U.S. patent application number 12/621363 was filed with the patent office on 2010-05-13 for systems and methods for automated diagnosis and decision support for breast imaging.
Invention is credited to Murat Dundar, Glenn Fung, Sriram Krishnan, R. Bharat Rao.
Application Number | 20100121178 12/621363 |
Document ID | / |
Family ID | 33551977 |
Filed Date | 2010-05-13 |
United States Patent
Application |
20100121178 |
Kind Code |
A1 |
Krishnan; Sriram ; et
al. |
May 13, 2010 |
Systems and Methods for Automated Diagnosis and Decision Support
for Breast Imaging
Abstract
CAD (computer-aided diagnosis) systems and applications for
breast imaging are provided, which implement methods to
automatically extract and analyze features from a collection of
patient information (including image data and/or non-image data) of
a subject patient, to provide decision support for various aspects
of physician workflow including, for example, automated diagnosis
of breast cancer other automated decision support functions that
enable decision support for, e.g., screening and staging for breast
cancer. The CAD systems implement machine-learning techniques that
use a set of training data obtained (learned) from a database of
labeled patient cases in one or more relevant clinical domains
and/or expert interpretations of such data to enable the CAD
systems to "learn" to analyze patient data and make proper
diagnostic assessments and decisions for assisting physician
workflow.
Inventors: |
Krishnan; Sriram; (Exton,
PA) ; Rao; R. Bharat; (Berwyn, PA) ; Dundar;
Murat; (Malvern, PA) ; Fung; Glenn; (Bryn
Mawr, PA) |
Correspondence
Address: |
SIEMENS CORPORATION;INTELLECTUAL PROPERTY DEPARTMENT
170 WOOD AVENUE SOUTH
ISELIN
NJ
08830
US
|
Family ID: |
33551977 |
Appl. No.: |
12/621363 |
Filed: |
November 18, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10877129 |
Jun 25, 2004 |
7640051 |
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12621363 |
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60482293 |
Jun 25, 2003 |
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60541360 |
Feb 3, 2004 |
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Current U.S.
Class: |
600/411 ;
600/443 |
Current CPC
Class: |
Y10S 128/922 20130101;
G06T 2207/10088 20130101; G06T 7/0012 20130101; G16H 50/20
20180101; Y10S 128/924 20130101; G06T 2207/10116 20130101; Y10S
128/92 20130101; G16H 40/63 20180101; G06T 2207/20084 20130101;
Y10S 128/923 20130101; G16H 30/20 20180101; G06T 2207/30068
20130101 |
Class at
Publication: |
600/411 ;
600/443 |
International
Class: |
A61B 5/055 20060101
A61B005/055; A61B 8/14 20060101 A61B008/14 |
Claims
1. A system for providing automatic diagnosis and decision support,
comprising: an image acquisition device for obtaining information
from image data of a subject patient; a phase spotting unit for
receiving non-image data records of the subject patient, extracting
an unstructured free-text source from the non-image data records,
and identifying phrases of interest from the free-text source; a
rules database including a list of rules for providing inferences
drawn from the identified phrases of interest; and an automatic
diagnosis unit for providing diagnosis using the information from
the image data and the inferences drawn from the identified phrases
of interest.
2. The system of claim 1, wherein the image data includes a breast
of the subject patient and the automatic diagnosis unit provides
diagnosis pertaining to abnormal breast tissue.
3. The system of claim 1, further comprising a patient management
module for recommending therapies to the patient based on the
provided diagnosis.
4. The system of claim 1, wherein the image data is ultrasound
image data and wherein the automatic diagnosis unit automatically
extracts one or more features from the ultrasound data in providing
diagnosis.
5. The system of claim 1, wherein the image data is acquired using
an ultrasound medical imaging device.
6. The system of claim 5, wherein the ultrasound medical imaging
device is a three-dimensional ultrasound.
7. The system of claim 1 wherein the automatic diagnosis unit is a
computer aided diagnosis system.
8. The system of claim 1, wherein the image data is acquired using
an X-ray mammography scanner, an MRI scanner, an ultrasound imager
or a combination thereof.
