Table Of Content

Machine and Deep Learning in Oncology, Medical Physics and Radiology Issam El Naqa Martin J. Murphy Editors Second Edition 123 Machine and Deep Learning in Oncology, Medical Physics and Radiology Issam El Naqa • Martin J. Murphy Editors Machine and Deep Learning in Oncology, Medical Physics and Radiology Second Edition Editors Issam El Naqa Martin J. Murphy Department of Machine Learning Department of Radiation Oncology Moffitt Cancer Center Virginia Commonwealth University Tampa, FL, USA Richmond, VA, USA ISBN 978-3-030-83046-5 ISBN 978-3-030-83047-2 (eBook) https://doi.org/10.1007/978-3-030-83047-2 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Foreword to the First Edition This book is based on one of the most consequential emergent results of the ongoing computer revolution, namely, that computers can be trained—under the right condi- tions—to reliably classify new data, such as patient data. This capability, called machine learning (or statistical learning), has been deployed in many areas of technology, commerce, and medicine. Data mining and statistical prediction models have already crept into many areas of modern life, including advertising, banking, sports, weather prediction, politics, science generally, and medicine in particular. The ability of computers to increasingly communicate with people in a natural way (understanding language and speaking to us), such as the famed IBM “Watson” appearance on Jeopardy, or “Siri” on iPhones, portends an accelerating role of sophisticated computer models that predict and respond to our requests. Fundamentally, these developments rely on the ability of statistical computer methods to pull (as Nate Silver puts it) “signal from the noise.” While traditional statistical methods typically attempt to ascertain the role of particular variables in determining an outcome of interest (hence, needing many data points for every vari- able included in the prediction model), machine learning represents a different goal, to reliably predict an outcome, for example that an imaging abnormality is benign with a high degree of certainty. The statisticians and computer scientists working in this emerging area are often happy to use large numbers of variables (or previous data instances) that essentially vote together in a nonlinear fashion. Simplicity is happily traded for an improved ability to predict. The chapters in this book comprehensively review machine learning and related modeling methods previously used in many areas of radiation oncology and diag- nostic radiology. The editors and authors are explorers in this new territory, and have performed a great service by surveying and mapping the many achievements to date and outline many areas of potential application. Early chapters review the fundamental characteristics, and varieties, of machine learning methods, including difficult issues regarding evaluation of predictive model performance. The most well-developed use of machine learning reviewed is the creation of computer-aided diagnosis (CAD) models to provide a reliable “second opinion” for radiologists reading mammograms to detect breast cancer. The increasing use of wider range of imaging features referred to as “radiomics,” in analogy to “genomics,” presented in radiomics for disease detection, and radiomics for diagnosis, or “theragnostic” [1] chapters, which are devoted to details of image-based informatics formats and v vi Foreword to the First Edition database systems, including tools to share and learn from institutional databases. Machine learning approaches to aid in the planning, delivery, and quality assurance of radiation therapy are reviewed. Efforts to predict response to radiation therapy are also reviewed in useful detail. Obtaining enough data of sufficient quality and diversity is the biggest challenge in predictive modeling. This is only possible if data are shared across institutional and national borders, both academic and community health-care systems [2]. Machine learning—coupled with computer vision and imaging processing techniques—has been demonstrated to be useful in diagnosis, treatment planning, and outcome prediction in radiation oncology and radiology. This is of particular importance since we know that doctors have increasing difficulties to predict the outcome of modernized complex patient treatments [3]. This book provides a wonderful summary of past achievements, current challenges, and emerging approaches in this important area of medicine. Unlike many other approaches to improving medicine, the use of improved and continu- ously updated prediction models put together in “Decision Support Systems” holds the potential of improved clinical decision making with minimal costs to patients [4]. An intuitively attractive characteristic of this approach is the user of all the data available (rather than using only one type of data such as dose or gene profile). We anticipate that predictive models-based Decisions Support Systems will ease the implementation of personalized (or precision) medicine. Despite investment in efforts to improve the skills of clinicians, patients continue to report low levels of involvement [5]. There is indeed evidence level 1 from a Cochrane systematic review evaluating 86 studies involving 20,209 participants included in published randomized controlled trials demonstrating that decision aids increase people’s involvement, support informed values-based choices in patient- practitioner communication, and improve knowledge and realistic percep- tion of outcomes. We therefore believe the next step will be to integrate, whenever possible, Shared Decision Making approaches (see, e.g., www.treatmentchoice. info; www.optiongrid.org) to include the patient perspective on the best treatment of choice [6]. We are sincerely convinced that this book will continue to advance precision medicine in oncology. Philippe Lambin Department of Radiation Oncology Research Institute GROW, Maastro Clinic, Maastricht University, Maastricht, The Netherlands Joseph O. Deasy Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, NY, USA Foreword to the First Edition vii References 1. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6. 2. Lambin P, Roelofs E, Reymen B, Velazquez ER, Buijsen J, Zegers CM, Carvalho S, Leijenaar RT, Nalbantov G, Oberije C, Scott Marshall M, Hoebers F, Troost EG, van Stiphout RG, van Elmpt W, van der Weijden T, Boersma L, Valentini V, Dekker A. Rapid Learning health care in oncology’ – an approach towards decision support systems enabling customised radiotherapy. Radiother Oncol. 2013;109(1):159–64. 3. Oberije C, Nalbantov G, Dekker A, Boersma L, Borger J, Reymen B, van Baardwijk A, Wanders R, De Ruysscher D, Steyerberg E, Dingemans AM, Lambin P. A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol. 2014;112(1):37–43. 4. Lambin P, van Stiphout RG, Starmans MH, et al. Predicting outcomes in radiation oncology–multifactorial decision support systems. Nat Rev Clin Oncol. 