صورة
Image_1_Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.pdf
| العنوان: | Image_1_Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model.pdf |
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| المؤلفون: | Chung-Man Moon, Yun Young Lee, Doo-Young Kim, Woong Yoon, Byung Hyun Baek, Jae-Hyun Park, Suk-Hee Heo, Sang-Soo Shin, Seul Kee Kim |
| سنة النشر: | 2023 |
| المجموعة: | Frontiers: Figshare |
| مصطلحات موضوعية: | Cancer, Cancer Cell Biology, Cancer Diagnosis, Cancer Genetics, Cancer Therapy (excl. Chemotherapy and Radiation Therapy), Chemotherapy, Haematological Tumours, Molecular Targets, Radiation Therapy, Solid Tumours, Oncology and Carcinogenesis not elsewhere classified, Ki-67, p53, meningioma, radiomics, machine learning |
| الوصف: | Purpose To investigate the utility of preoperative multiparametric magnetic resonance imaging (mpMRI)-based clinical-radiomic analysis combined with machine learning (ML) algorithms in predicting the expression of the Ki-67 proliferative index and p53 tumor suppressor protein in patients with meningioma. Methods This multicenter retrospective study included 483 and 93 patients from two centers. The Ki-67 index was classified into high (Ki-67≥5%) and low (Ki-67<5%)-expressed groups, and the p53 index was classified into positive (p53≥5%) and negative (p53<5%)-expressed groups. Clinical and radiological features were analyzed using univariate and multivariate statistical analyses. Six ML models were performed with different types of classifiers to predict Ki-67 and p53 status. Results In the multivariate analysis, larger tumor volumes (p<0.001), irregular tumor margin (p<0.001), and unclear tumor-brain interface (p<0.001) were independently associated with a high Ki-67 status, whereas the presence of both necrosis (p=0.003) and the dural tail sign (p=0.026) were independently associated with a positive p53 status. A relatively better performance was yielded from the model constructed by combined clinical and radiological features. The area under the curve (AUC) and accuracy of high Ki-67 were 0.820 and 0.867 in the internal test, and 0.666 and 0.773 in the external test, respectively. Regarding p53 positivity, the AUC and accuracy were 0.858 and 0.857 in the internal test, and 0.684 and 0.718 in the external test. Conclusion The present study developed clinical-radiomic ML models to non-invasively predict Ki-67 and p53 expression in meningioma using mpMRI features, and provides a novel non-invasive strategy for assessing cell proliferation. |
| نوع الوثيقة: | still image |
| اللغة: | unknown |
| العلاقة: | https://figshare.com/articles/figure/Image_1_Preoperative_prediction_of_Ki-67_and_p53_status_in_meningioma_using_a_multiparametric_MRI-based_clinical-radiomic_model_pdf/23085599Test |
| DOI: | 10.3389/fonc.2023.1138069.s001 |
| الإتاحة: | https://doi.org/10.3389/fonc.2023.1138069.s001Test https://figshare.com/articles/figure/Image_1_Preoperative_prediction_of_Ki-67_and_p53_status_in_meningioma_using_a_multiparametric_MRI-based_clinical-radiomic_model_pdf/23085599Test |
| حقوق: | CC BY 4.0 |
| رقم الانضمام: | edsbas.E0A67A1F |
| قاعدة البيانات: | BASE |
| DOI: | 10.3389/fonc.2023.1138069.s001 |
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