교모세포종 환자에서의 역동적 조영증강 자기공명영상의 라디오믹스 점수를 이용한 IDH 돌연변이 상태 독립적 고위험군 예측 연구

학위논문(박사) -- 서울대학교대학원 : 의과대학 의학과, 2021.8. 최승홍.Objective To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for the prediction high-risk group in glioblastoma patients. Materials and Methods One hundred fifty patients (92 men (61.3%); mean age, 60.5 ± 13.5 years) with glioblastoma who underwent a preoperative MRI were enrolled in the study. Six hundred forty-two radiomic features were extracted from Ktrans, Vp and Ve maps of DCE MRI, where regions of interest were based on both non-enhancing T2 hyperintense areas and T1-weighted contrast-enhancing areas. Using feature selection algorithms, significant radiomic features were selected. Subsequently, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105) and validated in the validation set (n = 45) by investigating the difference in prognosis between “radiomics risk score” groups. Finally, a multivariate Cox-regression for 1-year progression-free survival was performed using the radiomics risk score and clinical variables. Results Sixteen radiomic features obtained from non-enhancing T2 hyperintense areas were selected out of 642 features. The radiomics risk score stratified high- and low-risk groups in both the discovery and validation set in log rank test (both p < 0.001). The radiomics risk score increased the risk of progression in glioblastoma patients, independently of IDH-mutation status (HR = 3.56, p = 0.004; HR = 0.34, p = 0.022, respectively). Conclusion We developed and assessed the “radiomics risk score” from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of progression at 1 year in glioblastoma patients, which was independent of IDH mutational status.목적: 본 연구의 목적은 교모세포종 환자의 고위험군을 예측하기 위해 서 역동적 조영증강 자기공명영상 기반의 라디오믹스 점수를 개발하는 것이다. 방법: 본 연구에는 수술 전 DCE MRI를 시행받은 교모세포종 환자 150 명 (남성 92 명 (61.3 %), 평균 연령 60.5 ± 13.5 세)이 포함되었다. DCE MRI의 Ktrans, Vp 및 Ve 지도 각각에서 640 개의 radiomics 지표가 추출되었으며, 이를 위하여 종양 부위는 조영증강 T1WI 와 FLAIR 영상을 이용하여 segmentation 하였다. 지표 선택 알고리즘을 사용하여 642 개 지표 중 예후 예측에 특이적인 radiomics 지표를 선택했다. 다음으로, discovery set (n = 105)에서 선택된 지표의 가중치 조합을 사용하여 radiomics risk score를 개발하고 radiomics risk score에 기반하여 고위험 및 저위험 그룹 간의 예후 차이를 조사하여 validation set (n = 45)에서 검증하였다. 마지막으로, 1년 무진행 생존율 분석을 위한 다변량 Cox- 회귀 기법을 사용하여 임상 변수와 함께 radiomics risk score의 예후 예측력을 평가하였다. 결과: 비조영증강 T2 고신호 영역에서 얻은 16 가지 radiomics 지표가 642개 지표 중 선택되었다. 이 두가지 지표를 이용하여 Radiomics risk score를 만들었으며, 이를 이용하였을 때, 로그 순위 테스트에서 discovery 및 test set에서 고위험군과 저 위험군을 유의미하게 구분할 수 있었다 (p<0.001). Radiomics risk score는 isocitrate dehydrogenase (IDH) 돌연변이와 독립적인 예후 예측인자였다 (Hazard ratio (HR) = 3.56 (p = 0.004)). 결론: 교모세포종 환자에서 1년 무진행 생존율 예측에 있어 비조영증강 T2 고신호 영역에서의 DCE MRI 기반의 radiomics risk score 가 우수한 성적을 보였으며, 향후 이를 이용한 임상 이용 가능성이 기대된다.Introduction 4 Materials and methods 14 Results 23 Discussion 27 References 34 Tables 52 Figures 58 Supplementary materials 71 Abstract in Korean 90박

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD+r training were evaluated at the \emph{argmin} of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized root-mean square error, the concordance correlation coefficient (CCC), and the structural similarity index (SSIM). The CD+r reconstructions, chosen using the stopping condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs (p=0.95) across R and that were significantly higher than the L1 SSIMs (p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps, respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD+r. We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.Comment: 45 pages, 7 figures, 2 Tables, supplementary material included (10 figures, 4 tables

Measurement of subtle blood-brain barrier disruption in cerebral small vessel disease using dynamic contrast-enhanced magnetic resonance imaging

