Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
Artikel i vetenskaplig tidskrift, 2020

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.

artificial intelligence

PCa

fluorocholine

PET

lymph node metastases

Författare

Pablo Borrelli

Sahlgrenska universitetssjukhuset

Måns Larsson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Johannes Ulen

Eigenvision AB

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Elin Tragardh

Lunds universitet

Skånes universitetssjukhus (SUS)

Mads Hvid Poulsen

Syddansk Universitet

Odense Univ Hosp

Mike Allan Mortensen

Odense Universitetshospital

Henrik Kjolhede

Göteborgs universitet

Poul Flemming Hoilund-Carlsen

Odense Universitetshospital

Syddansk Universitet

Lars Edenbrandt

Göteborgs universitet

Clinical Physiology and Functional Imaging

1475-0961 (ISSN)

Vol. In Press

Styrkeområden

Hälsa och teknik

Ämneskategorier

Urologi och njurmedicin

Radiologi och bildbehandling

Medicinsk bildbehandling

DOI

10.1111/cpf.12666

PubMed

32976691

Mer information

Senast uppdaterat

2020-11-12