In Clinical physiology and functional imaging
INTRODUCTION : Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions.
METHODS : A group of 399 patients with biopsy-proven PCa who had undergone 18 F-choline PET/CT for staging prior to treatment were used to train (n=319) and test (n=80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated.
RESULTS : The AI-based tool detected more lymph node lesions than Reader B (98 vs 87/117; p=0.045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs 87/111; p=0.63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment were significantly associated with PCa-specific survival.
CONCLUSION : This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers, and prognostic information in PCa patients.
Borrelli Pablo, Larsson Måns, Ulén Johannes, Enqvist Olof, Trägårdh Elin, Hvid Poulsen Mads, Mortensen Mike Allan, Kjölhede Henrik, Høilund-Carlsen Poul Flemming, Edenbrandt Lars
Artificial intelligence, Fluorocholine, Lymph node metastases, PCa, PET