Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians
Artikel i vetenskaplig tidskrift, 2022

Purpose: The aim of this study was to develop and validate an artificial intelligence (AI)-based method using convolutional neural networks (CNNs) for the detection of pelvic lymph node metastases in scans obtained using [18F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) from patients with high-risk prostate cancer. The second goal was to make the AI-based method available to other researchers. Methods: [18F]PSMA PET-CT scans were collected from 211 patients. Suspected pelvic lymph node metastases were marked by three independent readers. A CNN was developed and trained on a training and validation group of 161 of the patients. The performance of the AI method and the inter-observer agreement between the three readers were assessed in a separate test group of 50 patients. Results: The sensitivity of the AI method for detecting pelvic lymph node metastases was 82%, and the corresponding sensitivity for the human readers was 77% on average. The average number of false positives was 1.8 per patient. A total of 5–17 false negative lesions in the whole cohort were found, depending on which reader was used as a reference. The method is available for researchers at www.recomia.org. Conclusion: This study shows that AI can obtain a sensitivity on par with that of physicians with a reasonable number of false positives. The difficulty in achieving high inter-observer sensitivity emphasizes the need for automated methods. On the road to qualifying AI tools for clinical use, independent validation is critical and allows performance to be assessed in studies from different hospitals. Therefore, we have made our AI tool freely available to other researchers.

Artificial intelligence

PSMA

Convolutional neural network

Deep learning

Prostate cancer

Författare

E. Tragardh

Institutionen för Translationell Medicin

Skånes universitetssjukhus (SUS)

Olof Enqvist

Digitala bildsystem och bildanalys

Eigenvision AB

Johannes Ulén

Eigenvision AB

Erland Hvittfeldt

Institutionen för Translationell Medicin

Skånes universitetssjukhus (SUS)

Sabine Garpered

Skånes universitetssjukhus (SUS)

S. L. Belal

Institutionen för Translationell Medicin

Skånes universitetssjukhus (SUS)

Anders Bjartell

Skånes universitetssjukhus (SUS)

L. Edenbrandt

Sahlgrenska universitetssjukhuset

Göteborgs universitet

European Journal of Nuclear Medicine and Molecular Imaging

1619-7070 (ISSN) 1619-7089 (eISSN)

Vol. In Press

Ämneskategorier

Dermatologi och venereologi

Urologi och njurmedicin

Radiologi och bildbehandling

DOI

10.1007/s00259-022-05806-9

PubMed

35475912

Mer information

Senast uppdaterat

2022-05-12