文章：

基于多角度最大强度投影的3D PSMA PET体积中微小转移性前列腺癌病灶的计算机辅助检测

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

原文发布日期：3 May 2025

DOI: 10.3390/cancers17091563

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

目的：本研究旨在开发并评估一种新型计算机辅助检测方法，用于在PSMA-PET图像中识别小型转移性生化复发前列腺癌病灶。该方法通过多角度最大强度投影技术与前沿目标检测算法相结合，实现对病灶的精准定位。方法：我们从旋转三维PSMA-PET影像中提取多角度最大强度投影数据，对四大类模型架构中的16种前沿目标检测算法进行微调与评估。采用有序子集期望最大化算法将预测的二维边界框反投影至原始三维空间，并运用微调后的医学通用分割模型对边界框内已识别的病灶进行分割。结果：针对这一高难度检测任务，所提方法展现出优异的检测性能，其中FreeAnchor模型获得0.69的F1分数与0.74的召回率。在保持相当准确度的同时，其效率优于多种三维检测方法。在临床关键区域如局部复发灶与骨转移灶的检测中，分别达到0.82和0.80的高召回率。结论：本全自动计算机辅助检测工具可作为"第二阅片者"辅助临床医生检测PSMA-PET图像中的小型转移性生化复发前列腺癌病灶。该方法在保留PSMA-PET三维空间信息的同时，充分发挥了二维模型的计算效能优势，有望提升癌症诊疗过程中的病灶检出率并减轻临床工作负担。

原文链接：

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