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AI-guided histopathology predicts brain metastasis in lung cancer patients.
Zhou, Haowen; Watson, Mark; Bernadt, Cory T; Lin, Steven Siyu; Lin, Chieh-Yu; Ritter, Jon H; Wein, Alexander; Mahler, Simon; Rawal, Sid; Govindan, Ramaswamy; Yang, Changhuei; Cote, Richard J.
Afiliação
  • Zhou H; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Watson M; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
  • Bernadt CT; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
  • Lin SS; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Lin CY; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
  • Ritter JH; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
  • Wein A; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
  • Mahler S; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Rawal S; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
  • Govindan R; Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA.
  • Yang C; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Cote RJ; Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA.
J Pathol ; 263(1): 89-98, 2024 05.
Article em En | MEDLINE | ID: mdl-38433721
ABSTRACT
Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Carcinoma Pulmonar de Células não Pequenas / Neoplasias Pulmonares Limite: Humans Idioma: En Revista: J Pathol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Carcinoma Pulmonar de Células não Pequenas / Neoplasias Pulmonares Limite: Humans Idioma: En Revista: J Pathol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos