Expert Rev Med Devices. 2023 Nov 9. doi: 10.1080/17434440.2023.2280685. Online ahead of print.
ABSTRACT
AIM: To evaluate the relevance of Incidental prostate [18F]FDG uptake (IPU) and to explore the potential of radiomics and machine learning (ML) to predict prostate cancer (PCa).
METHODS: We retrieved [18F]FDG PET/CT scans with evidence of IPU performed in 2 institutions between 2015-2021. Patients were divided in PCa and non-PCa, according to biopsy. Clinical and PET/CT derived information (comprehensive of radiomic analysis) were acquired. 5 ML models were developed and their performance in discriminating PCa vs non-PCa IPU was evaluated. Radiomic analysis was investigated to predict ISUP Grade.
RESULTS: Overall 56 IPU were identified and 31 patients performed prostate biopsy. Eighteen of those were diagnosed as PCa. Only PSA and radiomic features (8 from CT and 9 from PET images, respectively) showed statistically significant difference between PCa/non-PCa patients. Eight features resulted robust between the two institutions. CT-based ML models showed a good performance, especially in terms of negative predictive value (NPV 0.733-0.867). PET-derived ML models resulted less accurate except the Random Forest model (NPV = 0.933). Radiomics could not accurately predict ISUP grade.
CONCLUSIONS: Paired with PSA, radiomic analysis seems to be promising to discriminate PCa/non-PCa IPU. ML could be a useful tool to identify non-PCa IPU, avoiding further investigations.
PMID:37942630 | DOI:10.1080/17434440.2023.2280685