Cerebral small vessel disease (SVD) is a common cause of strokes and dementia. The pathogenesis of SVD is poorly understood, but imaging and biochemical investigations suggest that subtle blood-brain barrier (BBB) leakage may contribute to tissue damage. The most widely-used imaging method for assessing BBB integrity and other microvascular properties is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI has primarily been applied in situations where contrast uptake in tissue is typically large and rapid (e.g. neuro-oncology); the optimal approach for quantifying BBB integrity in diseases where the BBB remains largely intact and the reliability of resulting measurements is unclear. The main purpose of this thesis was to assess and improve the reliability of quantitative assessment of subtle BBB disruption, in order to illuminate its potential role in cerebral SVD. Firstly, a systematic literature review was performed in order to provide an overview of DCE-MRI methods in the brain. This review found large variations in MRI procedures and data analysis methods, resulting in widely varying estimates of tracer kinetic parameters. Secondly, this thesis focused on the analysis of DCE-MRI data acquired in an on-site clinical study of mild stroke patients. After performing basic DCE-MRI processing (e.g. selection of a vascular input function), this work aimed to determine the tracer kinetic modelling approach most suitable for assessing subtle BBB disruption in this cohort. Using data-driven model selection and computer simulations, the Patlak model was found to provide accurate estimates of blood plasma volume and low-level BBB leakage. Thirdly, this thesis aimed to investigate two potential pitfalls in the quantification of subtle BBB disruption. Contrast-free measurements in healthy volunteers revealed that a signal drift of approximately 0.1 %/min occurs during the DCE-MRI acquisition; computer simulations showed that this drift introduces significant systematic errors when estimating low-level tracer kinetic parameters. Furthermore, tracer kinetic analysis was performed in an external patient cohort in order to investigate the inter-study comparability of DCE-MRI measurements. Due to the nature of the acquisition protocol it proved difficult to obtain reliable estimates of BBB leakage, highlighting the importance of study design. Lastly, this thesis examined the relationship between quantitative MRI parameters and clinical measurements in cerebral SVD, with a focus on the estimates of blood volume and BBB leakage obtained in the internal SVD patient cohort. This work did not provide evidence that BBB leakage in normal-appearing tissue increases with SVD burden or predicts disease progression; however, increased BBB leakage was found in white matter hyperintensities. Furthermore, this work raises the possibility of a role for blood plasma volume and dietary salt intake in cerebral SVD. The work described in this thesis has demonstrated that it is possible to estimate subtle BBB disruption using DCE-MRI, provided that the measurement and data analysis strategies are carefully optimised. However, absolute values of tracer kinetic parameters should be interpreted with caution, particularly when making comparisons between studies, and sources of error and their influence should be estimated where possible. The exact roles of BBB breakdown and other microvascular changes in SVD pathology remain to be defined; however, the work presented in this thesis contributes further insights and, together with technical advances, will facilitate improved study design in the future

Optimisation and applications of chemical exchange saturation transfer MRI techniques for cancer imaging on clinical scanners

Chemical Exchange Saturation Transfer (CEST) is receiving growing attention in the field of cancer imaging due to its ability to provide molecular information with good spatial resolution within clinically acceptable scan-times. Translation to the clinic requires a solid evidence-base demonstrating the clinical utility and a range of anatomical regions and pathologies have already been studied. These have traditionally been evaluated in terms of asymmetry-based metrics, the most common of which is the magnetization transfer ratio. However, alternative and potentially more informative metrics are also possible. Investigation of fitting metrics has not been reported at clinical field strengths and there is currently no standard approach for optimising the acquisition and post-processing protocols. The work described in this thesis focuses on the practical development and implementation of z-spectrum fitting methods in vivo at 3.0T. After the technical and clinical introductory chapters, chapter three describes the evaluation and comparison of the use of two different lineshapes for modelling the water direct saturation effect. Chapter four describes the optimization of an acquisition and post-processing protocol suitable for CEST imaging of the human prostate at 3.0T. The repeatability of the method is evaluated and in chapter five the optimized protocol is applied in two cancer patients. In chapter six a method is proposed for identification of CEST and NOE resonances in z- spectra acquired at low-field strengths. Chapter seven describes a pre-clinical study of healthy rat brains at 9.4T highlighting the need to consider the interplay between CEST and perfusion effects. In chapter eight the effects of gadolinium administration on CEST signal and contrast in glioma patients is investigated. I hope that the work described herein and the contributions stemming from it will be of some practical benefit to scientists and clinicians interested in exploring the future potential of the growing field of CEST imaging

Dynamic Contrast-Enhanced MRI in the Study of Brain Tumors. Comparison Between the Extended Tofts-Kety Model and a Phenomenological Universalities (PUN) Algorithm

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a well-established technique for studying blood\ue2\u80\u93brain barrier (BBB) permeability that allows measurements to be made for a wide range of brain pathologies, including multiple sclerosis and brain tumors (BT). This latter application is particularly interesting, because high-grade gliomas are characterized by increased microvascular permeability and a loss of BBB function due to the structural abnormalities of the endothelial layer. In this study, we compared the extended Tofts-Kety (ETK) model and an extended derivate class from phenomenological universalities called EU1 in 30 adult patients with different BT grades. A total of 75 regions of interest were manually drawn on the MRI and subsequently analyzed using the ETK and EU1 algorithms. Significant linear correlations were found among the parameters obtained by these two algorithms. The means of R2 obtained using ETK and EU1 models for high-grade tumors were 0.81 and 0.91, while those for low-grade tumors were 0.82 and 0.85, respectively; therefore, these two models are equivalent. In conclusion, we can confirm that the application of the EU1 model to the DCE-MRI experimental data might be a useful alternative to pharmacokinetic models in the study of BT, because the analytic results can be generated more quickly and easily than with the ETK model