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Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study.
Dacic, Sanja; Travis, William D; Giltnane, Jennifer M; Kos, Filip; Abel, John; Hilz, Stephanie; Fujimoto, Junya; Sholl, Lynette; Ritter, Jon; Khalil, Farah; Liu, Yi; Taylor-Weiner, Amaro; Resnick, Murray; Yu, Hui; Hirsch, Fred R; Bunn, Paul A; Carbone, David P; Rusch, Valerie; Kwiatkowski, David J; Johnson, Bruce E; Lee, Jay M; Hennek, Stephanie R; Wapinski, Ilan; Nicholas, Alan; Johnson, Ann; Schulze, Katja; Kris, Mark G; Wistuba, Ignacio I.
Afiliação
  • Dacic S; Department of Pathology, Yale School of Medicine, New Haven, Connecticut. Electronic address: sanja.dacic@yale.edu.
  • Travis WD; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Giltnane JM; Research Pathology, Genentech, Inc., South San Francisco, California.
  • Kos F; Department of Machine Learning, PathAI, Inc., Boston, Massachusetts.
  • Abel J; Department of Machine Learning, PathAI, Inc., Boston, Massachusetts.
  • Hilz S; Research Pathology, Genentech, Inc., South San Francisco, California.
  • Fujimoto J; Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Sholl L; Department of Anatomic Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
  • Ritter J; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
  • Khalil F; Department of Pathology, Moffitt Cancer Center, Tampa, Florida.
  • Liu Y; Department of Machine Learning, PathAI, Inc., Boston, Massachusetts.
  • Taylor-Weiner A; Department of Machine Learning, PathAI, Inc., Boston, Massachusetts.
  • Resnick M; Department of Pathology, PathAI, Inc., Boston, Massachusetts.
  • Yu H; Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
  • Hirsch FR; Department of Hematology and Medical Oncology, University of Colorado/Icahn School of Medicine, Mount Sinai, New York.
  • Bunn PA; Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
  • Carbone DP; Division of Medical Oncology, The Ohio State University Medical Center and Pelotonia Institute for Immuno-Oncology, Columbus, Ohio.
  • Rusch V; Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Kwiatkowski DJ; Department of Anatomic Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
  • Johnson BE; Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
  • Lee JM; Division of Thoracic Surgery, University of California, Los Angeles, Los Angeles, California.
  • Hennek SR; Department of Translational Research, PathAI, Inc., Boston, Massachusetts.
  • Wapinski I; Department of Translational Research, PathAI, Inc., Boston, Massachusetts.
  • Nicholas A; U.S. Medical Affairs, Genentech, Inc., South San Francisco, California.
  • Johnson A; U.S. Medical Affairs, Genentech, Inc., South San Francisco, California.
  • Schulze K; Research Pathology, Genentech, Inc., South San Francisco, California.
  • Kris MG; Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Wistuba II; Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
J Thorac Oncol ; 2023 Dec 07.
Article em En | MEDLINE | ID: mdl-38070597
INTRODUCTION: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate. METHODS: We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists. RESULTS: For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements. CONCLUSIONS: Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Thorac Oncol Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: J Thorac Oncol Ano de publicação: 2023 Tipo de documento: Article