ORIGINAL RESEARCH article
Front. Oncol.
Sec. Radiation Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1568755
A PET-CT r adiomics model for immunother apy r esponse and pr ognosis pr ediction in patients with metastatic color ectal cancer
Provisionally accepted- 1shenzhen people'hospital, Shenzhen, China
- 2Shenzhen Pingshan District People’s Hospital, Shenzhen, China
- 3The Tenth Affiliated Hospital of Southern Medical University, Dongguan, Guangdong, China
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Backgr ound: In recent years, radiomics, as a non-invasive method, has shown potential in predicting tumor response and prognosis by analyzing medical image data to extract high-dimensional features and reveal the heterogeneity of tumor microenvironment (TME).Objective: The aim of this study was to construct and validate a radiomic model based on PET/CT images for predicting immunotherapy response and prognosis in mCRC patients.Methods: This study included mCRC patients from multiple cohorts, including a training set (n=105), an internal validation set (n=60), a TME phenotype cohort (n=42), and an immunotherapy response cohort (n=99). High-dimensional radiomic features were extracted from PET/CT images using a deep neural network (DNN), and RNA-Seq was used to screen for features associated with TME phenotypes to construct a radiomic score (Rad-Score). At the same time, combined with immune scores (IHC staining results based on CD3 and CD8) and clinical features, a joint prediction model was developed to assess overall survival (OS) and progression-free survival (PFS). The predictive performance of the model was evaluated by area under receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA).Results: A radiomics signature to predict the TME phenotype was constructed in the training set and verified it in an internal validation set, with AUC of 0.855 and 0.844 respectively. In the TME phenotype external cohort, the radiomics signature can differentiate either immunopotentiation or immunosuppression tumor (AUC=0.814). In the immunotherapy response external cohort, the radiomics signature can predict response to immunotherapy (AUC=0.784). The combined nomograms can predict OS and PFS, with AUC of 0.860 and 0.875 respectively. The calibration curve and decision curve analysis (DCA) confirmed the predicting performance and clinical utility of the combined nomograms.Conclusion: In this study, a radiomic model based on PET/CT images was successfully constructed, which can effectively predict immunotherapy response and prognosis of mCRC patients. The model combines radiomic features, immune scores and clinical features, showing high prediction accuracy and clinical application value. In the future, the reliability and generalization ability of this model need to be further verified in larger prospective studies to promote its application in clinical practice.
Keywords: Metastatic colorectal cancer, PET-CT, Radiomics, TME, Immunotherapy response
Received: 30 Jan 2025; Accepted: 25 Apr 2025.
Copyright: © 2025 Chen, Zhu, Chen and Sun. 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: Wenbiao Chen, shenzhen people'hospital, Shenzhen, China
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