Purpose

We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

Methods

This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

Results

Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

Conclusions

MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.

中文翻译：

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磁共振成像特征的识别用于预测新诊断的胶质母细胞瘤的分子谱

目的

我们使用磁共振 (MR) 成像特征预测了新诊断的胶质母细胞瘤患者的分子谱，并探索了成像特征与主要分子改变之间的关联。

方法

这项回顾性研究包括新诊断的胶质母细胞瘤患者和可用的下一代测序结果。从术前 MR 成像中，获得了视觉上可识别的伦勃朗图像 (VASARI) 特征、体积参数和表观扩散系数 (ADC) 值。首先，执行单变量随机森林以识别可通过成像特征以高精度和稳定性预测的基因异常。接下来，训练多变量随机森林来预测发现队列中选定的基因，并在外部队列中进行验证。进行单变量逻辑回归以进一步探索成像特征与基因之间的关联。

结果

单变量随机森林识别出9个通过成像特征预测的基因，准确度和稳定性高。多变量随机森林模型在预测发现队列中的IDH和PTPN11突变方面表现出出色的性能，这在外部验证队列中得到了验证（IDH的接收者操作特征曲线下面积 [AUC] 为 0.855 ，PTPN11为0.88 ）。ATRX丢失和EGFR突变的预测 AUC 分别为 0.753 和 0.739，而 PTEN 无法可靠预测。基于单变量逻辑回归分析，IDH、ATRX和TP53根据它们共享的成像特征进行聚类，而EGFR和CDKN2A/B的聚类方向相反。

结论

MR 成像特征与特定的分子改变有关，可用于预测新诊断的胶质母细胞瘤患者的分子特征。