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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1395313
This article is part of the Research Topic Quantitative Imaging: Revolutionizing Cancer Management with biological sensitivity, specificity, and AI integration View all 11 articles

Pilot Study: Radiomic Analysis for Predicting Treatment Response to Whole-Brain Radiotherapy combined Temozolomide in Lung Cancer Brain Metastases

Provisionally accepted
Yichu Sun Yichu Sun 1Fei Liang Fei Liang 1Jing Yang Jing Yang 2Chong Zhou Chong Zhou 3*Youyou Xia Youyou Xia 1*
  • 1 Lianyungang Clinical Medical College, Nanjing Medical University, Lianyungang, China
  • 2 The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
  • 3 Xuzhou Central Hospital, Xuzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    Objective: The objective of this study is to assess the viability of utilizing radiomics for predicting the treatment response of lung cancer brain metastases (LCBM) to whole-brain radiotherapy (WBRT) combined with temozolomide (TMZ). Methods: Fifty-three patients diagnosed with LCBM and undergoing WBRT combined with TMZ were enrolled. Patients were divided into responsive and non-responsive groups based on the RANO-BM criteria. Radiomic features were extracted from contrast-enhanced the whole brain tissue CT images. Feature selection was performed using t-tests, Pearson correlation coefficients, and Least Absolute Shrinkage And Selection (LASSO) regression. Logistic regression was employed to construct the radiomics model, which was then integrated with clinical data to develop the nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves, and clinical utility was assessed using decision curve analysis (DCA).Results: A total of 1834 radiomic features were extracted from each patient's images, and 3 features with predictive value were selected. Both the radiomics and nomogram models exhibited satisfactory predictive performance and clinical utility, with the nomogram model demonstrating superior predictive value. The ROC analysis revealed that the AUC of the radiomics model in the training and testing sets were 0.776 and 0.767, respectively, while the AUC of the nomogram model were 0.799 and 0.833, respectively. DCA curves demonstrated that both models provided benefits to patients across various thresholds.Conclusion: Radiomic-defined image biomarkers can effectively predict the treatment response of WBRT combined with TMZ in patients with LCBM, offering potential to optimize treatment decisions for this condition.

    Keywords: brain metastases, Whole-brain radiation therapy, temozolomide, Radiomics, nomogram

    Received: 03 Mar 2024; Accepted: 23 Jul 2024.

    Copyright: © 2024 Sun, Liang, Yang, Zhou and Xia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Chong Zhou, Xuzhou Central Hospital, Xuzhou, 221000, Jiangsu Province, China
    Youyou Xia, Lianyungang Clinical Medical College, Nanjing Medical University, Lianyungang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.