Deep learning-based lesion characterization and outcome prediction of prostate cancer on [ 18 F]DCFPyL PSMA imaging

Background This study aimed to develop deep learning (DL) models for lesion characterization and outcome prediction in prostate cancer (PCa) patients using Prostate-Specific Membrane Antigen (PSMA) PET/CT imaging. Methods The study included 358 confirmed PCa patients who underwent [ ¹⁸ F]DCFPyL PET/CT imaging. Patients were divided into training and internal test sets (n = 275), prospective test set (n = 64), and external test set (n = 19). Lesions were evaluated using PSMA-Reporting and Data System (RADS) scores, malignancy classification, treatment response and survival prediction, followed by DL models trained for each of these tasks. The performance of multi-modality (PET + CT) models was compared to single-modality models, with the best models from the internal and prospective test sets applied to the external test set. Results The input concatenation model, incorporating both PET and CT data, demonstrated the highest performance across all tasks. For PSMA-RADS scoring, the area under the receiver operating characteristic curve (AUROC) was 0.81 (95% CI: 0.80–0.81) for the internal test set, 0.72 (95% CI: 0.69–0.75) for the prospective test set, and 0.68 (95% CI: 0.68–0.69) for the external test set. For malignancy classification, the model achieved AUROCs of 0.79 (95% CI: 0.78–0.80), 0.70 (95% CI: 0.68–0.71), and 0.62 (95% CI: 0.61–0.63) in the internal, prospective, and external test sets, respectively. The AUROC for treatment response prediction was 0.74 (95% CI: 0.73–0.77) for the internal test set, 0.70 (95% CI: 0.67–0.72) for the prospective test set, and 0.72 (95% CI: 0.70–0.73) for the external dataset. The C-index for survival was 0.58 (95% CI: 0.57–0.59), 0.60 (95% CI: 0.60–0.63) and 0.59 (95% CI: 0.57–0.62) in the internal, prospective, and external test sets, respectively. Conclusions The DL model utilizing input concatenation of PET and CT data outperformed single-modality models in PSMA-RADS scoring, malignancy classification, treatment response assessment, and survival prediction, highlighting its potential as a clinical tool.