Radiomics for Prediction of Radiation-induced Lung Injury After Robotic Stereotactic Body Radiotherapy of Lung Cancer: Results From Two Independent Institutions

Abstract

Objectives: To generate and validate a state-of-the-art radiomics model for prediction of radiation-induced lung injury and oncologic outcome in non-small cell lung cancer (NSCLC) patients treated with robotic stereotactic body radiation therapy (SBRT).Methods: A radiomics model was generated from the planning CT images of 110 patients with primary, inoperable stage I/IIa NSCLC who were treated with robotic SBRT using a risk-adapted fractionation scheme at the University Hospital Cologne (training cohort). In total, 851 radiomic features fulfilling the standards of the Image Biomarker Standardization Initiative (IBSI) were extracted from the outlined gross tumor volume (GTV) and used to build a model for prediction of local control (LC), disease-free survival (DFS), overall survival (OS) and development of local lung fibrosis (LF) by means of a gradient-boosted ensemble of regression trees. In addition, predictive clinical and dosimetric parameters were identified from a standard univariate Cox regression analysis. The radiomics model was validated in a comparable cohort of 71 patients treated by robotic SBRT at the Radiosurgery Center in Northern Germany (test cohort).Results: Oncologic outcome did not differ between the two cohorts (OS at 36 months 56% vs. 43%, p=0.065; median DFS 25 months vs. 23 months, p=0.43; LC at 36 months 90% vs. 93%, p=0.197). Local lung fibrosis developed in 33% vs. 35% of the patients (p=0.75), all events were observed within 36 months. In the training cohort, the radiomics model was able to distinguish low-risk from high risk patients for OS, DFS, LC and LF with a high accuracy (p < 0.001). In the test cohort, the model for development of lung fibrosis retained its predictive power and could differentiate patients with a high risk for developing LF from those with a low risk (p=0.016). In contrast, the radiomics model failed to predict OS, DFS and LC in the test cohort. Also, none of the clinical and dosimetric parameters predictive for development of LF in the training cohort (GTV-Dmean, GTV-Dmax, PTV-D95%, Lung-D1ml, age) had a significant impact on the occurrence of LF in the test cohort.Conclusion: Despite the obvious difficulties in generalizing predictive models for oncologic outcome and toxicity, this analysis shows that a carefully designed radiomics model for prediction of local lung fibrosis after SBRT of early stage lung cancer performs well across different institutions.