혈관신생 억제제로 치료한 재발성 교모세포종 환자의 혈관 크기와 관류 영상을 이용한 혈관 서식지를 통한 예후 예측: 전향적. 혈관신생 억제제로 치료한 재발성 교모세포종 환자의 혈관 크기와 관류 영상을 이용한 혈관 서식지를 통한 예후 예측: 전향적. 연구 제목: 혈관신생 억제제로 치료된 재발성 교모세포종 환자에서 혈관 크기 및 관류 영상을 이용한 혈관 서식지를 통한 예후 예측: 전향적 연구의 검증 배경: 혈관신생 억제제는 모든 재발성 교모세포종 환자에게 효과적입니다. 혈관신생에 대한 치료 반응을 평가하기 위한 방사선학적 지표. 현재 억제제를 사용할 수 있습니다.
따라서 혈관신생 억제제로 치료받은 재발성 교모세포종 환자의 경우. 관류 및 혈관 크기 정보를 사용하는 혈관 서식지를 개발하고 종양 진행 시간을 예측하기 위해 검증했습니다. 방법: 이 연구는 혈관 신생 억제제 치료를 시작하기 전에 동적 대비 민감도와 혈관 구조 영상 검사를 받은 재발성 교모세포종 환자 69명을 대상으로 수행되었습니다.
혈관 서식지에는 혈관 크기 지수(VSI)와 상대 뇌혈액량(rCBV) 데이터가 사용되었습니다. 또한, 우리는 전향적으로 선택된 환자 그룹에서 혈관 서식지를 사용하여 치료 반응 그룹을 계층화하려고 시도했습니다(ClinicalTrials.gov 식별자; NCT04143425). 혈관 서식지는 전향적 환자 집단에서 반응자와 비반응자로 분류되었습니다(P = 0.059).
결론: 관류 및 혈관 크기 정보를 사용하여 혈관 서식지는 혈관 신생 억제제로 치료된 재발성 교모세포종 환자와 전향적인 환자 코호트에서 분류된 치료 반응자에서 종양 진행까지의 시간을 예측했습니다.
Study population
MRI protocol and image processing
For simultaneous GRE-SE DSC perfusion, a dynamic bolus was administered as a standard dose of 0.1 mmol/kg gadoterate meglumine (Dotarem; Guerbet) delivered at a rate of 4 ml/s by an MRI-compatible power injector (Spectris; Medrad). The bolus of contrast material was followed by a 20 ml bolus of saline, injected at the same rate.
Definition of rCBV and vessel size
FLAIR data (non-enhancing tumor) using a 3D method based on UNet. https://github.com/MIC-DKFZ/nnUNet) 16 from version 1.1 of the PyTorch package in Python 3.7 (www.python.org).
Vascular habitats
The region of interest included solid tumor consisting of FLAIR hyperintensity and contrast-enhancing lesion, and necrotic portion was excluded. The 3D CE-T1WI and FLAIR images were co-registered to the rCBV and vessel size index maps using rigid. FLAIR = fluid-attenuated inversion recovery image, CNN = convolutional neural network, VSI = vessel size index, rCBV = relative cerebral blood volume, TTP = time to progression.
Response assessment and time to progression
Separately, the pattern of progression was classified by two neuroradiologists in consensus (J.E.P. and H.S.K. with 6 and 21 years of experience in neuro-oncology imaging, respectively) at the time of. Briefly, there were three patterns of progression: (a) locally enhancing progression (focus of contrast enhancement at or within 3 cm of the primary site), (b) diffuse non-enhancing progression (locally enhancing tumor remains stable, but an area of abnormal FLAIR hyperintensity beyond 3 cm from the primary site appears) and (c) distant progression (new focus of contrast enhancement or area of abnormal FLAIR hyperintensity appears beyond 3 cm from the primary site with intervening normal white matter). All results are reported as mean with standard deviation or range or median with interquartile range for continuous variables and as frequencies or percentages for categorical variables.
Differences in clinicopathological factors between the derivation and validation cohorts were assessed using the chi-square test for discrete variables and Student's t -test for continuous variables. Association of vascular habitats with TTP: Univariate analysis was performed to analyze associations of imaging parameters or vascular habitats with TTP using Cox proportional hazard regression analysis. A concordance probability index (C-index) of the vascular habitat, intertwining high and medium angiogenic habitats, was calculated and compared with that for rCBV alone.
