Integrating 3D Volumetric Segmentation and LLM-Based Classification for csPCa Detection on mpMRI: Multi-Institutional External Validation.

RATIONALE AND

OBJECTIVES

To develop and externally validate integrated models combining three-dimensional (3D) volumetric segmentation and large language model (LLM)-based slice-wise classification for improved detection of clinically significant Prostate Cancer (csPCa) on multiparametric Magnetic Resonance Imaging (mpMRI).

MATERIALS AND

METHODS

This retrospective multi-institutional study included 5050 patients (3896 for model development and 1154 for external validation) who underwent mpMRI. A 3D V-Net was trained for voxel-wise csPCa segmentation, generating three patient-level metrics: positive volume, positive slice count, and positive slice rate. A four-billion-parameter MedGemma-IT LLM was fine-tuned for slice-level classification, producing positive slice count and rate metrics. The optimal V-Net and LLM metrics were integrated into two combined models: Combined Model 1 (logistic regression of V-Net and LLM positive slice rates) and Combined Model 2 (zero-adjusted Gamma regression of V-Net positive volume and LLM positive slice rate). Performance was evaluated in the external validation cohort using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

In external validation, Combined Model 1 achieved an AUROC of 0.900 (95% CI: 0.882-0.918) and Combined Model 2 achieved 0.885 (95% CI: 0.865-0.905), significantly outperforming all individual V-Net and LLM metrics (ΔAUROC: 0.060-0.145; all P < 0.001). Both combined models demonstrated significant improvements in NRI and IDI for most comparisons (P < 0.05 for most) and provided superior standardized net benefit across clinically relevant risk thresholds (0.3-0.9) on DCA.

CONCLUSION

Integrating 3D volumetric segmentation with LLM-based slice-wise classification significantly improves csPCa detection accuracy and clinical utility compared to single-modality approaches, demonstrating promising generalizability in an independent, multi-institutional external validation cohort with substantial scanner and protocol heterogeneity.