Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study.

Abstract

To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). A convolutional neural network (CNN) was trained for automated measurements in 18 F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax ), mean standardized uptake value of voxels considered abnormal (SUVmean ) and volume of abnormal voxels (Volabn ). The product SUVmean ×Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1g≈1ml of tissue. The mean (range) weight of the prostate specimens was 44g (20-109), while CNN-estimated volume was 62ml (31-108) with a mean difference of 13·5g or ml (95% CI: 9·78-17·32). The two measures were significantly correlated (r=0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Volabn (ml) and TLU were 0·37 (-0·01 to 0·75), -0·08 (-0·30 to 0·14), 1·40 (-2·26 to 5·06) and 9·61 (-3·95 to 23·17), respectively. PET findings Volabn and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.