Construction and validation of the nomogram predictive model for recurrence risk after primary meningioma resection

doi:10.11958/20211806

Abstract

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

CLC Number:

R739.45

Figures/Tables 6

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

Tab.2 Multivariate Logistic regression analysis of postoperative recurrence

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

Fig.2 Internal verification of nomogram

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

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

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