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基于剂量测定、生理和代谢磁共振成像识别胶质母细胞瘤进展风险的体素

Identifying Voxels at Risk for Progression in Glioblastoma Based on Dosimetry, Physiologic and Metabolic MRI.

作者信息

Anwar Mekhail, Molinaro Annette M, Morin Olivier, Chang Susan M, Haas-Kogan Daphne A, Nelson Sarah J, Lupo Janine M

机构信息

Departments of a   Radiation Oncology, University of California, San Francisco, California.

b   Neurosurgery, Division of Epidemiology and Biostatistics, University of California, San Francisco, California.

出版信息

Radiat Res. 2017 Sep;188(3):303-313. doi: 10.1667/RR14662.1. Epub 2017 Jul 19.

Abstract

Despite the longstanding role of radiation in cancer treatment and the presence of advanced, high-resolution imaging techniques, delineation of voxels at-risk for progression remains purely a geometric expansion of anatomic images, missing subclinical disease at risk for recurrence while treating potentially uninvolved tissue and increasing toxicity. This remains despite the modern ability to precisely shape radiation fields. A striking example of this is the treatment of glioblastoma, a highly infiltrative tumor that may benefit from accurate identification of subclinical disease. In this study, we hypothesize that parameters from physiologic and metabolic magnetic resonance imaging (MRI) at diagnosis could predict the likelihood of voxel progression at radiographic recurrence in glioblastoma by identifying voxel characteristics that indicate subclinical disease. Integrating dosimetry can reveal its effect on voxel outcome, enabling risk-adapted voxel dosing. As a system example, 24 patients with glioblastoma treated with radiotherapy, temozolomide and an anti-angiogenic agent were analyzed. Pretreatment median apparent diffusion coefficient (ADC), fractional anisotropy (FA), relative cerebral blood volume (rCBV), vessel leakage (percentage recovery), choline-to-NAA index (CNI) and dose of voxels in the T2 nonenhancing lesion (NEL), T1 post-contrast enhancing lesion (CEL) or normal-appearing volume (NAV) of brain, were calculated for voxels that progressed [NAV→NEL, CEL (N = 8,765)] and compared against those that remained stable [NAV→NAV (N = 98,665)]. Voxels that progressed (NAV→NEL) had significantly different (P < 0.01) ADC (860), FA (0.36) and CNI (0.67) versus stable voxels (804, 0.43 and 0.05, respectively), indicating increased cell turnover, edema and decreased directionality, consistent with subclinical disease. NAV→CEL voxels were more abnormal (1,014, 0.28, 2.67, respectively) and leakier (percentage recovery = 70). A predictive model identified areas of recurrence, demonstrating that elevated CNI potentiates abnormal diffusion, even far (>2 cm) from the tumor and dose escalation >45 Gy has diminishing benefits. Integrating advanced MRI with dosimetry can identify at voxels at risk for progression and may allow voxel-level risk-adapted dose escalation to subclinical disease while sparing normal tissue. When combined with modern planning software, this technique may enable risk-adapted radiotherapy in any disease site with multimodal imaging.

摘要

尽管放射治疗在癌症治疗中具有长期作用,且存在先进的高分辨率成像技术,但对于有进展风险的体素的描绘仍仅仅是解剖图像的几何扩展,在治疗潜在未受累组织并增加毒性的同时,遗漏了有复发风险的亚临床疾病。尽管现代具备精确塑造放射野的能力,但情况依然如此。胶质母细胞瘤的治疗就是一个显著例子,这是一种高度浸润性肿瘤,可能受益于亚临床疾病的准确识别。在本研究中,我们假设诊断时生理和代谢磁共振成像(MRI)的参数可通过识别表明亚临床疾病的体素特征,预测胶质母细胞瘤放射学复发时体素进展的可能性。整合剂量测定可揭示其对体素结果的影响,实现风险适应性体素给药。作为一个系统示例,分析了24例接受放疗、替莫唑胺和抗血管生成药物治疗的胶质母细胞瘤患者。计算了进展的体素[正常脑组织容积(NAV)→T2加权像非强化病灶(NEL)、T1加权像增强病灶(CEL)(N = 8765)]与保持稳定的体素[NAV→NAV(N = 98665)]在治疗前的中位表观扩散系数(ADC)、分数各向异性(FA)、相对脑血容量(rCBV)、血管渗漏(百分比恢复)、胆碱与N-乙酰天门冬氨酸指数(CNI)以及T2非强化病灶、T1增强病灶或正常脑组织容积中的体素剂量。进展的体素(NAV→NEL)与稳定的体素相比,ADC(860)、FA(0.36)和CNI(0.67)有显著差异(P < 0.01)(分别为804、0.43和0.05),表明细胞更新增加、水肿和方向性降低,与亚临床疾病一致。NAV→CEL体素更异常(分别为1014、0.28、2.67)且渗漏更严重(百分比恢复 = 70)。一个预测模型识别出复发区域,表明升高的CNI会增强异常扩散,即使远离肿瘤(>2 cm),且剂量增加>45 Gy的获益递减。将先进的MRI与剂量测定相结合可识别有进展风险的体素,并可能允许对亚临床疾病进行体素水平的风险适应性剂量增加,同时 sparing正常组织。当与现代治疗计划软件结合时,该技术可能在任何疾病部位通过多模态成像实现风险适应性放疗。

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