Machine and Deep Learning in Oncology, Medical Physics and Radiology

514 Pages·2022·15.534 MB·English

by  Issam El Naqa, Martin J. Murphy

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
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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 tech-
nology, 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 meth-
ods to pull (as Nate Silver puts it) “signal from the noise.” While traditional statisti-
cal 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 com-
munity health-care systems [2].
Machine learning—coupled with computer vision and imaging processing
techniques—has been demonstrated to be useful in diagnosis, treatment plan-
ning, 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 con-
tinue 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 deci-
sion 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 mod-
els 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 radia-
tion 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, con-
trol 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 previ-
ous 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 impor-
tant 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 appli-
cations that touch almost every area in radiotherapy from treatment planning,
quality assurance, image guidance, and respiratory motion management to treat-
ment 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 radio-
therapy 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 prin-
ciples using PAC or VC theories (Chap. 2), presentation of the most commonly
used supervised and unsupervised learning algorithms with demonstrative appli-
cations drawn from the radiotherapy field (Chap. 3), and descriptions of different
methods and techniques used for evaluating the performance of learning meth-
ods (Chap. 4). The ever-growing role of informatics infrastructure in radiother-
apy 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 learn-
ing 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 treat-
ment planning as a tool for image-guided radiotherapy (Chap. 9) and a computa-
tional vehicle for knowledge-based planning (Chap. 10).
• The fourth section demonstrates the application of machine learning to respira-
tory 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 treat-
ment 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 com-
plex 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 chal-
lenges 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