Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case

Korelace mezi kvantitativními in vivo MR parametry v různých tkáních (MR spektroskopické zobrazování, MR difúzometrie, MR relaxometrie aj.)

Koregistrace MR spektroskopických (SI), difúzních (DTI) a relaxačních obrazů a jejich následné korelace založené na kvantitativním zpracování obrazu bod po bodu mají potenciál rozlišit patologické stavy a zdravou tkáň, a pomoci tak stanovit rozsah patologie. Použití této metody v klinické praxi bylo vyzkoušeno u pacientů s tumorem mozku a s temporální epilepsií (TLE). 30 pacientů s nově diagnostikovanou lézí, 22 pacientů s léčeným tumorem (diagnóza stanovena na základě histologie či radiologickým sledováním), 20 pacientů s TLE a 59 zdravých dobrovolníků bylo vyšetřeno v magnetickém poli 3T. Vyšetřovací protokol obsahoval T2-vážené MR obrazy, SI, DTI a T2 relaxometrii. Korelace byly analyzovány programem CORIMA umožňující automatickou identifikaci oblasti zdravé tkáně dle kontrolních dat. Mozkové léze: Specifické tvary korelací metabolitů, MD a T2 relaxačních časů (T2) byly nalezeny pro danou lokalizaci léze i pro daný typ tumoru. Tyto korelace vznikají v důsledku zastoupení různých typů tkání ve zkoumané oblasti. Korelační grafy rekurentních tumorů vykazovaly charakteristiku stejnou jako u tumorů neléčených, ale se změněnými hodnotami parametrů vlivem terapie. Metabolické hodnoty nekorelovaly s MD nebo T2 v případě radiační nekrózy. TLE: V hipokampu v předozadním směru se MR parametry měnily...Coregistration of MR spectroscopic (SI), diffusion (DTI), relaxation images and their subsequent correlations based on pixel-by-pixel quantitative analysis have the potential to distinguish between pathological states and healthy tissue and therefore can help assessing brain pathology extent. Patients with brain tumours and temporal lobe epilepsy (TLE) were involved in the study to validate the use of this method in clinical practice. 30 patients with a new diagnosed brain lesion, 22 patients with a treated tumour (diagnosis assessed by histology or by radiological follow-up), 20 TLE patients and 59 healthy subjects were examined on a 3T system. The measurement protocol consisted of T2-weighted MR images, SI, DTI and T2 relaxometry. Correlations were analysed with the CORIMA programme with automatic identification of pixels in the normal tissue according to control data. Brain lesions: Specific correlation patterns between metabolites, MD and T2 relaxation times (T2) were found for a given lesion localisation and tumour type. The patterns depend on different tissue states involved in the examined area. Recurrent tumours exhibited the same patterns as untreated ones but with changed parameter values caused by therapy. Metabolic values did not correlate with MD and T2 in radiation necrosis. TLE: MR...Institute for Clinical and Experimental MedicineInstitut klinické a experimentální medicínyFirst Faculty of Medicine1. lékařská fakult

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

Advanced Neuroimaging in Brain Tumors : Diffusion, Spectroscopy, Perfusion and Permeability MR imaging for the evaluation of tumor characterization and surgical treatment planning.

