Abstract 7530: Harmonization radiomics models to predict tumor response in non-small cell lung cancer (NSCLC) patients treated with immunotherapy

Abstract

Abstract Background: Precise prediction of immunotherapy response in non-small cell lung cancer (NSCLC) is crucial for personalized treatment. This study investigates the application of pretreatment CT-based radiomics and use of harmonization model in predicting these outcomes in NSCLC patients receiving immunotherapy. Methods: This retrospective study examined data from 152 stage III-IV NSCLC patients undergoing immunotherapy. Patient responses were categorized using immune-related RECIST (irRECIST) criteria into durable responders (complete response [CR], partial response [PR], or stable disease [SD]) for at least 24 weeks and non-responders. Tumor segmentation was performed using LIFEx software (IMIV/CEA, Orsay, France). 3D-radiomic features were extracted from both the tumor and surrounding 1 cm thick peritumoral regions. The Random Forest (RF) algorithm was employed to develop a classification model to differentiate between responders and non-responders. A harmonization model was deployed to account for the scanner-related differences by using spleen and normal lung signals. The dataset was divided into a training set (75%) and a test set (25%). Bootstrapping with 1,000 iterations was performed to estimate the model's performance by calculating the median and 95% confidence interval (CI) estimate. The accuracy of the model's predictions was evaluated by creating a confusion matrix. The model's performance was assessed by calculating the sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and area under the ROC curve (AUC) for response prediction. Results: A total of 152 patients were analyzed. 87 (57.23%) were female and 65 (42.76%) were male. The median age was 59 years. Histology types included were: 111 Adenocarcinoma (73%), 27 Squamous cell carcinoma (17.8%), and 14 other types (9.2%). The model achieved a sensitivity of 0.63, a specificity of 0.59, a PPV of 0.51, and NPV of 0.7 for response prediction of NSCLC patients receiving immunotherapy. The AUC of 0.61 indicates that the model may discriminate between responders and non-responders with an accuracy of 61%. Conclusion: This study demonstrates the potential of radiomics and the use of harmonization models to predict immunotherapy responses in NSCLC patients, offering insights into personalized treatment approaches. Larger studies are needed to validate our findings and the utility of harmonization models in predicting a tumor response. Citation Format: Monica Yadav, Jeeyeon Lee, Peter Haseok Kim, Seyoung Lee, Taegyu Um, Salie Lee, Maria Jose Chuchuca, Trie Arni Djunadi, Liam Il-Young Chung, Jisang Yu, Darren Rodrigues, Nicolo Gennaro, Leeseul Kim, Myungwoo Nam, Youjin Oh, Sungmi Yoon, Zunairah Shah, Yuchan Kim, Ilene Hong, Jessica Jang, Grace Kang, Amy Cho, Soowon Lee, Timothy Hong, Cecilia Nam, Yury S Velichko, Young Kwang Chae. Harmonization radiomics models to predict tumor response in non-small cell lung cancer (NSCLC) patients treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7530.