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Zhao Z, Zhang J, Yuan S, Zhang H, Yin H, Wang G, Pan Y, Li Q. The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression. Neurosurg Rev 2024; 47:235. [PMID: 38795181 DOI: 10.1007/s10143-024-02482-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 05/17/2024] [Accepted: 05/21/2024] [Indexed: 05/27/2024]
Abstract
PURPOSE This study investigated the value of whole tumor apparent diffusion coefficient (ADC) histogram parameters and magnetic resonance imaging (MRI) semantic features in predicting meningioma progesterone receptor (PR) expression. MATERIALS AND METHODS The imaging, pathological, and clinical data of 53 patients with PR-negative meningiomas and 52 patients with PR-positive meningiomas were retrospectively reviewed. The whole tumor was outlined using Firevoxel software, and the ADC histogram parameters were calculated. The differences in ADC histogram parameters and MRI semantic features were compared between the two groups. The predictive values of parameters for PR expression were assessed using receiver operating characteristic curves. The correlation between whole-tumor ADC histogram parameters and PR expression in meningiomas was also analyzed. RESULTS Grading was able to predict the PR expression in meningiomas (p = 0.012), though the semantic features of MRI were not (all p > 0.05). The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were able to predict meningioma PR expression (all p < 0.05). The predictive performance of the combined histogram parameters improved, and the combination of grade and histogram parameters provided the optimal predictive value, with an area under the curve of 0.849 (95%CI: 0.766-0.911) and sensitivity, specificity, ACC, PPV, and NPV of 73.08%, 81.13%, 77.14%, 79.20%, and 75.40%, respectively. The mean, Perc.01, Perc.05, Perc.10, Perc.25, and Perc.50 histogram parameters were positively correlated with PR expression (all p < 0.05). CONCLUSION Whole tumor ADC histogram parameters have additional clinical value in predicting PR expression in meningiomas.
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Affiliation(s)
- Zhiyong Zhao
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Jinglong Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Shuai Yuan
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - He Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Hang Yin
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Gang Wang
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China
| | - Yawen Pan
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China.
| | - Qiang Li
- Department of Neurosurgery and Laboratory of Neurosurgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China.
- Institute of Neurology, Lanzhou University, Lanzhou, Gansu, China.
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Bai L, Jiang J, Zhou J. Assessment of Ki-67 expression levels in IDH-wildtype glioblastoma using logistic regression modelling of VASARI features. Neurosurg Rev 2023; 47:20. [PMID: 38135816 DOI: 10.1007/s10143-023-02258-z] [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: 09/26/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 12/24/2023]
Abstract
To investigate the value of using VASARI signs preoperatively to assess Ki-67 proliferation index levels in patients with IDH-wildtype glioblastoma (GB).Pathological and imaging data of 154 patients with GB confirmed by surgical pathology were retrospectively analysed, and the level of Ki-67 proliferative index was assessed in tumour tissue samples from patients using immunohistochemistry (IHC) staining. Patients were divided into a high and low Ki-67 proliferation index expression group. Two radiologists analysed MRI images of patients with IDH-wildtype GB using the VASARI features system. VASARI parameters between the two groups were statistically analysed to identify characteristic parameters with significant differences and their predictive performance was determined using ROC curves.Among the obtained clinical and VASARI features of IDH-wildtype GB patients, the distribution of Maximum diameter, Proportion of necrosis and Hemorrhage was significantly different between the two groups (all p < 0.05). Multivariate logistic regression analysis showed that Maximum diameter and Hemorrhage were independent risk factors distinguishing the group with high and low expression of Ki-67 proliferative index. ROC curve analysis showed that the logistic regression model achieved an AUC value of 0.730 (95% CI: 0.639, 0.822), sensitivity of 0.628 and specificity of 0.756.Logistic regression modelling of preoperative VASARI features can be used as a reliable tool for predicting the level of Ki-67 proliferative index in IDH-wildtype GB patients, which can help in preoperative development of treatment and follow-up strategies for patients.
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Affiliation(s)
- Liangcai Bai
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jian Jiang
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, The Second Hospital of Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
- Second Clinical School, Lanzhou University, Lanzhou, China.
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