Predicting T790M mutation status in non-small cell lung cancer based on radiomics: A systematic review and meta-analysis - Hongyang Chen; Bingjie Fan; Mengqi Yuan; Dandan Wang; Chenxi Qiao; Na Qiu; Xiaomin Quan; Wei Hou (PLoS ONE, 2026)
Background Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have revolutionized the prognosis for patients with EGFR-mutant lung cancer. The emergence of the T790M resistance mutation compromises the efficacy of EGFR-TKI therapy. Therefore, assessing EGFR T790M mutation status during non-small cell lung cancer (NSCLC) treatment is crucial for improving NSCLC prognosis. Method PubMed, Embase, Web of Science databases, China National Knowledge Infrastructure, and Wanfang as primary sources were systematically searched up to January 1, 2026. To assess the risk of bias and study quality, we employed the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and the Radiomics Quality Score version 2.0 (RQS). The diagnostic accuracy of radiomics for detecting T790M in NSCLC patients was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy for each study. Results This meta-analysis analyzed 13 studies with 2,654 patients. The pooled AUC, sensitivity, and specificity of internal validation models were 0.91, 0.73, and 0.95, respectively. The pooled AUC, sensitivity, and specificity of external validation models were 0.81, 0.73, and 0.87, respectively. Subgroup analysis revealed that imaging examinations derived from lung and mediastinal metastases achieved the highest sensitivity (0.76; 95% CI, 0.73–0.79), whereas those based on brain metastases exhibited the highest specificity (0.95; 95% CI, 0.95–0.96). The high specificity of the lung/mediastinal models was further confirmed in external validation (0.96; 95% CI, 0.95–0.98). Compared with CT, MRI-based models demonstrated a trade-off in internal validation: lower sensitivity (0.72 vs. 0.75) but significantly higher specificity (0.96 vs. 0.80). Notably, in external validation, CT achieved superior sensitivity (0.96, 95% CI 0.94–0.99). ITK-SNAP demonstrated higher sensitivity (internal: 0.76 [95% CI, 0.73–0.79]; external: 0.76 [95% CI, 0.67–0.84]) and lower specificity (internal: 0.80 [95% CI, 0.76–0.85]; external: 0.83 [95% CI, 0.70–0.95]). When stratified by a median RQS exceeding 20, higher-scoring studies were associated with higher pooled sensitivity (0.76 [95% CI, 0.70–0.82]) but a lower specificity (0.85 [95% CI, 0.79–0.90]). While in external validation, RQS ≤ 20 demonstrated higher sensitivity (0.75 [95% CI, 0.68–0.82], P < 0.001). Integrating clinical factors with radiomics improved sensitivity but reduced specificity compared with radiomics-only models (0.79 vs. 0.72 and 0.81 vs. 0.95, respectively). A similar sensitivity-specificity trade-off was observed with standardized data processing (sensitivity: 0.76 vs. 0.72; specificity: 0.80 vs 0.95). Conclusion Radiomics, as a non-invasive detection method, has demonstrated significant potential in predicting the T790M mutation status in NSCLC, showing promising clinical application prospects based on retrospective evidence. However, further standardization and validation are required in future studies. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251130164 (CRD420251130164).