Purpose: Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS). Materials and Methods: Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion. Results: A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction. Conclusion Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.

Purpose: Radiomic models elaborate geometric and texture features of tumors extracted from imaging to develop predictors for clinical outcomes. Stereotactic body radiation therapy (SBRT) has been increasingly applied in the ablative treatment of thoracic tumors. This study aims to identify predictors of treatment responses in patients affected by early stage non-small cell lung cancer (NSCLC) or pulmonary oligo-metastases treated with SBRT and to develop an accurate machine learning model to predict radiological response to SBRT. Materials and Methods: Computed tomography (CT) images of 85 tumors (stage I–II NSCLC and pulmonary oligo-metastases) from 69 patients treated with SBRT were analyzed. Gross tumor volumes (GTV) were contoured on CT images. Patients that achieved complete response (CR) or partial response (PR) were defined as responders. One hundred ten radiomic features were extracted using PyRadiomics module based on the GTV. The association of features with response to SBRT was evaluated. A model using support vector machine (SVM) was then trained to predict response based solely on the extracted radiomics features. Receiver operating characteristic curves were constructed to evaluate model performance of the identified radiomic predictors. Results: Sixty-nine patients receiving thoracic SBRT from 2008 to 2018 were retrospectively enrolled. Skewness and root mean squared were identified as radiomic predictors of response to SBRT. The SVM machine learning model developed had an accuracy of 74.8%. The area under curves for CR, PR, and non-responder prediction were 0.86 (95% confidence interval [CI], 0.794–0.921), 0.946 (95% CI, 0.873–0.978), and 0.857 (95% CI, 0.789–0.915), respectively. Conclusion Radiomic analysis of pre-treatment CT scan is a promising tool that can predict tumor response to SBRT.