The human metabolome represents a largely unexplored area with respect to prediction of disease. I have conducted a targeted study of metabolic compounds in the human metabolome that are linked to patients with non small cell lung cancer. Through the use of machine learning techniques such as SVMs, Random Forests, and Decision Trees, I have determined models can be trained to correctly classify new patients with an F1--score above 0.95 in case vs. control classification. From these models I have produced a select subset of compounds using peak analysis as well as recursive feature extraction such that when the prediction is done on this smaller subset of compounds an F1--score of above 0.95 can still be achieved. These compounds represent potential biomarkers for future studies and clinical applications.

Lung cancer has a high incidence and mortality rate. The five-year relative survival rate for all lung cancers is 18%. Due to the high mortality and incidence rate of lung cancer worldwide, early detection is essential. Low dose Computed Tomography (CT) is a commonly used technique for screening, diagnosis, and prognosis of non-small cell lung cancer (NSCLC). The National Lung Screening Trial (NLST) compared low-dose helical computed tomography (LDCT) and standard chest radiography (CXR) for three annual screens and reported a 20% relative reduction in lung cancer mortality for LDCT compared to CXR. As such, LDCT screening for lung cancer is an effective way of mitigating lung cancer mortality and is the only imaging option for those at high risk. Lung cancer screening for high-risk patients often detects a large number of indeterminate pulmonary nodules, of which only a subset will be identified as cancer. As such, reliable and reproducible biomarkers determining which indeterminate pulmonary nodules will be identified as cancer would have significant translational implications as a therapeutic method to enhance lung cancer screening for nodule detection. Radiomics is an approach to extract high-dimensional quantitative features from medical images, which can be used individually or merged with clinical data for predictive and diagnostic analysis. Quantitative radiomics features (size, shape, and texture) extracted from lung CT scans have been shown to predict cancer incidence and prognosis. Deep learning is an emerging machine learning approach, which has been applied to the classification and analysis of various cancers and tumors. To generate generic features (blobs, edges, etc.) from an image, different convolutional kernels are applied over the input image, and then those generic feature-based images are passed through some fully connected neural layers. This category of the neural network is called a convolutional neural network (CNN), which has achieved high accuracy on image data. With the advancement of deep learning and convolutional neural networks (CNNs), deep features can be utilized to analyze lung CTs for prognosis prediction and diagnosis. In this dissertation, deep learning-based approaches were presented for lung nodule malignancy prediction. A subset of cases from the NLST was chosen as a dataset in our study. We experimented with three different pre-trained CNNs for extracting deep features and used five different classifiers. Experiments were also conducted with deep features from different color channels of a pre-trained CNN. Selected deep features were combined with radiomics features. Three CNNs were designed and trained. Combinations of features from pre-trained, CNNs trained on NLST data, and classical radiomics were used to build classifiers. The best accuracy (76.79%) was obtained using feature combinations. An area under the receiver operating characteristic curve of 0.87 was obtained using a CNN trained on an augmented NLST data cohort. After that, each of the three CNNs was trained using seven different seeds to create the initial weights. These enabled variability in the CNN models, which were combined to generate a robust, more accurate ensemble model. Augmenting images using only rotation and flipping and training with images from T0 yielded the best accuracy to predict lung cancer incidence at T2 from a separate test cohort (Accuracy = 90.29%; AUC = 0.96) based on an ensemble 21 models. From this research, five conclusions were obtained, which will be utilized in future research. First, we proposed a simple and effective CNN architecture with a small number of parameters useful for smaller (medical) datasets. Second, we showed features obtained using transfer learning with all the channels of a pre-trained CNN performed better than features extracted using any single channel and we also constructed a new feature set by fusing quantitative features with deep features, which in turn enhanced classification performance. Third, ensemble learning with deep neural networks was a compelling approach that accurately predicted lung cancer incidence at the second screening after the baseline screen, mostly two years later. Fourth, we proposed a method for deep features to have a recognizable definition via semantic or quantitative features. Fifth, deep features were dependent on the scanner parameters, and the dependency was removed using pixel size based normalization.

This integrated book covers the entire spectrum of cancer biomarkers in development and clinical use. Predictive and prognostic markers are explored in the context of colon cancer, breast cancer, lung cancer, prostate cancer, and GIST. International experts provide insight into toxicity markers and surrogate markers. Attention is also given to biomarker assay development, validation, and strategies. A powerful tool for determining decisions on therapy, selecting drug regimens, monitoring the efficacy of treatment, and performing individualized surveillance, biomarkers represent the forefront of cancer research and treatment. As these technologies become increasingly available for clinical use, this book will be an essential resource for oncologists and translational researchers.

Giving a fresh, substantial and in-depth overview of the topic, this book brings together the latest results in the field of metabolomics. It comprehensively presents the current state of the metabolomics field by underscoring experimental methods, analysis techniques, standardization practices, and advances in specific model systems. As a result, it helps to significantly broaden our perspective on the principles and strategies underpinning this emerging field.

Cancer metabolomics is a rapidly evolving field that aims for a comprehensive dissection of the metabolic phenotypes and functional network of metabolites in human cancers. State of the art metabolomics tools have been developed and applied to studying cancer metabolism and developing metabolic targets for improved diagnosis, prognosis and therapeutic treatment of human cancers. Chapters are written by subject experts in the field of cancer metabolomics with cross-disciplinary contributions. Coverage includes advanced metabolomics technologies and methodologies, including chemical isotope labelling liquid chromatography - mass spectrometry, capillary ion chromatography - mass spectrometry, 2-D gas chromatography – mass spectrometry, capillary electrophoresis – mass spectrometry, nuclear magnetic resonance spectroscopy, shotgun lipidomics, tracer-based metabolomics, microbial metabolomics, mass spectrometry imaging for single cell metabolomics and functional metabolomics. In addition, the book highlights new discoveries in cancer metabolism such as hypoxia inducible factor pathway, isocitrate dehydrogenase 1 mutation and oncometabolites. Finally, contributors focus on the translational applications of metabolomics in human cancers such as glioma, head and neck cancer, and gastric cancer. This new volume will be a unique reference source for cancer researchers and promote applications of metabolomics in understanding cancer metabolism.

Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.