An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma

Ningfang Du; Weiquan Shu; Kefeng Li; Yao Deng; Xinxin Xu; Yao Ye; Feng Tang; Renling Mao; Guangwu Lin; Shihong Li; Xuhao Fang

doi:10.1186/s12967-023-03950-w

An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma

J Transl Med. 2023 Feb 11;21(1):119. doi: 10.1186/s12967-023-03950-w.

Authors

Ningfang Du^#¹, Weiquan Shu^#², Kefeng Li^{3

4}, Yao Deng², Xinxin Xu⁵, Yao Ye⁶, Feng Tang², Renling Mao², Guangwu Lin⁷, Shihong Li⁸, Xuhao Fang⁹

Affiliations

¹ Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
² Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China.
³ School of Medicine, University of California, San Diego, CA, USA.
⁴ Faculty of Health Sciences and Sports, Macao Polytechnic University, Macao SAR, China.
⁵ Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China.
⁶ Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China.
⁷ Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China. lingw01000@163.com.
⁸ Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China. lishihong@fudan.edu.cn.
⁹ Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China. steve.fong@foxmail.com.

^# Contributed equally.

Abstract

Background and purpose: Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics.

Methods: Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis.

Results: ADC_min, ADC_mean, rADC_min, rADC_mean and Ki-67 LI showed a negative correlation (r = - 0.478, r = - 0.369, r = - 0.488, r = - 0.388, all P < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933-0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group (P < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721-0.879).

Conclusions: There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.

Keywords: Apparent diffusion coefficient; Diffusion-weighted magnetic resonance imaging; Glioma; Ki-67 labeling index; Magnetic resonance imaging; Peritumoral edema.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain Neoplasms* / diagnostic imaging
Brain Neoplasms* / pathology
Diffusion Magnetic Resonance Imaging / methods
Glioma* / diagnostic imaging
Glioma* / pathology
Humans
Ki-67 Antigen
Magnetic Resonance Imaging / methods
Neoplasm Grading
Retrospective Studies

Substances

Ki-67 Antigen
poly R-478