CT-based imaging metrics for identification of radiation-induced lung damage

Purpose This study investigates the feasibility of radiomics for identifying textural changes of radiation-induced lung damage (RILD) after chemoradiotherapy. Methods The severity of RILD on each CT scan was graded on a scale from 0 (similar to the baseline CT scan) to 5 (lung fibrosis). The delineation of abnormal areas inside the lung on CT images was performed semi-automatically using a median filter. We extracted a total of 138 quantitative image features from this delineated region of interest and ran a random forest algorithm as a classifier for identifying the severity of RILD. After training and testing the model, we validated the model using a separate dataset. Results The classification accuracies for identifying grade 0 from grades 1 ∼ 5 were 70% for the test dataset and 85% for the validation dataset; for identifying grade 1 from grades 2∼5, 90% for the test dataset and 95% for the validation dataset; and for identifying grade 5 from grades 2∼4, 80% for the test dataset and 85% for the validation dataset. Conclusions Our preliminary study shows that the classification accuracy was robust, the model was most useful for distinguishing grade 1 from other grades, and the results demonstrated the feasibility of radiomics for identifying the severity of lung damage after chemoradiotherapy. This approach could be a potential tool for helping diagnostic radiologists identify RILD and its severity on CT images. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Institutional Review Boards of the University of Texas MD Anderson Cancer Center. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.