Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV <i>EGFR</i> Variant–Positive Non–Small Cell Lung Cancer
Authors: Jiangdian Song, Lu Wang, Nathan Norton Ng, Mingfang Zhao, Jingyun Shi, Ning Wu, Weimin Li, Zaiyi Liu, Kristen W. Yeom, Jie Tian
Journal: JAMA Network Open (2020)

<h3>Importance</h3> An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitor (TKI) therapy in patients with stage IV<i>EGFR</i>variant–positive non–small cell lung cancer (NSCLC) is lacking. <h3>Objective</h3> To propose a clinically applicable large-scale bidirectional generative adversarial network for predicting the efficacy of EGFR-TKI therapy in patients with NSCLC. <h3>Design, Setting, and Participants</h3> This diagnostic/prognostic study enrolled 465 patients from January 1, 2010, to August 1, 2017, with follow-up from February 1, 2010, to June 1, 2020. A deep learning (DL) semantic signature to predict progression-free survival (PFS) was constructed in the training cohort, validated in 2 external validation and 2 control cohorts, and compared with the radiomics signature. <h3>Exposures</h3> An end-to-end bidirectional generative adversarial network framework was designed to predict the progression risk in patients with NSCLC. <h3>Main Outcomes and Measures</h3> The primary end point was PFS, considering the time from the initiation of therapy to the date of recurrence, confirmed disease progression, or death. <h3>Results</h3> A total of 342 patients with stage IV<i>EGFR</i>variant–positive NSCLC receiving EGFR-TKI therapy met the inclusion criteria. Of these, 145 patients from 2 of the hospitals (n = 117 and 28) formed a training cohort (mean [SD] age, 61 [11] years; 87 [60.0%] female), and the patients from 2 other hospitals comprised 2 external validation cohorts (validation cohort 1: n = 101; mean [SD] age, 57 [12] years; 60 [59.4%] female; and validation cohort 2: n = 96, mean [SD] age, 58 [9] years; 55 [57.3%] female). Fifty-six patients with advanced-stage<i>EGFR</i>variant–positive NSCLC (mean [SD] age, 52 [11] years; 26 [46.4%] female) and 67 patients with advanced-stage<i>EGFR</i>wild-type NSCLC (mean [SD] age, 54 [10] years; 10 [15.0%] female) who received first-line chemotherapy were included. A total of 90 (26%) receiving EGFR-TKI therapy with a high risk of rapid disease progression were identified (median [range] PFS, 7.3 [1.4-32.0] months in the training cohort, 5.0 [0.6-34.6] months in validation cohort 1, and 6.4 [1.8-20.1] months, in validation cohort 2) using the DL semantic signature.The PFS decreased by 36% (hazard ratio, 2.13; 95% CI, 1.30-3.49;<i>P</i> &lt; .001) compared with that in other patients (median [range] PFS, 11.5 [1.5-64.2] months in the training cohort, 10.9 [1.1-50.5] in validation cohort 1, and 8.9 [0.8-40.6] months in validation cohort 2. No significant differences were observed when comparing the PFS of high-risk patients receiving EGFR-TKI therapy with the chemotherapy cohorts (median PFS, 6.9 vs 4.4 months;<i>P</i> = .08). In terms of predicting the tumor progression risk after EGFR-TKI therapy, clinical decisions based on the DL semantic signature led to better survival outcomes than those based on radiomics signature across all risk probabilities by the decision curve analysis. <h3>Conclusions and Relevance</h3> This diagnostic/prognostic study provides a clinically applicable approach for identifying patients with stage IV<i>EGFR</i>variant–positive NSCLC who are not likely to benefit from EGFR-TKI therapy. The end-to-end DL-derived semantic features eliminated all manual interventions required while using previous radiomics methods and have a better prognostic performance.