SU-F-R-53: CT-Based Radiomics Analysis of Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy




Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable non-small cell lung cancer (NSCLC) patients and has demonstrated excellent local control and survival. However, some patients still develop distant metastases and local recurrence, and therefore, there is a clinical need to identify patients at high-risk of disease recurrence. The aim of the current study is to use a radiomics approach to identify imaging biomarkers, based on tumor phenotype, for clinical outcomes in SBRT patients.


Radiomic features were extracted from free breathing computed tomography (CT) images of 113 Stage I-II NSCLC patients treated with SBRT. Their association to and prognostic performance for distant metastasis (DM), locoregional recurrence (LRR) and survival was assessed and compared with conventional features (tumor volume and diameter) and clinical parameters (e.g. performance status, overall stage). The prognostic performance was evaluated using the concordance index (CI). Multivariate model performance was evaluated using cross validation. All p-values were corrected for multiple testing using the false discovery rate.


Radiomic features were associated with DM (one feature), LRR (one feature) and survival (four features). Conventional features were only associated with survival and one clinical parameter was associated with LRR and survival. One radiomic feature was significantly prognostic for DM (CI=0.670, p<0.1 from random), while none of the conventional and clinical parameters were significant for DM. The multivariate radiomic model had a higher median CI (0.671) for DM than the conventional (0.618) and clinical models (0.617).


Radiomic features have potential to be imaging biomarkers for clinical outcomes that conventional imaging metrics and clinical parameters cannot predict in SBRT patients, such as distant metastasis. Development of a radiomics biomarker that can identify patients at high-risk of recurrence could facilitate personalization of their treatment regimen for an optimized clinical outcome.

R.M. had consulting interest with Amgen (ended in 2015).