Computer-Aided Detection (CADe) of Small Metastatic Prostate Cancer Lesions on 3D PSMA PET Volumes Using Multi-Angle Maximum Intensity Projections

Abstract

We aimed to develop an automated computer-aided detection (CADe) system to help doctors detect small metastatic prostate cancer (PCa) lesions more efficiently, ultimately acting as a “second reader” to improve diagnosis and reduce workload in cancer care. Our method used multi-angle Maximum Intensity Projections (MA-MIPs) and explored state-of-the-art (SOTA) object detection AI algorithms. We evaluated 16 SOTA models across four categories. The system identified lesions in 2D images and then mapped them back into 3D space. A fine-tuned segmentation model further refined the results. Our best model, FreeAnchor, achieved a stronger detection performance. It was more efficient than many 3D methods while maintaining high accuracy, and it performed especially well for local relapses and bone metastases.