Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images

Epidermal growth factor receptor (EGFR) genotyping and programmed death ligand-1 (PD-L1) expressions are of paramount importance for treatment guidelines such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional identification of EGFR or PD-L1 status requires surgical or biopsied tumor specimens, which are obtained through invasive procedures associated with risk of morbidities and may be unavailable to access tissue samples. Here, we developed an artificial intelligence (AI) system that can predict EGFR and PD-L1 status in using non-invasive computed tomography (CT) images.

A multitask AI system including deep learning (DL) module, radiomics (RA) module, and joint (JO) module combining the DL, RA, and clinical features was developed, trained, and optimized with CT images to predict the EGFR and PD-L1 status. We used feature selectors and feature fusion methods to find the best model among combinations of module types. The models were evaluated using the areas under the receiver operating characteristic curves (AUCs).

Our multitask AI system yielded promising performance for gene expression status, subtype classification, and joint prediction. The AUCs of DL module achieved 0.842 (95% CI, 0.825-0.855) in the EGFR mutated status and 0.805 (95% CI, 0.779-0.829) in the mutated-EGFR subtypes discrimination (19Del, L858R, other mutations). DL module also demonstrated the AUCs of 0.799 (95% CI, 0.762-0.854) in the PD-L1 expression status and 0.837 (95% CI, 0.775-0.911) in the positive-PD-L1 subtypes (PD-L1 tumor proportion score, 1%-49% and ≥50%). Furthermore, the JO module of our AI system performed well in the EGFR and PD-L1 joint cohort, with an AUC of 0.928 (95% CI, 0.909-0.946) for distinguishing EGFR mutated status and 0.905 (95% CI, 0.886-0.930) for discriminating PD-L1 expression status.

Our AI system has demonstrated the encouraging results for identifying gene status and further assessing the genotypes. Both clinical indicators and radiomics features showed a complementary role in prediction and provided accurate estimates to predict EGFR and PD-L1 status. Furthermore, this non-invasive, high-throughput, and interpretable AI system can be used as an assistive tool in conjunction with or in lieu of ancillary tests and extensive diagnostic workups to facilitate early intervention.