Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

Abstract

Aim To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/ CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-na€ ıve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18 F-choline PET images above a standardized uptake value (SUV) of 2Á65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results The SDI between the automated and the manual volume segmentations was 0Á78 and 0Á79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0Á02), whereas age, PSA, and Gleason score were not. Conclusion Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

“…Mortensen et al [9] and Polymeri et al [10] used automated segmentation of the prostate. A CNN was trained for automated measurements in [18F]-choline PET/CT scans obtained before radical prostatectomy in patients with newly diagnosed prostate cancer [9].…”

“…The automated CNN segmentation and the PET measurements provided similar measurements to manually derived measurements. Polymeri et al [10] then used the method to explore the potential of automatic PET/CT measurements as prognostic biomarkers. These authors found that automated PET/CT measurements reflecting total lesion uptake were significantly associated with overall survival, whereas age, prostate-specific antigen, and Gleason score were not.…”

“…The CNN-based organ segmentation in CT studies in RECOMIA has been used in multiple studies [7][8][9][10][11][12]. These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki.…”

“…These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki. Patients and image acquisition have been described previously [7,8,10,11].…”

Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

“…The vision is that AI methods applied to whole‐body PET/CT scans can provide a single number, the global disease score, to reflect the entire disease burden in the body or part of the body (Høilund‐Carlsen et al., 2019). We have used CNNs to automatically detect and quantify PET tracer activity in the prostate gland and skeleton of patients with PCa (Lindgren Belal et al., 2017; Polymeri et al., 2020). Both methods provided volumetric measurements of tumour burden, which is associated with overall survival.…”