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

• Published in: Diagnostics (Basel) Vol. 13; no. 18; p. 3013
• Main Authors: Rovera, Guido, Grimaldi, Serena, Oderda, Marco, Finessi, Monica, Giannini, Valentina, Passera, Roberto, Gontero, Paolo, Deandreis, Désirée
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 21.09.2023; MDPI
• Subjects: 68Ga-PSMA-11; Accuracy; Algorithms; Artificial intelligence; automatic segmentation; Automation; Cancer therapies; Clustering; Good Manufacturing Practice; Libraries; Lymphatic system; Machine learning; Metastasis; Nuclear medicine; Patients; PET/CT; PET/CT specimen images; Pilot projects; Prostate cancer; Robots; Scintigraphy; Tomography; Belgium
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13183013

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.