The goal of this research was to propose and evaluate solutions to four important aspects of adaptive radiation therapy in order to make it more reliable, accurate, and efficient in clinical environment. The first study focused on the evaluation of several deformable image registration algorithms. Results demonstrated that the Dense Anatomical Block Matching registration outperformed the other methods making it a very promising alternative to the existing registration methods for challenging CT-to-CBCT registration and its applications for radiation dose calculation, dose mapping and contour propagation in adaptive radiation therapy (ART) of the pelvic region. The second study focused on the quantitative evaluation of eight proposed adaptive radiation therapy approaches for prostate cancer patients treated with hypofractionated VMAT. The ART strategies included online and offline methods. The comprehensive analysis showed that daily on-line adaptation approaches were the most impactful. The findings of this study provided applicable insights into the selection of the optimal ART strategy, improving the quality of the decision-making process based on the quantitatively evaluated dosimetric benefits. The third study aimed to utilize a deep learning network to automatically contour critical organs on the computed tomography (CT) scans of head and neck cancer patients. Proposed model achieved expert level accuracy and was able to segment 25 critical organs on unseen CT images in approximately 7 seconds per patient. High accuracy and short contouring time allow for the implementation of the model within a clinical ART workflow, which would lead to a significant decrease in the time required to create a new adapted treatment plan. The objective of the fourth study was to use artificial intelligence methods to build a decision making support system that would classify previously delivered plans of brain tumor patients into those that met treatment planning objectives and those for which objectives were not met due to the priority given to one or more organs-at-risk. Among evaluated machine learning algorithms, the Logistic Regression model achieved the highest accuracy and can be used by radiation oncologists to support their decision-making process in terms of treatment plan adaptations and plan approvals in a data-driven quality assurance program.

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

In this book we provide insights into liver – cancer and immunology. Experts in the field provide an overview over fundamental immunological questions in liver cancer and tumorimmunology, which form the base for immune based approaches in HCC, which gain increasing interest in the community due to first promising results obtained in early clinical trials. Hepatocellular carcinoma (HCC) is the third most common cause of cancer related death in the United States. Treatment options are limited. Viral hepatitis is one of the major risk factors for HCC, which represents a typical “inflammation-induced” cancer. Immune-based treatment approaches have revolutionized oncology in recent years. Various treatment strategies have received FDA approval including dendritic cell vaccination, for prostate cancer as well as immune checkpoint inhibition targeting the CTLA4 or the PD1/PDL1 axis in melanoma, lung, and kidney cancer. Additionally, cell based therapies (adoptive T cell therapy, CAR T cells and TCR transduced T cells) have demonstrated significant efficacy in patients with B cell malignancies and melanoma. Immune checkpoint inhibitors in particular have generated enormous excitement across the entire field of oncology, providing a significant benefit to a minority of patients.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Cell Surface Proteases provides a comprehensive overview of these important enzymes that catalyze the hydrolysis of a protein as it degrades to a simpler substance. In the 1990s, an explosion of new discoveries shed light on the role of cell surface proteases and extended it beyond degradation of extracellular matrix components to include its influence on growth factors, cell signaling, and other cellular events. This volume unites the scientific literature from across disciplines and teases out unified themes of interactions between cell surface proteases and interconnecting cell surface-related systems -- including integrins and other adhesion molecules. Scientists and students involved in developmental biology, cell biology and disease processes will find this an indispensable resource. * Provides an overview of the entire field of cell surface proteases in a single volume* Presents major issues and astonishing discoveries at the forefront of modern developmental biology and developmental medicine * A thematic volume in the longest-running forum for contemporary issues in developmental biology with over 30 years of coverage