Integrating clinical features with 11 C-methionine PET-MRI radiomics for non-invasive prediction of H3 K27M mutation in brainstem gliomas

Sep 30, 2026

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Accurate identification of H3 K27M mutation status is essential for prognosis assessment and treatment planning in brainstem gliomas. A retrospective study was conducted to build a preoperative, non-invasive clinical-radiomics model by integrating 11C-methionine PET, MRI, and clinical features. Seventy-seven newly diagnosed patients underwent preoperative imaging, with training and test cohorts. Model performance was assessed with AUC, calibration curves, and decision curve analysis, showing strong predictive capability, improved calibration, and superior net clinical benefit.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

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Abstract

Background

Methods

Results

Conclusions

Introduction

Zhang et al. BMC Medical Imaging (2026) 26:13 [https://doi.org/10.1186/s12880-025-02103-3](https://doi.org/10.1186/s12880-025-02103-3) BMC Medical Imaging RESEARCH Open Access Integrating clinical features with 11 C-methionine PET-MRI radiomics for non-invasive prediction of H3 K27M mutation in brainstem gliomas Shu Zhang 1, Xiaotong Li 1, Yanmei Wang2, Yuliang Wu3, Lin Ai 1*, Liwei Zhang4,5* and Xiaobin Zhao 1* Abstract Background Accurate determination of H3 K27M mutation status is critical for prognosis evaluation and treatment planning in brainstem gliomas (BSGs) . We developed a clinical-radiomics model integrating 11C-methionine ( 11CMET) positron emission tomography (PET), magnetic resonance imaging (MRI), and clinical features for non-invasive, preoperative prediction of H3 K27M mutation status in BSGs. Methods This retrospective study included 77 patients with newly diagnosed BSG who underwent preoperative 11C-MET PET imaging and MRI. Among them, 53 had a histologically confirmed H3 K27M mutation. Patients were randomly divided into training (n = 55) and test (n = 22) cohorts. Clinical variables significantly associated with H3 K27M mutation status were used to construct a clinical model using the extreme gradient boosting (XGBoost) algorithm. Radiomics features were extracted separately from PET and MRI images to construct individual and combined models. A clinical-radiomics integrated model was then developed by combining clinical features and the radiomics model with the best predictive performance. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) . Results The clinical model achieved AUCs of 0.795 and 0.790 in the training and test cohorts, respectively. The combined PET-MRI radiomics model outperformed individual PET or MRI model, with AUCs of 0.876 and 0.848 in the training and test cohorts, respectively. The clinical-radiomics integrated model achieved the highest performance with AUCs of 0.886 in the training cohort and 0.876 in the test cohort. The calibration curve showed good agreement between predicted and actual outcomes, and DCA demonstrated a superior net clinical benefit of the integrated model. *Correspondence: Lin Ai [ailin@bjtth.org](ailin@bjtth.org)[ ](ailin@bjtth.org)Liwei Zhang [zlwtt@aliyun.com](zlwtt@aliyun.com)[ ](zlwtt@aliyun.com)Xiaobin Zhao [flxzxb@163.com](flxzxb@163.com) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Zhang et al. BMC Medical Imaging (2026) 26:13 Page 2 of 12 Conclusions Radiomics analysis based on 11C-MET PET and MRI can effectively predict H3 K27M mutation status in BSGs. Incorporating clinical features further enhances predictive performance, supporting its use in non-invasive, preoperative molecular diagnosis. Clinical trial number Not applicable. Ke

FAQs

Why is determining H3 K27M mutation status important in brainstem gliomas?

It is critical for prognosis evaluation and treatment planning because the mutation is linked to aggressive tumor behavior and poor clinical outcomes.

What data and modeling approach were used for non-invasive prediction?

Preoperative 11C-methionine PET, MRI radiomics features, and clinically associated variables were integrated, with clinical modeling using XGBoost and combined models using PET-MRI radiomics and clinical features.

How was the predictive performance evaluated?

Performance was assessed using AUC, calibration curves, and decision curve analysis (DCA) to quantify discrimination, agreement, and net clinical benefit.

Sep 30, 2026

Uploaded by Elsa