Integrating AI-powered automated neurovascular bundle segmentation and radiomics for prostate cancer staging
Authors
Affiliations (1)
Affiliations (1)
- Quantitative Imaging Biomarkers in Medicine, Quibim S.L, Av. d'Arago 30, 46026 Valencia, Spain
Abstract
BackgroundAssessment 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. PurposeTo 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. MethodsThis 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 nnU-Net 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. ResultsThe 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. ConclusionsAutomated neurovascular bundle segmentation enabled quantitative tumor proximity assessment and radiomics-based prediction of biochemical recurrence, perineural invasion, and extraprostatic extension.