Region-specific Multi-Omics Modeling for Predicting Acute Radiation-Induced Proctitis in Cervical Cancer Radiotherapy: A Retrospective Analysis
Introduction
To develop and evaluate a multi-omics machine-learning model that integrates clinical variables, dose-volume histogram (DVH) metrics, radiomics, and dosiomics from both the rectum and rectal wall regions of interest (ROIs) to improve prediction of acute radiation proctitis (ARP) in cervical cancer patients receiving radiotherapy.
Methods
In this single-center retrospective cohort, 107 cervical cancer patients were randomly split into a training set (n = 85) and a testing set (n = 22) in an 8:2 ratio. Radiomic were extracted from planning CT, and dosiomic features from 3-D RT-dose distributions, for both rectum and rectal wall ROIs. Features were z-score standardized; redundant features were filtered by Pearson correlation, followed by least absolute shrinkage and selection operator (LASSO) for selection. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers were trained using stratified five-fold cross-validation within the training set. Model performance was assessed on the held-out test set using receiver operating characteristic (ROC) analysis; clinical utility was evaluated with decision-curve analysis (DCA). The primary endpoint was Common Terminology Criteria for Adverse Events (CTCAE,version 5.0) grade ≥2 ARP.
Results
Multi-omics fusion outperformed single-modality models across ROIs and classifiers. The rectal-wall multi-omics SVM achieved the best discrimination with AUC 0.867 (95% Confidence Interval [CI]:0.709-1.000) in the test set; performance for the whole-rectum region of interest (ROI) was lower (AUC 0.714). DVH-only models showed limited discrimination, and no DVH feature was retained after penalized selection in the multi-omics pipeline. DCA demonstrated the greatest net clinical benefit for the rectal-wall multi-omics model across threshold probabilities 0.20–0.50.
Conclusion
A rectal-wall, region-specific multi-omics approach integrating clinical, radiomic, and dose-based descriptors improves prediction of radiotherapy-induced ARP compared with single-modality and whole-rectum analyses. These findings highlight the importance of ROI selection and multi-omics integration for precision toxicity assessment and support future external validation and prospective evaluation.