Machine Learning CT-Based Automatic Nodal Segmentation and PET Semi-Quantification of Intraoperative 68Ga-PSMA-11 PET/CT Images in High-Risk Prostate Cancer: A Pilot Study

Abstract

High-resolution intraoperative PET/CT specimen imaging, coupled with prostate-specific membrane antigen (PSMA) molecular targeting, holds great potential for the rapid ex vivo identification of disease localizations in high-risk prostate cancer patients undergoing surgery. However, the accurate analysis of radiotracer uptake would require time-consuming manual volumetric segmentation of 3D images. The aim of this study was to test the feasibility of using machine learning to perform automatic nodal segmentation of intraoperative 68Ga-PSMA-11 PET/CT specimen images. Six (n = 6) lymph-nodal specimens were imaged in the operating room after an e.v. injection of 2.1 MBq/kg of 68Ga-PSMA-11. A machine learning-based approach for automatic lymph-nodal segmentation was developed using only open-source Python libraries (Scikit-learn, SciPy, Scikit-image). The implementation of a k-means clustering algorithm (n = 3 clusters) allowed to identify lymph-nodal structures by leveraging differences in tissue density. Refinement of the segmentation masks was performed using morphological operations and 2D/3D-features filtering. Compared to manual segmentation (ITK-SNAP v4.0.1), the automatic segmentation model showed promising results in terms of weighted average precision (97–99%), recall (68–81%), Dice coefficient (80–88%) and Jaccard index (67–79%). Finally, the ML-based segmentation masks allowed to automatically compute semi-quantitative PET metrics (i.e., SUVmax), thus holding promise for facilitating the semi-quantitative analysis of PET/CT images in the operating room.

Comparison of Digital versus Analog 68Ga-PSMA-11 PET/CT Performance in Hormone-Sensitive Prostate Cancer Patients with Early Biochemical Recurrence or Persistence after Radical Treatment

“…Finally, the data on dPET performance presented in this study were obtained using the 68 Ga-PSMA-11 radiotracer, but further investigations with other PSMA-based radiopharmaceuticals should be performed before generalizing these results to other radiolabeled PSMA inhibitors. Despite these limitations, the enhanced performance of dPET holds promise for improving molecular imaging diagnostic accuracy and opens new perspectives for further investigations on novel technological developments such as intraoperative PET/CT imaging [53,54] and long axial field-of-view (LAFOV) PET/CT scanners [55]. Proper economic analyses will also be needed to evaluate the cost-effectiveness of dPET imaging in this setting: they will have to consider the costs and the utilities resulting from the impact of dPET higher restaging accuracy on patients' management and prognosis.…”