Developing novel quantitative imaging analysis schemes based machine learning for cancer research

The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of developing several new novel CAD schemes for different cancer research purposes. First, I investigated the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to predict short-term breast cancer risk. For this study, an existing CAD scheme was applied “as is” to process each image. From CAD-generated results, some detection features were computed from each image. Two logistic regression models were then trained and tested using a leave-one-case-out cross-validation method to predict each testing case's likelihood of being positive in the next subsequent screening. This study demonstrated that CAD-generated false-positives contain valuable information to predict short-term breast cancer risk. Second, I identified and applied quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. For this study, a CAD scheme was developed to perform tumor segmentation and image feature analysis. The study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies. Last, I optimized a machine learning model for predicting peritoneal metastasis in gastric cancer. For this purpose, I have developed a CAD scheme to segment the tumor volume and extract quantitative image features automatically. Then, I reduced the dimensionality of features with a new method named random projection to optimize the model's performance. Finally, the gradient boosting machine model was applied along with a synthetic minority oversampling technique to predict peritoneal metastasis risk. Results suggested that the random projection method yielded promising results in improving the accuracy performance in peritoneal metastasis prediction. In summary, in my Ph.D. studies, I have investigated and tested several innovative approaches to develop different CAD schemes and identify quantitative imaging markers with high discriminatory power in various cancer research applications. Study results demonstrated the feasibility of applying CAD technology to several new application fields, which can help radiologists and gynecologists improve accuracy and consistency in disease diagnosis and prognosis assessment of using the medical image

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Computer-aided diagnosis in mammography : correlation of regions in multiple standard mammographic views of the same breast.

Thesis (Ph.D.)-University of KwaZulu-Natal, 2006.Abstract available in PDF file

Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

Computer-aided Diagnosis of Pulmonary Nodules in Thoracic Computed Tomography.

Lung cancer is the leading cause of cancer death in the United States. The five-year survival rate is 15% because most patients present with advanced disease. If lung cancer is detected and treated at its earliest stage, the five-year survival rate has been reported as high as 92%. Computed tomography (CT) has been shown to be more sensitive than chest radiography in detecting abnormal lung lesions (nodules), especially when they are small. However, each thin-slice thoracic CT scan can contain hundreds of images. Large amounts of image data, radiologist fatigue, and diagnostic uncertainty may lead to missed cancers or unnecessary biopsies. We address these issues by developing a computer-aided diagnosis (CAD) system that would serve as a second reader for radiologists by analyzing nodules and providing a malignancy estimate using computer vision and machine learning techniques. To segment the nodules, we extended the active contour (AC) model to 3D by adding new energy terms. The classification accuracy, quantified by the area (Az) under the receiver operating characteristic curve, was used as the figure-of-merit to guide segmentation parameter optimization. The effect of CT acquisition parameters on 3DAC segmentation was systematically studied by imaging simulated nodules in chest phantoms. We conducted simulation studies to compare the relative performance of feature selection and classification methods and to examine the bias and variance introduced due to limited training sample sizes. We also designed new feature descriptors to describe the nodule surface, which were combined with texture and morphological features extracted from the nodule volume and the surrounding tissue to characterize the nodule. Stepwise feature selection was used to search for the subset of most effective features to be used in the linear discriminant analysis classifier. The CAD system achieved a test Az of 0.86±0.02 in a leave-one-case-out resampling scheme for 256 nodules from 152 patients. We conducted an observer study with six thoracic radiologists and found that their average Az in assessing nodule malignancy increased significantly (p<0.05) from 0.83±0.03 without CAD to 0.85±0.04 with CAD. These results indicate the potential usefulness of CAD as a second reader for radiologists in characterizing lung nodules.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60814/1/tway_1.pd

Effect of nutritional factors on the growth and production of biosurfactant by Pseudomonas aeruginosa strain 181

The growth and production of biosurfactant by P. seudomonas aeruginosa (181) was dependant on nutritional factors. Among the eleven carbon sources tested, glucose supported the maximum growth (0.25 g/L) with the highest biosurfactant yield and this was followed by glycerol. Glucose reduced the surface tension to 35.3 dyne/ cm and gave an E24 reading of 62.7%. Butanol gave the lowest growth and had no biosurfactant production. For the nitrogen sources tested, casamino acid supported a growth of 0.21 g/L which reduced the surface tension to 41.1 dyne/cm and gave an E24 reading of 56%. Soytone was assimilated similarly, with good growth and high biosurfactant production. Corn steep liquor gave the lowest growth and did not show any biosurfactant activity