An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer

An interpretable CT-based machine learning model for predicting recurrence risk in stage II colorectal cancer

Abstract Objectives This study aimed to develop an interpretable 3-year disease-free survival risk prediction tool to stratify patients with stage II colorectal cancer (CRC) by integrating CT images and clinicopathological factors. Methods A total of 769 patients with pathologically confirmed stage...

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Bibliographic Details
Main Authors: Ziqi Wu, Liya Gong, Jingwen Luo, Xiaobo Chen, Fan Yang, Junyan Wen, Yanyu Hao, Zhishan Wang, Ruozhen Gu, Yuqin Zhang, Hai Liao, Ge Wen
Format: Article
Language:English
Published: SpringerOpen 2025-07-01
Series:Insights into Imaging
Subjects:
Colorectal cancer
Computed tomography
Machine learning
Radiomics
Prognosis
Online Access:https://doi.org/10.1186/s13244-025-02009-2
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https://doi.org/10.1186/s13244-025-02009-2

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