|Emerging Tools for Computer-aided Diagnosis and
|The ability to more accurately predict and prevent disease has
the potential to transform clinical practice by improving response to
specific treatment regimens and decreasing morbidity and mortality.
Part of what limits the accuracy to which we can predict and prevent
disease results from our limited understanding of the relationship
between clinical presentation and disease progression .
|Although vast amounts of data are collected at clinical presentation,
ranging from macro-scale Magnetic Resonance Imaging (MRI) scans,
to micro-scale pathology slides, to nano-scale proteins and genes, there
are challenges associated with analyzing, combining, and correlating
these data to make diagnostic, prognostic, and theranostic predictions
[2-4]. Computerized image analysis and data integration methods have
the potential to improve our understanding of the relationship between
these heterogeneous multi-format, multi-scale data to better predict
disease outcomes and treatment responses.
|Computer-based Image Analysis
|Advances in imaging hardware and computational processing have
catalyzed the growth of digital imaging and computer-based image
analysis in pathology. Digitization of entire glass slides (whole-slide
imaging) has increased the amount of morphologic data that can be
obtained from tissue . Whole-slide imaging has also aided pathologists
with automated field selection and has begun to allow pathologists to
supplement steps in image analysis (i.e., feature extraction, feature
selection, dimensionality reduction, and classification) with automated
machine-learning algorithms to minimize subjectivity and augment
quality assurance [3,5,6].
|One such tool, developed, evaluated, and applied by Beck et al., is
an unbiased image analysis system called C-Path . C-Path has been
used to identify feature sets in tissue microarrays to predict 5-year
survival of patients with breast carcinoma. Using a machine-learning
algorithm and thousands of morphologic descriptors, the C-Path
prognostic model accurately predicted good and poor prognosis
patients and identified clinically significant morphologic features,
some of which were not previously recognizable using traditional
quantitative pathology techniques. Although the molecular basis for
the prognositically significant morphologic phenotypes has yet to
be elucidated, and the effectiveness of computer-aided pathological
interpretation has yet to be established on whole-slide images and tested
on a diverse set of images, this approach shows great potential because
it has predicted survival outcomes with a high degree of statistical
significance and has the potential for further refinement. This example
illustrates the potential for using automated, unbiased image analysis
and machine-learning systems for producing standardized, objective,
reproducible results that could eventually support clinical practice .
|Heterogeneous Data Integration
|Advances in computational processing have enabled quantitative
integration of heterogeneous, multi-format, multi-scale dataparticularly
imaging and genomic data [2,9-12].
|In one of the first applications to combine imaging and nonimaging
(protein expression) data, Lee and Madabhushi developed
a Generalized Fusion Framework (GFF) to integrate the micro-scale
morphological features obtained from digital histopathology slides with
nano-scale protein expression measurements from mass spectrometry
. This GFF was created to observe whether quantitative integration
of image-based signatures from digital histopathology slides with
corresponding peptide measurements from mass spectrometry could
be used to differentiate prostate cancer progressors with prostate
cancer non-progressors. The challenge of integrating this multi-scale,
multi-modal, multi-protocol data was overcome by combining the 3
data modalities (architectural histopathology features, morphological
histopathology features, and m/z mass spectrometry features in 51, 100,
and 570 dimensions, respectively) into a common low-dimensional
meta-space projection with 3 dimensions using principal component
analysis. This projection was then normalized, concatenated, and
reduced a second time with principal component analysis to yield the
low-dimensional integration product of the original high-dimensional
data. Results reflected the suitability of using this GFF to integrate
heterogeneous multi-format, multi-scale data for differentiating
between patients with different disease profiles.
|Later applications by Madabhushi et al., have explored additional
methods for combining data modalities beyond principal component
analysis (e.g., non-linear dimensionality reduction methods) and
correlations between disease and markers in digital pathology ,
gene and protein expression , spectroscopy [12,14], ultrasound
, and MRI [9,14,16].
|While computer-based image analysis, heterogeneous data
integration methods, and computer-aided prognostics are currently
demonstrating their efficacy in the pre-operative or pre-therapeutic
cancer population, they will inevitably have applicability in other fields.
|In cardiovascular medicine, for instance, large amounts of
macro-scale heart morphology and phenotype data (from MRI,
hemodynamics, and echocardiograms), micro-scale whole-slide
imaging data (from biopsies, donors, explants, and device placements),
and nano-scale gene expression and transcriptome data are being
collected at several institutions for clinical and research purposes
. Because typical cardiac pathology scoring systems are rather rudimentary, such as the Dallas criteria for myocarditis  and the
International Society for Heart and Lung Transplantation scoring
of rejection in cardiac allografts , there is rich opportunity for
computer-aided interpretation and multi-modality integration to
provide new insights into myocardial disease mechanisms, severity and
prognosis. As with the oncology applications described above, a key
step in these myocardial applications will be correlation with clinical
outcomes and current clinical reference standards. As heterogeneous
data integration tools become increasingly sophisticated and validated,
they could provide a rational basis for the identification of interpatient
distinctions necessary for greater individualization of therapeutics.
|Computers are becoming increasingly ready to supplement
and enhance imaging (MRI, ultrasound), morphologic information
(tissue), and molecular classification (whole-genome sequencing,
expression profiling, proteomics, and metabolomics) with diagnostic,
prognostic, and theragnostic predictions . These computer-based
tools for heterogeneous data integration have begun to demonstrate
their effectiveness in large retrospective studies and will soon be ready
for prospective, multi-institutional validation studies as the next step
before adoption into clinical practice.
|This work was supported by the Myocardial Applied Genomics
Network (MAGNet) National Institutes of Health grant R01HL105993.
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- Myocardial Applied Genomics Network (MAGNet) (2011)
- Baughman KL (2006) Diagnosis of myocarditis: death of Dallas criteria. Circulation 113: 593-595.
- Stewart S1, Winters GL, Fishbein MC, Tazelaar HD, Kobashigawa J, et al . (2005) Revision of the 1990 working formulation for the standardization of nomenclature in the diagnosis of heart rejection. J Heart Lung Transplant 24: 1710-1720.