Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Second Clinical School, Lanzhou University, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
Department of Radiology, Lanzhou University Second Hospital, Gansu, China; Key Laboratory of Medical Imaging of Gansu Province, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, China.
World Neurosurg. 2022 Aug;164:e619-e628. doi: 10.1016/j.wneu.2022.05.039. Epub 2022 May 16.
The objective of the study was to develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors, and magnetic resonance imaging parameters.
One hundred fifty-four patients with HGG were classified into recurrence and nonrecurrence groups based on the pathological diagnosis and Response Assessment in Neuro-Oncology criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status scores, grade, and cell proliferation index (Ki-67). Gene information included P53, isocitrate dehydrogenase 1 (IDH1), O6-methylguanine-DNA methyltransferase, and telomerase reverse transcriptase expression status. All patients underwent baseline magnetic resonance imaging before treatment, including T1-weighted imaging, T2-weighted imaging, contrast-enhanced T1WI, fluid attenuated inversion recovery, and diffusion-weighted imaging examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient value, and minimum apparent diffusion coefficient values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build a nomogram.
Univariate analysis showed that the number of tumors (odds ratio [OR], 0.258; 95% confidence interval [CI]: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI: 0.942, 0.988; P = 0.003; mean in the recurrence group = 22.04 ± 17.21 mm; mean in the nonrecurrence group = 14.22 ± 12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI: 1.874, 10.353; P = 0.001) and O6-methylguanine-DNA methyltransferase (OR, 2.389; 95% CI: 1.234, 4.628; P = 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI: 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI: 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI: 1.602, 10.013; P = 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%.
A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG.
本研究旨在基于临床病理、遗传因素和磁共振成像参数，建立预测高级别胶质瘤（HGG）早期复发的列线图。
根据病理诊断和神经肿瘤反应评估标准，将 154 例 HGG 患者分为复发组和非复发组。临床病理信息包括年龄、性别、术前卡诺夫斯基表现状态评分、分级和细胞增殖指数（Ki-67）。基因信息包括 P53、异柠檬酸脱氢酶 1（IDH1）、O6-甲基鸟嘌呤-DNA 甲基转移酶和端粒酶逆转录酶的表达状态。所有患者在治疗前均进行基线磁共振成像检查，包括 T1 加权成像、T2 加权成像、对比增强 T1WI、液体衰减反转恢复和弥散加权成像检查。评估肿瘤位置、单发/多发肿瘤、肿瘤直径、瘤周水肿、坏死性囊肿、出血、平均表观弥散系数值和最小表观弥散系数值。采用单因素和多因素逻辑回归分析确定早期复发的预测因素，并构建列线图。
单因素分析显示，肿瘤数量（比值比 [OR]，0.258；95%置信区间 [CI]：0.104，0.639；P=0.003）和瘤周水肿（OR，0.965；95%CI：0.942，0.988；P=0.003；复发组平均值为 22.04±17.21mm；非复发组平均值为 14.22±12.84mm）在早期复发患者中差异具有统计学意义。与早期复发相关的遗传因素包括 IDH1（OR，4.405；95%CI：1.874，10.353；P=0.001）和 O6-甲基鸟嘌呤-DNA 甲基转移酶（OR，2.389；95%CI：1.234，4.628；P=0.010）。多因素逻辑回归分析显示，肿瘤数量（OR，0.227；95%CI：0.084，0.616；P=0.004）、瘤周水肿（OR，0.969；95%CI：0.945，0.993；P=0.013）和 IDH1（OR，4.200；95%CI：1.602，10.013；P=0.004）是早期复发的独立危险因素。列线图显示，当阈值概率小于 60%时，净获益最高。
列线图预测模型可有效辅助新诊断的 HGG 患者的临床治疗决策。
