Artificial intelligence-based recurrence prediction outperforms classical histopathological methods in pulmonary adenocarcinoma biopsies.

Akram, F; Wolf, J L; Trandafir, T E; Dingemans, Anne-Marie C; Stubbs, A P; von der Thüsen, J H

Akram, F; Wolf, J L; Trandafir, T E; Dingemans, Anne-Marie C; Stubbs, A P; von der Thüsen, J H.

Afiliação

Akram F; Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands.
Wolf JL; Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands; Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland.
Trandafir TE; Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands.
Dingemans AC; Department of Pulmonary Diseases, Erasmus MC Cancer Center, University Medical Center, Rotterdam, The Netherlands.
Stubbs AP; Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands.
von der Thüsen JH; Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, The Netherlands. Electronic address: j.vonderthusen@erasmusmc.nl.

Lung Cancer ; 186: 107413, 2023 12.

Article em En | MEDLINE | ID: mdl-37939498

Assuntos

Adenocarcinoma de Pulmão; Neoplasias Pulmonares; Humanos; Inteligência Artificial; Neoplasias Pulmonares/patologia; Adenocarcinoma de Pulmão/cirurgia; Redes Neurais de Computação; Biópsia

Palavras-chave

Artificial intelligence; Convolutional neural network; Lung adenocarcinoma; Recurrence prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Adenocarcinoma de Pulmão / Neoplasias Pulmonares Limite: Humans Idioma: En Revista: Lung Cancer Assunto da revista: NEOPLASIAS Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Holanda

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