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Updated: Aug 5, 2026
Establishing Dual Resistance to EGFR-TKI and MET-TKI in Lung Adenocarcinoma Cells In Vitro with a 2-step Dose-escalation Procedure
Published on: August 11, 2017
Predicting response to immune checkpoint inhibitor plus chemotherapy in EGFR-mutant lung adenocarcinoma following
Shuai Qie1, Yasong Shi1, Jingyun Li1
1Department of Radiation Oncology, Hebei University Affiliated Hospital, Baoding, Hebei, China.
Background:
Patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma who develop resistance to first-generation tyrosine kinase inhibitors (TKIs) without a T790M mutation face a therapeutic dilemma with limited and suboptimal options.
Methods:
In this multicenter retrospective study, the final analyzable modeling cohort included 490 patients with complete eligible CT imaging and outcome labels, comprising a training cohort of 326 patients, validation cohort 1 of 70 patients, and validation cohort 2 of 94 patients. Model performance was evaluated using AUC, decision curve analysis, and PFS stratification analyses.
Results:
The 2.5D axial model achieved AUCs of 0.885, 0.819, and 0.863 in the training cohort, validation cohort 1, and validation cohort 2, respectively, based on the locked source prediction files.
Conclusion:
A CT-based 2.5D deep learning model showed promising performance for treatment-response prediction after EGFR-TKI resistance, but prospective validation and clinical-variable benchmarking remain necessary before clinical implementation.
Insights
A new deep learning model using CT scans shows promise in predicting treatment response for EGFR-mutant lung cancer patients resistant to tyrosine kinase inhibitors (TKIs). Further validation is needed before clinical use.
Area of Science:
- Radiology
- Oncology
- Artificial Intelligence
Background:
- Patients with EGFR-mutant lung adenocarcinoma resistant to first-generation TKIs without T790M mutation have limited treatment options.
- This resistance presents a significant therapeutic challenge in managing lung cancer.
Purpose of the Study:
- To develop and evaluate a CT-based deep learning model for predicting treatment response in EGFR-TKI resistant lung adenocarcinoma.
- To address the unmet need for better treatment selection in this patient population.
Main Methods:
- A multicenter retrospective study involving 490 patients with EGFR-mutant lung adenocarcinoma.
- Development of a 2.5D deep learning model using CT imaging.
- Model performance assessed using AUC, decision curve analysis, and progression-free survival (PFS) stratification.
Main Results:
- The 2.5D axial model achieved high AUCs: 0.885 (training), 0.819 (validation 1), and 0.863 (validation 2).
- The model demonstrated promising predictive performance for treatment response.
Conclusions:
- A CT-based 2.5D deep learning model shows potential for predicting treatment response after EGFR-TKI resistance.
- Prospective validation and benchmarking against clinical variables are required for clinical implementation.