Background: Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity.
Methods: CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed.
Results: All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81-0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, "kurtosis" had a high predictive value of early death (AUC at first year: 0.70-0.75 in two independent cohorts), negative association with histopathological grade (Spearman's r: - 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915-11.561) than histopathological staging and grading.
Conclusions: We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.
Keywords: Lung neoplasms; Precision medicine; Prognosis; Tomography (X-ray computed).
© 2022. The Author(s).