1
|
Rovera G, Grimaldi S, Oderda M, Finessi M, Giannini V, Passera R, Gontero P, Deandreis D. 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. Diagnostics (Basel) 2023; 13:3013. [PMID: 37761380 PMCID: PMC10529304 DOI: 10.3390/diagnostics13183013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
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.
Collapse
Affiliation(s)
- Guido Rovera
- Nuclear Medicine, Department of Medical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, 10126 Turin, Italy; (G.R.)
| | - Serena Grimaldi
- Nuclear Medicine, Department of Medical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, 10126 Turin, Italy; (G.R.)
| | - Marco Oderda
- Urology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, Molinette Hospital, University of Turin, 10126 Turin, Italy
| | - Monica Finessi
- Nuclear Medicine, Department of Medical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, 10126 Turin, Italy; (G.R.)
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
| | - Roberto Passera
- Nuclear Medicine, Department of Medical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, 10126 Turin, Italy; (G.R.)
| | - Paolo Gontero
- Urology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, Molinette Hospital, University of Turin, 10126 Turin, Italy
| | | |
Collapse
|