2022 World Conference on Lung Cancer (ePosters)-EP16.04-032. Indoleamine 2,3-dioxygenase 1 (IDO1) Blood Metabolites May Improve Predictive Accuracy for Radiation Pneumonitis

EP16.04-032. Indoleamine 2,3-dioxygenase 1 (IDO1) Blood Metabolites May Improve Predictive Accuracy for Radiation Pneumonitis

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Radiation pneumonitis (RP) is a significant risk for lung cancer patients undergoing radiotherapy, but the factors that contribute to its development are not well understood. Previous studies have shown that radiomic features and baseline radiomic can be predictive factors for RP, but these models have not been externally validated. Additionally, there is limited research on the biological factors associated with inflammation.<br /><br />One potential biomarker for RP is indoleamine 2,3-dioxygenase 1 (IDO1), which is known for its role in suppressing inflammation. The circulating levels of IDO1 and its metabolites have been associated with overall survival and may play a role in the risk of radiation-induced inflammation.<br /><br />In this study, researchers hypothesized that a combined model of computed tomography (CT) radiomics features and IDO1 biomarkers could improve the predictive accuracy for RP. They conducted the study on a population of lung cancer patients treated with thoracic radiotherapy at two different centers. The primary endpoint was the development of Grade 2 RP.<br /><br />The results showed that the combined model of IDO1 and radiomics had a higher area under the curve (AUC) value compared to radiomics alone in the training dataset. However, this improvement was not observed in the external validation dataset. The authors concluded that larger studies with more RP events are needed to further validate the predictive accuracy of the combined model. They also suggested refining the model with dynamic changes in IDO1 and using deep machine learning in a larger number of patients.<br /><br />In conclusion, this study suggests that a combined model of CT radiomics, radiation dosiomics, and IDO1 metabolites may improve the prediction accuracy for RP in lung cancer patients undergoing radiotherapy. However, further research is needed to validate these findings and refine the model. The study was supported by several grants, including those from the National Institutes of Health and the China National Science Foundation.

Asset Subtitle

Feng-Ming Spring Kong

Meta Tag

Speaker Feng-Ming Spring Kong

Topic Tumour Biology and Biomarkers - Tumour Biology & Preclinical Studies

Keywords

Radiation pneumonitis

lung cancer patients

radiotherapy

radiomic features

IDO1 biomarkers

CT radiomics features

predictive accuracy

Grade 2 RP

external validation dataset

prediction accuracy