Uncertainty Estimation in Classification of MGNT Using Radiogenomics for Glioblastoma Patients

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework for MGMT classification with uncertainty analysis utilizing imaging features extracted from multimodal magnetic resonance imaging (mMRI). The imaging features include conventional texture, volumetric, and sophisticated fractal, and multi-resolution fractal texture features. The proposed method is evaluated with publicly available BraTS-TCIA-GBM pre-operative scans and TCGA datasets with 114 patients. The experiment with 10-fold cross-validation suggests that the fractal and multi-resolution fractal texture features offer an improved prediction of MGMT status. The uncertainty analysis using an ensemble of Stochastic Gradient Langevin Boosting models along with multi-resolution fractal features offers an accuracy of 71.74% and area under the curve of 0.76. Finally, analysis shows that our proposed method with uncertainty analysis offers improved predictive performance when compared with different well-known methods in the literature

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas Using MR Imaging Features

Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out cross-validation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83 ± 0.04, 0.84 ± 0.03, 0.80 ± 0.04, 0.70 ± 0.09, and 0.82 ±0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification

Radiomic analysis to predict outcome in recurrent glioblastoma based on multi-center MR imaging from the prospective DIRECTOR trial

Background: Based on promising results from radiomic approaches to predict O6-methylguanine DNA methyltransferase promoter methylation status (MGMT status) and clinical outcome in patients with newly diagnosed glioblastoma, the current study aimed to evaluate radiomics in recurrent glioblastoma patients. Methods: Pre-treatment MR-imaging data of 69 patients enrolled into the DIRECTOR trial in recurrent glioblastoma served as a training cohort, and 49 independent patients formed an external validation cohort. Contrast-enhancing tumor and peritumoral volumes were segmented on MR images. 180 radiomic features were extracted after application of two MR intensity normalization techniques: fixed number of bins and linear rescaling. Radiomic feature selection was performed via principal component analysis, and multivariable models were trained to predict MGMT status, progression-free survival from first salvage therapy, referred to herein as PFS2, and overall survival (OS). The prognostic power of models was quantified with concordance index (CI) for survival data and area under receiver operating characteristic curve (AUC) for the MGMT status. Results: We established and validated a radiomic model to predict MGMT status using linear intensity interpolation and considering features extracted from gadolinium-enhanced T1-weighted MRI (training AUC = 0.670, validation AUC = 0.673). Additionally, models predicting PFS2 and OS were found for the training cohort but were not confirmed in our validation cohort. Conclusions: A radiomic model for prediction of MGMT promoter methylation status from tumor texture features in patients with recurrent glioblastoma was successfully established, providing a non-invasive approach to anticipate patient's response to chemotherapy if biopsy cannot be performed. The radiomic approach to predict PFS2 and OS failed. Keywords: DIRECTOR trial; MGMT status; linear intensity interpolation; radiomics; recurrent glioblastoma

Model-Based Approach for Diffuse Glioma Classification, Grading, and Patient Survival Prediction

The work in this dissertation proposes model-based approaches for molecular mutations classification of gliomas, grading based on radiomics features and genomics, and prediction of diffuse gliomas clinical outcome in overall patient survival. Diffuse gliomas are types of Central Nervous System (CNS) brain tumors that account for 25.5% of primary brain and CNS tumors and originate from the supportive glial cells. In the 2016 World Health Organization’s (WHO) criteria for CNS brain tumor, a major reclassification of the diffuse gliomas is presented based on gliomas molecular mutations and the growth behavior. Currently, the status of molecular mutations is determined by obtaining viable regions of tumor tissue samples. However, an increasing need to non-invasively analyze the clinical outcome of tumors requires careful modeling and co-analysis of radiomics (i.e., imaging features) and genomics (molecular and proteomics features). The variances in diffuse Lower-grade gliomas (LGG), which are demonstrated by their heterogeneity, can be exemplified by radiographic imaging features (i.e., radiomics). Therefore, radiomics may be suggested as a crucial non-invasive marker in the tumor diagnosis and prognosis. Consequently, we examine radiomics extracted from the multi-resolution fractal representations of the tumor in classifying the molecular mutations of diffuse LGG non-invasively. The proposed radiomics in the decision-tree-based ensemble machine learning molecular prediction model confirm the efficacy of these fractal features in glioma prediction. Furthermore, this dissertation proposes a novel non-invasive statistical model to classify and predict LGG molecular mutations based on radiomics and count-based genomics data. The performance results of the proposed statistical model indicate that fusing radiomics to count-based genomics improves the performance of mutations prediction. Furthermore, the radiomics-based glioblastoma survival prediction framework is proposed in this work. The survival prediction framework includes two survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) challenges in 2017 and 2018. The first survival prediction pipeline offered the best overall performance in the 2017 Challenge, and the second survival prediction pipeline offered the best performance using the validation dataset. In summary, in this work, we develop non-invasive computational and statistical models based on radiomics and genomics to investigate overall survival, tumor progression, and the molecular classification in diffuse gliomas. The methods discussed in our study are important steps towards a non-invasive approach to diffuse brain tumor classification, grading, and patient survival prediction that may be recommended prior to invasive tissue sampling in a clinical setting

