Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on Ga PSMA PET/CT Delayed Imaging by Analyzing Radiomic Features.

Purpose: To assess the utility of convoluted neural network (CNN) in differentiating clinically significant and insignificant prostate cancer in patients with  Ga PSMA PET/CT-targeted prostate biopsy-proven prostate cancer.

Methods: In this retrospective study, 142 patients with clinical suspicion of prostate cancer were evaluated who underwent  Ga-PSMA PET/CT imaging followed by  Ga-PSMA PET/CT-targeted prostate biopsy from the PSMA-avid prostate lesion. Twenty patients with no PSMA-avid lesions were excluded. Local Image Features Extraction (LifeX) software was used to extract radiomic features (RF) from delayed  Ga-PSMA PET/CT images of 122 patients. LifeX failed to extract radiomic features in 24 patients, and the remaining 98 were evaluated. RFs were fed to an in-built CNN of the software for computation and results were achieved. Patients with Gleason Score ≥ 7 on histopathology were labeled clinically significant prostate cancer (csPCa). The diagnostic values of radiomic features were evaluated.

Results: The csPCa was revealed in 69/98 (70.4%) patients, and insignificant PCa was noticed in 29/98 (29.6%) patients. The software extracted 124 RF from the delayed  Ga-PSMA PET/CT images. The accuracy of the CNN was 80.7% to differentiate clinically significant and clinically insignificant prostate cancer, with an error percentage ( %) of 19.3%. The sensitivity, specificity, positive predictive, and negative predictive values were 90.3%, 57.7%, 83.6%, and 71.4%, respectively, to detect csPCa.

Conclusion: CNN is a feasible pre-biopsy screening tool for identifying clinically significant prostate cancer and can be used as an adjunct in the initial diagnosis and early treatment planning.

Supplementary Information: The online version contains supplementary material available at 10.1007/s13139-023-00832-3.