Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randoml...

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Main Authors: Xiao-Yong Chen, Jin-Yuan Chen, Yin-Xing Huang, Jia-Heng Xu, Wei-Wei Sun, Yue- Chen, Chen-Yu Ding, Shuo-Bin Wang, Xi-Yue Wu, De-Zhi Kang, Hong-Hai You, Yuan-Xiang Lin
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-10-01
Series:Frontiers in Oncology
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2021.754937/full
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language English
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author Xiao-Yong Chen
Jin-Yuan Chen
Yin-Xing Huang
Jia-Heng Xu
Wei-Wei Sun
Yue- Chen
Chen-Yu Ding
Chen-Yu Ding
Shuo-Bin Wang
Xi-Yue Wu
De-Zhi Kang
De-Zhi Kang
De-Zhi Kang
Hong-Hai You
Yuan-Xiang Lin
spellingShingle Xiao-Yong Chen
Jin-Yuan Chen
Yin-Xing Huang
Jia-Heng Xu
Wei-Wei Sun
Yue- Chen
Chen-Yu Ding
Chen-Yu Ding
Shuo-Bin Wang
Xi-Yue Wu
De-Zhi Kang
De-Zhi Kang
De-Zhi Kang
Hong-Hai You
Yuan-Xiang Lin
Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma
Frontiers in Oncology
atypical meningioma
recurrence
predict
LASSO
nomogram
model
author_facet Xiao-Yong Chen
Jin-Yuan Chen
Yin-Xing Huang
Jia-Heng Xu
Wei-Wei Sun
Yue- Chen
Chen-Yu Ding
Chen-Yu Ding
Shuo-Bin Wang
Xi-Yue Wu
De-Zhi Kang
De-Zhi Kang
De-Zhi Kang
Hong-Hai You
Yuan-Xiang Lin
author_sort Xiao-Yong Chen
title Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma
title_short Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma
title_full Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma
title_fullStr Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma
title_full_unstemmed Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma
title_sort establishment and validation of an integrated model to predict postoperative recurrence in patients with atypical meningioma
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2021-10-01
description BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.
topic atypical meningioma
recurrence
predict
LASSO
nomogram
model
url https://www.frontiersin.org/articles/10.3389/fonc.2021.754937/full
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spelling doaj-3a1670a18ed6440b867ca2ac6f17ee2b2021-10-07T07:10:32ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-10-011110.3389/fonc.2021.754937754937Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical MeningiomaXiao-Yong Chen0Jin-Yuan Chen1Yin-Xing Huang2Jia-Heng Xu3Wei-Wei Sun4Yue- Chen5Chen-Yu Ding6Chen-Yu Ding7Shuo-Bin Wang8Xi-Yue Wu9De-Zhi Kang10De-Zhi Kang11De-Zhi Kang12Hong-Hai You13Yuan-Xiang Lin14Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Ophthalmology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaFujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaFujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaKey Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaDepartment of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, ChinaBackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.https://www.frontiersin.org/articles/10.3389/fonc.2021.754937/fullatypical meningiomarecurrencepredictLASSOnomogrammodel