Non-invasive decision support for NSCLC treatment using PET/CT radiomics

Wei Mu, Lei Jiang, JianYuan Zhang, Yu Shi, Jhanelle E. Gray, Ilke Tunali, Chao Gao, Yingying Sun, Jie Tian, Xinming Zhao (), Xilin Sun (), Robert J. Gillies () and Matthew B. Schabath ()
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Wei Mu: H. Lee Moffitt Cancer Center and Research Institute
Lei Jiang: Tongji University School of Medicine
JianYuan Zhang: the Fourth Hospital of Hebei Medical University
Yu Shi: H. Lee Moffitt Cancer Center and Research Institute
Jhanelle E. Gray: H. Lee Moffitt Cancer Center and Research Institute
Ilke Tunali: H. Lee Moffitt Cancer Center and Research Institute
Chao Gao: Harbin Medical University
Yingying Sun: Harbin Medical University
Jie Tian: Beihang University
Xinming Zhao: the Fourth Hospital of Hebei Medical University
Xilin Sun: Harbin Medical University
Robert J. Gillies: H. Lee Moffitt Cancer Center and Research Institute
Matthew B. Schabath: H. Lee Moffitt Cancer Center and Research Institute

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a 18F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments.

Date: 2020
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DOI: 10.1038/s41467-020-19116-x

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