<scp>MRI</scp> Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer

BACKGROUND: Accurate preoperative assessment of perineural invasion (PNI) remains challenging in rectal cancer. PURPOSE: To develop assessment models based on preoperative multiparametric MRI (mpMRI) habitat analysis for evaluating PNI status and to explore their prognostic value. STUDY TYPE: Retrospective. POPULATION: Six hundred and twenty-one rectal cancer patients were enrolled from two centers, divided into a training set (n = 330; 65.8 ± 11.22 years; 215 males), an internal validation set (in-vad, n = 152; 67.85 ± 12.43 years; 105 males), and an external validation set (ex-vad, n = 139; 62.82 ± 11.79 years; 96 males). FIELD STRENGTH/SEQUENCE: 1.5T, 3T, T2-weighted imaging using turbo spin-echo sequence, diffusion-weighted imaging using echo planar imaging, and contrast-enhanced T1-weighted imaging using 3D spoiled gradient echo sequence. ASSESSMENT: Tumor voxels were partitioned into subregions using k-means clustering, and habitat-based submodels were developed with deep learning. The Boruta algorithm combined with univariate and multivariate analyses identified key variables. STATISTICAL TESTS: Student's t test, Mann-Whitney U test, chi-square test, Boruta analysis, and DeLong's test. Significance was defined as p < 0.05. A clinical model was constructed from selected significant variables, and a nomogram integrating the clinical model with habitat-based submodels was subsequently developed. RESULTS: Tumors were divided into three imaging-derived subregions, generating three habitat submodels. Habitat 1, 2, 3, mrN, and mrEMVI were independent PNI variables. The nomogram exhibited the highest performance, with area under the curve (AUC) values of 0.967 (95% confidence interval [CI], 0.950-0.983), 0.965 (0.941-0.990), and 0.977 (0.949-1.000) in the training, in-vad, and ex-vad sets, respectively. Kaplan-Meier analysis further confirmed its effective stratification of 3-year disease-free survival. CONCLUSION: The MRI-based habitat analysis model and the derived nomogram demonstrate high predictive value for preoperative assessment of PNI in rectal cancer. The nomogram also shows promising capability for prognostic risk stratification. TECHNICAL EFFICACY STAGE: 3.