TORNA ALL’ELENCO

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 in Diagnostics

2023
AOU Città della Salute di Torino

Tipo pubblicazione

Article

Autori/Collaboratori (8)Vedi tutti...

Rovera G
Nuclear Medicine, Department of Medical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
Grimaldi S
Nuclear Medicine, Department of Medical Sciences, AOU Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
Oderda M
Urology Unit, Department of Surgical Sciences, AOU Città della Salute e della Scienza di Torino, Molinette Hospital, University of Turin, Turin, Italy

et alii...

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.

DOI : 10.3390/diagnostics13183013

Keywords

AURA 10; PET-CT scanner; radionuclide generator; robotic surgical system; gallium gozetotide ga 68; article; automation; clinical article; controlled study; feasibility study; high risk patient; human; image segmentation; intraoperative period; k means clustering; lymph node; machine learning; male; maximum standardized uptake value; pilot study; positron emission tomography-computed tomography; prostate cancer; retrospective study; three-dimensional imaging; two-dimensional imaging; AURA 10; Da Vinci Xi;