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Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians.
Trägårdh, Elin; Enqvist, Olof; Ulén, Johannes; Hvittfeldt, Erland; Garpered, Sabine; Belal, Sarah Lindgren; Bjartell, Anders; Edenbrandt, Lars.
Afiliación
  • Trägårdh E; Department of Translational Medicine and Wallenberg Centre of Molecular Medicine, Lund University, Malmö, Sweden. elin.tragardh@med.lu.se.
  • Enqvist O; Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02, Malmö, Sweden. elin.tragardh@med.lu.se.
  • Ulén J; Eigenvision AB, Malmö, Sweden.
  • Hvittfeldt E; Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
  • Garpered S; Eigenvision AB, Malmö, Sweden.
  • Belal SL; Department of Translational Medicine and Wallenberg Centre of Molecular Medicine, Lund University, Malmö, Sweden.
  • Bjartell A; Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02, Malmö, Sweden.
  • Edenbrandt L; Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02, Malmö, Sweden.
Eur J Nucl Med Mol Imaging ; 49(10): 3412-3418, 2022 08.
Article en En | MEDLINE | ID: mdl-35475912
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Médicos / Neoplasias de la Próstata / Medicina Nuclear Límite: Humans / Male Idioma: En Revista: Eur J Nucl Med Mol Imaging Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Médicos / Neoplasias de la Próstata / Medicina Nuclear Límite: Humans / Male Idioma: En Revista: Eur J Nucl Med Mol Imaging Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: Suecia Pais de publicación: Alemania