Preoperative prediction of Ki-67 expression and risk stratification in gliomas using multiparametric MRI and intratumor heterogeneity-based habitat imaging: a multicenter study
Year: 2025
Type: article
Study design: Observational Study
Abstract: PURPOSE: To investigate the feasibility of multiparametric magnetic resonance imaging (MRI)-based characterization of intratumor heterogeneity (ITH) for noninvasive prediction of Ki-67 proliferation index (PI) and assessment of its prognostic significance in gliomas. MATERIALS AND METHODS: This study included 205 patients with pathologically confirmed gliomas. Dynamic contrast-enhanced MRI and diffusion-weighted imaging were used to generate volume transfer constant (K trans ) and apparent diffusion coefficient (ADC) maps. A voxel-wise k -means clustering algorithm was applied to segment tumors into three biologically distinct intratumor habitats based on K trans and ADC values. Logistic regression and elastic net models were developed to predict Ki-67 PI. Model performance was validated through 10-fold cross-validation (CV) and two independent test cohorts, with diagnostic accuracy assessed by receiver operating characteristic curves and area under the curve (AUC). The prognostic value of habitat-derived biomarkers for progression-free survival (PFS) and overall survival (OS) was evaluated using Kaplan-Meier analysis and Cox proportional hazards models. A composite risk score was calculated for patient stratification. RESULTS: Three spatial habitats were identified: H1 (hypo-vasopermeability, hypo-cellularity habitat), H2 (hypo-vasopermeability, hypercellularity habitat), and H3 (hyper-vasopermeability habitat). The elastic net model demonstrated high predictive accuracy for Ki-67 PI, with AUCs of 0.924, 0.875, 0.881, and 0.869 in the training, CV, and two test sets, respectively. Patients classified as high-risk by the risk score exhibited markedly shorter PFS (median 6.3 vs. 52.4 months) and OS (median 13.2 vs. 76.4 months) compared to low-risk patients. High-risk status was significantly associated with poorer prognosis [PFS: hazard ratio (HR) = 4.338, 95% confidence interval (CI): 2.596-7.249; OS: HR = 4.471, 95% CI: 2.572-7.772; both P < 0.001). CONCLUSION: Multiparametric MRI-based habitat imaging effectively enables preoperative noninvasive prediction of Ki-67 expression and risk stratification in gliomas, with potential to offer insights into tumor biological behavior and inform individualized treatment planning.
Source: International Journal of Surgery
Authors: Xingrui Wang, Hao Wu, Yuanzheng Wang, Wenzhong Hu, Shiteng Suo, Liemei Guo, Dongxu Zhao, Shilei Zhang, Chen Li, Zhenhui Xie, Yang Song, Wentao Hu, Xusha Wu, Yi Zhu, Yan Ren, Yibin Xi, Yan Zhou, Mengqiu Cao
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W4415505025
