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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Xue C, Zhou Q, Zhang B, Ke X, Zhang P, Liu X, Li S, Deng J, Zhou J. Vasari-Based Features Nomogram to Predict the Tumor-Infiltrating CD8+ T Cell Levels in Glioblastoma. Acad Radiol 2023:S1076-6332(23)00602-5. [PMID: 37985291 DOI: 10.1016/j.acra.2023.10.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/22/2023]
Abstract
RATIONALE AND OBJECTIVES Tumor-infiltrating CD8 + T cells play a key role in glioblastoma (GB) development, malignant progression, and recurrence. The aim of the study was to establish nomograms based on the Visually AcceSAble Rembrandt Images (VASARI) features of multiparametric magnetic resonance imaging (MRI) to determine the expression levels of tumor-infiltrating CD8 + T cells in patients with GB. MATERIALS AND METHODS Pathological and imaging data of 140 patients with GB confirmed by surgery and pathology were retrospectively analyzed. The levels of tumor-infiltrating CD8 + T cells in tumor tissue samples obtained from patients were quantified using immunohistochemical staining. Patients were divided into high and low CD8 expression groups. The MRI images of patients with GB were analyzed by two radiologists using the VASARI scoring system. RESULTS A total of 25 MRI-based VASARI imaging features were evaluated by two neuroradiologists. The features with the greatest predictive power for CD8 expression levels were, cystic (OR, 3.063; 95% CI: 1.387, 6.766; P = 0.006), hemorrhage (OR, 2.980; 95% CI: 1.172, 7.575; P = 0.022), and ependymal extension (OR, 0.257; 95% CI: 0.114 0.581; P = 0.001). A logistic regression model based on these three features showed better sample predictive performance (AUC=0.745; 95% CI: 0.665, 0.825; Sensitivity=0.527; Specificity=0.857). CONCLUSION The VASARI feature-based nomogram model can show promise to predict the level of infiltrative CD8 expression in GB tumors non-invasively for earlier tissue diagnosis and more aggressive treatment.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, 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 Imaging Artificial Intelligence, Lanzhou, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, 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
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, 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 Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, 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
| | - Peng Zhang
- Department of Pathology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, 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 Imaging Artificial Intelligence, Lanzhou, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, 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 Imaging Artificial Intelligence, Lanzhou, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, 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 Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, 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 Imaging Artificial Intelligence, Lanzhou, China.
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