Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
Journal article, 2021

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 = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111;p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was 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.

fluorocholine

PCa

artificial intelligence

PET

lymph node metastases

Author

Pablo Borrelli

Sahlgrenska University Hospital

Måns Larsson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Johannes Ulen

Eigenvision AB

Olof Enqvist

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Elin Tragardh

Lund University

Skåne University Hospital

Mads Hvid Poulsen

University of Southern Denmark

Odense University Hospital

Mike Allan Mortensen

Odense Universitetshospital

Henrik Kjolhede

University of Gothenburg

Poul Flemming Hoilund-Carlsen

Odense Universitetshospital

University of Southern Denmark

Lars Edenbrandt

University of Gothenburg

Clinical Physiology and Functional Imaging

1475-0961 (ISSN)

Vol. 41 1 62-67

Areas of Advance

Health Engineering

Subject Categories

Urology and Nephrology

Radiology, Nuclear Medicine and Medical Imaging

Medical Image Processing

DOI

10.1111/cpf.12666

PubMed

32976691

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Latest update

1/4/2021 1