‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).

1. Technology Trend Analysis through
the RI-Biomics Technology Professional Workforce Development
Program and Information System Development
Vol.04 November 2014KARA ISSUE PAPERAdvanced Research Center for Nuclear Excellence Issue Paper
RI-Biomics Based Bio-sensing Application Technology Development

2. Working Definition
The term, “Radiomics” can be thought of as analogous to “genomics” whereby medical images are broken down into a
large number of complex component parts and tagged and made available in a database.This is similar to the process in
which patient DNA information is acquired and made available for analysis in a genomic database.The term “radiome”
is similarly analogous to the genome. Both the image “radiome” and the patient or tumor genome can be analyzed and
combined resulting in paired gene expression data and medical images including diagnostic information from a study
cohort.These combined data can be then correlated with a public database of genomic data and image data and this can
be utilized to improve clinical outcomes using a closely matched population. This will ultimately result in the ultimate goal
of personalized/precision based medical practice.
Methodology
Radiomics (tagged medical images) can be utilized along with genomic data to integrate big data from both imaging
informatics and genomic data with 3-dimensional 3D or 4D ComputerAided Detection/Diagnosis(CADe/CADx) for Clinical
Decision Support(CDS) through a publically available free, on-line software platform such as ePAD(Stanford university).
A super computer(IBM) such as the one currently being introduced at Memorial Sloan-Kettering Cancer Center can
read medical records and recommend treatment, particularly for cancer patients as a clinician assistant.The field of
Cognitive computing is emerging to overcome such limitations of the current generation of computers, resulting in a new
generation of computer applications and technologies that are much better able to “understand” medical data and its
relationships.
Perspectives
A technique, referred to as a “liquid biopsy,” (Johns Hopkins) has been applied to find telltale genetic material from a vial
of blood for diagnostic purposes.This technique and others have allowed us to delve into the“cockpit of cancer” showing
how a series of genetic mutations, adding up silently over decades, turn cells cancerous. Radiomics particularly combined
with “liquid biopsy” genomics from these types of blood tests will provide much earlier diagnosis and will allow a new
era of mass screening for various diseases through preclinical and clinical stages.
Radiomics : Novel Paradigm of Deep Learning
for Clinical Decision Support02
Advanced Research Center for Nuclear Technology
KARA Issue Paper Vol.04 Radiomics Novel Paradigm of Deep Learning
for Clinical Decision Support
03 | Introduction
04 | Background & Need
06 | Overview
07 | Cases
08 | Perspectives
10 | Expectation
11 | References
CONTENTS
Abstract
Jongho Kim, MD, PhD
Division of Nuclear Medicine
Department of Diagnostic Radiology and
Nuclear Medicine
Wook Jin Choi, PhD
Department of Radiation Oncology
University of Maryland School of Medicine.
University of Maryland Medical Center.
22 S Greene St, Baltimore, MD, 21201, USA

3. Novel radiomics based approaches can be used to identify new imaging biomarkers
when long-term clinical outcomes such as mortality are not known, relying on first
acquiring paired gene expression data and medical images at diagnosis from a study
cohort, then leveraging the public gene expression data that contain clinical outcomes
from a closely matched population. A critical step in this approach involves predicting
the image features in the study cohort in terms of gene signatures.The prognostic
significance of these gene signatures in public data sets for which gene expression and
survival data are available revolutionize and accelerate the application of personalized
medicine(Figure 1).
Korean Association for
Radiation Application
Introduction
03Advanced Research Center for Nuclear Technology
KARA Issue Paper Vol.04
Public
radiomic
data into
personalized
medicine
Imaging
Informatics
Figure 1. Radiomics as public-to-person data communication
Radiomics refers to the comprehensive quantification of disease phenotypes by
applying a large number of quantitative image features from medical images such
as positron emission tomography(PET), computed tomography(CT), and magnetic
resonance imaging(MRI), etc. In the genomic and epigenomic era, personalized
medicine aims to tailor medical care to the individual by characterizing tissue
heterogeneity through advanced high-throughput molecular technologies. Within this
context, the fusion of the radiome and genome would be an essential part of clinical
science and practice in a near future.

