Abstract
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 EGFR variant-positive non-small cell lung cancer (NSCLC) is lacking. To propose a clinically applicable large-scale bidirectional generative adversarial network for predicting the efficacy of EGFR-TKI therapy in patients with NSCLC. 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. An end-to-end bidirectional generative adversarial network framework was designed to predict the progression risk in patients with NSCLC. The primary end point was PFS, considering the time from the initiation of therapy to the date of recurrence, confirmed disease progression, or death. A total of 342 patients with stage IV EGFR 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 EGFR variant-positive NSCLC (mean [SD] age, 52 [11] years; 26 [46.4%] female) and 67 patients with advanced-stage EGFR 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; P < .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; P = .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. This diagnostic/prognostic study provides a clinically applicable approach for identifying patients with stage IV EGFR 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.
Highlights
Epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitor (TKI) therapy plays an important role in treating clinical stage IV epidermal growth factor receptor (EGFR) (OMIM 131550) variant–positive non–small cell lung cancer (NSCLC).1,2 Previous studies3,4 have found that EGFR-TKIs, such as erlotinib, gefitinib, and icotinib, promote longer progression-free survival (PFS) in patients with EGFR variant–positive NSCLC than conventional chemotherapy
A total of 90 (26%) receiving EGFR-TKI therapy with a high risk of rapid disease progression were identified using the deep learning (DL) semantic signature.The PFS decreased by 36% compared with that in other patients
Response to Tyrosine Kinase Inhibitors in Non–Small Cell Lung Cancer led to better survival outcomes than those based on radiomics signature across all risk probabilities by the decision curve analysis. This diagnostic/prognostic study provides a clinically applicable approach for identifying patients with stage IV EGFR variant–positive NSCLC who are not likely to benefit from EGFR-TKI therapy
Summary
Epidermal growth factor receptor (EGFR)–tyrosine kinase inhibitor (TKI) therapy plays an important role in treating clinical stage IV EGFR (OMIM 131550) variant–positive non–small cell lung cancer (NSCLC). Previous studies have found that EGFR-TKIs, such as erlotinib, gefitinib, and icotinib, promote longer progression-free survival (PFS) in patients with EGFR variant–positive NSCLC than conventional chemotherapy. On the basis of these results, the current clinical guidelines recommend EGFR-TKI therapy in patients with stage IV EGFR variant–positive NSCLC.. Studies have reported that 30% of patients with stage IV EGFR variant–positive NSCLC developed rapid tumor progression after EGFR-TKI therapy, and TKIs were deemed ineffective or less effective in these patients.. There are no known biomarkers predictive of which patients might not benefit from TKIs and are at risk for rapid disease progression. Next-generation TKIs (osimertinib) or intercalated therapy may be considered in patients with EGFR T790M variants or those more likely to develop tumor progression.. Recent clinical studies have suggested that the efficacy of EGFR-TKIs should be evaluated before clinical decision-making and alternative treatments potentially prioritized in patients at high risk for rapid tumor progression Next-generation TKIs (osimertinib) or intercalated therapy may be considered in patients with EGFR T790M variants or those more likely to develop tumor progression. Recent clinical studies have suggested that the efficacy of EGFR-TKIs should be evaluated before clinical decision-making and alternative treatments potentially prioritized in patients at high risk for rapid tumor progression
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