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

• Published in: Clinical physiology and functional imaging Vol. 41; no. 1; pp. 62 - 67
• Main Authors: Borrelli, Pablo, Larsson, Måns, Ulén, Johannes, Enqvist, Olof, Trägårdh, Elin, Poulsen, Mads Hvid, Mortensen, Mike Allan, Kjölhede, Henrik, Høilund‐Carlsen, Poul Flemming, Edenbrandt, Lars
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-01-2021
• Subjects: Artificial intelligence; Artificial neural networks; Biopsy; Cancer and Oncology; Cancer och onkologi; Choline; Clinical Medicine; Computed tomography; Feasibility studies; fluorocholine; Image analysis; Image processing; Klinisk medicin; Lesions; lymph node metastases; Lymph nodes; Lymphatic system; Medical and Health Sciences; Medical imaging; Medicin och hälsovetenskap; Metastases; Metastasis; Neural networks; PCa; PET; Positron emission; Prostate cancer; Radiologi och bildbehandling; Radiology, Nuclear Medicine and Medical Imaging; Survival; Tomography; lymph node metastases; PET; artificial intelligence; fluorocholine; PCa
• ISSN: 1475-0961; 1475-097X; 1475-097X
• DOI: 10.1111/cpf.12666

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 18F‐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.