TY - JOUR
T1 - Artificial Intelligence–Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC
T2 - Results From the LCMC3 Study
AU - Dacic, Sanja
AU - Travis, William D.
AU - Giltnane, Jennifer M.
AU - Kos, Filip
AU - Abel, John
AU - Hilz, Stephanie
AU - Fujimoto, Junya
AU - Sholl, Lynette
AU - Ritter, Jon
AU - Khalil, Farah
AU - Liu, Yi
AU - Taylor-Weiner, Amaro
AU - Resnick, Murray
AU - Yu, Hui
AU - Hirsch, Fred R.
AU - Bunn, Paul A.
AU - Carbone, David P.
AU - Rusch, Valerie
AU - Kwiatkowski, David J.
AU - Johnson, Bruce E.
AU - Lee, Jay M.
AU - Hennek, Stephanie R.
AU - Wapinski, Ilan
AU - Nicholas, Alan
AU - Johnson, Ann
AU - Schulze, Katja
AU - Kris, Mark G.
AU - Wistuba, Ignacio I.
N1 - Publisher Copyright:
© 2023 International Association for the Study of Lung Cancer
PY - 2024
Y1 - 2024
N2 - 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 less than or equal to 10% 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.
AB - 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 less than or equal to 10% 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.
KW - Artificial intelligence
KW - Convolutional neural network
KW - Digital pathology
KW - Neoadjuvant
KW - NSCLC
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UR - http://www.scopus.com/inward/citedby.url?scp=85182457538&partnerID=8YFLogxK
U2 - 10.1016/j.jtho.2023.12.010
DO - 10.1016/j.jtho.2023.12.010
M3 - Article
C2 - 38070597
AN - SCOPUS:85182457538
SN - 1556-0864
JO - Journal of Thoracic Oncology
JF - Journal of Thoracic Oncology
ER -