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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 |
doi: 10.3389/fonc.2024.1425837
This article is part of the Research Topic Quantitative Imaging: Revolutionizing Cancer Management with biological sensitivity, specificity, and AI integration View all 11 articles
Radiomics Based on 18 F-FDG PET/CT for Prediction of Pathological Complete Response to Neoadjuvant Therapy in Non-small Cell Lung Cancer
Provisionally accepted- 1 Tianjin Medical University Cancer Institute and Hospital, Tianjin, Tianjin, China
- 2 Tianjin Cancer Hospital Airport Hospital, Tianjin, China
Purpose: This study aimed to establish and evaluate the value of integrated models involving 18 F-FDG PET/CT-based radiomics and clinicopathological information in prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for NSCLC.: A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2016 PET-based and 2016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. Results: The hybrid PET/CT derived radiomic model outperformed PET alone and CT alone radiomic models in prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application.The 18 F-FDG PET/CT-based SVM-radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.
Keywords: 18 F-FDG PET/CT, Radiomics, NSCLC, Neoadjuvant Therapy, Pathological complete response
Received: 30 Apr 2024; Accepted: 09 Jul 2024.
Copyright: © 2024 Liu, Sui, Bian, Li, Wang, Fu, Qi, Chen, Xu and Li. 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:
Xiaofeng Li, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300070, Tianjin, 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.
Chunxiao Sui
1