Radiomics combined with interpretable machine learning in predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
10.3969/j.issn.1005-202X.2025.05.011
- VernacularTitle:基于影像组学和可解释机器学习的局部进展期直肠癌新辅助放化疗疗效预测
- Author:
Jianfeng LI
1
;
Meijuan SUN
;
Haiyan PENG
;
Wenyou HU
;
Fu JIN
;
Zhaoxia LI
;
Ning WANG
Author Information
1. 锦州医科大学中国人民解放军火箭军特色医学中心研究生培养基地,北京 100088
- Publication Type:Journal Article
- Keywords:
locally advanced rectal cancer;
neoadjuvant chemoradiotherapy;
radiomics;
machine learning;
interpretability
- From:
Chinese Journal of Medical Physics
2025;42(5):625-631
- CountryChina
- Language:Chinese
-
Abstract:
The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.
