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

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 at...

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Veröffentlicht in:	Journal of thoracic oncology 2024-05, Vol.19 (5), p.719-731
Hauptverfasser:	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.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Convolutional neural network Digital pathology Neoadjuvant NSCLC
Online-Zugang:	Volltext
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