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Systematic review of spectral computed tomography quantitative parameters for predicting EGFR mutation status in
Jiapeng Li1, Lihua Gao1, Xinyao Jiao1
1Department of Radiology, The Second Hospital of Jilin University, Changchun, China.
Background:
Epidermal growth factor receptor (EGFR) gene mutations are important therapeutic targets in non-small cell lung cancer (NSCLC). Spectral computed tomography (spectral CT) has shown promise in the non-invasive prediction of EGFR status; however, the diagnostic value of these parameters remains unclear. This systematic review aimed to evaluate the utility of key quantitative spectral CT parameters for predicting EGFR mutation status in NSCLC.
Methods:
A comprehensive search of several Chinese and English databases was conducted to retrieve relevant diagnostic studies published up to October 1, 2025 that used pathology as the gold standard to assess the predictive value of spectral CT quantitative parameters for EGFR mutation detection in NSCLC. Two researchers conducted the literature screening and data extraction independently. Due to study heterogeneity, results were synthesized narratively. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool.
Results:
The quantitative parameters of spectral CT, including iodine concentration (IC), normalized iodine concentration (NIC), slope of the spectral Hounsfield unit curve (λHU), and effective atomic number (Zeff), were higher in the EGFR-mutant cohort than in the wild-type cohort (P<0.05). Multi-parameter models combining quantitative parameters with clinical and imaging features showed improved diagnostic performance. The results were unaffected by the various brands of CT scanners and their scanning parameters.
Conclusions:
Several quantitative parameters derived from spectral CT are associated with EGFR mutations in NSCLC. Predictive models integrating these quantitative parameters with imaging features and clinical variables may further improve diagnostic efficacy. This may inform epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) planing. Large-scale prospective studies are needed to validate these models.