Multi-parametric MRI radiomics to predict sensitivity to neoadjuvant chemoradiotherapy based on multi-regions of interest in locally advanced rectal cancer: a multicenter study.
Authors
Affiliations (13)
Affiliations (13)
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu City, 610041, Sichuan, China.
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Chengdu Institute of Computer Application, Chinese Academy of Science, No. 1369, Kezhi Road, Xinglong Subdistrict, Tianfu New Area, Chengdu, 610213, Sichuan, China.
- University of Chinese Academy of Sciences, Chengdu, Sichuan, China.
- China Zhenhua Research Institute Co., Ltd., Guiyang, Guizhou, China.
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China.
- Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
- Department of Oncology, No.4 West China Teaching Hospital of Sichuan University, Chengdu, Sichuan, China.
- Chengdu Institute of Computer Application, Chinese Academy of Science, No. 1369, Kezhi Road, Xinglong Subdistrict, Tianfu New Area, Chengdu, 610213, Sichuan, China. [email protected].
- University of Chinese Academy of Sciences, Chengdu, Sichuan, China. [email protected].
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu City, 610041, Sichuan, China. [email protected].
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China. [email protected].
Abstract
To evaluate whether baseline multi-parametric MRI radiomics models, integrating multiple regions of interest (ROIs), can predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC), and to validate performance externally. In this multicenter, retrospective cohort study, 510 patients with LARC treated with NCRT followed by total mesorectal excision were enrolled (330 for training, 83 for internal validation, and 97 for external validation). Radiomic features were extracted from three ROIs (primary tumor, mesorectal region, and lateral lymph node regions). After redundancy reduction and clustering, the top 30 features were selected using gradient boosting decision trees via cross-validation. Four classifiers, logistic regression, random forest, support vector machine, and extra trees, were evaluated, with SMOTE applied to address class imbalance. Model performance was compared across ROI combinations, MRI sequences, and algorithms using the area under the receiver operating characteristic curve (AUC). Overall, 98 of 510 patients (19.2%) achieved pCR. The multi-ROI, multi-parametric model based on extra trees achieved an AUC of 0.717 in the external validation set. Among single-ROI models, tumor ROI yielded the highest AUC (0.630). Among MRI sequences, the ADC-based model performed best (AUC 0.629). Baseline multi-parametric MRI radiomics models integrating multiple ROIs showed modest predictive performance for pCR in LARC and demonstrated similar predictive utility in an independent external cohort. This approach may provide pre-treatment risk stratification and support future research on individualized treatment strategies and organ preservation.