DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES

Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories. In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

While cross-sectional imaging has seen continuous progress and plays an undiscussedpivotal role in the diagnostic management and treatment planning of patients with rectal cancer, alargely unmet need remains for improved staging accuracy, assessment of treatment response andprediction of individual patient outcome. Moreover, the increasing availability of target therapies hascalled for developing reliable diagnostic tools for identifying potential responders and optimizingoverall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fullyevolving research topic, which could harness the power of modern computer technology to generatequantitative information from imaging datasets based on advanced data-driven biomathematicalmodels, potentially providing an added value to conventional imaging for improved patient manage-ment. The present study aimed to illustrate the contribution that current radiomics methods appliedto magnetic resonance imaging can offer to managing patients with rectal cancer

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Novel Statistical Learning Methods for Multi-Modality Heterogeneous Data Fusion in Health Care Applications

abstract: With the development of computer and sensing technology, rich datasets have become available in many fields such as health care, manufacturing, transportation, just to name a few. Also, data come from multiple heterogeneous sources or modalities. This is a common phenomenon in health care systems. While multi-modality data fusion is a promising research area, there are several special challenges in health care applications. (1) The integration of biological and statistical model is a big challenge; (2) It is commonplace that data from various modalities is not available for every patient due to cost, accessibility, and other reasons. This results in a special missing data structure in which different modalities may be missed in “blocks”. Therefore, how to train a predictive model using such a dataset poses a significant challenge to statistical learning. (3) It is well known that different modality data may contain different aspects of information about the response. The current studies cannot afford to solve this problem. My dissertation includes new statistical learning model development to address each of the aforementioned challenges as well as application case studies using real health care datasets, included in three chapters (Chapter 2, 3, and 4), respectively. Collectively, it is expected that my dissertation could provide a new sets of statistical learning models, algorithms, and theory contributed to multi-modality heterogeneous data fusion driven by the unique challenges in this area. Also, application of these new methods to important medical problems using real-world datasets is expected to provide solutions to these problems, and therefore contributing to the application domains.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Review on the Application of Machine Learning to Cancer Research

This study reviews the application of machine learning through different algorithms in cancer research. In recent years, the introduction of machine learning has been an exciting tool that enhances cancer research which has improved statistical method of speeding up both fundamental and applied research considerably. The application of machine learning goes around in predicting the future events and outcomes with the available datasets. There is an indication that on yearly bases up to 14 million new cancer patients are diagnosed by Pathologists round the world, and they are people whose conditions are uncertain. Definitely, the diagnoses and prognoses of cancer have been performed by Pathologists. The research on machine learning flourished in 1980s and 1990s and information become digitalized through improved artificial network connectivity and computational power