Mike A Mortensen1,2, Pablo Borrelli3, Mads Hvid Poulsen1, Oke Gerke4, Olof Enqvist5, Johannes Ulén6, Elin Trägårdh7,8, Caius Constantinescu4, Lars Edenbrandt3, Lars Lund1,2, Poul Flemming Høilund-Carlsen2,4. 1. Department of Urology, Odense University Hospital, Odense, Denmark. 2. Department of Clinical Research, University of Southern Denmark, Odense, Denmark. 3. Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden. 4. Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark. 5. Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. 6. Eigenvision AB, Malmö, Sweden. 7. Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden. 8. Department of Translational Medicine, Lund University, Malmö, Sweden.
Abstract
AIM: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). METHODS: 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 1 g ≈ 1 ml of tissue. RESULTS: The mean (range) weight of the prostate specimens was 44 g (20-109), while CNN-estimated volume was 62 ml (31-108) with a mean difference of 13·5 g 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. CONCLUSION: 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.
AIM: To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). METHODS: 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 1 g ≈ 1 ml of tissue. RESULTS: The mean (range) weight of the prostate specimens was 44 g (20-109), while CNN-estimated volume was 62 ml (31-108) with a mean difference of 13·5 g 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. CONCLUSION: 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.
Authors: Matteo Ferro; Ottavio de Cobelli; Gennaro Musi; Francesco Del Giudice; Giuseppe Carrieri; Gian Maria Busetto; Ugo Giovanni Falagario; Alessandro Sciarra; Martina Maggi; Felice Crocetto; Biagio Barone; Vincenzo Francesco Caputo; Michele Marchioni; Giuseppe Lucarelli; Ciro Imbimbo; Francesco Alessandro Mistretta; Stefano Luzzago; Mihai Dorin Vartolomei; Luigi Cormio; Riccardo Autorino; Octavian Sabin Tătaru Journal: Ther Adv Urol Date: 2022-07-04