2013;10:27–40. 5. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011;10:CD001431. 6. Stiggelbout AM, Van der Weijden T, De Wit MP, et al. Shared decision making: really putting patients at the centre of healthcare. BMJ. 2012;344:e256. Preface to the First Edition Radiotherapy is a major treatment modality for cancer and is currently the main option for treating local disease at advanced stages. More than half of all cancer patients receive irradiation as part of their treatment, with curative or palliative intent to eradicate cancer or reduce pain, respectively, while sparing uninvolved normal tissue from detrimental side effects. Despite significant technological advances in treatment planning and delivery using image-guided techniques, the complex nature of radiotherapy processes and the massive amount of structured and unstructured heterogeneous data generated during radiotherapy from early patient consultation to patient simulation, to treatment planning and delivery, to monitoring response, to follow-up visits, invite the application of more advanced computational methods that can mimic human cognition and intelligent decision making to ensure safe and effective treatment. In addition, these computational methods need to com- pensate for human limitations in handling a large amount of flowing information in an efficient manner, in which simple errors can make the difference between life and death. Machine learning is a technology that aims to develop computer algorithms that are able to emulate human intelligence by incorporating ideas from neuroscience, probability and statistics, computer science, information theory, psychology, control theory, and philosophy with successful applications in computer vision, robot- ics, entertainment, ecology, biology, and medicine. The essence of this technology is to humanize computers by learning from the surrounding environment and previous experiences, with or without a teacher. The development and application of machine learning has undergone a significant surge in recent years due to the expo- nential growth and availability of “big data” with machine learning techniques occupying the driver’s seat to steer the understanding of such data in many fields, including radiation oncology. The growing interest in applying machine learning algorithms to radiotherapy has been highlighted by special sessions at the annual meeting of the American Association of Physicists in Medicine (AAPM) and at the International Conference on Machine Learning and Applications (ICMLA). Ensuing discussions of compil- ing these disparate applications of machine learning in radiotherapy into a single succinct monograph led to the idea of this book. The goal is to provide interested readers with a comprehensive and accessible text on the subject to fill in an important existing void in radiotherapy and machine learning literature. Even as these ix x Preface to the First Edition discussions were taking place, the subject of machine learning in radiotherapy con- tinued its growth from a peripheral subfield in radiotherapy into widespread applications that touch almost every area in radiotherapy from treatment planning, quality assurance, image guidance, and respiratory motion management to treatment response modeling and outcomes prediction. This rapid growth has driven the compilation of this textbook. The textbook is intended to be an introductory learning guide for students and residents in medical physics and radiation oncology who are interested in exploring this new field of machine learning for their own curiosity or their research projects. In addition, the book is intended to be a useful and informative resource for more experienced practitioners, researchers, and members of both radiotherapy and applied machine learning as a two-way bridge between these communities. This is manifested by the fact that the book has been written by experts from both the radiotherapy and machine learning domains. The book is structured into five sections: • The first section provides an introduction to machine learning and is a must-read for individuals who are new to the field. It begins with a machine learning defini- tion (Chap. 1), followed by a discussion of the main computational learning principles using PAC or VC theories (Chap. 2), presentation of the most commonly used supervised and unsupervised learning algorithms with demonstrative applications drawn from the radiotherapy field (Chap. 3), and descriptions of different methods and techniques used for evaluating the performance of learning methods (Chap. 4). The ever-growing role of informatics infrastructure in radiotherapy and its application to machine learning are presented in Chap. 5. Finally, given the realistic challenges related to data sharing from a global radiotherapy network, this section concludes with a discussion of how machine learning could be extended to a distributed multicenter rapid learning framework. • The second section summarizes years of successful application of machine learning in radiological sciences—a sister field to radiotherapy—as a computational tool for computer-aided detection (Chap. 7) and computer-aided diagnosis (Chap. 8). • The third section presents applications of machine learning in radiotherapy treatment planning as a tool for image-guided radiotherapy (Chap. 9) and a computational vehicle for knowledge-based planning (Chap. 10). • The fourth section demonstrates the application of machine learning to respiratory motion management—a rather challenging problem for accurate delivery of irradiation to a moving target—by discussing predictive respiratory models (Chap. 11) and image-based compensation techniques (Chap. 12). • Quality assurance is at the heart of safe delivery of radiotherapy and is a major part of a medical physicist’s job. Examples for application of machine learning to QA for detection and prediction of radiotherapy errors (Chap. 13), for treatment planning (Chap. 14), and for delivery (Chap. 15) validation are presented and discussed. • In the era of personalized evidence-based medicine, machine learning predictive analytics can play an important role in the understanding of radiotherapy Preface to the First Edition xi response (Chap. 16). Examples of successful machine learning applications to normal tissue complication probability (Chap. 17) and tumor control probability (Chap. 18) highlight the inherent power of this technology in deciphering complex radiobiological response. This book is the product of a coordinated effort by the editors, authors, and pub- lishing team to present the principles and applications of machine learning to a new generation of practitioners in radiation therapy and to present the present-day challenges of radiotherapy to the computer science community, with the hope of driving advancements in both fields. Montreal, QC, Canada Issam El Naqa Stanford, CA, USA Ruijiang Li Richmond, VA, USA Martin J. Murphy