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

Pablo Borrelli, Måns Larsson, Johannes Ulén, Olof Enqvist, Elin Trägårdh, Mads Hvid Poulsen, Mike Allan Mortensen, Henrik Kjölhede, Poul Flemming Høilund-Carlsen, Lars Edenbrandt

Research output: Contribution to journalArticlepeer-review

9 Citations (SciVal)

Abstract

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.

Original languageEnglish
Pages (from-to)62-67
Number of pages6
JournalClinical Physiology and Functional Imaging
Volume41
Issue number1
Early online date2020 Sep 25
DOIs
Publication statusPublished - 2021 Jan

Subject classification (UKÄ)

  • Cancer and Oncology
  • Radiology, Nuclear Medicine and Medical Imaging

Keywords

  • artificial intelligence
  • fluorocholine
  • lymph node metastases
  • PCa
  • PET

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