A Review

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given

Adaptive Feature Engineering Modeling for Ultrasound Image Classification for Decision Support

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually significantly underrepresented compared to the non-target class. This makes it difficult to train standard classification models like Support Vector Machine (SVM), Decision Trees, and Nearest Neighbor techniques on biomedical datasets because they assume an equal class distribution or an equal misclassification cost. Resampling techniques by either oversampling the minority class or under-sampling the majority class have been proposed to mitigate the class imbalance problem but with minimal success. We propose a method of resolving the class imbalance problem with the design of a novel data-adaptive feature engineering model for extracting, selecting, and transforming textural features into a feature space that is inherently relevant to the application domain. We hypothesize that by maximizing the variance and preserving as much variability in well-engineered features prior to applying a classifier model will boost the differentiation of the thyroid nodules (benign or malignant) through effective model building. Our proposed a hybrid approach of applying Regression and Rule-Based techniques to build our Feature Engineering and a Bayesian Classifier respectively. In the Feature Engineering model, we transformed images pixel intensity values into a high dimensional structured dataset and fitting a regression analysis model to estimate relevant kernel parameters to be applied to the proposed filter method. We adopted an Elastic Net Regularization path to control the maximum log-likelihood estimation of the Regression model. Finally, we applied a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of the thyroid lesion. This is performed to establish the conditional influence on the textural feature to the random factors generated through our feature engineering model and to evaluate the success criterion of our approach. The proposed approach was tested and evaluated on a public dataset obtained from thyroid cancer ultrasound diagnostic data. The analyses of the results showed that the classification performance had a significant improvement overall for accuracy and area under the curve when then proposed feature engineering model was applied to the data. We show that a high performance of 96.00% accuracy with a sensitivity and specificity of 99.64%) and 90.23% respectively was achieved for a filter size of 13 × 13

Mass spectral imaging of clinical samples using deep learning

A better interpretation of tumour heterogeneity and variability is vital for the improvement of novel diagnostic techniques and personalized cancer treatments. Tumour tissue heterogeneity is characterized by biochemical heterogeneity, which can be investigated by unsupervised metabolomics. Mass Spectrometry Imaging (MSI) combined with Machine Learning techniques have generated increasing interest as analytical and diagnostic tools for the analysis of spatial molecular patterns in tissue samples. Considering the high complexity of data produced by the application of MSI, which can consist of many thousands of spectral peaks, statistical analysis and in particular machine learning and deep learning have been investigated as novel approaches to deduce the relationships between the measured molecular patterns and the local structural and biological properties of the tissues. Machine learning have historically been divided into two main categories: Supervised and Unsupervised learning. In MSI, supervised learning methods may be used to segment tissues into histologically relevant areas e.g. the classification of tissue regions in H&E (Haemotoxylin and Eosin) stained samples. Initial classification by an expert histopathologist, through visual inspection enables the development of univariate or multivariate models, based on tissue regions that have significantly up/down-regulated ions. However, complex data may result in underdetermined models, and alternative methods that can cope with high dimensionality and noisy data are required. Here, we describe, apply, and test a novel diagnostic procedure built using a combination of MSI and deep learning with the objective of delineating and identifying biochemical differences between cancerous and non-cancerous tissue in metastatic liver cancer and epithelial ovarian cancer. The workflow investigates the robustness of single (1D) to multidimensional (3D) tumour analyses and also highlights possible biomarkers which are not accessible from classical visual analysis of the H&E images. The identification of key molecular markers may provide a deeper understanding of tumour heterogeneity and potential targets for intervention.Open Acces

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

Mass spectrometry data mining for cancer detection

Early detection of cancer is crucial for successful intervention strategies. Mass spectrometry-based high throughput proteomics is recognized as a major breakthrough in cancer detection. Many machine learning methods have been used to construct classifiers based on mass spectrometry data for discriminating between cancer stages, yet, the classifiers so constructed generally lack biological interpretability. To better assist clinical uses, a key step is to discover ”biomarker signature profiles”, i.e. combinations of a small number of protein biomarkers strongly discriminating between cancer states. This dissertation introduces two innovative algorithms to automatically search for a signature and to construct a high-performance signature-based classifier for cancer discrimination tasks based on mass spectrometry data, such as data acquired by MALDI or SELDI techniques. Our first algorithm assumes that homogeneous groups of mass spectra can be modeled by (unknown) Gibbs distributions to generate an optimal signature and an associated signature-based classifier by robust log-likelihood analysis; our second algorithm uses a stochastic optimization algorithm to search for two lists of biomarkers, and then constructs a signature-based classifier. To support these two algorithms theoretically, this dissertation also studies the empirical probability distributions of mass spectrometry data and implements the actual fitting of Markov random fields to these high-dimensional distributions. We have validated our two signature discovery algorithms on several mass spectrometry datasets related to ovarian cancer and to colorectal cancer patients groups. For these cancer discrimination tasks, our algorithms have yielded better classification performances than existing machine learning algorithms and in addition,have generated more interpretable explicit signatures.Mathematics, Department o

Mathematical approaches for the clinical translation of hyperpolarised 13C imaging in oncology