9. A method for providing automatic diagnosis and decision support,
comprising: obtaining information from image data of a subject
patient; receiving non-image data records of the subject patient,
extracting an unstructured free-text source from the non-image data
records, and identifying phrases of interest from the free-text
source; referencing a rules database to provide inferences drawn
from the identified phrases of interest; and automatically
providing diagnosis using the information from the image data and
the inferences drawn from the identified phrases of interest, where
in the above steps are performed using a computer assisted
diagnosis system.
10. The method of claim 9, wherein the image data includes a breast
of the subject patient and the automatic diagnosis comprises
diagnosis pertaining to abnormal breast tissue.
11. The method of claim 9, wherein one or more therapies are
recommended to the patient based on the provided diagnosis.
12. The method of claim 11, wherein the recommending of the one or
more therapies is influenced using user criteria provided through a
user interface.
13. The method of claim 11, wherein the recommending of the one or
more therapies is influenced clinical knowledge provided through a
clinical knowledge database.
14. The method of claim 11, wherein the recommending of the one or
more therapies utilizes an interactive diagnostic workflow
assistance module.
15. The method of claim 9, wherein automatically providing the
diagnosis is influenced by user criteria provided through a user
interface.
16. The method of claim 9, wherein automatically providing the
diagnosis is influenced by clinical knowledge provided through a
clinical knowledge database.
17. The method of claim 9, wherein automatically providing the
diagnosis utilizes an interactive diagnostic workflow assistance
module.
18. A method for providing automatic diagnosis and decision
support, comprising: obtaining information from image data of a
subject patient; receiving non-image data records of the subject
patient, extracting an unstructured free-text source from the
non-image data records, and identifying phrases of interest from
the free-text source; automatically providing diagnosis using the
information from the image data and the identified phrases of
interest; and recommending one or more therapies based on the
provided diagnosis and user criteria, where in the above steps are
performed using a computer assisted diagnosis system.
19. The method of claim 18, wherein the image data includes a
breast of the subject patient and the automatic diagnosis comprises
diagnosis pertaining to abnormal breast tissue.
20. The method of claim 18, wherein the image data is ultrasound
image data, X-ray mammography image data, MRI data, or a
combination thereof.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a Continuation of co-pending U.S. patent
application Ser. No. 10/877,129, filed Jun. 25, 2004, which claims
priority to U.S. Provisional Application Ser. No. 60/482,293, filed
on Jun. 25, 2003, and U.S. Provisional Application Ser. No.
60/541,360, filed on Feb. 3, 2004, all of which are fully
incorporated herein by reference.
TECHNICAL FIELD OF THE INVENTION
[0002] The present invention relates generally to systems and
methods for providing automated diagnosis and decision support for
medical imaging and, in particular, to CAD (computer-aided
diagnosis) systems and applications for breast imaging, which use
machine-learning techniques that enable such systems and
application to "learn" to analyze parameters extracted from image
data and/or non-image patient data of a subject patient for
purposes of providing automated decision support functions to
assist a physician in various aspects of physician workflow
including, but not limited to, diagnosing medical conditions
(breast tumors) and determining efficacious healthcare or
diagnostic or therapeutic paths for the subject patient.
BACKGROUND
[0003] Today, in most countries, women over a certain age (usually
40) are screened for breast cancer using X-ray mammography. If the
results of the X-ray mammography present suspicious or potentially
cancerous breast tissue, the patient is sent for a diagnostic
workup. Alternatively, the patient can be sent for a diagnostic
workup through other paths, such as the result of a physical
examination in which the examining physician feels or otherwise
identifies some abnormal feature (e.g., lump) in a patient's
breast, or in circumstance in which the patient has an extremely
high risk of cancer as determined through the patient's clinical,
history, or other means.
