Integrating AI-powered automated neurovascular bundle segmentation and radiomics for prostate cancer staging
Journal:
medRxiv
Published Date:
Mar 11, 2026
Abstract
Background: Assessment of the prostatic neurovascular bundles on MRI is clinically relevant for staging and treatment planning but remains technically challenging and underexplored in automated imaging pipelines. Purpose: To develop and evaluate an automated framework for neurovascular bundle segmentation, proximity based invasion risk stratification, and radiomics based prediction of biochemical recurrence, perineural invasion, and extraprostatic extension. Methods: This retrospective study included 808 prostate MRI examinations from three datasets acquired between 2015 and 2020. Among them, 470 T2-weighted image sequences were manually annotated to train a 3D full resolution nnUNet segmentation model. Tumor to neurovascular bundle distance was used to define invasion risk categories (low, intermediate, high). Machine learning models were developed using radiomics features extracted from neurovascular bundles, lesions, and combined regions, with optional inclusion of prostate specific antigen and age at MRI. Model performance was evaluated using area under the receiver operating characteristic curve and accuracy. Model interpretability was assessed using Shapley additive explanations. Results: The median patient age was 69 years (interquartile range, 63,73). Automatic neurovascular bundles segmentation achieved anatomically plausible contours, with average surface distance below 1 mm and volume difference under 0.4 cc. The resulting tumor to neurovascular bundle invasion risk classification reached 90% accuracy, supporting usability. Radiomics models showed predictive value across endpoints, with moderate testing performance for biochemical recurrence (AUC = 0.73), and higher discrimination for perineural invasion (AUC = 0.80) and extraprostatic extension (AUC = 0.80). Interpretability analysis revealed that tumor to neurovascular bundle proximity and neurovascular bundles imaging features among the most relevant contributors to outcome prediction. Conclusions: Automated neurovascular bundle segmentation enabled quantitative tumor proximity assessment and radiomics based prediction of biochemical recurrence, perineural invasion, and extraprostatic extension.
