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A vision for global multimedia structured reporting
Poster No.:
C-1784
Congress:
ECR 2013
Type:
Scientific Exhibit
Authors:
D. J. Vining , U. Salem , C. DURAN , L. Jiang , A. Pitici , I.
1
1
3
3
1
3
2
3
3 1
Aghenitei , C. Popovici , M. Jurca , R. Rosu ; Houston, TX/US,
2
3
Beijing/CN, Chapel Hill, NC/US
Keywords:
Computer applications, eHealth, Professional issues, RIS, PACS,
Teleradiology, Structured reporting, Computer ApplicationsGeneral, Health policy and practice
DOI:
10.1594/ecr2013/C-1784
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Purpose
The need for structured reporting in diagnostic radiology is becoming paramount
as electronic medical records evolve worldwide (1-3). However, attempts to develop
structured reporting solutions have not been widely adopted due to the tedious and timeconsuming nature of how a radiologist must interact with these systems (4-5).
We introduce a new multimedia structured reporting system, called ViSion, which allows
a radiologist to view and speak naturally about image findings while simultaneously
capturing structured data in order to generate a multimedia report that can be delivered in
multiple languages and used to support advanced applications such as disease tracking
(Figure 1) (6).
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Fig. 1: ViSion multimedia structured reporting.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
Images for this section:
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Fig. 1: ViSion multimedia structured reporting.
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Methods and Materials
We developed a client-server software solution that allows a radiologist to record
key image findings from any picture archiving and communication system (PACS) or
advanced imaging workstation while he or she dictates descriptions of those findings.
The ViSion client software runs in parallel with any PACS or advanced imaging software
that operates on a computer workstation using the Windows® operating system (Figure
2).
Fig. 2: The ViSion system works by capturing key images from any PACS or
advanced imaging workstation, tagging the images with anatomy and pathology
terminology extracted from verbal dictations, and assembling a multimedia report on a
computer server that is accessible via a web browser.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
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As a radiologist identifies image findings, the he or she presses a speech microphone
or keyboard button to initiate a screen capture and record a verbal description of each
finding. The image and speech data are uploaded to a cloud-based server where
metadata describing the anatomical location, radiological observation/diagnosis, disease
metrics, medical priority, and target lesion designation are extracted from the transcribed
speech and used to tag the images in a database (Figure 3).
Fig. 3: A picture is worth 1000 words in whatever language is spoken, but ViSion tags
each key image with just two words describing a finding's anatomical location and
pathology (i.e., radiological observation or diagnosis).
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
Alternatively, a radiologist may use pull-down menus in the reporting system to tag
images. The data are then assembled into a multimedia structured report that organizes
the image findings by anatomical categories. Alternatively, ViSion can display the image
findings in a graphical representation of a patient with image icons linked to specific
anatomical sites. The assignment of a medical priority to each finding on a 5-point scale
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(from Incidental to Life-threatening) enables the automatic notification of critical results
when a ViSion report is signed by the radiologist.
The medical ontology (controlled vocabulary with defined relationships between terms)
used to support ViSion is described in another EPOS poster, "Development of the ViSion
Ontology." The basic principle of the ViSion ontology is that anatomy and pathology terms
are paired to create radiological observations and diagnoses (Figure 4).
Fig. 4: An observation or diagnosis in the ViSion system is created by the pairing of an
anatomy term with a pathology term. The ViSion ontology contains over 12,000 such
observations and diagnoses. Secondary characteristics can also be assigned in order
to provide additional detail for a particular finding. In this example, the terms "Colon"
+ "Polyp" are combined, and the secondary characteristics that may be assigned are
specific to colonic polyps.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
The ViSion ontology contains over 12,000 observations and diagnoses that have been
translated to multiple languages. As a result, a ViSion report can be automatically
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translated to any language covered by the system. ViSion does not translate the entire
narrative dictation associated with each finding but only the metadata which allows for
the communication of essential medical information (Figures 5-7).
Fig. 5: This is an example of a ViSion report generated in English. In addition to
the labeling of an image finding with anatomy and pathology terms, a finding may
be labeled with a priority level, disease metric, and target lesion designation. The
incorporation of the radiologist's verbal dictation, found under the audio column, allows
for the radiologist's unlimited freedom of expression.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
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Fig. 6: Translation of the ViSion report in Figure 5 to Chinese.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
Fig. 7: Translation of the ViSion report in Figure 5 to Arabic.
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References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
ViSion provides the ability to link image findings from serial radiological examinations
in order to generate disease timelines illustrating progression of disease at specific
anatomical sites (Figure 8).
Fig. 8: ViSion provides the ability to link image findings from serial examinations
in order to create disease timelines that illustrate the progression of disease at a
particular anatomical site with images and graphed metrics.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
Particular image findings in a ViSion report can be designated by the radiologist as "target
lesions" from which ViSion automatically calculates response evaluation criteria in solid
tumors (RECIST) curves (Figure 9) (7).
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Fig. 9: Specific image findings in a ViSion report can be designated as "target lesions"
that are used to calculate RECIST curves. Analysis of individual target lesions may
identify mixed response to targeted therapies.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
The concept of linking image findings may also be used to associate image data from
different medical disciplines, including pathology, histology, and genomics (Figure 10).
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Fig. 10: The linking of image findings is not limited to radiological images but can be
applied to multiple image-based medical disciplines. In this example, a colonoscopy
image is linked to CT colonography, pathology, histology, and genomic images. Note
how the diagnosis changes from a non-specific entity "Mass" to "Adenocarcinoma" as a
more specific diagnosis is rendered.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
Finally, ViSion is capable of creating a "composite" report that shows the most recent
image findings specific to anatomy, regardless of modality, so that the entire history of a
patient can be shown in a single display (Figure 11).
