin Weibin, Fang Junwei, Wang Meiping et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

An interpretable machine learning model for predicting distant organ metastasis after radical resection of colorectal cancer

Huang Guoliang, Lin Weibin, Fang Junwei, Wang Meiping, Xiao Chunhong, Ni Weixiang, Zhou Junwei, Hong Weixuan
article en

Abstract

This study established a high-accuracy and interpretable ML model capable of effectively predicting the 5-year cumulative risk of distant organ metastasis after radical colorectal cancer surgery, while further external validation is necessary to confirm its clinical utility.

Frontiers in OncologyVol. 16
Fujian University of Traditional Chinese Medicine (CN), Fujian Medical University (CN), Xiamen University (CN), Army Sustainment University (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 45%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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