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

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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.

Detaljer

Författare
  • Pablo Borrelli
  • Måns Larsson
  • Johannes Ulén
  • Olof Enqvist
  • Elin Trägårdh
  • Mads Hvid Poulsen
  • Mike Allan Mortensen
  • Henrik Kjölhede
  • Poul Flemming Høilund-Carlsen
  • Lars Edenbrandt
Enheter & grupper
Externa organisationer
  • Sahlgrenska University Hospital
  • Chalmers Tekniska Högskola
  • Eigenvision AB
  • Skåne University Hospital
  • Odense University Hospital
  • University of Southern Denmark
  • Göteborgs universitet
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Cancer och onkologi
  • Radiologi och bildbehandling

Nyckelord

Originalspråkengelska
Sidor (från-till)62-67
Antal sidor6
TidskriftClinical Physiology and Functional Imaging
Volym41
Utgåva nummer1
Tidigt onlinedatum2020 sep 25
StatusPublished - 2021 jan
PublikationskategoriForskning
Peer review utfördJa