4. Radiomics : Novel Paradigm of Deep Learning
for Clinical Decision Support04
Korean Association for
Radiation Application
Background & Need
By assessing tissue characteristics noninvasively, medical imaging is likely to be a
major factor in the advancement of clinical practice and science for human health
taking advantage of seamless integration with genomic data particularly from then
public domain.
Radiomics data can be extracted from multi-modal imaging data and combined
with genomics by leveraging existing already funded public gene expression data
containing clinical outcomes such as mortality from a closely matched population,
representing 4th
generation imaging informatics following anatomic, functional and
molecular imaging.
With the advance in computer technologies to deal with exponentially rising data even
from a large volume of unstructured data(Giga 109
->Tera, 1012
->Peta, 1015
->Exa, EB, 1018
byte) through high performance processors,
- ‌IBM’s Watson computer vanquished human contests on the TV quiz show named
Jeopardy! with its performance at 80 TeraFLOPs and 500 gigabytes, the equivalent of a
million books per second.
-‌To overcome the weakness of the standard way of building computers, von Neumann
computers, such as limited comprehension, cognitive computing is emerging. IBM recently
unveiled a new processor that uses only a fraction of the energy that today’s processors do,
but that can deliver radically greater returns on a brain-like synaptic scale.
- ‌Cognitive computing can be compared to the standard von Neumann computer(Figure
2) as tracked in the red line consisting of three components, a CPU, hard drive and
RAM(fast accessible memory). Today the von Neumann plot(solid red) is well ahead
on the development curve when compared to the futuristic cognitive(neuromorphic)
computer(solid green) but where the computing power of traditional computers and
neuromorphic computer intersect will be usher in the dawn of a new computing paradigm.
4th
generation
imaging
informatics
Computer
technology
Cognitive
Computer
IBM developed a system that can read medical records to guide management,
particularly for cancer patients. This has been launched at hospitals in the U.S.
including Memorial Sloan-Kettering Cancer Center, NewYork, functioning as a clinician
assistant. Recently, cognitive computing has emerged to overcome such weakness
of the standard way of building computers, von Neumann computers, as limitations
including comprehension. This has occurred in parallel with new IBM processing
capabilities that can deliver radically greater returns using biological mimicking
approaches such as a brain-like synaptic scale.

5. 05Advanced Research Center for Nuclear Technology
KARA Issue Paper Vol.04
Figure 2. Neuromorphic vs. standard von Neumann computer

6. Radiomics : Novel Paradigm of Deep Learning
for Clinical Decision Support06
PlanA vs. B
Due to the spatial and temporal heterogeneity of diseases such as tumors, invasive
techniques such as biopsies to extract small portions of tissue do not allow for a
complete characterization as a whole, therefore a comprehensive view of medical
imaging has great potential for monitoring disease progression including response
to therapy if associated with genomics or proteomics already available in the public
domain. One of the greatest challenges is to incorporate the personalized/precision
approach into routine clinical practice.
ePAD, a free software for quantitative imaging informatics, was developed for the
National Cancer Institute by researchers at Stanford in the radiology department.
It can serve as a model-platform for radiomics for a wide range of imaging-based
projects combined with on-line genetic data, by making the content of images(imaging
informatics) machine accessible and intelligible to allow electronic correlation with
other biomics such as genomics as well as clinical informatics. In parallel, development
and translation of basic biomedical informatics will improve radiology practice and
decision making tools to efficiently and thoroughly capture the semantic terms
radiologists use to describe lesions.
The idea of treating the advanced rather than early diseases is not ideal as a primary
approach, but instead it is preferable to reduce cancer deaths using screening to catch
cancer early. All these preventive steps represent “Plan B” but unfortunately much
less attention and funding has been devoted to early detection of cancer but when
prevention works, it has better results than any drug. In the U.S., the chance of dying
from colorectal cancer is 40 percent lower than it was in 1975; a decrease mostly due
to colonoscopy screening as well as melanoma, and skin cancer, treatable with surgery
if caught early.Therefore, the Plan B needs to be Plan A and screening should be the
primary approach for treating diseases.This is already the case in screening patients
for breast cancer,The National Lung ScreeningTrial(NLST) is an excellent example
of this approach for lung cancer. Radiomics, and imaging informatics combining
with genomics from blood test, “liquid biopsy” will propel personalized medicine for
prevention, early detection by screening and treatment response of various disease
along with preclinical stage of drug development using genetically modified animal
models.
Radiomics
regarding
Heterogeneity
Stanford ePAD
Platform for
Radiomics
Korean Association for
Radiation Application
Overview

7. 07Advanced Research Center for Nuclear Technology
KARA Issue Paper Vol.04
Harvard
Radiomics at
Dana-Farber
Cancer
Institute
National
Lung Screening
Trial (NLST)
To identify prognostic imaging biomarkers in non-small cell lung cancer(NSCLC) using
PET/CT by means of a radiomics strategy, gene expression and medical images were
integrated in patients for whom survival outcomes are not available by leveraging
survival data in public gene expression data sets. A radiomics strategy for associating
image features with clusters of co-expressed genes(metagenes) was defined. First,
a radiomics correlation map is created for a pairwise association between image
features and metagenes. Next, predictive models of metagenes are built in terms
of image features by using sparse linear regression. Similarly, predictive models of
image features are built in terms of metagenes. Finally, the prognostic significance
of the predicted image features are evaluated in a public gene expression data set
with survival outcomes.The predicted image features were mapped to a public gene
expression data set with survival outcomes, tumor size, edge shape, and sharpness
ranked for prognostic significance.
Human cancers exhibit strong phenotypic differences that can be visualized
noninvasively by medical imaging. Radiomics refers to the comprehensive
quantification of tumor phenotypes by applying a large number of quantitative
image features, which reveal prognostic radiomic signature as well as capture tumor
heterogeneity, associated with underlying gene-expression patterns.
The National Lung Screening Trial(NLST) was a randomized trial of lung-cancer-
specific mortality among participants in an asymptomatic high-risk cohort who
underwent screening with the use of low-dose helical computed tomography(CT) as
compared with screening with the use of single-view poster anterior chest radiography.
It is possible to perform research analysis of pulmonary nodules(0.3 mm - 3cm) for
lung cancer diagnosis, particularly radiomics research and identify the association
of a nodule’s radiome(personalized CAD development) and genome(gene signature
for early diagnosis), malignancy of nodule, and subsequently predict prognosis
and therapy response using NLST metadata. Moreover, the use of NLST data can
contribute to personalized medicine for screening in terms of tumor heterogeneity of
adenocarcinoma vs. squamous cell cancer with further characterization using PET/CT
along with developing genomic biomarkers.
Stanford
Radiomics
using PET/CT
Korean Association for
Radiation Application
Cases

