原发性脑膜瘤一期切除术后复发风险列线图预测模型的构建及验证

doi:10.11958/20211806

摘要/Abstract

摘要：

目的构建原发性脑膜瘤患者一期切除术后复发风险的列线图预测模型并进行外部验证。方法纳入经病理确诊的脑膜瘤患者328例为建模组,构建一期切除术后复发风险的列线图预测模型并进行内部验证;另外纳入同样方式确诊的脑膜瘤患者62例为验证组,对模型进行外部验证;2组均根据术后随访是否复发分为复发组和未复发组。结果建模组术后复发41例（12.5%）。与未复发组相比,复发组男性占比升高、术前Karnofsky评分（KPS）降低、肿瘤最大直径>42 mm比例增加,磁共振成像显示肿瘤不规则外形、瘤周血管、不均匀强化、规则或不规则肿瘤-皮质界面、脑浸润增多,瘤周水肿（EI）>4、肿瘤基底部直径>42 mm比例增加,Simpson切除分级Ⅱ~Ⅳ和病理分级Ⅱ~Ⅲ比例升高,Ki-67指数≥5%比例升高（P<0.05）。建模组多因素Logistic回归分析显示,不均匀强化、脑浸润、高Simpson切除分级（Ⅱ~Ⅳ）和高病理分级（Ⅱ~Ⅲ）是术后复发的独立危险因素。构建的列线图预测模型采用Bootstrap内部验证,H-L检验示χ²=6.958,P=0.421,Calibration校准曲线拟合良好,受试者工作曲线（ROC）显示曲线下面积（AUC）为0.856（95%CI：0.767~0.901）。外部验证显示列线图模型的AUC值为0.833（95%CI：0.779~0.896）。结论根据构建列线图预测模型能够指导临床医生早期识别复发高风险患者并采取针对性干预策略,有较好的临床应用价值。

关键词: 脑膜瘤, 复发, 列线图, 预测价值

Abstract:

Objective To construct a nomogram predictive model for recurrence risk in patients with primary meningioma resection and verified externally. Methods A total of 328 patients with meningiomas confirmed pathologically were included in the model group. The nomogram predictive model of recurrence risk after primary resection was constructed and internally verified. In addition, another 62 patients with meningiomas diagnosed in the same way were included as the verification group, and the model was externally verified. The two groups were sub-divided into the recurrence group and the non-recurrence group. Results The postoperative recurrence was 41 (12.5%) in the model group. Compared with the non-recurrence group, the proportion of men was more, preoperative Karnofsky Performance Scale (KPS) score was lower, the maximum diameter of tumor (>42 mm) was larger in the recurrence group. MRI showed more irregular shape of tumor, peritumoral vessels, uneven reinforcement, regular or irregular tumor-cortical interface, brain invasion, higher peritumoral edema (EI>4) and tumor basal diameter (>42 mm) increase in the recurrence group. Simpson resection grade (Ⅱ-Ⅳ) and pathological grade (Ⅱ-Ⅲ) were increased. Ki-67 index≥5% was more in the recurrence group (P<0.05). Multivariate Logistic regression analysis showed that uneven reinforcement, brain invasion, Simpson resection grade (Ⅱ-Ⅳ) and pathological grade (Ⅱ-Ⅲ) were the independent risk factors of postoperative recurrence in the model group. The nomogram predictive model was internally verified by Bootstrap, and H-L test showed (χ²=6.958, P=0.421). The calibration curve fitted well. The area under the curve (AUC) was 0.856 (95%CI: 0.767-0.901). External verification showed that the AUC value was 0.833 (95%CI: 0.779-0.896). Conclusion The prediction model based on the the construction of nomogram can earlily guide clinicians to identify patients with high risk of recurrence early and take targeted interventive strategies, which has good clinical application value.

Key words: meningioma, recurrence, nomograms, predictive value

中图分类号:

R739.45

图/表 6

表1 建模组中复发与未复发患者的临床资料比较

Tab.1 Comparison of clinical data between relapsed patients and non-relapsed patients in the modeling group

表2 术后复发的多因素Logistic回归分析

Tab.2 Multivariate Logistic regression analysis of postoperative recurrence

图1 预测脑膜瘤切除术后复发的列线图风险模型的建立

Fig.1 The establishment of a nomogram risk model for predicting recurrence after meningioma resection

图2 列线图的内部验证 A：Calibration校准曲线;B：ROC曲线。

Fig.2 Internal verification of nomogram

表3 验证组中复发与未复发患者的临床资料比较 [例（%）]

Tab.3 Comparison of clinical data between recurrent patients and non-recurrent patients in the validation group

图3 列线图外部验证的ROC曲线

Fig.3 The ROC curve of external verification in the nomogram model

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