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For: Winter F, Furtner J, Pleyel A, Woehrer A, Callegari K, Hosmann A, Herta J, Roessler K, Dorfer C. How to predict the consistency and vascularity of meningiomas by MRI: an institutional experience. Neurol Res 2021;43:693-699. [PMID: 33906575 DOI: 10.1080/01616412.2021.1922171] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]

For:	Winter F, Furtner J, Pleyel A, Woehrer A, Callegari K, Hosmann A, Herta J, Roessler K, Dorfer C. How to predict the consistency and vascularity of meningiomas by MRI: an institutional experience. Neurol Res 2021;43:693-699. [PMID: 33906575 DOI: 10.1080/01616412.2021.1922171] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]

Number

Cited by Other Article(s)

Han T, Liu X, Sun J, Long C, Jiang J, Zhou F, Zhao Z, Zhang B, Jing M, Deng L, Zhang Y, Zhou J. T2-Weighted Imaging and Apparent Diffusion Coefficient Histogram Parameters Predict Meningioma Consistency. Acad Radiol 2024;31:2511-2520. [PMID: 38155025 DOI: 10.1016/j.acra.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/30/2023]

Abstract

RATIONALE AND OBJECTIVES

Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency.

MATERIALS AND METHODS

We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann-Whitney's U test, or independent samples t-test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters.

RESULTS

Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADCmean, ADCp1, ADCp10, and ADCp50 among ADC histogram parameters, and T2mean, T2p1, T2p10, T2p50, T2p90, and T2p99 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P < 0.05). We found that all combined variables (combinedall) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804-0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combinedT2 is the most beneficial for predicting meningioma consistency.

CONCLUSION

CombinedT2 demonstrated better predictive performance for meningioma consistency than combinedADC. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency.

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Affiliation(s)

Tao Han Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Xianwang Liu Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Jiachen Sun Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Changyou Long Image Center of Affiliated Hospital of Qinghai University, Xining 810001, China (C.L.)
Jian Jiang Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Fengyu Zhou Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Zhiyong Zhao Department of Neurosurgery, The Second Hospital of Lanzhou University, Lanzhou 730000, China (Z.Z.)
Bin Zhang Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Mengyuan Jing Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Liangna Deng Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Yuting Zhang Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Second Clinical School, Lanzhou University, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.)
Junlin Zhou Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730000, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.); Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China (T.H., X.L., J.S., J.J., F.Z., B.Z., M.J., L.D., Y.Z., J.Z.).

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Han T, Liu X, Xu Z, Geng Y, Zhang B, Deng L, Jing M, Zhou J. Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging. World Neurosurg 2024;181:e203-e213. [PMID: 37813337 DOI: 10.1016/j.wneu.2023.09.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/29/2023] [Indexed: 10/11/2023]

Abstract

OBJECTIVE

We sought to investigate the value of a clinical-radiomics model based on magnetic resonance imaging in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas.

METHODS

Clinical, imaging, and postoperative pathologic data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n = 296) and validation (n = 127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE) were drawn to evaluate the performance. The optimal radiomics model was selected. Calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors.

RESULTS

Thirteen radiomics features were selected from contrast-enhanced T1-weighted imaging and T2-weighted imaging after dimensionality reduction. The prediction performance of random forest radiomics model is slightly lower than that of the clinical-radiomics model. The area under the curve, SEN, SPE, and ACC of the clinical-radiomics model training set were 0.836 (95% confidence interval, 0.795-0.878), 0.922, 0.583, and 0.686, respectively. The area under the curve, SEN, SPE, and ACC of the validation set were 0.756 (95% confidence interval, 0.660-0.846), 0.816, 0.596, and 0.661, respectively.

CONCLUSIONS

The diagnostic efficacy of the clinical-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.

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Affiliation(s)

Tao Han Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
Xianwang Liu Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
Zhendong Xu Shukun (Beijing) Technology Co., Ltd., Beijing, China
Yayuan Geng Shukun (Beijing) Technology Co., Ltd., Beijing, China
Bin Zhang Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
Liangna Deng Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
Mengyuan Jing Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China
Junlin Zhou Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Gansu International Scientific and Technological Cooperation Base of Medical, Lanzhou, China.

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Han T, Long C, Liu X, Zhang Y, Zhang B, Deng L, Jing M, Zhou J. Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas. Clin Imaging 2023;104:110019. [PMID: 37976629 DOI: 10.1016/j.clinimag.2023.110019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 10/05/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023]

Affiliation(s)

Tao Han Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
Changyou Long Image Center of affiliated Hospital of Qinghai University, Xining 810001, China
Xianwang Liu Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
Yuting Zhang Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
Bin Zhang Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
Liangna Deng Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
Mengyuan Jing Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
Junlin Zhou Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.

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Ahmeti H, Caliebe A, Trigui N, Alati I, Röcken C, Jansen O, Synowitz M, Mehdorn MH. Surgical approach for convexity meningiomas: An analysis of the preoperative clinical signs, radiological features and surgical outcomes of these tumors. Clin Neurol Neurosurg 2023;233:107914. [PMID: 37531753 DOI: 10.1016/j.clineuro.2023.107914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/23/2023] [Accepted: 07/25/2023] [Indexed: 08/04/2023]

Abstract

BACKGROUND

Convexity meningiomas (CM) can be successfully treated with neurosurgery. However, clinical complications due to CM have been reported. Moreover, systematic investigations of CM with respect to all relevant clinical factors are currently lacking.

METHODS

We performed a systematic investigation in 210 patients with supratentorial CM considering all relevant clinical and radiological factors, with a follow-up time of 19.5 years.

RESULTS

Among 812 patients with intracranial meningiomas treated in our department (2003-2020), 28.2 % of intracranial meningiomas were located over the supratentorial convexity, and the patients had a median age of 62 years (95 % CI:59-64). The median follow-up was 30.4 months (95 % CI:21.6-37.1). Tumor-related symptoms were observed in 88.1 % of patients. The most common preoperative symptom was headache (28.1 %), followed by seizure (19.5 %). Symptomatic patients had significantly higher tumor volumes than asymptomatic patients (p = 0.0003; 24.5 cm3 and 6.98 cm3, respectively). Complete tumor resection was achieved in 92.9 % of patients. The most common postoperative complication was bleeding (7.1 %) in the approach area. Of all bleedings, only three were intracerebral hemorrhages and did not require surgical intervention. The second most common complication was postoperative seizure (4.7 %). The multiple logistic regression analyses showed that tumor volume (OR:1.007; 95 % CI:1.001-1.013; p = 0.02) and brain infiltration by the tumor (OR:1.961; 95 % CI:1.028-3.741; p = 0.04) had a significant impact on postoperative complications. The postoperative and final KPS scores significantly improved (p < 0.001). The tumor recurrence rate was 6.2 %, with a median time of 38 months. No surgery-related deaths occurred.

CONCLUSION

A large tumor volume and brain infiltration by the tumor were significant factors for postoperative complications. The clinical conditions significantly improved postoperatively and further during the follow-up period.

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