Li Jiamin, Lan Zhihong, Zhang Xiao, Liang Xiaoyun, Chen Hanwei, Yu Xiangrong
Department of Radiology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated With Jinan University, Zhuhai, China.
Department of Radiology, Guangzhou Panyu Health Management Center (Guangzhou Panyu Rehabilitation Hospital), Guangzhou, China.
Cancer Med. 2025 Aug;14(15):e71063. doi: 10.1002/cam4.71063.
Accurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic efficacy of radiomics utilizing pre-surgery contrast-enhanced T1-weighted imaging (CE-T1WI) for predicting p53 status in IDH-mt gliomas.
A total of seventy-eight patients with pathologically confirmed IDH-mutant glioma were admitted to our institution between January 2011 and October 2018. For each patient, three types of volumes of interest (VOIs) were segmented: (i) VOI: the entire tumor (including the necrotic area within the tumor); (ii) VOI: the peritumoral edema; (iii) VOI: the entire tumor and peritumoral edema. A total of 962 radiomic features were extracted for each VOI, followed by feature selection and modeling (Rad_VOI, Rad_VOI, and Rad_VOI models) using machine learning algorithms. A nomogram was developed to integrate significant clinical factors and radiomic predictors for p53 status prediction. Akaike Information Criterion (AIC) was leveraged as the stopping rule. The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis.
The VOI model, which included the eight best-performing features, demonstrated the highest predictive performance among the three VOI-based models, with AUCs of 0.811 (95% CI: 0.782-0.840) and 0.810 (95% CI: 0.786-0.834) in the training and validation cohorts, respectively. Age was identified as the only significant clinical factor and was combined with the Rad-scores from VOI, VOI, and VOI to construct a clinical-radiomics nomogram with the most notable discriminative ability for p53 status (AIC = -120.19). The AUCs for this nomogram were 0.969 (95% CI: 0.942-0.996) in the training cohort and 0.929 (95% CI: 0.898-0.960) in the validation cohort.
The CE-T1WI-based radiomic model can noninvasively predict p53 mutation status in IDH-mt gliomas. The textural differences of peritumoral edema may more accurately reflect the underlying tumor heterogeneity associated with p53 status.
准确、无创地检测异柠檬酸脱氢酶突变（IDH-mt）胶质瘤中的p53状态对胶质瘤的分子分层具有临床意义，但仍具有挑战性。我们旨在研究利用术前对比增强T1加权成像（CE-T1WI）的放射组学对IDH-mt胶质瘤中p53状态的诊断效能。
2011年1月至2018年10月期间，共有78例经病理证实的IDH突变型胶质瘤患者入住我院。对于每位患者，分割三种类型的感兴趣体积（VOI）：（i）VOI：整个肿瘤（包括肿瘤内的坏死区域）；（ii）VOI：瘤周水肿；（iii）VOI：整个肿瘤和瘤周水肿。为每个VOI提取总共962个放射组学特征，然后使用机器学习算法进行特征选择和建模（Rad_VOI、Rad_VOI和Rad_VOI模型）。开发了一个列线图，以整合用于p53状态预测的重要临床因素和放射组学预测因子。使用赤池信息准则（AIC）作为停止规则。使用受试者操作特征（ROC）曲线分析评估模型的预测性能。
包含八个表现最佳特征的VOI模型在三个基于VOI的模型中表现出最高的预测性能，在训练队列和验证队列中的AUC分别为0.811（95%CI：0.782-0.840）和0.810（95%CI：0.786-0.834）。年龄被确定为唯一显著的临床因素，并与来自VOI、VOI和VOI的Rad分数相结合，构建了对p53状态具有最显著判别能力的临床放射组学列线图（AIC = -120.19）。该列线图在训练队列中的AUC为0.969（95%CI：0.942-0.996），在验证队列中的AUC为0.929（95%CI：0.898-0.960）。
基于CE-T1WI的放射组学模型可以无创地预测IDH-mt胶质瘤中的p53突变状态。瘤周水肿的纹理差异可能更准确地反映与p53状态相关的潜在肿瘤异质性。