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Fig. 11: ViSion's "composite" view shows the entire history of a patient in a single view
by displaying the most recent image findings specific to anatomy.
References: Department of Diagnostic Radiology, University of Texas MD Anderson
Cancer Center - Houston/US
Images for this section:
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Fig. 2: The ViSion system works by capturing key images from any PACS or advanced
imaging workstation, tagging the images with anatomy and pathology terminology
extracted from verbal dictations, and assembling a multimedia report on a computer
server that is accessible via a web browser.
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Fig. 3: A picture is worth 1000 words in whatever language is spoken, but ViSion
tags each key image with just two words describing a finding's anatomical location and
pathology (i.e., radiological observation or diagnosis).
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Fig. 4: An observation or diagnosis in the ViSion system is created by the pairing of an
anatomy term with a pathology term. The ViSion ontology contains over 12,000 such
observations and diagnoses. Secondary characteristics can also be assigned in order
to provide additional detail for a particular finding. In this example, the terms "Colon"
+ "Polyp" are combined, and the secondary characteristics that may be assigned are
specific to colonic polyps.
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Fig. 5: This is an example of a ViSion report generated in English. In addition to the
labeling of an image finding with anatomy and pathology terms, a finding may be labeled
with a priority level, disease metric, and target lesion designation. The incorporation of the
radiologist's verbal dictation, found under the audio column, allows for the radiologist's
unlimited freedom of expression.
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Fig. 6: Translation of the ViSion report in Figure 5 to Chinese.
Fig. 7: Translation of the ViSion report in Figure 5 to Arabic.
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Fig. 8: ViSion provides the ability to link image findings from serial examinations in
order to create disease timelines that illustrate the progression of disease at a particular
anatomical site with images and graphed metrics.
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Fig. 9: Specific image findings in a ViSion report can be designated as "target lesions"
that are used to calculate RECIST curves. Analysis of individual target lesions may
identify mixed response to targeted therapies.
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Fig. 10: The linking of image findings is not limited to radiological images but can be
applied to multiple image-based medical disciplines. In this example, a colonoscopy
image is linked to CT colonography, pathology, histology, and genomic images. Note
how the diagnosis changes from a non-specific entity "Mass" to "Adenocarcinoma" as a
more specific diagnosis is rendered.
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Fig. 11: ViSion's "composite" view shows the entire history of a patient in a single view
by displaying the most recent image findings specific to anatomy.
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Results
ViSion provides a simple solution for structured reporting that can be used worldwide
and applied to multiple medical disciplines. ViSion's fundamental concept of linking
"image findings" supports many advanced applications including quantitative disease
assessment, electronic notification of critical results, and data mining of structured
radiologic information.
Conclusion
The adoption of structured reporting is essential to transforming radiology from a
qualitative to quantitative process. ViSion offers a unique solution for creating multimedia
structured reports with worldwide potential due to its ability to operate in multiple
languages and to support advanced applications that will add value to the radiologist's
role in the healthcare enterprise.
Images for this section:
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Fig. 1: ViSion multimedia structured reporting.
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References
1.
2.
3.
4.
5.
6.
7.
Hall FM. The radiology report of the future. Radiology 2009;251:313-316.
Schwartz LH et al. Improving communication of diagnostic radiology findings
through structured reporting. Radiology 2011; 260: 174-181.
RSNA Informatics. Radiology Reporting Initiative. Radiological Society of
North America Web site. http://www.rsna.org/Reporting_Initiative.aspx.
Accessed January 27, 2013.
Johnson AJ. All structured reporting systems are not created equal.
Radiology 2012;262:726-727.
Johnson AJ et al. Cohort study of structured reporting compared with
conventional dictation. Radiology 2009; 253:74-80.
Martino A. Sketching a new reality: what will the radiology report of the
future look like? http://www.acr.org/News-Publications/News/NewsArticles/2012/ACR-Bulletin/201203-Rad-Report-of-Future Accessed January
28, 2013.
Eisenhauer EA et al. New response evaluation criteria in solid tumours:
revised RECIST guideline (version 1.1). EJC 2009;45:228-247.
Personal Information
Disclosure: David J. Vining, MD, is the founder, CEO, and a major stockholder of
VisionSR which has an option agreement with the University of Texas MD Anderson
Cancer Center to license the ViSion technology for commercialization.
David J. Vining, MD, Department of Diagnostic Radiology, UT MD Anderson
Cancer Center, Houston, Texas, USA, [email protected], www.facebook.com/
ViSionReporting
Usama Salem, MD, Department of Diagnostic Radiology, UT MD Anderson Cancer
Center, Houston, Texas, USA, [email protected]
Cihan Duran, MD, Department of Diagnostic Radiology, UT MD Anderson Cancer Center,
Houston, Texas, USA, [email protected]
Liming Jiang, MD, Department of Radiology, Chinese Academy of Medical Sciences,
Beijing, China, [email protected]
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Andrea
Pitici,
Eloquentix,
[email protected]
Iulian Aghenitei, Eloquentix,
[email protected]
Cristi
Popovici,
Eloquentix,
[email protected]
Inc,
Inc,
Inc,
Chapel
Hill,
North
Carolina,
USA,
Chapel
Hill,
North
Carolina,
USA,
Chapel
Hill,
North
Carolina,
USA,
Mihai Jurca, Eloquentix, Inc, Chapel Hill, North Carolina, USA, [email protected]
Radu Rosu, Eloquentix, Inc, Chapel Hill, North Carolina, USA, [email protected]
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