Dissolution dynamic nuclear polarisation is an emerging clinical technique which enables the metabolism of hyperpolarised 13C-labelled molecules to be dynamically and non- invasively imaged in tissue. The first molecule to gain clinical approval is [1-13C]pyruvate, the conversion of which to [1-13C]lactate has been shown to detect early treatment re- sponse in cancers and correlate with tumour grade. As the technique has recently been translated into humans, accurate and reliable quantitative methods are required in order to detect, analyse and compare regions of altered metabolism in patients. Furthermore, there is a requirement to understand the biological processes which govern lactate pro- duction in tumours in order to draw reliable conclusions from this data. This work begins with a comprehensive analysis of the quantitative methods which have previously been applied to hyperpolarised 13C data and compares these to some novel approaches. The most appropriate kinetic model to apply to hyperpolarised data is determined and some simple, robust quantitative metrics are identified which are suitable for clinical use. A means of automatically segmenting 5D hyperpolarised imaging data using a fuzzy Markov random field approach is presented in order to reliably identify regions of abnormal metabolic activity. The utility of the algorithm is demonstrated on both in silico and animal data. To gain insight into the processes driving lactate metabolism, a mathematical model is developed which is capable of simulating tumour growth and treatment response under a range of metabolic and tissue conditions, focusing on the interaction between tumour and stroma. Finally, hyperpolarised 13C-pyruvate imaging data from the first human subjects to be imaged in Cambridge is analysed. The ability to detect and quantify lactate production in patients is demonstrated through application of the methods derived in earlier chapters. The mathematical approaches presented in this work have the potential to inform both the analysis and interpretation of clinical hyperpolarised 13C imaging data and to aid in the clinical translation of this technique.Joint funded by GlaxoSmithKline and the Cambridge Biomedical Research Centre

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Computer-aided diagnosis of gynaecological abnormality using B-mode ultrasound images

Ultrasound scan is one of the most reliable imaging for detecting/diagnosing of gynaecological abnormalities. Ultrasound imaging is widely used during pregnancy and has become central in the management of the problems of early pregnancy, particularly in miscarriage diagnosis. Also ultrasound is considered as the most important imaging modality in the evaluation of different types of ovarian tumours. The early detection of ovarian carcinoma and miscarriage continues to be a challenging task. It mostly relies on manual examination, interpretation by gynaecologists, of the ultrasound scan images that may use morphology features extracted from the region of interest. Diagnosis depends on using certain scoring systems that have been devised over a long time. The manual diagnostic process involves multiple subjective decisions, with increased inter- and intra-observer variations which may lead to serious errors and health implications. This thesis is devoted to developing computer-based tools that use ultrasound scan images for automatic classification of Ovarian Tumours (Benign or Malignant) and automatic detection of Miscarriage cases at early stages of pregnancy. Our intended computational tools are meant to help gynaecologists to improve accuracy of their diagnostic decisions, while serving as a tool for training radiology students/trainees on diagnosing gynaecological abnormalities. Ultimately, it is hoped that the developed techniques can be integrated into a specialised gynaecology Decision Support System. Our approach is to deal with this problem by adopting a standard image-based pattern recognition research framework that involve the extraction of appropriate feature vector modelling of the investigated tumours, select appropriate classifiers, and test the performance of such schemes using sufficiently large and relevant datasets of ultrasound scan images. We aim to complement the automation of certain parameters that gynaecologist experts and radiologists manually determine, by image-content information attributes that may not be directly accessible without advanced image transformations. This is motivated by, and benefit from, advances in computer vision that led the emergence of a variety of image processing/analysis techniques together with recent advances in data mining and machine learning technologies. An expert observer makes a diagnostic decision with a level of certainty, and if not entirely certain about their diagnostic decisions then often other experts’ opinions are sought and may be essential for diagnosing difficult “Inconclusive cases”. Here we define a quantitative measure of confidence in decisions made by automatic diagnostic schemes, independent of accuracy of decision. In the rest of the thesis, we report on the development of a variety of innovative diagnostic schemes and demonstrate their performances using extensive experimental work. The following is a summary of the main contributions made in this thesis. 1. Using a combination of spatial domain filters and operations as pre-processing procedures to enhance ultrasound images for both applications, namely miscarriage identification and ovarian tumour diagnosis. We show that the Non-local means filter is effective in reducing speckle noise from ultrasound images, and together with other filters we succeed in enhancing the inner border of malignant tumours and reliably segmenting the gestational sac. 2. Developing reliable automated procedures to extract several types of features to model gestational sac dimensional measurements, few of which are manually determined by radiologist and used by gynaecologists to identify miscarriage cases. We demonstrate that the corresponding automatic diagnostic schemes yield excellent accuracy when classified by the k-Nearest Neighbours. 3. Developing several local as well as global image-texture based features in the spatial as well as the frequency domains. The spatial domain features include the local versions of image histograms, first order statistical features and versions of local binary patterns. From the frequency domain, we propose a novel set of Fast Fourier Geometrical Features that encapsulates the image texture information that depends on all image pixel values. We demonstrate that each of these features define Ovarian Tumour diagnostic scheme that have relatively high power of discriminating Benign from Malignant tumours when classified by Support Vector Machine. We show that the Fast Fourier Geometrical Features are the best performing scheme achieving more than 85% accuracy. 4. Introducing a simple measure of confidence to quantify the goodness of the automatic diagnostic decision, regardless of decision accuracy, to emulate real life medical diagnostics. Experimental work in this theis demonstrate a strong link between this measure and accuracy rate, so that low level of confidence could raise an alarm. 5. Conducting sufficiently intensive investigations of fusion models of multi-feature schemes at different level. We show that feature level fusion yields degraded performance compared to all its single components, while score level fusion results in improved results and decision level fusion of three sets of features using majority rule is slightly less successful. Using the measure of confidence is useful in resolving conflicts when two sets of features are fused at the decision level. This leads to the emergence of a Not Sure decision which is common in medical practice. Considering the Not Sure label is a good practice and an incentive to conduct more tests, rather than misclassification, which leads to significantly improved accuracy. The thesis concludes with an intensive discussion on future work that would go beyond improving performance of the developed scheme to deal with the corresponding multi-class diagnostics essential for a comprehensive gynaecology Decision Support System tool as the ultimate goal

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models