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

• Published in: Journal of translational medicine Vol. 21; no. 1; pp. 119 - 11
• Main Authors: Du, Ningfang, Shu, Weiquan, Li, Kefeng, Deng, Yao, Xu, Xinxin, Ye, Yao, Tang, Feng, Mao, Renling, Lin, Guangwu, Li, Shihong, Fang, Xuhao
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.02.2023; BioMed Central; BMC
• Subjects: Analysis; Apparent diffusion coefficient; Biopsy; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Brain tumors; Care and treatment; Cell cycle; Cell growth; Cell proliferation; Classification; Diagnosis; Diffusion coefficient; Diffusion Magnetic Resonance Imaging - methods; Diffusion-weighted magnetic resonance imaging; Edema; Glioma; Glioma - diagnostic imaging; Glioma - pathology; Glioma cells; Gliomas; Humans; Ki-67 Antigen; Ki-67 labeling index; Labeling; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical prognosis; Neoplasm Grading; Nervous system; Patients; Peritumoral edema; Retrospective Studies; Statistical analysis; Surgery; Testing; Tumors; Taiwan; Germany; Ki-67 labeling index; Glioma; Magnetic resonance imaging; Peritumoral edema; Diffusion-weighted magnetic resonance imaging; Apparent diffusion coefficient
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-023-03950-w

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. 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. ADC , ADC , rADC , rADC 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). 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.