Advanced Neuroimaging in Brain Tumors: Diffusion, Spectroscopy, Perfusion and Permeability for the evaluation of tumor characterization and surgical treatment planning. The current standard of neuroimaging for brain tumor evaluation is anatomy-based MRI. Unfortunately, MRI does not fully reflect the complicated biology of infiltrative glioma, and has a limited capacity to differentiate a high-grade glioma (HGG) from a single brain metastasis. Grading of gliomas is important for the determination of appropriate treatment strategies and in the assessment of prognosis. It is clinically important to distinguish HGG from a single brain metastasis, because medical staging, surgical planning, and therapeutic decisions are different for each tumor type. The basis for this thesis was 208 patients admitted at Oslo University Hospital-Ullevål with the diagnosis of brain tumor between 2006 and 2010. The aim of this thesis was to evaluate in terms of diagnostic examination performance in the clinical decision-making process the use of advanced MRI techniques, namely, diffusion-weighted imaging (DWI), magnetic resonance spectroscopic imaging (MRSI), and T2*-weighted first pass dynamic susceptibility contrast-enhanced perfusion MRI (DSC MRI) in the diagnosis and preoperative planning of brain tumors, with focus in the grading and characterization of gliomas, as well as in the assessment of the peri-enhancing region aiming to demonstrate tumor-infiltration and tumor-free edema. In this thesis, we have demonstrated that MRSI and DSC MRI can be helpful to discriminate HGG from solitary metastases, supporting the hypothesis that these advanced MRI techniques can detect infiltration of tumor cells in the peri-enhancing region. We have demonstrated that combining DWI and MRSI increases the accuracy in the determination of glioma grade. We identified differences among all glial tumor grades for the parameters cerebral blood volume (rCBV) and microvascular leakage (MVL) derived from DSC MRI. Our correlation analysis indicate that MVL, rCBV, and cerebral blood flow (rCBF) may be related to different aspects of tumor angiogeneseis

The added value of advanced multi-modal magnetic resonance imaging in the diagnosis and management of childhood cancer

Background: Magnetic Resonance Imaging (MRI) provides images with excellent structural detail, but imparts limited information about the characteristics of paediatric tumours. MRI based functional imaging probes tissue properties to provide clinically important information about metabolites, structure and cellularity. Aim: To determine the added value of advanced MRI, particularly diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS), in non-invasive diagnosis and management of paediatric tumours, and facilitate integration into clinical practice. Methods: Children with newly diagnosed body and brain tumours were imaged using multi b-value DWI and MRS respectively. Imaging data was used to develop a clinical decision support system for presentation to clinicians. Added diagnostic and clinical value of additional information was ascertained through retrospective and prospective evaluation. Results: Quantitative DWI confers added diagnostic accuracy beyond conventional MRI, allowing discrimination of benign from malignant body tumours through morphological and quantifiably significant differences in Apparent Diffusion Coefficient (ADC) histograms. Chemotherapeutic response is reflected through visually apparent and quantifiably significant histogram changes. Review of MRS improves diagnostic accuracy of paediatric brain tumours, adding therapeutic value through avoiding biopsy of indolent lesions, aiding tumour characterisation, and allowing earlier treatment planning and clinical decision-making. Conclusion: Advanced MRI adds value to non-invasive diagnosis and management of paediatric tumours in a real-time clinical setting. Presentation of complex information through a decision support system makes it accessible and comprehensible for clinicians, overcoming barriers precluding clinical use. Multicentre assessment is needed to promote integration of these techniques into the clinical workflow to improve care of children with cancer

Texture analysis of multimodal magnetic resonance images in support of diagnostic classification of childhood brain tumours

Primary brain tumours are recognised as the most common form of solid tumours in children, with pilocytic astrocytoma, medulloblastoma and ependymoma being found most frequently. Despite their high mortality rate, early detection can be facilitated through the use of Magnetic Resonance Imaging (MRI), which is the preferred scanning technique for paediatric patients. MRI offers a variety of imaging sequences through structural and functional imaging, as well as providing complementary tissue information. However visual examination of MR images provides limited ability to characterise distinct histological types of brain tumours. In order to improve diagnostic classification, we explore the use of a computer-aided system based on texture analysis (TA) methods. TA has been applied on conventional MRI but has been less commonly studied on diffusion MRI of brain-related pathology. Furthermore, the combination of textural features derived from both imaging approaches has not yet been widely studied. In this thesis, the aim of the research is to investigate TA based on multi-centre multimodal MRI, in order to provide more comprehensive information and develop an automated processing framework for the classification of childhood brain tumours

Advanced imaging and artificial intelligence for diagnostic and prognostic biomarkers in glioblastoma

