Kamel Serageldin, Humbert-Vidan Laia, Kaffey Zaphanlene, Mirbahaeddin Sarah, Abusaif Abdulrahman, Fuentes David T A, Wahid Kareem, Dede Cem, Naser Mohamed A, He Renjie, Moawad Ahmed W, Elsayes Khaled M, Chen Melissa M, Otun Adegbenga O, Rigert Jillian, Chambers Mark S, Hope Andrew, Watson Erin, Brock Kristy K, Hutcheson Katherine, van Dijk Lisanne, Moreno Amy C, Lai Stephen Y, Fuller Clifton D, Mohamed Abdallah S R
The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA.
The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, USA.
Oral Oncol. 2025 Aug;167:107337. doi: 10.1016/j.oraloncology.2025.107337. Epub 2025 Jun 13.
This study aims to identify radiomic features from contrast-enhanced CT (CECT) scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer (HNC) patients treated with radiotherapy (RT).
CECT images from 150 patients with confirmed ORN diagnosis (2008-2018) at MD Anderson Cancer Center (MDACC) were analyzed (80 % train, 20 % test). Radiomic features were extracted using PyRadiomics from manually segmented ORN regions and automated contralateral healthy mandible regions. Correlation analysis (r > 0.95) reduced features for model training. A random Forest (RF) classifier with Recursive Feature Elimination identified discriminative features. Explainability was assessed using SHapley Additive exPlanations (SHAP) analysis on the 20 most important features identified by the trained RF classifier.
Of the 1316 radiomic features extracted, 810 features were excluded for high collinearity. From a set of 506 pre-selected radiomic features, 67 were optimal for RF classification, yielding 88% accuracy and a ROC AUC of 0.96. The model well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue.
This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on detecting subclinical ORNJ regions to guide earlier interventions.
本研究旨在从对比增强CT（CECT）扫描中识别放射组学特征，以区分接受放疗（RT）的头颈癌（HNC）患者的放射性骨坏死（ORN）与正常下颌骨。
分析了MD安德森癌症中心（MDACC）150例确诊为ORN的患者（2008 - 2018年）的CECT图像（80%用于训练，20%用于测试）。使用PyRadiomics从手动分割的ORN区域和对侧自动分割的健康下颌骨区域提取放射组学特征。相关性分析（r>0.95）减少了用于模型训练的特征。具有递归特征消除的随机森林（RF）分类器识别出判别性特征。使用SHapley加性解释（SHAP）分析对训练后的RF分类器识别出的20个最重要特征进行可解释性评估。
在提取的1316个放射组学特征中，810个因高共线性被排除。从一组506个预选的放射组学特征中，67个对RF分类是最优的，准确率达88%，ROC曲线下面积（AUC）为0.96。该模型校准良好（对数损失0.296，预期校准误差0.125），准确率为88%，ROC AUC为0.96。SHAP分析显示，小波-LLH一阶均值和中位数的较高值与颌骨放射性骨坏死（ORNJ）相关。相反，指数广义局部二值模式（GLDM）依赖熵较高和平方一阶峰度较低是正常下颌组织更具特征性的表现。
本研究成功开发了一种基于CECT的放射组学模型，用于区分RT后HNC患者的ORNJ与健康下颌组织。未来的工作将集中于检测亚临床ORNJ区域以指导早期干预。
