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Neuro-oncology broadly encompasses life-threatening brain and
spinal cord malignancies, including primary lesions and lesions
metastasizing to the central nervous system. It is well suited for
diagnosis, classification, and prognosis as well as assessing
treatment response. Radiomics and Radiogenomics (R-n-R) have become
two central pillars in precision medicine for
neuro-oncology.Radiomics is an approach to medical imaging used to
extract many quantitative imaging features using different data
characterization algorithms, while Radiogenomics, which has
recently emerged as a novel mechanism in neuro-oncology research,
focuses on the relationship of imaging phenotype and genetics of
cancer. Due to the exponential progress of different computational
algorithms, AI methods are composed to advance the precision of
diagnostic and therapeutic approaches in neuro-oncology.The field
of radiomics has been and definitely will remain at the lead of
this emerging discipline due to its efficiency in the field of
neuro-oncology. Several AI approaches applied to conventional and
advanced medical imaging data from the perspective of radiomics are
very efficient for tasks such as survival prediction, heterogeneity
analysis of cancer, pseudo progression analysis, and infiltrating
tumors. Radiogenomics advances our understanding and knowledge of
cancer biology, letting noninvasive sampling of the molecular
atmosphere with high spatial resolution along with a systems-level
understanding of causal heterogeneous molecular and cellular
processes. These AI-based R-n-R tools have the potential to
stratify patients into more precise initial diagnostic and
therapeutic pathways and permit better dynamic treatment monitoring
in this period of personalized medicine. While extremely promising,
the clinical acceptance of R-n-R methods and approaches will
primarily hinge on their resilience to non-standardization across
imaging protocols and their capability to show reproducibility
across large multi-institutional cohorts.Radiomics and
Radiogenomics in Neuro-Oncology: An Artificial Intelligence
Paradigm provides readers with a broad and detailed framework for
R-n-R approaches with AI in neuro-oncology, the description of
cancer biology and genomics study of cancer, and the methods
usually implemented for analyzing. Readers will also learn about
the current solutions R-n-R can offer for personalized treatments
of patients, limitations, and prospects. There is comprehensive
coverage of information based on radiomics, radiogenomics, cancer
biology, and medical image analysis viewpoints on neuro-oncology,
so this in-depth coverage is divided into two Volumes.Volume 1:
Radiogenomics Flow Using Artificial Intelligence provides coverage
of genomics and molecular study of brain cancer, medical imaging
modalities and analysis in neuro-oncology, and prognostic and
predictive models using radiomics.Volume 2: Genetics and Clinical
Applications provides coverage of imaging signatures for brain
cancer molecular characteristics, clinical applications of R-n-R in
neuro-oncology, and Machine Learning and Deep Learning AI
approaches for R-n-R in neuro-oncology.
Developing an effective computer-aided diagnosis (CAD) system for
lung cancer is of great clinical importance and can significantly
increase the patient's chance for survival. For this reason, CAD
systems for lung cancer have been investigated in a large number of
research studies. A typical CAD system for lung cancer diagnosis is
composed of four main processing steps: segmentation of the lung
fields, detection of nodules inside the lung fields, segmentation
of the detected nodules, and diagnosis of the nodules as benign or
malignant. This book overviews the current state-of-the-art
techniques that have been developed to implement each of these CAD
processing steps. Overviews the latest state-of-the-art diagnostic
CAD systems for lung cancer imaging and diagnosis Offers detailed
coverage of 3D and 4D image segmentation Illustrates unique fully
automated detection systems coupled with 4D Computed Tomography
(CT) Written by authors who are world-class researchers in the
biomedical imaging sciences Includes extensive references at the
end of each chapter to enhance further study Ayman El-Baz is a
professor, university scholar, and chair of the Bioengineering
Department at the University of Louisville, Louisville, Kentucky.
He earned his bachelor's and master's degrees in electrical
engineering in 1997 and 2001, respectively. He earned his doctoral
degree in electrical engineering from the University of Louisville
in 2006. In 2009, he was named a Coulter Fellow for his
contributions to the field of biomedical translational research. He
has 17 years of hands-on experience in the fields of bio-imaging
modeling and noninvasive computer-assisted diagnosis systems. He
has authored or coauthored more than 500 technical articles (132
journals, 23 books, 57 book chapters, 211 refereed-conference
papers, 137 abstracts, and 27 U.S. patents and disclosures). Jasjit
S. Suri is an innovator, scientist, a visionary, an industrialist,
and an internationally known world leader in biomedical
engineering. He has spent over 25 years in the field of biomedical
engineering/devices and its management. He received his doctorate
from the University of Washington, Seattle, and his business
management sciences degree from Weatherhead School of Management,
Case Western Reserve University, Cleveland, Ohio. He was awarded
the President's Gold Medal in 1980 and named a Fellow of the
American Institute of Medical and Biological Engineering for his
outstanding contributions in 2004. In 2018, he was awarded the
Marquis Life Time Achievement Award for his outstanding
contributions and dedication to medical imaging and its management.
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Lung Cancer and Imaging (Hardcover)
Ayman El-Baz, Jasjit Suri; Contributions by Ahmed Shaffie, Ahmed Soliman, Ali Mahmoud, …
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R3,442
Discovery Miles 34 420
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Ships in 12 - 17 working days
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The book covers novel strategies of state of the art in engineering
and clinical analysis and approaches for analyzing abdominal
imaging, including lung, mediastinum, pleura, liver, kidney and
gallbladder. In the last years the imaging techniques have
experienced a tremendous improvement in the diagnosis and
characterization of the pathologies that affect abdominal organs.
In particular, the introduction of extremely fast CT scanners and
high Magnetic field MR Systems allow imaging with an exquisite
level of detail the anatomy and pathology of liver, kidney,
pancreas, gallbladder as well as lung and mediastinum. Moreover,
thanks to the development of powerful computer hardware and
advanced mathematical algorithms the quantitative and
automated\semi automated diagnosis of the pathology is becoming a
reality. Medical image analysis plays an essential role in the
medical imaging field, including computer-aided diagnosis,
organ/lesion segmentation, image registration, and image-guided
therapy. This book will cover all the imaging techniques, potential
for applying such imaging clinically, and offer present and future
applications as applied to the abdomen and thoracic imaging with
the most world renowned scientists in these fields. The main aim of
this book is to help advance scientific research within the broad
field of abdominal imaging. This book focuses on major trends and
challenges in this area, and it presents work aimed to identify new
techniques and their use in medical imaging analysis for abdominal
imaging.
This book deals with the acquisition and extraction of the
various morphological features of the electrocardiogram
signals.
In the first chapters the book first presents data fusion and
different data mining techniques that have been used for the
cardiac state diagnosis. The second part deals with heart rate
variability (HRV), a non-invasive measurement of cardiovascular
autonomic regulation.
Next, visualization of ECG data is discussed, an important part
of the display in life threatening state. Here, the handling of
data is discussed which were acquired during several hours.
In the following chapters the book discusses aortic pressure
measurement which is of significant clinical importance. It
presents non-invasive methods for analysis of the aortic pressure
waveform, indicating how it can be employed to determine cardiac
contractility, arterial compliance, and peripheral resistance. In
addition, the book demonstrates methods to extract diagnostic
parameters for assessing cardiac function. Further the measurement
strategies for contractile effort of the left ventricle are
presented.
Finally, the book concludes about the future of cardiac signal
processing leading to next generation research topics which
directly impacts the cardiac health care.
The editors thank Biocom Technologies for the provided
scientific material and help in writing the book.
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