[0004] In a diagnostic workup, the patient's breasts will be imaged
with one of several imaging modalities, including X-ray mammography
(digital or analog), MRI, or ultrasound, for the purposes of
screening or evaluating for anatomical abnormalities in breast
tissue including microcalcifications or masses in breast tissue,
and various other lesions or abnormalities that are potentially
cancerous. Newer techniques are also being developed for diagnostic
purposes, including X-ray tomosynthesis, optical imaging, strain
imaging, nuclear imaging, etc, which can be used to obtain
diagnostic images of the patient's breast for evaluation by the
physician determine whether a particular lesion in breast tissue is
benign or malignant.
[0005] After reviewing a diagnostic image, if the physician
believes that a lesion may be malignant, a biopsy will be performed
to remove a piece of the lesion tissue for analysis. This process
is assumed to be a "gold standard" for characterization of benign
or malignant tissue. However, it is preferable to minimize the
number of biopsies that are performed for various reasons. For
instance, a biopsy procedure causes pain and scarring for the
patient, and the long period of time between the time of the biopsy
procedure and the time the results are provided to the patient
(usually at least a few days), the patient may be become severely
stressed in anticipation of potentially obtaining negative results.
On the other hand, biopsy procedures enable physicians to
accurately diagnose a large percentage of patients with breast
cancer. Thus, there is some trade-off or balance between
sensitivity and specificity that is typically maintained.
[0006] In the field of medical imaging, although various imaging
modalities and systems can be used for generating diagnostic images
of anatomical structures for purposes of screening and evaluating
medical conditions, with respect to breast cancer detection, each
diagnostic imaging modality has its own advantages and
disadvantages, and the optimal choice of imaging modality may not
be the same for every patient. Ideally, the imaging modality for a
given patient is selected to maximize sensitivity and specificity
for the patient. For each patient, there may be one or more
"optimal" imaging modalities for such purpose. Unfortunately, due
to cost, it is not possible to image every patient using multiple
imaging modalities, and then choose which modality would provide
the optimal balance between sensitivity and specificity.
[0007] The choice of diagnostic imaging modality is usually made by
the referring physician based on a number of factors, including,
for example, (i) availability and cost, (ii) comfort level and
experience of the referring physician, or (ii) a physician's "gut
feeling" as to which imaging modality would be optimal to obtain
information for the patient. While the first factor is unavoidable,
the second and third factors can lead to a sub-optimal choice of
imaging modality for the individual patient.
SUMMARY OF THE INVENTION
[0008] Exemplary embodiments of the invention generally include
systems and methods for providing automated diagnosis and decision
support for breast imaging. More specifically, exemplary
embodiments of the invention include CAD (computer-aided diagnosis)
systems and applications for breast imaging, which implement
automated methods for extracting and analyzing relevant
features/parameters from a collection of patient information
(including image data and/or non-image data) of a subject patient
to provide automated assistance to a physician for various aspects
of physician workflow in breast care. For example, a CAD system can
provide automated diagnosis of breast cancer and other related
conditions, assessments with regard to the risk of a subject
patient having breast cancer and/or developing breast cancer in the
future, and other automated decision support functions to assist a
physician in determining efficacious healthcare or diagnostic or
therapeutic paths for a subject patient based on a current state of
the patient.
[0009] In other exemplary embodiments of the invention, CAD systems
and methods for breast imaging implement machine-learning
techniques which use training data that is obtained (learned) from
a database of previously diagnosed (labeled) patient cases in one
or more relevant clinical domains and/or expert interpretations of
such data to enable the CAD systems to "learn" to properly and
accurately analyze patient data and make proper diagnostic and/or
therapeutic assessments and decisions for assisting physician
workflow.
[0010] These and other exemplary embodiments, features and
advantages of the present invention will be described or become
apparent from the following detailed description of exemplary
embodiments, which is to be read in connection with the
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 is a block diagram of a system for providing
automatic diagnostic and decision support for breast imaging
according to an exemplary embodiment of the invention.
[0012] FIG. 2 is a block diagram of a system for providing
automatic diagnostic and decision support for breast imaging
according to another exemplary embodiment of the invention.
[0013] FIG. 3 is a block diagram of a system for providing
automatic diagnostic and decision support for breast imaging
according to another exemplary embodiment of the invention.
