Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study

Abstract

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retrospectively collected data from 108 patients with pathologically confirmed rectal adenocarcinoma who underwent preoperative multiparametric MRI at the First Affiliated Hospital of Bengbu Medical College between April 2019 and August 2023. This dataset was subsequently divided into training and validation sets following a ratio of 7:3. Both univariate and multivariate logistic regression analyses were implemented to identify independent clinical risk factors associated with perineural invasion (PNI) in rectal cancer. We manually delineated the region of interest (ROI) layer-by-layer on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences and extracted the image features. Five machine learning algorithms were used to construct radiomics model with the features selected by least absolute shrinkage and selection operator (LASSO) method. The optimal radiomics model was then selected and combined with clinical features to formulate a nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and its clinical value was assessed via decision curve analysis (DCA). Our final selection comprised 10 optimal radiological features and the SVM model showcased superior predictive efficiency and robustness among the five classifiers. The area under the curve (AUC) values of the nomogram model were 0.945 (0.899, 0.991) and 0.846 (0.703, 0.99) for the training and validation sets, respectively. The nomogram model developed in this study exhibited excellent predictive performance in foretelling PNI of rectal cancer, thereby offering valuable guidance for clinical decision-making. The nomogram could predict the perineural invasion status of rectal cancer in early stage.

Keywords: Machine learning; Magnetic resonance imaging; Perineural invasion; Radiomics; Rectal cancer.