Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China.
Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
Neurosurg Rev. 2023 Dec 22;47(1):20. doi: 10.1007/s10143-023-02258-z.
To investigate the value of using VASARI signs preoperatively to assess Ki-67 proliferation index levels in patients with IDH-wildtype glioblastoma (GB).Pathological and imaging data of 154 patients with GB confirmed by surgical pathology were retrospectively analysed, and the level of Ki-67 proliferative index was assessed in tumour tissue samples from patients using immunohistochemistry (IHC) staining. Patients were divided into a high and low Ki-67 proliferation index expression group. Two radiologists analysed MRI images of patients with IDH-wildtype GB using the VASARI features system. VASARI parameters between the two groups were statistically analysed to identify characteristic parameters with significant differences and their predictive performance was determined using ROC curves.Among the obtained clinical and VASARI features of IDH-wildtype GB patients, the distribution of Maximum diameter, Proportion of necrosis and Hemorrhage was significantly different between the two groups (all p < 0.05). Multivariate logistic regression analysis showed that Maximum diameter and Hemorrhage were independent risk factors distinguishing the group with high and low expression of Ki-67 proliferative index. ROC curve analysis showed that the logistic regression model achieved an AUC value of 0.730 (95% CI: 0.639, 0.822), sensitivity of 0.628 and specificity of 0.756.Logistic regression modelling of preoperative VASARI features can be used as a reliable tool for predicting the level of Ki-67 proliferative index in IDH-wildtype GB patients, which can help in preoperative development of treatment and follow-up strategies for patients.
为了研究术前使用 VASARI 征象评估 IDH 野生型胶质母细胞瘤(GB)患者 Ki-67 增殖指数水平的价值。回顾性分析了 154 例经手术病理证实的 GB 患者的病理和影像学资料,并通过免疫组织化学(IHC)染色评估了患者肿瘤组织样本中的 Ki-67 增殖指数水平。患者被分为 Ki-67 增殖指数高表达和低表达组。两名放射科医生使用 VASARI 特征系统分析 IDH 野生型 GB 患者的 MRI 图像。对两组间 VASARI 参数进行统计学分析,以确定具有显著差异的特征参数,并通过 ROC 曲线确定其预测性能。在获得 IDH 野生型 GB 患者的临床和 VASARI 特征中,两组间最大直径、坏死比例和出血的分布差异有统计学意义(均 P < 0.05)。多因素 logistic 回归分析显示,最大直径和出血是区分 Ki-67 增殖指数高表达和低表达组的独立危险因素。ROC 曲线分析显示,logistic 回归模型的 AUC 值为 0.730(95%CI:0.639,0.822),敏感度为 0.628,特异度为 0.756。术前 VASARI 特征的 logistic 回归模型可作为预测 IDH 野生型 GB 患者 Ki-67 增殖指数水平的可靠工具,有助于术前为患者制定治疗和随访策略。