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Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET.
- Source :
-
EJNMMI research [EJNMMI Res] 2022 Dec 29; Vol. 12 (1), pp. 76. Date of Electronic Publication: 2022 Dec 29. - Publication Year :
- 2022
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Abstract
- Background: Accurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci of physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level and patient-level classification on PSMA PET images of patients with PCa.<br />Methods: This was an IRB-approved, HIPAA-compliant, retrospective study. Lesions on [ <superscript>18</superscript> F]DCFPyL PET/CT scans were assigned to PSMA reporting and data system (PSMA-RADS) categories and randomly partitioned into training, validation, and test sets. The framework extracted image features, radiomic features, and tissue type information from a cropped PET image slice containing a lesion and performed PSMA-RADS and PCa classification. Performance was evaluated by assessing the area under the receiver operating characteristic curve (AUROC). A t-distributed stochastic neighbor embedding (t-SNE) analysis was performed. Confidence and probability scores were measured. Statistical significance was determined using a two-tailed t test.<br />Results: PSMA PET scans from 267 men with PCa had 3794 lesions assigned to PSMA-RADS categories. The framework yielded AUROC values of 0.87 and 0.90 for lesion-level and patient-level PSMA-RADS classification, respectively, on the test set. The framework yielded AUROC values of 0.92 and 0.85 for lesion-level and patient-level PCa classification, respectively, on the test set. A t-SNE analysis revealed learned relationships between the PSMA-RADS categories and disease findings. Mean confidence scores reflected the expected accuracy and were significantly higher for correct predictions than for incorrect predictions (Pā<ā0.05). Measured probability scores reflected the likelihood of PCa consistent with the PSMA-RADS framework.<br />Conclusion: The framework provided lesion-level and patient-level PSMA-RADS and PCa classification on PSMA PET images. The framework was interpretable and provided confidence and probability scores that may assist physicians in making more informed clinical decisions.<br /> (© 2022. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2191-219X
- Volume :
- 12
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- EJNMMI research
- Publication Type :
- Academic Journal
- Accession number :
- 36580220
- Full Text :
- https://doi.org/10.1186/s13550-022-00948-1