Feature selection in proton magnetic resonance spectroscopy data of brain tumors

In cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted toxic or aggressive treatments. Moreover, the correct prediction of cancer types using non-invasive information –e.g. 1H-MRS data– could avoid patients to suffer collateral problems derived from exploration techniques that require surgery. A Feature Selection Algorithm specially designed to be use in 1H-MRS Proton Magnetic Resonance Spectroscopy data of brain tumors is presented. It takes advantage of a highly distinctive aspect in this data: some metabolite levels are notoriously different between types of tumors. Experimental read- ings on an international dataset show highly competitive models in terms of accuracy, complexity and medical interpretability.Postprint (author’s final draft

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.Funding from project MARESCAN (SAF2011-23870) from Ministerio de Economia y Competitividad in Spain. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. JRG acknowledges support from Cancer Research UK, the University of Cambridge and Hutchison Whampoa Ltd.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/nbm.343

Machine learning methods for classifying normal vs. tumorous tissue with spectral data

Machine learning is a powerful paradigm within which to analyze 1H-MRS spectral data for the automated classi¯cation of tumor pathologies aimed to facilitate clinical diagnosis. The high dimensionality of the involved data sets makes the discover of computational models a challenging task. In this study we apply a feature selection algorithm in order to reduce the complexity of the problem. The obtained experimental results yield a remarkable classification performance of the final induced models, both in terms of prediction accuracy and number of involved spectral frequencies. A dimensionality reduction technique that preserves the class discrimination capabilities is used for the visualization of the final selected frequencies, thus enhancing their interpretability.Peer ReviewedPostprint (author’s final draft

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

Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning

In neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due to the localization of the tumour in an extremely sensitive and difficult to reach organ such as the brain. The clinical analysis of brain tumours often requires the use of non-invasive measurement methods, the most common of which resort to imaging techniques. The discrimination between high-grade malignant tumours of different origin but similar characteristics, such as glioblastomas and metastases, is a particularly difficult problem in this context. This is because imaging techniques are often not sensitive enough and their spectroscopic signal is overall too similar. In spite of this, machine learning techniques, coupled with robust feature selection procedures, have recently made substantial inroads into the problem. In this study, magnetic resonance spectroscopy data from an international, multicentre database were used to discriminate between these two types of malignant brain tumours using ensemble learning techniques, with a focus on the definition of a feature selection method specifically designed for ensembles. This method, Breadth Ensemble Learning, takes advantage of the fact that many of the frequencies of the available spectra convey no relevant information for the discrimination of the tumours. The potential of the proposed method is supported by some of the best results reported to date for this problem.Postprint (author's final draft

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction

Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed by survival regression and classification using these abnormal tumor tissue segments and other relevant clinical features. The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI. The survival prediction step includes two representative 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) global challenges in 2017 and 2018. The best overall survival pipeline in the proposed framework achieves leave-one-out cross-validation (LOOCV) accuracy of 0.73 for training datasets and 0.68 for validation datasets, respectively. These training and validation accuracies for tumor patient survival prediction are among the highest reported in literature. Finally, a critical analysis of radiomics features and efficacy of these features in segmentation and survival prediction performance is presented as lessons learned