Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study
Maliazurina B Saad1, Lingzhi Hong1,2, Muhammad Aminu1, Natalie I Vokes2,3, Pingjun Chen1, Morteza Salehjahromi1, Kang Qin2, Sheeba J Sujit1, Xuetao Lu4, Elliana Young5, Qasem Al-Tashi1, Rizwan Qureshi1, Carol C Wu6, Brett W Carter6, Steven H Lin7, Percy P Lee7,8, Saumil Gandhi7, Joe Y Chang7, Ruijiang Li9, Michael F Gensheimer9
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Tx, USA
2Department of Thoracic, Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
3Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
4Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
5Department of Enterprise Data Engineering and Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
6Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
7Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
8Department of Radiation Oncology, City of Hope National Medical Center, Los Angeles, CA, USA
9Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
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The Lancet Digital Health
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