Vascular habitats and progression type: Voxel counts of significant habitats identified in univariate Cox regression were compared. Stratification of patients using vascular habitat: For significant habitat identified in univariate Cox regression, the optimal cutoff for stratifying high- and low-risk groups was estimated using maxstat in R with 10-fold cross-validation, which provided an unbiased within-sample prediction of 20 .Using this cutoff from the exploratory analysis, a habitat risk score was developed for risk stratification of patients in the validation set; each predictor was assigned a discrete score of 1 if it was higher than the cutoff and 0 if it was lower.
Patient demographics
Vascular habitats associated with TTP in the derivation set
Note: The hazard ratio for vascular habitats indicates the relative change in hazard ratio that a 1-unit (30,000 voxels) or 1% increase in each vascular habit brings. A 49-year-old man with recurrent glioblastoma (IDH wild-type) showed a high number of voxels of high and intermediate angiogenic habitats on the MRI scan before initiation of bevacizumab treatment and at time. A 64-year-old woman with relapsed glioblastoma (IDH wild type) showed a small number of voxels of high and intermediate angiogenic habitats on MRI before initiation of bevacizumab treatment and at time.
Vascular habitats and progression type
Stratification using vascular habitats
Risk stratification based on high angiogenic habitat predicts treatment resistance to bevacizumab therapy in the derivation set. Risk stratification based on intermediate angiogenic habitat predicted treatment resistance to bevacizumab therapy in the derivation set.
Validation in a prospective clinical cohort
In this study, we demonstrated that in recurrent glioblastomas treated with anti-angiogenic therapy, TTP could be predicted by analysis of perfusion-derived vascular habitat and vessel size. Not only high angiogenic habitat with high rCBV and vessel size, but intermediate angiogenic habitat with intermediate rCBV and vessel size were significantly associated with treatment resistance and shorter TTP. This was validated in a prospective clinical cohort, and vascular habitat analysis revealed a trend for predicting treatment resistance to anti-angiogenic therapy.
Previous studies have investigated perfusion parameters as a potential prognostic imaging biomarker of antiangiogenic therapy in recurrent glioblastoma 7 - 9 . In addition, voxel-based quantitative image features derived from rCBV were clustered to identify prognostic patient subgroups for survival and response to anti-angiogenic therapy 9. Our study was an exploratory analysis of perfusion- and vessel-size-derived vascular habitat, and high angiogenic vascular habitat was most significantly associated with a shorter TTP (HR CI.
Vascular habitat incorporated information on perfusion parameter from DSC MRI and vessel size from VAI, allowing for a more sophisticated depiction of complex tumor vascularity in recurrent glioblastoma 21. By grouping voxels based on rCBV and vessel size into high, intermediate, and low angiogenic vascular habitats , vascular habitats better reflect the complex interplay of vascular mimicry, co-option and vasculogenesis in the tumor vasculature 11,22,23. Our study showed that high and medium angiogenic vascular habitats, characterized by large and intermediate vessel sizes, are important predictors of treatment resistance to antiangiogenic therapy.
Higher proportion of vessels with large and intermediate size before initiation of anti-angiogenic therapy represents tissue hypoxia and impaired vascular function, and was able to predict treatment resistance to anti-angiogenic therapy. Second, vascular habitats constructed based on rCBV and vessel size must be pathologically confirmed for their spatial localization for future targeted therapy. In conclusion, perfusion and vessel size-derived vascular habitat analysis predicted TTP in recurrent glioblastoma treated with anti-.
High angiogenic and intermediate angiogenic habitats may better reflect the complex tumor vasculature of recurrent glioblastomas, aiding patient stratification for anti-angiogenic therapy. Vessel size and perfusion-derived vascular habitat refine prediction of resistance to bevacizumab in recurrent glioblastomas: validation in a prospective cohort. Background: Antiangiogenic therapy may not benefit all patients with recurrent glioblastomas, and imaging biomarkers that predict treatment response to antiangiogenic therapy are currently limited.
We aimed to develop and validate vascular habitats based on perfusion and vessel size to predict time to progression (TTP) in patients with recurrent glioblastomas treated with bevacizumab. Vascular habitats were constructed using vessel size index (VSI) and relative cerebral blood volume (rCBV).
