Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.
Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.
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DOI：
10.1227/neu.0000000000001202
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发表时间：
2016-04
期刊：
影响因子：
4.8
通讯作者：
Davatzikos C
中科院分区：
文献类型：
--
作者：
Akbari H;Macyszyn L;Da X;Bilello M;Wolf RL;Martinez-Lage M;Biros G;Alonso-Basanta M;OʼRourke DM;Davatzikos C
胶质母细胞瘤是一种侵袭性和高度浸润性的脑癌。标准的手术切除是通过增强T1加权（T1）磁共振成像（MRI），这是不足以划定周围浸润性肿瘤的增强。开发描绘肿瘤浸润区域并预测瘤周组织早期复发的成像生物标志物。这些标志物将使密集的，但有针对性的，手术和放射治疗，从而有可能延迟复发和延长生存。使用机器学习方法将31例患者的术前多参数MRI（T1、T1-Gad、T2加权[T2]、T2液体衰减反转恢复[FLAIR]、扩散张量成像（DTI）和动态磁敏感对比增强[DSC]-MRI）结合起来，从而创建浸润瘤周组织的预测空间图。在回顾性队列中使用交叉验证以获得可推广的生物标志物。随后，将从回顾性研究中获得的成像特征用于34例新患者的重复队列。将代表肿瘤浸润和未来早期复发可能性的空间图与经病理学证实的切除术后随访研究的复发区域进行比较。该技术预测了早期复发，平均曲线下面积（AUC）为0.84，灵敏度为91%，特异性为93%，重复研究中预测严重浸润组织的比值比估计值为9.29（99%CI，8.95-9.65）。当通过模式分析和机器学习进行定量分析时，发现肿瘤复发区域具有微妙但相当独特的多参数成像特征。通过多参数模式分析方法发现的视觉上难以察觉的成像模式被发现可以估计浸润程度和未来肿瘤复发的位置，为改进靶向治疗铺平道路。
Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging (MRI), which is insufficient for delineating surrounding infiltrating tumor. To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. Preoperative multiparametric MRIs (T1, T1-Gad, T2-weighted [T2], T2-fluid-attenuated inversion recovery [FLAIR], diffusion tensor imaging (DTI), and dynamic susceptibility contrast-enhanced [DSC]-MRI) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing likelihood of tumor infiltration and future early recurrence were compared to regions of recurrence on postresection follow-up studies with pathology confirmation. This technique produced predictions of early recurrence with a mean area under the curve (AUC) of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% CI, 8.95–9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.