Preoperative prediction of Ki-67 and p53 status in meningioma using a multiparametric MRI-based clinical-radiomic model

Frontiers in Oncology

Oncology

Doo-Young Kim¹  Yun Young Lee²  Jae-Hyun Park²  Sang-Soo Shin³  Woong Yoon³  Byung Hyun Baek³  Seul Kee Kim⁴  Suk-Hee Heo⁴  Chung-Man Moon⁵ 

[1] Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Republic of Korea;Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea;Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea;Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea;Department of Radiology, Chonnam National University Medical School, Gwangju, Republic of Korea;Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea;Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of Korea;

关键词: Ki-67;    p53;    meningioma;    radiomics;    machine learning;   

DOI  :  10.3389/fonc.2023.1138069

 received in 2023-01-06, accepted in 2023-02-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Moon, Lee, Kim, Yoon, Baek, Park, Heo, Shin and Kim