MRI-Based Radiomics and Machine Learning for Predicting Pathological Tumor Invasion and Nodal Status in Rectal Cancer: A Retrospective Study
TL;DR
The clinico-radiological variables analyzed using ML showed a predictive performance similar to that of a radiologist in this setting, and the radiomic and combined models achieved a moderate and heterogeneous performance.
Abstract
Introduction: MRI is the gold-standard imaging modality for rectal cancer (RC) local staging, but the ability to determine tumor invasion (pT) and nodal status (pN) remains limited in clinical practice. The main objective of this study is to evaluate the performance of different machine learning (ML) models based on clinical–radiological and radiomic variables for predicting these categories from preoperative MRI. Methods: A retrospective observational study was conducted involving 152 patients with RC (70 without neoadjuvant therapy and 82 with neoadjuvant therapy). Radiomic features were extracted from high-resolution T2 sequences using two independent segmentations: tumor and tumor + mesorectum. Twenty-three ML algorithms were evaluated using cross-validation to predict pT and pN. For each combination of outcome, cohort, data source and segmentation, an optimal model was selected based on the area under the curve (AUC). Results: Models based on clinical and radiological variables showed the most consistent performance, particularly in the overall cohort, with AUCs of 0.767 for pT and 0.764 for pN. The radiomic and combined models achieved a moderate and heterogeneous performance, with maximum AUCs of 0.770 for pT and 0.732 for pN. Conclusions: The clinico-radiological variables analyzed using ML showed a predictive performance similar to that of a radiologist. Radiomics did not show significant improvement in this setting.