8. Radiomics : Novel Paradigm of Deep Learning
for Clinical Decision Support08
Korean Association for
Radiation Application
Perspectives
In order to break the current computing paradigms, neuromorphic chips have the
capacity to “learn”.The object recognition software used at Baidu and Google, for
example “Google Cat”, is trained on the ImageNet database, boasting thousands of
object categories and it’s definitely an achievement to make a state-of-art chip of the
scale but it still falls far short of achieving the capabilities of the human brain.
-‌With support from DARPA(Defense Advanced Research Projects Agency, U.S), IBM’s
chip research project, the SyNAPSE(Systems of Neuromorphic Adaptive Plastic Scalable
Electronics) aims to create a human brain-like hardware/software system that breaks the
von Neumann paradigm. Hence neuromorphic chips such as TrueNorth(IBM) pack the
memory, computation and communication components into little modules to proced locally
but communicate with each other easily and quickly using a revolutionary new technology
inspired by the human brain traditional and cognitive computer as left and right brain
respectively(Figure 3).
Can cognitive
computing
learn to defeat
the problem
in the
real world ?
Figure 3. Cognitive or synaptic computer

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Radiomics
platform
Figure 4. Radiomics platform with ePAD and pCAD
A medical image informatics platform will be developed based on radiomics as
shown in Figure 4. The platform process useful information by integration of big
data from both imaging and genomic(and epigenomic) along with development of
3-dimensional(3D) or 4D Computer Assisted Diagnosis(CAD) and/or Clinical Decision
Support(CDS), and provide information through a free software for quantitative
imaging informatics such as ePAD.This radiomics strategy for identifying imaging
biomarkers for prognostic phenotype may enable more rapid evaluation and
development of novel imaging modalities, thereby accelerating their translation to
personalized medicine.

10. 01 | ‌Radiomic, novel imaging biomarkers, can be combined digitally with genomics
particularly in the public domain as well as clinical informatics in the private
domain by leveraging the public gene expression data containing clinical
outcomes such as mortality from a closely matched population.This will lead to
the advancement of public health through mass screening for early detection of
the diseases.
02 | ‌Radiation technology(RT) can be utilized to substantially integrate biotechnology
(BT) and information technology(IT) particularly by developing a futuristic
neuromorphic chip for cognitive computing as a Korean secure advanced
technology, which will lead to new demands and advances in semiconductor
technology.
03 | ‌Fusion technology of neuroscience and cognitive computing, as a mutually
beneficial platform will advance future Korean science and technology in
parallel with U.S. Brain initiatives(Brain Research through Advancing Innovative
Neurotechnologies) and E.U. Human Brain Project as well as Japanese Brain/
MINDS(Brain Mapping by Integrated Neurotechnologies for Disease Studies).
Korean Association for
Radiation Application
Expectation
Radiomics : Novel Paradigm of Deep Learning
for Clinical Decision Support10

11. ⊙ ‌Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics
approach, Nature Communications 2014
⊙ ‌Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by
Leveraging Public Gene Expression Microarray Data-Methods and Preliminary
Results, Radiology 2012
⊙ ‌Results of theTwo Incidence Screenings in the National Lung ScreeningTrial, New
England Journal of Medicine 2013
⊙ ‌Detection of circulating tumor DNA in early- and late-stage human malignancies,
ScienceTranslational Medicine 2014
Korean Association for
Radiation Application
References
※Please contact the following regarding inquiries about this report.
Korean Association for Radiation Application
Project supervisor : Tai-Jin Park
Phone : +82-2-3490-7120
Email : tjpark@ri.or.kr
Supervisor: Sol-Ah Jang
Phone : +82-2-3490-7106
Email : sol8485@ri.or.kr
Korea Atomic Energy Research Institute
Project supervisor : Sang-Hyun Park
Phone: +82-63-570-3370
Email : parksh@kaeri.re.kr
11Advanced Research Center for Nuclear Technology
KARA Issue Paper Vol.04

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