Conventional magnetic resonance imaging (MRI) has a pivotal role in diagnosis and post-treatment management of glioblastoma, however it has limitations. This work investigates the use of advanced MRI techniques that assess the tumour microenvironment, and artificial intelligence (AI) techniques that compute quantitative features, as potential imaging biomarkers in key clinical issues faced by clinicians, through several retrospective studies. Results show that advanced multiparametric MRI is superior to current standard-of-care imaging for the diagnosis of glioblastoma, and in treatment response assessment. Results of AI techniques on pre-operative imaging show the ability to differentiate between glioblastoma and metastasis with an accuracy of 88.7%, prediction of overall survival with a high level of accuracy, and stratification of patients into high- and low-level groups of MGMT promoter methylation with accuracies between 45-67%. In the early post-treatment phase, AI analysis of imaging can distinguish between disease progression and pseudoprogression with an accuracy of 73.7%, compared to neuroradiologist accuracy of 32.9%. Integrating these techniques into routine clinical practice is essential to improve patient outcomes. Further work is required to validate advanced imaging and AI biomarkers, towards the longer-term goal of using these as clinical decision support tools, to benefit patients with glioblastoma and other brain tumours

Magnetic Resonance Imaging Studies of Angiogenesis and Stem Cell Implantations in Rodent Models of Cerebral Lesions

Molecular biology and stem cell research have had an immense impact on our understanding of neurological diseases, for which little or no therapeutic options exist today. Manipulation of the underlying disease-specific molecular and cellular events promises more efficient therapy. Angiogenesis, i.e. the regrowth of new vessels from an existing vascular network, has been identified as a key contributor for the progression of tumor and, more recently, for regeneration after stroke. Donation of stem cells has proved beneficial to treat cerebral lesions. However, before angiogenesis-targeted and stem cell therapies can safely be used in patients, underlying biological processes need to be better understood in animal models. Noninvasive imaging is essential in order to follow biological processes or stem cell fate in both space and time. We optimized steady state contrast enhanced magnetic resonance imaging (SSCE MRI) to monitor vascular changes in rodent models of tumor and stroke. A modification of mathematical modeling of MR signal from the vascular network allowed for the first time simultaneous measurements of relaxation time T2 and SSCE MRI derived blood volume, vessel size, and vessel density. Limitations of SSCE MRI in tissues with high blood volume and non-cylindrically shaped vessels were explored. SSCE MRI detected angiogenesis and response to anti-angiogenic treatment in two rodent tumor models. In both tumor models, reduction of blood volume in small vessels and a shift towards larger vessels was observed upon treatment. After stroke, decreased vessel density and increased vessel size was found, which was most pronounced one week after the infarct. This is in agreement with two initial, recently published clinical studies. Overall, very little signs of angiogenesis were found. Furthermore, superparamagnetic iron oxide (SPIO) labels were used to study neural stem cells (NSCs) in vivo with MRI. SPIO labeling revealed a decrease in volume of intracerebral grafts over 4 months, assessed by T2* weighted MRI. Since SPIO labels are challenging to quantify and their MR contrast can easily be confounded, we explored the potential of in vivo 19F MRI of 19F labeled NSCs. Hardware was developed for in vitro and in vivo 19F MRI. NSCs were labeled with little effect on cell function and in vivo detection limits were determined at ~10,000 cells within 1 h imaging time. A correction for the inhomogeneous magnetic field profile of surface coils was validated in vitro and applied for both sensitive and quantitative in vivo cell imaging. As external MRI labels do not provide information on NSC function we combined 19F MRI with bioluminescence imaging (BLI). The BLI signal allowed quantification of viable cells whereas 19F MRI provided graft location and density in 3D over 4 weeks both in the healthy and stroke brain. A massive decrease in number of viable cells was detected independent of the microenvironment. This indicates that functional recovery reported in many studies of NSC implantation after stroke, is rather due to release of factors by NSCs than direct tissue replacement. In light of these indirect effects, combination of the imaging methods developed in this dissertation with other functional and structural imaging methods is suggested in order to further elucidate interactions of NSCs with the vasculature