Toosi Amirhosein, Harsini Sara, Divband Ghasemali, Bénard François, Uribe Carlos F, Oviedo Felipe, Dodhia Rahul, Weeks William B, Lavista Ferres Juan M, Rahmim Arman
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada.
Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
Cancers (Basel). 2025 May 3;17(9):1563. doi: 10.3390/cancers17091563.
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.
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.
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).
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.
我们旨在开发并评估一种新型的计算机辅助检测（CADe）方法，该方法利用多角度最大强度投影（MA-MIPs）和先进的（SOTA）目标检测算法，在PSMA-PET图像上识别小的生化复发（BCR）前列腺癌（PCa）转移病灶。
我们对16种SOTA目标检测算法（从四种主要模型类型类别中选择）进行了微调与评估，这些算法应用于从旋转的3D PSMA-PET体积中提取的MA-MIPs。使用有序子集期望最大化（OSEM）算法将预测的2D边界框反向投影到原始3D空间。然后还使用了经过微调的医学分割一切模型（MedSAM）对边界框内识别出的病灶进行分割。
所提出的方法在这项艰巨任务中实现了较高的检测性能，FreeAnchor模型的F1分数达到0.69，召回率达到0.74。它在效率上优于几种3D方法，同时保持了相当的准确性。在临床相关区域，如局部复发（0.82）和骨转移（0.80），观察到了较高的召回率。
我们的全自动CADe工具作为PSMA-PET图像上检测小的转移性BCR PCa病灶的“第二阅片者”，在协助医生方面显示出前景。通过利用2D模型的优势和计算效率，同时保留PSMA-PET体积的3D空间信息，所提出的方法有可能提高癌症诊断和管理中的可检测性并减少工作量。
Zotero 插件
