Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer on ⁶⁸ Ga PSMA PET/CT Delayed...

To assess the utility of convoluted neural network (CNN) in differentiating clinically significant and insignificant prostate cancer in patients with ⁶⁸ Ga PSMA PET/CT-targeted prostate biopsy-proven prostate cancer.

In this retrospective study, 142 patients with clinical suspicion of prostate cancer were evaluated who underwent ⁶⁸ Ga-PSMA PET/CT imaging followed by ⁶⁸ 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 ⁶⁸ 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.

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 ⁶⁸ 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 (E %) 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.

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.

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