本研究旨在评估细胞周期蛋白依赖性激酶6（CDK6）表达水平的预后价值，并建立基于机器学习的放射组学模型来预测高级别胶质瘤（HGG）中CDK6的表达水平。临床参数和基因组数据从癌症基因组图谱 (TCGA) 数据库中的 310 名 HGG 患者和分子脑肿瘤数据存储库 (REMBRANDT) 数据库中的 27 名患者中提取。采用单变量和多变量 Cox 回归以及 Kaplan-Meier 分析进行预后分析。使用斯皮尔曼相关分析评估免疫细胞浸润与CDK6之间的相关性。放射组学特征是从癌症成像档案 (TCIA) 数据库 (n = 82) 和 REMBRANDT 数据库 (n = 27) 的对比增强磁共振成像 (CE-MRI) 中提取的。采用逻辑回归（LR）和支持向量机（SVM）建立预测CDK6表达的放射组学模型。利用受试者工作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）来评估放射组学模型的预测性能。基于 LR 模型生成放射组学评分 (RS)。构建基于 RS 的列线图来预测 HGG 的预后。CDK6 在 HGG 组织中显着过表达，并且与较低的总生存率相关。在 CDK6 高组中观察到浸润 M0 巨噬细胞显着升高 (P < 0.001)。利用三个放射组学特征建立了预测 CDK6 表达水平的 LR 放射组学模型（训练队列中 AUC=0.810，交叉验证后 AUC=0.784，测试队列中 AUC=0.750）。基于RS的列线图的AUC预测效率分别为1年0.769、3年0.815和5年0.780。CDK6的表达水平可以影响HGG患者的预后。 HGG 的表达水平可以利用放射组学模型进行无创预测。版权所有 © 2024 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

This study aims to assess the prognostic value of Cyclin-dependent kinases 6 (CDK6) expression levels and establish a machine learning-based radiomics model for predicting the expression levels of CDK6 in high-grade gliomas (HGG).Clinical parameters and genomic data were extracted from 310 HGG patients in the Cancer Genome Atlas (TCGA) database and 27 patients in the Repository of Molecular Brain Neoplasia Data (REMBRANDT) database. Univariate and multivariate Cox regression, as well as Kaplan-Meier analysis, were performed for prognosis analysis. The correlation between immune cell Infiltration with CDK6 was assessed using spearman correlation analysis. Radiomic features were extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) in the Cancer Imaging Archive (TCIA) database (n = 82) and REMBRANDT database (n = 27). Logistic regression (LR) and support vector machine (SVM) were employed to establish the radiomics model for predicting CDK6 expression. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were utilized to assess the predictive performance of the radiomics model. Generate radiomic scores (RS) based on the LR model. An RS-based nomogram was constructed to predict the prognosis of HGG.CDK6 was significantly overexpressed in HGG tissues and was related to lower overall survival. A significant elevation in infiltrating M0 macrophages was observed in the CDK6 high group (P < 0.001). The LR radiomics model for the prediction of CDK6 expression levels (AUC=0.810 in the training cohort, AUC = 0.784 after cross-validation, AUC=0.750 in the testing cohort) was established utilizing three radiomic features. The predictive efficiencies of the RS-based nomogram, as measured by AUC, were 0.769 for 1-year, 0.815 for 3-year, and 0.780 for 5-year, respectively.The expression level of CDK6 can impact the prognosis of patients with HGG. The expression level of HGG can be noninvasively prognosticated utilizing a radiomics model.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.