Radiomics analyses for outcome prediction in patients with locally advanced rectal cancer and glioblastoma multiforme using multimodal imaging data

Personalized treatment strategies for oncological patient management can improve outcomes of patient populations with heterogeneous treatment response. The implementation of such a concept requires the identification of biomarkers that can precisely predict treatment outcome. In the context of this thesis, we develop and validate biomarkers from multimodal imaging data for the outcome prediction after treatment in patients with locally advanced rectal cancer (LARC) and in patients with newly diagnosed glioblastoma multiforme (GBM), using conventional feature-based radiomics and deep-learning (DL) based radiomics. For LARC patients, we identify promising radiomics signatures combining computed tomography (CT) and T2-weighted (T2-w) magnetic resonance imaging (MRI) with clinical parameters to predict tumour response to neoadjuvant chemoradiotherapy (nCRT). Further, the analyses of externally available radiomics models for LARC reveal a lack of reproducibility and the need for standardization of the radiomics process. For patients with GBM, we use postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w MRI for the detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS). We show that DL models built on MET-PET have an improved diagnostic and prognostic value as compared to MRI

Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Purpose: Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. Materials and methods: One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. Results: Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values &lt; 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). Conclusion: ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.</p

Robustness of pet radiomics features: Impact of co-registration with mri

Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coeffi-cient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction

Robustness of PET Radiomics Features: Impact of Co-Registration with MRI

Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction

Radiomic Features to Predict Overall Survival Time for Patients with Glioblastoma Brain Tumors Based on Machine Learning and Deep Learning Methods

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for patients with Glioblastoma brain tumors from medical imaging and non-imaging data. This system is developed to enhance and speed-up the diagnosis process, as well as to increase understanding of the behavior of Glioblastoma brain tumors. The proposed OST prediction system is developed based on a classification process to categorize a GBM patient into one of the following three survival time groups: short-term (months), mid-term (10-15 months), and long-term (\u3e15 months). The Brain Tumor Segmentation challenge (BraTS) dataset is used to develop the automatic OST prediction system. This dataset consists of multimodal preoperative Magnetic Resonance Imaging (mpMRI) data, and clinical data. The training data is relatively small in size to train an accurate OST prediction model based on DL method. Therefore, traditional ML methods such as Support Vector Machine (SVM), Neural Network, K-Nearest Neighbor (KNN), Decision Tree (DT) were used to develop the OST prediction model for GBM patients. The main contributions in the perspective of ML field include: developing and evaluating five novel radiomic feature extraction methods to produce an automatic and reliable OST prediction system based on classification task. These methods are volumetric, shape, location, texture, histogram-based, and DL features. Some of these radiomic features can be extracted directly from MRI images, such as statistical texture features and histogram-based features. However, preprocessing methods are required to extract automatically other radiomic features from MRI images such as the volume, shape, and location information of the GBM brain tumors. Therefore, a three-dimension (3D) segmentation DL model based on modified U-Net architecture is developed to identify and localize the three glioma brain tumor subregions, peritumoral edematous/invaded tissue (ED), GD-enhancing tumor (ET), and the necrotic tumor core (NCR), in multi MRI scans. The segmentation results are used to calculate the volume, location and shape information of a GBM tumor. Two novel approaches based on volumetric, shape, and location information, are proposed and evaluated in this dissertation. To improve the performance of the OST prediction system, information fusion strategies based on data-fusion, features-fusion and decision-fusion are involved. The best prediction model was developed based on feature fusions and ensemble models using NN classifiers. The proposed OST prediction system achieved competitive results in the BraTS 2020 with accuracy 55.2% and 55.1% on the BraTS 2020 validation and test datasets, respectively. In sum, developing automatic CADiag systems based on robust features and ML methods, such as our developed OST prediction system, enhances the diagnosis process in terms of cost, accuracy, and time. Our OST prediction system was evaluated from the perspective of the ML field. In addition, preprocessing steps are essential to improve not only the quality of the features but also boost the performance of the prediction system. To test the effectiveness of our developed OST system in medical decisions, we suggest more evaluations from the perspective of biology and medical decisions, to be then involved in the diagnosis process as a fast, inexpensive and automatic diagnosis method. To improve the performance of our developed OST prediction system, we believe it is required to increase the size of the training data, involve multi-modal data, and/or provide any uncertain or missing information to the data (such as patients\u27 resection statuses, gender, etc.). The DL structure is able to extract numerous meaningful low-level and high-level radiomic features during the training process without any feature type nominations by researchers. We thus believe that DL methods could achieve better predictions than ML methods if large size and proper data is available

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