Huang Yingqian, He Siyuan, Hu Hangtong, Ma Hui, Huang Zihuan, Zeng Shanmei, Mazu Liwei, Zhou Wenwen, Zhao Chen, Zhu Nengjin, Wu Jiajing, Liu Qiuchan, Yang Zhiyun, Wang Wei, Shen Guoping, Zhang Nu, Chu Jianping
Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):8423-8439. doi: 10.21037/qims-2025-242. Epub 2025 Aug 19.
Ki-67 labelling index (LI), a critical marker of tumor proliferation, is vital for grading adult-type diffuse gliomas and predicting patient survival. However, its accurate assessment currently relies on invasive biopsy or surgical resection. This makes it challenging to non-invasively predict Ki-67 LI and subsequent prognosis. Therefore, this study aimed to investigate whether histogram analysis of multi-parametric diffusion model metrics-specifically diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI)-could help predict Ki-67 LI in adult-type diffuse gliomas and further predict patient survival.
A total of 123 patients with diffuse gliomas who underwent preoperative bipolar spin-echo diffusion magnetic resonance imaging (MRI) were included. Diffusion metrics (DTI, DKI and NODDI) and their histogram features were extracted and used to develop a nomogram model in the training set (n=86), and the performance was verified in the test set (n=37). Area under the receiver operating characteristics curve of the nomogram model was calculated. The outcome cohort, including 123 patients, was used to evaluate the predictive value of the diffusion nomogram model for overall survival (OS). Cox proportion regression was performed to predict OS.
Among 123 patients, 87 exhibited high Ki-67 LI (Ki-67 LI >5%). The patients had a mean age of 46.08±13.24 years, with 39 being female. Tumor grading showed 46 cases of grade 2, 21 cases of grade 3, and 56 cases of grade 4. The nomogram model included eight histogram features from diffusion MRI and showed good performance for prediction Ki-67 LI, with area under the receiver operating characteristic curves (AUCs) of 0.92 [95% confidence interval (CI): 0.85-0.98, sensitivity =0.85, specificity =0.84] and 0.84 (95% CI: 0.64-0.98, sensitivity =0.77, specificity =0.73) in the training set and test set, respectively. Further nomogram incorporating these variables showed good discrimination in Ki-67 LI predicting and glioma grading. A low nomogram model score relative to the median value in the outcomes cohort was independently associated with OS (P<0.01).
Accurate prediction of the Ki-67 LI in adult-type diffuse glioma patients was achieved by using multi-modal diffusion MRI histogram radiomics model, which also reliably and accurately determined survival.
ClinicalTrials.gov Identifier: NCT06572592.
Ki-67标记指数（LI）是肿瘤增殖的关键标志物，对成人型弥漫性胶质瘤分级和预测患者生存至关重要。然而，其准确评估目前依赖于侵入性活检或手术切除。这使得非侵入性预测Ki-67 LI及后续预后具有挑战性。因此，本研究旨在探讨多参数扩散模型指标（特别是扩散张量成像（DTI）、扩散峰度成像（DKI）和神经突方向离散度与密度成像（NODDI））的直方图分析是否有助于预测成人型弥漫性胶质瘤的Ki-67 LI，并进一步预测患者生存。
共纳入123例接受术前双极自旋回波扩散磁共振成像（MRI）的弥漫性胶质瘤患者。提取扩散指标（DTI、DKI和NODDI）及其直方图特征，并用于在训练集（n = 86）中建立列线图模型，在测试集（n = 37）中验证其性能。计算列线图模型的受试者操作特征曲线下面积。包括123例患者的结果队列用于评估扩散列线图模型对总生存期（OS）的预测价值。进行Cox比例回归以预测OS。
123例患者中，87例表现为高Ki-67 LI（Ki-67 LI>5%）。患者平均年龄为46.08±13.24岁，女性39例。肿瘤分级显示2级46例，3级21例，4级56例。列线图模型包括来自扩散MRI的八个直方图特征，对预测Ki-67 LI表现出良好性能，训练集和测试集的受试者操作特征曲线下面积（AUC）分别为0.92[95%置信区间（CI）：0.85 - 0.98，灵敏度 = 0.85，特异性 = 0.84]和0.84（95%CI：0.64 - 0.98，灵敏度 = 0.77，特异性 = 0.73）。纳入这些变量的进一步列线图在Ki-67 LI预测和胶质瘤分级方面显示出良好的区分度。结果队列中相对于中位数的低列线图模型评分与OS独立相关（P<0.01）。
通过使用多模态扩散MRI直方图放射组学模型实现了对成人型弥漫性胶质瘤患者Ki-67 LI的准确预测，该模型也可靠且准确地确定了生存期。
ClinicalTrials.gov标识符：NCT06572592。
