Joe Y Chang, Professor and Clinical Section Chief at MD Anderson Cancer Center, shared a post on X:
“How to predict PD-L1 expression level without biopsy for your patients?
Houston, We Have a Solution.
We just published ‘Deep Learning of CT Imaging Predicts PD-L1 Expression and Immunotherapy Response in Metastatic NSCLC: A Multi-Center Study‘ in Cancer Letters by team leader Jia Wu.
- SCENT, an ensemble transformer model, predicts PD-L1 (≥50% vs <50%) from routine CT as a noninvasive virtual biopsy in metastatic NSCLC.
- SCENT achieved strong discrimination for PD-L1 status in the MDACC cohort (AUC 0.84) and generalized it to external cohorts (AUC 0.80 Mayo; 0.78 LONESTAR trail).
- SCENT-derived PD-L1 stratified immunotherapy outcomes, associated with both PFS (HR 1.49) and OS (HR 1.40), comparable to tissue PD-L1 IHC.
Title: Deep learning of CT imaging predicts PD-L1 expression and immunotherapy response in metastatic NSCLC: A multi-center study
Authors: Amgad Muneer, Eman Showkatian, Maliazurina B. Saad, Lingzhi Hong, Shenduo Li, Morteza Salehjahromi, Muhammad Aminu, Sheeba J. Sujit, Hui Xu, Muhammad Waqas, Anas Zafar, Girish S. Shroff, Carol C. Wu, Brett W. Carter, Joe Y. Chang, Zhongxing Liao, Mehmet Altan, Natalie I. Vokes, Tina Cascone, Xiuning Le, Cara L. Haymaker, Ignacio I. Wistuba, Caroline Chung, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Yanyan Lou, John V. Heymach, Jianjun Zhang, Jia Wu
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