Magnetic resonance analysis of malignant transformation in recurrent glioma.
Magnetic resonance analysis of malignant transformation in recurrent glioma.
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DOI：
10.1093/neuonc/now008
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发表时间：
2016-08
期刊：
影响因子：
15.9
通讯作者：
Nelson SJ
中科院分区：
文献类型：
--
作者：
Jalbert LE;Neill E;Phillips JJ;Lupo JM;Olson MP;Molinaro AM;Berger MS;Chang SM;Nelson SJ
低级别胶质瘤（LGG）患者的生存期相对较长，通常在治疗肿瘤和影响生活质量之间取得平衡。虽然病变可能保持稳定多年，但它们也可能在复发时发生恶性转化（MT），需要更积极的干预。在这里，我们报告了最新的多参数MRI研究复发性LGG患者。111例先前诊断为LGG的患者在复发时进行了1.5 T或3 T MR扫描。根据解剖、扩散、灌注和代谢MR数据估计体积和强度参数。将图像引导组织样本的组织病理学标记物与体内图像上相应位置的指标进行直接比较。应用生物信息学方法可视化和解释这些结果，包括成像热图和网络分析。采用多元线性回归模型进行预测。许多高级成像参数被发现在接受MT的肿瘤患者与未接受MT的肿瘤患者之间存在显著差异。在组织样本位置计算的成像指标突出了成像的独特生物学意义和复发性LGG中存在的异质性，而多变量建模在预测MT方面的准确率为76.04%。这些多参数MR数据的获取和定量分析最终可以改善复发性LGG患者的临床评估和治疗分层。
Patients with low-grade glioma (LGG) have a relatively long survival, and a balance is often struck between treating the tumor and impacting quality of life. While lesions may remain stable for many years, they may also undergo malignant transformation (MT) at the time of recurrence and require more aggressive intervention. Here we report on a state-of-the-art multiparametric MRI study of patients with recurrent LGG. One hundred and eleven patients previously diagnosed with LGG were scanned at either 1.5 T or 3 T MR at the time of recurrence. Volumetric and intensity parameters were estimated from anatomic, diffusion, perfusion, and metabolic MR data. Direct comparisons of histopathological markers from image-guided tissue samples with metrics derived from the corresponding locations on the in vivo images were made. A bioinformatics approach was applied to visualize and interpret these results, which included imaging heatmaps and network analysis. Multivariate linear-regression modeling was utilized for predicting transformation. Many advanced imaging parameters were found to be significantly different for patients with tumors that had undergone MT versus those that had not. Imaging metrics calculated at the tissue sample locations highlighted the distinct biological significance of the imaging and the heterogeneity present in recurrent LGG, while multivariate modeling yielded a 76.04% accuracy in predicting MT. The acquisition and quantitative analysis of such multiparametric MR data may ultimately allow for improved clinical assessment and treatment stratification for patients with recurrent LGG.