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Updated: Jun 19, 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 immunochemotherapy in EGFR-mutant lung adenocarcinoma after third-generation TKI resistance
Shuai Qie1, Yasong Shi1, Jingyun Li1
1Department of Radiation Oncology, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
Background:
Third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) resistance poses a significant therapeutic challenge in advanced lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based habitat radiomics model for predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma patients after TKI resistance.
Methods:
This retrospective multicenter study enrolled 475 patients from two medical centers. Patients were allocated to train (N = 332) and external validation (N = 143) cohorts. Habitat imaging was performed using K-means clustering to partition tumors into three distinct subregions. Radiomic features were extracted from both whole-tumor volumes and habitat subregions. A combined model combining clinical, conventional radiomics, and habitat features was constructed using machine learning algorithms and validated through cross-validation and external testing. The primary endpoint was objective response rate (ORR) based on Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria, and overall survival (OS) was used as a secondary endpoint.
Results:
The combined model demonstrated superior predictive performance with area under the curve (AUC) of 0.904 (95% CI: 0.871-0.937) in the train cohort and 0.890 (95% CI: 0.838-0.942) in the validation cohort, significantly outperforming the clinical model, conventional whole-tumor radiomics model, and habitat model (all P < 0.001). Moreover, Kaplan-Meier analysis based on the risk groups stratified by the combined model revealed significant survival differences, with high-risk groups showing markedly shorter overall survival in both cohorts (training HR = 3.688, validation HR = 2.823, both log-rank P < 0.0001).
Conclusion:
This study developed and externally validated a CT-based habitat radiomics model for predicting response to immunochemotherapy in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance. The combined model achieved improved predictive performance compared with single-modality approaches. These findings suggest that incorporating habitat-based features may enhance the characterization of intratumoral heterogeneity and improve treatment response prediction. Notably, the model demonstrated a high negative predictive value, suggesting its potential to reduce unnecessary treatment in predicted non-responders. Further prospective and multi-center validation is warranted.
Insights
A new CT-based radiomics model accurately predicts immunochemotherapy response in advanced lung adenocarcinoma patients resistant to EGFR-TKI therapy. This model, incorporating tumor habitats, improves prediction and identifies patients likely to benefit from treatment.
Area of Science:
- Medical Imaging
- Oncology
- Radiomics
Background:
- Third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) resistance presents a major challenge in advanced lung adenocarcinoma.
- Predicting treatment response in these patients is crucial for effective therapeutic strategies.
Purpose of the Study:
- To develop and validate a computed tomography (CT)-based habitat radiomics model.
- To predict the response to immunochemotherapy in EGFR-mutant lung adenocarcinoma patients post-TKI resistance.
Main Methods:
- A retrospective multicenter study included 475 patients (332 training, 143 validation).
- Habitat imaging utilized K-means clustering; radiomic features were extracted from whole tumors and habitat subregions.
- A combined model integrating clinical, conventional radiomics, and habitat features was built using machine learning and validated.
Main Results:
- The combined model showed superior predictive performance (AUC 0.904 training, 0.890 validation), outperforming other models (P < 0.001).
- High-risk groups identified by the model had significantly shorter overall survival (HR 3.688 training, 2.823 validation; P < 0.0001).
Conclusions:
- A CT-based habitat radiomics model was successfully developed and validated for predicting immunochemotherapy response in EGFR-TKI resistant lung adenocarcinoma.
- The combined model enhances prediction by characterizing intratumoral heterogeneity, with potential to reduce unnecessary treatments.
- Further prospective, multi-center validation is recommended.
