Radiomics Based Theranostics In Cancer Precision Medicine

Over the past few decades, there have been many dramatic innovations in cancer diagnosis and treatment strategies. Medical imaging plays a pivotal role in the diagnosis and treatment of cancer. It provides a comprehensive assessment of the tumors and their environments. Multiple imaging modalities are used for theranostics including optical (fluorescence or bioluminescence), nuclear (PET or SPECT), ultrasound, photoacoustic, CT, and MR imaging techniques. Radiomics is an approach for high-throughput extraction of quantitative imaging features or textures from imaging to decode histopathology and create high-dimensional datasets for feature extraction. Therefore, Radiomics may provide quantitative and objective support for decisions surrounding cancer detection and treatment.

"In this thesis, the major aim is to develop radiomic-based models for the accurate prediction of tumour outcomes via advanced machine learning. We first showed that the optimization of how texture features are extracted from medical images (different isotropic voxel sizes, image quantization schemes, etc.) is fundamental for best tumour outcome prediction. We then integrated the texture optimization process into a robust multivariable modeling methodology developed for the construction of radiomic-based prediction models. This multivariable modeling methodology employs logistic regression to linearly combine radiomic features. Using this methodology, we were able to develop a model that can predict the development of lung metastases in soft-tissue sarcomas with high accuracy. This model combines texture features extracted from functional FDG-PET and anatomical MRI pre-treatment images. Following this initial work, we demonstrated how the predictive properties of imaging textures composing such prediction models could be further enhanced by optimizing the way images are acquired. The proof of concept for the enhancement of the prediction of lung metastases in soft-tissue sarcomas was carried out using computerized simulations of FDG-PET and MR image acquisitions with tumour and clinical scanner models, by varying different physical parameters employed during image acquisitions. Next, in another study, we developed a strategy for personalizing treatments for soft-tissue sarcoma patients identified at diagnosis to be at higher risks of developing lung metastases (using radiomic-based prediction models); specifically, we verified the feasibility of applying double nested radiation dose boosting to the hypermetabolic and hypoxic soft-tissue sarcoma sub-regions to counteract the progression of more aggressive parts of tumours. For the purpose of radiation treatment planning, contours defining the hypermetabolic and hypoxic tumour sub-regions were obtained from FDG-PET and low-perfusion DCE-MRI functional images. Finally, in our last study, we developed a methodology allowing to integrate radiomic imaging data with clinical prognostic factors into comprehensive prediction models using a random forest algorithm. We tested our methodology in head-and-neck cancer to better assess the risk of locoregional recurrences and distant metastases, this time using functional FDG-PET and anatomical CT pre-treatment images in conjunction to clinical data. The clinically-integrated radiomic models that we developed possess high prognostic power, leading to patient stratification into two sub-groups for the risk assessment of locoregional recurrences (low, high) in head-and-neck cancer, and into three groups for distant metastases (low, medium, high).Overall, in this thesis, we demonstrated that radiomics analysis is an enabling method towards precision medicine. The different radiomic techniques and models developed in this work could have a major impact on the design of new clinical trials aiming at a better personalization of cancer treatments. One can envision different treatment regimens being delivered to patients based on different radiomic-based risk assessments of specific tumour outcomes." --

This multidisciplinary textbook is designed to be the standard on the subject and is geared for use by physicians who are involved in the care and/or diagnosis of cancer patients. Comprehensive coverage is provided on all aspects of radioguided surgery. Practical information is readily accessible and throughout there is an emphasis on improved decision making. Tables present the indications, performance, and interpretation of procedures at a glance. A wealth of illustrations, including a full-color insert, enhances the application of new concepts.

This encompassing book is designed to contribute to a teamwork approach by promoting understanding between radiologists and clinical oncologists. All of the currently available imaging modalities of relevance in clinical oncology are covered, and the presentation of a broad spectrum of oncologic diseases (of most organ systems) on these modalities is discussed and illustrated. The role of multiparametric and multimodality imaging approaches providing both morphologic and functional information is considered in detail, and careful attention is paid to the latest developments in higher field (3T) MR imaging and advanced MR techniques such as diffusion-weighted imaging, diffusion tensor imaging, perfusion-weighted imaging and spectroscopy. The major challenge of incorporating progress in quantitative imaging technology into radiotherapy treatment planning, guidance, and monitoring is also addressed. This book will assist in refining the treatment approach in various oncologic diseases and organ systems based on specific imaging features. It will be of value to radiologists, oncologists, and other medical professionals involved in the diagnosis and treatment of oncology patients. ​

Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms

Neutron capture therapy (NCT) is based on the ability of the non-radioactive isotope boron-10 to capture thermal neutrons with very high probability and immediately to release heavy particles with a path length of one cell diameter, which in principle allows for tumor cell-selective high-LET particle radiotherapy. This book provides a comprehensive summary of the progress made in NCT in recent years. Individual sections cover all important aspects, including neutron sources, boron chemistry, drugs for NCT, dosimetry, and radiation biology. The use of NCT in a variety of malignancies and also some non-malignant diseases is extensively discussed. NCT is clearly shown to be a promising modality at the threshold of wider clinical application. All of the chapters are written by experienced specialists in language that will be readily understood by all participating disciplines.

In the fast-changing age of precision medicine, PET/CT is increasingly important for accurate cancer staging and evaluation of treatment response. Fundamentals of Oncologic PET/CT, by Dr. Gary A. Ulaner, offers an organized, systematic introduction to reading and interpreting PET/CT studies, ideal for radiology and nuclear medicine residents, practicing radiologists, medical oncologists, and radiation oncologists. Synthesizing eight years’ worth of cases and lectures from one of the largest cancer centers in the world, this title provides a real-world, practical approach, taking you through the body organ by organ as it explains how to integrate both the FDG PET and CT findings to best interpret each lesion.

This comprehensive text focuses exclusively on the management of metastatic spinal disease, evaluating the most recent literature and providing patient-centered treatment algorithms. Beginning with initial imaging, classification and clinical decision-making, the spine is approached anatomically from the upper cervical to the sacrum, describing the unique considerations and approaches appropriate to each region, such as laminectomy and stabilization, en bloc spondylectomy and resection and reconstruction. Less invasive and minimally invasive approaches are discussed throughout the text. Radiation therapy modalities and other adjuvant treatments are also discussed, as well as reconstructive flap coverage and the management of complications. The spinal column is the most common site of metastatic cancer, and a multidisciplinary approach is required to provide patients with reasonable management options to prevent and treat the disabling symptoms caused by this debilitating condition. This compendium of experience from thought leaders in the treatment of metastatic spine disease will provide spine surgeons, oncologists, radiation oncologists, physiatrists and palliative care specialists with up-to-date information to guide their patients through the multidisciplinary management of metastatic spinal disease.