MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study
BackgroundHigh expression of Ki-67 in meningioma is significantly associated with higher histological grade and worse prognosis. The non-invasive and dynamic assessment of Ki-67 expression levels in meningiomas is of significant clinical importance and is urgently required. This study aimed to devel...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1554539/full |
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| author | Ming Luo Ming Luo Guihan Lin Duoning Chen Duoning Chen Weiyue Chen Shuiwei Xia Junguo Hui Pengjun Chen Minjiang Chen Wangyang Ye Wangyang Ye Jiansong Ji |
| author_facet | Ming Luo Ming Luo Guihan Lin Duoning Chen Duoning Chen Weiyue Chen Shuiwei Xia Junguo Hui Pengjun Chen Minjiang Chen Wangyang Ye Wangyang Ye Jiansong Ji |
| author_sort | Ming Luo |
| collection | DOAJ |
| description | BackgroundHigh expression of Ki-67 in meningioma is significantly associated with higher histological grade and worse prognosis. The non-invasive and dynamic assessment of Ki-67 expression levels in meningiomas is of significant clinical importance and is urgently required. This study aimed to develop a predictive model for the Ki-67 index in meningioma based on preoperative magnetic resonance imaging (MRI).MethodsThis study included 196 patients from one center (internal cohort) and 92 patients from another center (external validation cohort). Meningioma had to have been pathologically confirmed for inclusion. The Ki-67 index was classified as high (Ki-67 ≥ 5%) and low (Ki-67 < 5%). The internal cohort was randomly assigned to training and validation sets at a 7:3 ratio. Radiomics features were selected from contrast-enhanced T1-weighted MRI using the least-absolute shrinkage and selection operator and random forest methods. Then, we constructed a predictive model based on the identified semantic and radiomics features, aiming to distinguish high and low Ki-67 expression. The model’s performance was evaluated through internal cross-validation and validated in the external cohort.ResultsAmong the clinical features, peritumoral edema (p = 0.001) and heterogeneous enhancement (p = 0.001) were independent predictors of the Ki-67 index in meningiomas. The radiomics model using a combined 8 mm volume of interest demonstrated optimal performance in the training (area under the receiver operating characteristic curve [AUC] = 0.883) and validation (AUC = 0.811) sets. A nomogram integrating clinical and radiomic features was constructed, achieving an AUC of 0.904 and enhancing the model’s predictive accuracy for high Ki-67 expression.ConclusionThis study developed clinical-radiomic models to non-invasively predict Ki-67 expression in meningioma and provided a novel preoperative strategy for assessing tumor proliferation. |
| format | Article |
| id | doaj-art-acae540b60834170b49dc76b368e54c6 |
| institution | DOAJ |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-acae540b60834170b49dc76b368e54c62025-08-20T03:15:20ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-07-011610.3389/fneur.2025.15545391554539MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center studyMing Luo0Ming Luo1Guihan Lin2Duoning Chen3Duoning Chen4Weiyue Chen5Shuiwei Xia6Junguo Hui7Pengjun Chen8Minjiang Chen9Wangyang Ye10Wangyang Ye11Jiansong Ji12Zhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaDepartment of Neurosurgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaDepartment of Neurosurgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaDepartment of Neurosurgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaZhejiang Key Laboratory of Imaging and Interventional Medicine, Key Laboratory of Precision Medicine of Lishui City, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, ChinaBackgroundHigh expression of Ki-67 in meningioma is significantly associated with higher histological grade and worse prognosis. The non-invasive and dynamic assessment of Ki-67 expression levels in meningiomas is of significant clinical importance and is urgently required. This study aimed to develop a predictive model for the Ki-67 index in meningioma based on preoperative magnetic resonance imaging (MRI).MethodsThis study included 196 patients from one center (internal cohort) and 92 patients from another center (external validation cohort). Meningioma had to have been pathologically confirmed for inclusion. The Ki-67 index was classified as high (Ki-67 ≥ 5%) and low (Ki-67 < 5%). The internal cohort was randomly assigned to training and validation sets at a 7:3 ratio. Radiomics features were selected from contrast-enhanced T1-weighted MRI using the least-absolute shrinkage and selection operator and random forest methods. Then, we constructed a predictive model based on the identified semantic and radiomics features, aiming to distinguish high and low Ki-67 expression. The model’s performance was evaluated through internal cross-validation and validated in the external cohort.ResultsAmong the clinical features, peritumoral edema (p = 0.001) and heterogeneous enhancement (p = 0.001) were independent predictors of the Ki-67 index in meningiomas. The radiomics model using a combined 8 mm volume of interest demonstrated optimal performance in the training (area under the receiver operating characteristic curve [AUC] = 0.883) and validation (AUC = 0.811) sets. A nomogram integrating clinical and radiomic features was constructed, achieving an AUC of 0.904 and enhancing the model’s predictive accuracy for high Ki-67 expression.ConclusionThis study developed clinical-radiomic models to non-invasively predict Ki-67 expression in meningioma and provided a novel preoperative strategy for assessing tumor proliferation.https://www.frontiersin.org/articles/10.3389/fneur.2025.1554539/fullmeningiomasradiomicsintratumoralperitumoralKi-67 |
| spellingShingle | Ming Luo Ming Luo Guihan Lin Duoning Chen Duoning Chen Weiyue Chen Shuiwei Xia Junguo Hui Pengjun Chen Minjiang Chen Wangyang Ye Wangyang Ye Jiansong Ji MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study Frontiers in Neurology meningiomas radiomics intratumoral peritumoral Ki-67 |
| title | MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study |
| title_full | MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study |
| title_fullStr | MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study |
| title_full_unstemmed | MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study |
| title_short | MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study |
| title_sort | mri based multiregional radiomics for preoperative prediction of ki 67 expression in meningiomas a two center study |
| topic | meningiomas radiomics intratumoral peritumoral Ki-67 |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1554539/full |
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