Computational pathology-based tumor microenvironment score for predicting EGFR-TKIs efficacy in patients with EGFR-mutant non-small cell lung cancer
10.12354/j.issn.1000-8179.2025.20250803
- VernacularTitle:基于计算病理学的肿瘤微环境评分预测EGFR突变阳性非小细胞肺癌患者EGFR-TKIs疗效
- Author:
Ding ZHUMIN
1
;
Wang HANYANG
;
Xia CONG
;
Wang JUNMEI
;
Lu LILI
;
Zhou JIE
;
Wang XIAOMING
Author Information
1. 皖南医学院第一附属医院放射科(安徽省 芜湖市 241000)
- Publication Type:Journal Article
- Keywords:
non-small cell lung cancer(NSCLC);
tumor microenvironment(TME);
computational pathology;
epidermal growth factor re-ceptor(EGFR);
epidermal growth factor receptor tyrosine kinase inhibitors(EGFR-TKIs);
efficacy prediction
- From:
Chinese Journal of Clinical Oncology
2025;52(16):826-833
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the utility of a computational pathology-based tumor microenvironment(TME)score derived from whole slide images(WSIs)in predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitors(EGFR-TKIs)in patients with EGFR mutation-positive non-small cell lung cancer(NSCLC).Methods:This retrospective study collected 240 EGFR-mutant NSCLC pa-tients treated with EGFR-TKIs at The First Affiliated Hospital of Wannan Medical College and analyzed hematoxylin-eosin(H&E)-stained WSIs of biopsy specimens,along with clinical and imaging data.The patients were randomly assigned into a training cohort(n=160)and an inde-pendent validation cohort(n=80)in a 2:1 ratio.Treatment response was assessed based on CT findings at 3 months after EGFR-TKIs initi-ation.Computational pathology was employed to automatically quantify the proportions of four TME components(tumor epithelium,stroma,lymphocytes,and vasculature)within the tumor regions of WSIs.Multivariate Logistic regression in the training cohort identified TME components independently predictive of treatment response(P<0.05),which were then integrated into a TME-score.The predictive performance was evaluated using receiver operating characteristic(ROC)curve analysis and area under the curve(AUC).The TME-score model was compared with a clinical-feature-based model and a combined model(TME-score+clinical features).Finally,the models were val-idated in the independent cohort.Results:In the training cohort,the TME-score,incorporating epithelial and stromal proportions,achieved an AUC of 0.827(95%CI:0.749-0.892)for predicting treatment response,while the validation cohort yielded an AUC of 0.845(95%CI:0.735-0.937).Both outperformed the clinical model(AUCs=0.730[95%CI:0.645-0.804]and 0.712[95%CI:0.586-0.824],respectively).The combined model(TME-score+clinical features,including cytokeratin 19 fragment and non-contrast CT values)further improved predictive performance(AUCs=0.884[95%CI:0.827-0.932]and 0.882[95%CI:0.798-0.950],respectively).Delong's test for pairwise model comparis-ons showed significant differences(all P<0.05)except TME-score and the combined model in the validation cohort(P=0.289).Conclusions:TME-score outperformed clinical models in predicting EGFR-TKIs efficacy in EGFR mutation-positive NSCLC patients and may serve as a novel tool for identifying patients likely to benefit from targeted therapy.
