Researchers report a high-throughput, noninvasive, deep learning model that accurately predicts non-small cell lung cancer (NSCLC) patient sensitivity to tyrosine kinase inhibitor (TKI) treatment and identifies those who are likely to benefit from immune checkpoint inhibitor (ICI) treatment in a study published in Nature Communications (2020;11[1]:5228. doi:10.1038/s41467-020-19116-x).
In the study by Matthew Schabath, PhD, H. Lee Moffitt Cancer Center and Research Institute (Tampa, FL) and colleagues, an 18F-FDG PET/CT-based deep learning model was developed using two retrospective cohorts of patients from the Shanghai Pulmonary Hospital (SPH) (Shanghai, China) and the Fourth Hospital of Hebei Medical University (HBMU) (Hebei, China). The performance of the epidermal growth factor receptor (EGFR) prediction model was evaluated with an external test cohort from the Fourth Hospital of Harbin Medical University (HMU; Harbin, China).
“This type of imaging, 18F-FDG PET/CT, is widely used in determining the staging of patients with non-small cell lung cancer. The glucose radiotracer used is also known to be affected by EGFR activation and inflammation,” said Dr Schabath. “EGFR, or epidermal growth factor receptor, is a common mutation found in non-small cell lung cancer patients. EGFR mutation status can be a predictor for treatment, as patients with an active EGFR mutation have better response to tyrosine kinase inhibitor treatment.”
The deep learning score was positively associated with longer progression-free survival (PFS) in patients treated with TKIs and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS in patients treated with ICIs.
Thus, the model is efficient in identifying patients sensitive to TKIs or ICIs and provides a noninvasive method for quantification of mutation status.
“We would like to perform further studies but believe this model could serve as a clinical decision support tool for different treatments,” said coauthor Robert Gillies, PhD, H. Lee Moffitt Cancer Center and Research Institute (Tampa, FL).—Lisa Kuhns
