[18F]FDG PET-TC radiomics and machine learning in the evaluation of prostate incidental uptake

Nieri, Alberto; Manco, Luigi; Bauckneht, Matteo; Urso, Luca; Caracciolo, Matteo; Donegani, Maria Isabella; Borgia, Francesca; Vega, Kevin; Colella, Alessandro; Ippolito, Carmelo; Cittanti, Corrado; Morbelli, Silvia; Sambuceti, Gianmario; Turra, Alessandro; Panareo, Stefano; Bartolomei, Mirco

doi:10.1080/17434440.2023.2280685

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…In the field of nuclear medicine, ML Models developed using PET/CT data have already demonstrated numerous applications in multiple anatomical districts and for different purposes (i.e., for the diagnosis, staging or prediction of response to treatment) [ 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

ML Models Built Using Clinical Parameters and Radiomic Features Extracted from 18F-Choline PET/CT for the Prediction of Biochemical Recurrence after Metastasis-Directed Therapy in Patients with Oligometastatic Prostate Cancer

Urso,

Cittanti,

Manco

et al. 2024

Diagnostics

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Oligometastatic patients at [18F]F-Fluorocholine (18F-choline) PET/CT may be treated with metastasis-directed therapy (MDT). The aim of this study was to combine radiomic parameters extracted from 18F-choline PET/CT and clinical data to build machine learning (ML) models able to predict MDT efficacy. Methods: Oligorecurrent patients (≤5 lesions) at 18F-choline PET/CT and treated with MDT were collected. A per-patient and per-lesion analysis was performed, using 2-year biochemical recurrence (BCR) after MDT as the standard of reference. Clinical parameters and radiomic features (RFts) extracted from 18F-choline PET/CT were used for training five ML Models for both CT and PET images. The performance metrics were calculated (i.e., Area Under the Curve—AUC; Classification Accuracy—CA). Results: A total of 46 metastases were selected and segmented in 29 patients. BCR after MDT occurred in 20 (69%) patients after 2 years of follow-up. In total, 73 and 33 robust RFTs were selected from CT and PET datasets, respectively. PET ML Models showed better performances than CT Models for discriminating BCR after MDT, with Stochastic Gradient Descent (SGD) being the best model (AUC = 0.95; CA = 0.90). Conclusion: ML Models built using clinical parameters and CT and PET RFts extracted via 18F-choline PET/CT can accurately predict BCR after MDT in oligorecurrent PCa patients. If validated externally, ML Models could improve the selection of oligorecurrent PCa patients for treatment with MDT.

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