Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

Sanja Dacic; William D Travis; Jennifer M Giltnane; Filip Kos; John Abel; Stephanie Hilz; Junya Fujimoto; Lynette Sholl; Jon Ritter; Farah Khalil; Yi Liu; Amaro Taylor-Weiner; Murray Resnick; Hui Yu; Fred R Hirsch; Paul A Bunn Jr; David P Carbone; Valerie Rusch; David J Kwiatkowski; Bruce E Johnson; Jay M Lee; Stephanie R Hennek; Ilan Wapinski; Alan Nicholas; Ann Johnson; Katja Schulze; Mark G Kris; Ignacio I Wistuba

doi:10.1016/j.jtho.2023.12.010

Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

J Thorac Oncol. 2023 Dec 7:S1556-0864(23)02415-2. doi: 10.1016/j.jtho.2023.12.010. Online ahead of print.

Authors

Sanja Dacic¹, William D Travis², Jennifer M Giltnane³, Filip Kos⁴, John Abel⁴, Stephanie Hilz³, Junya Fujimoto⁵, Lynette Sholl⁶, Jon Ritter⁷, Farah Khalil⁸, Yi Liu⁴, Amaro Taylor-Weiner⁴, Murray Resnick⁹, Hui Yu¹⁰, Fred R Hirsch¹¹, Paul A Bunn Jr¹², David P Carbone¹³, Valerie Rusch¹⁴, David J Kwiatkowski⁶, Bruce E Johnson¹⁵, Jay M Lee¹⁶, Stephanie R Hennek¹⁷, Ilan Wapinski¹⁷, Alan Nicholas¹⁸, Ann Johnson¹⁸, Katja Schulze³, Mark G Kris¹⁹, Ignacio I Wistuba⁵

Affiliations

¹ Department of Pathology, Yale School of Medicine, New Haven, Connecticut. Electronic address: sanja.dacic@yale.edu.
² Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
³ Research Pathology, Genentech, Inc., South San Francisco, California.
⁴ Department of Machine Learning, PathAI, Inc., Boston, Massachusetts.
⁵ Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
⁶ Department of Anatomic Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
⁷ Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
⁸ Department of Pathology, Moffitt Cancer Center, Tampa, Florida.
⁹ Department of Pathology, PathAI, Inc., Boston, Massachusetts.
¹⁰ Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
¹¹ Department of Hematology and Medical Oncology, University of Colorado/Icahn School of Medicine, Mount Sinai, New York.
¹² Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
¹³ Division of Medical Oncology, The Ohio State University Medical Center and Pelotonia Institute for Immuno-Oncology, Columbus, Ohio.
¹⁴ Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center, New York, New York.
¹⁵ Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
¹⁶ Division of Thoracic Surgery, University of California, Los Angeles, Los Angeles, California.
¹⁷ Department of Translational Research, PathAI, Inc., Boston, Massachusetts.
¹⁸ U.S. Medical Affairs, Genentech, Inc., South San Francisco, California.
¹⁹ Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.

PMID: 38070597
DOI: 10.1016/j.jtho.2023.12.010

Abstract

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.

Keywords: Artificial intelligence; Convolutional neural network; Digital pathology; NSCLC; Neoadjuvant.