PO.PR01.02 · 预防研究
评估CBV在识别胶质母细胞瘤早期复发风险中的价值
Assessing the value of CBV in identifying early recurrence risk in glioblastoma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
意义:量化胶质母细胞瘤内高CBV区域的比例,可能提供一种早期、实用的基于影像的复发风险估计,从而解决在预判肿瘤复发和指导及时临床干预方面的一个关键未满足挑战。引言:胶质母细胞瘤(GBM)具有高度侵袭性,尽管采取了最大限度的标准治疗,复发仍很常见。来自DSC-MRI的脑血容量(CBV)可量化肿瘤血管化程度,并有助于区分真性进展与假性进展。经Anil等人2024年针对影像定位组织病理学验证的标准化相对CBV(sRCBV)阈值,可区分复发(sRCBV >1)并提示存活肿瘤的存在(sRCBV >1.37,约88%概率)。本研究评估高sRCBV体素的比例是否可作为复发的早期指标,并识别与复发起始相关的阈值。
方法:在IRB批准下(2012-0441,PI为Puduvalli博士),回顾性分析了来自六名IDH1野生型GBM患者(五名女性,一名男性;年龄49-81岁)的十五个治疗后MRI时间点。所有患者均接受了Stupp方案放化疗(VMAT 6000cGy/30分次)和替莫唑胺治疗,随后随访监测直至复发。仅纳入放化疗后采集的DSC-MRI扫描。所有检查均采用共识的单剂量、低翻转角(30°)方案,不使用预负荷。使用IB Neuro结合BSW渗漏校正生成标准化RCBV图,并配准至增强T1加权(T1CE)图像。在T1CE上半自动定义肿瘤掩膜。高sRCBV体素比例(fH)计算为sRCBV >1.37的体素数除以肿瘤体素总数。记录每次扫描与影像学复发之间的间隔(Δt)。使用二次回归检验fH与Δt之间的关联,采用ROC分析区分早期(≤120天)与晚期复发,Youden指数阈值法,以及Cox比例风险模型。
结果:在15个时间点中,fH与复发时间呈现临界显著的负相关(R² =0.35,p = 0.073)。ROC分析得出AUC = 0.74(95% CI 0.44-0.96),最佳阈值为fH ≈ 0.39。较高的fH与复发风险增加相关(beta = 4.26,每增加1.0 fH的HR = 70.47)。约27%高sRCBV体素的肿瘤对应120天的中位无复发生存期。
结论:初步结果表明,增强肿瘤内高sRCBV体素的比例可作为胶质母细胞瘤复发风险的定量指标。尽管受样本量小和回顾性设计的限制,一致的趋势提示较高的血管比例对应较短的复发间隔和更高的复发风险。正在进行的工作正将该分析扩展到一个更大的、多时间点的队列,以提高统计效力并界定用于整合入CBV可视化工具的临床阈值。
查看英文原文 English abstract
Impact: Quantifying the fraction of high-CBV regions within glioblastoma may provide an early, practical imaging-based estimate of risk of recurrence, addressing a key unmet challenge in anticipating tumor regrowth and guiding timely clinical intervention. Introduction: Glioblastoma (GBM) is highly aggressive, with recurrence common despite maximal standard therapy. Cerebral blood volume (CBV) from DSC-MRI quantifies tumor vascularity and helps distinguish true progression from pseudoprogression. Standardized relative CBV (sRCBV) thresholds validated by Anil et al., 2024, against image-localized histopathology differentiate recurrence (sRCBV >1) and indicate viable tumor presence (sRCBV >1.37, ~88% probability). This study evaluates whether the fraction of high-sRCBV voxels serves as an early indicator of recurrence, identifying thresholds linked to recurrence onset.
Methods: Fifteen post-therapy MRI time points from six IDH1-wild-type GBM patients (five female, one male; age 49-81 years) were retrospectively analyzed under IRB approval (2012-0441, PI Dr. Puduvalli). All received Stupp-protocol chemoradiotherapy (VMAT 6000cGy/30 fractions) and temozolomide, followed by surveillance until recurrence. Only DSC-MRI scans acquired after chemoradiation were included. All studies used the consensus single-dose, low-flip-angle (30°) protocol without preload. Standardized RCBV maps were generated using IB Neuro with BSW leakage correction and registered to contrast-enhanced T1-weighted (T1CE) images. Tumor masks were defined semi-automatically on T1CE. The fraction of high-sRCBV voxels (fH) was calculated as voxels with sRCBV >1.37 divided by total tumor voxels. The interval between each scan and radiologic recurrence (Δt) was recorded. Associations between fH and Δt were tested using quadratic regression, ROC analysis for early (≤120 days) vs late recurrence, Youden-index thresholding, and Cox proportional-hazards modeling.
Results: Across 15 time points, fH showed a borderline-significant inverse relationship with time to recurrence (R² =0.35, p = 0.073). ROC analysis yielded AUC = 0.74 (95% CI 0.44-0.96) with an optimal threshold of fH ≈ 0.39. Higher fH correlated with increased recurrence hazard (beta = 4.26, HR = 70.47 per +1.0 fH). Tumors with ~27% high-sRCBV voxels corresponded to a 120-day median recurrence-free survival.
Conclusion: Preliminary findings indicate that the fraction of high-sRCBV voxels within the enhancing tumor may serve as a quantitative indicator of glioblastoma recurrence risk. Although limited by small sample size and retrospective design, consistent trends suggest that higher vascular fractions correspond to shorter recurrence intervals and increased risk of regrowth. Ongoing work is expanding this analysis to a larger, multi-timepoint cohort to improve statistical power and define clinical thresholds for integration into CBV visualization tools.
利益披露 Disclosure
M. Servati, None..
Z. Fu, None..
A. Anil, None..
C. Mokashi, None..
N. K. Majd, None..
V. K. Puduvalli, None..
C. Quarles, None.