Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model
PurposeTo 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 meningi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1138069/full |
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| author | Chung-Man Moon Yun Young Lee Doo-Young Kim Woong Yoon Woong Yoon Byung Hyun Baek Byung Hyun Baek Jae-Hyun Park Suk-Hee Heo Suk-Hee Heo Sang-Soo Shin Sang-Soo Shin Seul Kee Kim Seul Kee Kim |
| author_facet | Chung-Man Moon Yun Young Lee Doo-Young Kim Woong Yoon Woong Yoon Byung Hyun Baek Byung Hyun Baek Jae-Hyun Park Suk-Hee Heo Suk-Hee Heo Sang-Soo Shin Sang-Soo Shin Seul Kee Kim Seul Kee Kim |
| author_sort | Chung-Man Moon |
| collection | DOAJ |
| description | PurposeTo 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.MethodsThis 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.ResultsIn 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.ConclusionThe 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. |
| first_indexed | 2024-03-13T10:02:37Z |
| format | Article |
| id | doaj.art-d5946ff715354cddac3a3af6598350d4 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-13T10:02:37Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-d5946ff715354cddac3a3af6598350d42023-05-23T04:30:55ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-05-011310.3389/fonc.2023.11380691138069Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic modelChung-Man Moon0Yun Young Lee1Doo-Young Kim2Woong Yoon3Woong Yoon4Byung Hyun Baek5Byung Hyun Baek6Jae-Hyun Park7Suk-Hee Heo8Suk-Hee Heo9Sang-Soo Shin10Sang-Soo Shin11Seul Kee Kim12Seul Kee Kim13Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Hospital, Gwangju, Republic of KoreaDepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Hospital, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Hospital, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Hospital, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of KoreaDepartment of Radiology, Chonnam National University Hospital, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Medical School, Gwangju, Republic of KoreaDepartment of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of KoreaPurposeTo 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.MethodsThis 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.ResultsIn 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1138069/fullKi-67p53meningiomaradiomicsmachine learning |
| spellingShingle | Chung-Man Moon Yun Young Lee Doo-Young Kim Woong Yoon Woong Yoon Byung Hyun Baek Byung Hyun Baek Jae-Hyun Park Suk-Hee Heo Suk-Hee Heo Sang-Soo Shin Sang-Soo Shin Seul Kee Kim Seul Kee Kim Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model Frontiers in Oncology Ki-67 p53 meningioma radiomics machine learning |
| title | Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model |
| title_full | Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model |
| title_fullStr | Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model |
| title_full_unstemmed | Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model |
| title_short | Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model |
| title_sort | preoperative prediction of ki 67 and p53 status in meningioma using a multiparametric mri based clinical radiomic model |
| topic | Ki-67 p53 meningioma radiomics machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1138069/full |
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