Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.

introductionSince the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate the utility of multi-parametric MRI radiomics models and identify the most accurate machine learning (ML) algorithms for predicting pT stage of RC.

methodThis retrospective study analyzed pretreatment clinical features of 171 RC patients who underwent 3 T MRI prior to neoadjuvant therapy and subsequent total mesorectal excision. Tumors were manually drawn as regions of interest (ROI) layer by layer on high-resolution T2-weighted image (T2WI) and contrast-enhanced T1-weighted image (CE-T1WI) using ITK-SNAP software. The most relevant features of pT stage from CE-T1WI, T2WI, and fusion features (combination of clinical features, CE-T1WI, and T2WI radiomics features) were extracted by the Least Absolute Shrinkage and Selection Operator method. Clinical, CE-T1WI radiomics, T2WI radiomics, and fusion models were established by ML multiple classifiers.

resultsIn the clinical model, the LightGBM algorithm demonstrated the highest efficiency, with AUC values of 0.857 and 0.702 for the training and test cohorts, respectively. For the T2WI and CE-T1WI models, the SVM algorithm was the most efficient; AUC = 0.969 and 0.868 in the training cohort, and 0.839 and 0.760 in the test cohort, respectively. The fusion model yielded the highest predictive performance using the LR algorithm; AUC = 0.967 and 0.932 in the training and test cohorts, respectively.

conclusionRadiomics features extracted from CE-T1WI and T2WI images and clinical features were effective predictors of pT stage in patients with rectal cancer who underwent neoadjuvant therapy. ML-based multi-parameter MRI radiomics model incorporating relevant clinical features can improve the pT stage prediction accuracy of RC.