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

TL;DR

This paper introduces a new automated system to detect small prostate cancer metastases in PET scans using advanced AI techniques, aiming to improve diagnosis and reduce workload for doctors.

Contribution

The novel approach combines multi-angle projections with 2D object detection models to efficiently detect small metastatic lesions in 3D PSMA-PET volumes.

Findings

The FreeAnchor model achieved an F1-score of 0.69 and a recall of 0.74 for lesion detection.

The system showed strong recall rates for local relapses (0.82) and bone metastases (0.80).

The method outperformed several 3D methods in efficiency while maintaining high accuracy.

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.

Objectives: We aimed to develop and evaluate a novel computer-aided detection (CADe) approach for identifying small metastatic biochemically recurrent (BCR) prostate cancer (PCa) lesions on PSMA-PET images, utilizing multi-angle Maximum Intensity Projections (MA-MIPs) and state-of-the-art (SOTA) object detection algorithms. Methods: We fine-tuned and evaluated 16 SOTA object detection algorithms (selected across four main categories of model types) applied to MA-MIPs as extracted from rotated 3D PSMA-PET volumes. Predicted 2D bounding boxes were back-projected to the original 3D space using the Ordered Subset Expectation Maximization (OSEM) algorithm. A fine-tuned Medical Segment-Anything Model (MedSAM) was then also used to segment the identified lesions within the bounding boxes. Results: The proposed method achieved a high detection performance for this difficult task, with the FreeAnchor model reaching an F1-score of 0.69 and a recall of 0.74. It outperformed several 3D methods in efficiency while maintaining comparable accuracy. Strong recall rates were observed for clinically relevant areas, such as local relapses (0.82) and bone metastases (0.80). Conclusion: Our fully automated CADe tool shows promise in assisting physicians as a “second reader” for detecting small metastatic BCR PCa lesions on PSMA-PET images. By leveraging the strength and computational efficiency of 2D models while preserving 3D spatial information of the PSMA-PET volume, the proposed approach has the potential to improve detectability and reduce workload in cancer diagnosis and management.

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