Topographical Mapping of 436 Newly Diagnosed IDH Wildtype Glioblastoma With vs. Without MGMT Promoter Methylation

Introduction: O6-methylguanine-methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase (IDH) mutation status are important prognostic factors for patients with glioblastoma. There are conflicting reports about a differential topographical distribution of glioblastoma with vs. without MGMT promoter methylation, possibly caused by molecular heterogeneity in glioblastoma populations. We initiated this study to re-evaluate the topographical distribution of glioblastoma with vs. without MGMT promoter methylation in light of the updated WHO 2016 classification. Methods: Preoperative T2-weighted/FLAIR and postcontrast T1-weighted MRI scans of patients aged 18 year or older with IDH wildtype glioblastoma were collected. Tumors were semi-automatically segmented, and the topographical distribution between glioblastoma with vs. without MGMT promoter methylation was visualized using frequency heatmaps. Then, voxel-wise differences were analyzed using permutation testing with Threshold Free Cluster Enhancement. Results: Four hundred thirty-six IDH wildtype glioblastoma patients were included; 211 with and 225 without MGMT promoter methylation. Visual examination suggested that when compared with MGMT unmethylated glioblastoma, MGMT methylated glioblastoma were more frequently located near bifrontal and left occipital periventricular area and less frequently near the right occipital periventricular area. Statistical analyses, however, showed no significant difference in topographical distribution between MGMT methylated vs. MGMT unmethylated glioblastoma. Conclusions: This study re-evaluated the

Characterising Heterogeneity of Glioblastoma using Multi-parametric Magnetic Resonance Imaging

A better understanding of tumour heterogeneity is central for accurate diagnosis, targeted therapy and personalised treatment of glioblastoma patients. This thesis aims to investigate whether pre-operative multi-parametric magnetic resonance imaging (MRI) can provide a useful tool for evaluating inter-tumoural and intra-tumoural heterogeneity of glioblastoma. For this purpose, we explored: 1) the utilities of habitat imaging in combining multi-parametric MRI for identifying invasive sub-regions (I & II); 2) the significance of integrating multi-parametric MRI, and extracting modality inter-dependence for patient stratification (III & IV); 3) the value of advanced physiological MRI and radiomics approach in predicting epigenetic phenotypes (V). The following observations were made: I. Using a joint histogram analysis method, habitats with different diffusivity patterns were identified. A non-enhancing sub-region with decreased isotropic diffusion and increased anisotropic diffusion was associated with progression-free survival (PFS, hazard ratio [HR] = 1.08, P < 0.001) and overall survival (OS, HR = 1.36, P < 0.001) in multivariate models. II. Using a thresholding method, two low perfusion compartments were identified, which displayed hypoxic and pro-inflammatory microenvironment. Higher lactate in the low perfusion compartment with restricted diffusion was associated with a worse survival (PFS: HR = 2.995, P = 0.047; OS: HR = 4.974, P = 0.005). III. Using an unsupervised multi-view feature selection and late integration method, two patient subgroups were identified, which demonstrated distinct OS (P = 0.007) and PFS (P < 0.001). Features selected by this approach showed significantly incremental prognostic value for 12-month OS (P = 0.049) and PFS (P = 0.022) than clinical factors. IV. Using a method of unsupervised clustering via copula transform and discrete feature extraction, three patient subgroups were identified. The subtype demonstrating high inter-dependency of diffusion and perfusion displayed higher lactate than the other two subtypes (P = 0.016 and P = 0.044, respectively). Both subtypes of low and high inter-dependency showed worse PFS compared to the intermediate subtype (P = 0.046 and P = 0.009, respectively). V. Using a radiomics approach, advanced physiological images showed better performance than structural images for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status. For predicting 12-month PFS, the model of radiomic features and clinical factors outperformed the model of MGMT methylation and clinical factors (P = 0.010). In summary, pre-operative multi-parametric MRI shows potential for the non-invasive evaluation of glioblastoma heterogeneity, which could provide crucial information for patient care.The Cambridge Trust and China Scholarship Council ; Clare College; the British Neuro-Oncology Society; the EG Fearnsides Trust; the International Society for Magnetic Resonance in Medicin

Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis

PURPOSE:We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients.METHODS:A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis.RESULTS:A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%–90%) and 84% (95% CI 78%–88%) in the training cohort (n=15) and 84% (95% CI 70%–92%) and 78% (95% CI 63%–88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88–0.93) in the training cohort and 0.88 (95% CI 0.85–0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance.CONCLUSION:This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods

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