[0014] FIG. 4 is a block diagram of a system for providing
automatic diagnostic and decision support for breast imaging
according to another exemplary embodiment of the invention.
[0015] FIG. 5 is an exemplary diagram illustrating a classification
method according to an exemplary embodiment of the invention.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0016] In general, exemplary embodiments of the invention as
described below include systems and methods for providing automated
diagnosis and decision support for breast imaging. More
specifically, exemplary embodiments of the invention as described
below with reference to FIGS. 1-4, for example, include CAD
(computer-aided diagnosis) systems and applications for breast
imaging, which implement automated methods for extracting and
analyzing relevant features/parameters from a collection of patient
information (including image data and/or non-image data) of a
subject patient to provide automated assistance to a physician for
various aspects of physician workflow including, for example,
automated assistance to a physician for various aspects of
physician workflow where decisions must be made respecting
healthcare or diagnosis paths and/or therapeutic paths for the
patient. Various methods have been developed which attempt to
provide decision support for physicians using only information from
images. However, these techniques ignore the fact that there is a
significant amount of information contained in the patient record
in the form of non-image data. Advantageously, as described in
detail below, CAD systems and methods according to exemplary
embodiments of the invention provide automated decision support
methods that combine both imaging and non-imaging data. Here,
non-imaging data is taken to include all information found in a
patient's record other than images, which can include but not be
limited to, demographic data, history and physical information,
physician notes, lab results, results from blood tests, results
from proteomic analysis, and results from genetic assays. For
example, in the specific case of breast imaging, two women with
identical images with suspicions findings may be treated
differently if, for example, one patient is a young woman with no
history or risk factors for cancer, while the other patient is an
elderly woman with genetic disposition for breast cancer (such as
the presence of the BRCA gene) and a known family history of breast
cancer. Combining the clinical and imaging information provides the
most valuable assistance for the physician.
[0017] For instance, given a set of information that is collected
for a given patient, CAD systems according to exemplary embodiments
of the invention can extract and analyze relevant features from
such patient information to automatically assess the current state
of the patient (e.g. probability and confidence of diagnosis of a
disease or a likelihood of having a particular disease given
history, age, etc.), automatically determine which additional
test(s) or features(s), if any, would be useful to increase the
confidence in a diagnosis, and otherwise provide decision support
to a physician in other aspects of physician workflow.
[0018] Exemplary CAD systems and applications according to the
invention implement machine-learning techniques that use training
data obtained (learned) from a database of labeled patient cases in
one or more relevant clinical domains and/or expert interpretations
of such data to enable the CAD systems to "learn" to properly and
accurately analyze patient data and make proper diagnostic
assessments and decisions for assisting physician workflow. For
example, with respect to breast imaging a diagnosis of breast
cancer, exemplary CAD systems described below can "learn" to
provide proper assessments in the areas of screening, diagnosis
and/or staging of breast cancer. For illustrative purposes,
exemplary embodiments of the invention will be described with
specific reference to breast imaging and physician workflow for
breast care. It is to be understood, however, that the present
invention is not limited to any particular medical fields. Rather,
the invention is more generally applicable to any medical field of
practice in which physician workflow requires the physician to
determine or assess the current state of a patient and determine
workflow paths would result in a more accurate assessment of the
current state of the patient for purposes of providing the
appropriate care. Those of ordinary skill in the art will readily
appreciate that CAD systems according to exemplary embodiments of
the invention provide a powerful tool to assist physician
workflow.
[0019] It is to be understood that the systems and methods
described herein in accordance with the present invention may be
implemented in various forms of hardware, software, firmware,
special purpose processors, or a combination thereof. In one
exemplary embodiment of the invention, the systems and methods
described herein are implemented in software as an application
comprising program instructions that are tangibly embodied on one
or more program storage devices (e.g., magnetic floppy disk, RAM,
CD Rom, DVD, ROM and flash memory), and executable by any device or
machine comprising suitable architecture, wherein the application
may be a distributed network application with n-tier client-server
architecture for a distributed network application, etc.
[0020] It is to be further understood that because the constituent
system modules and method steps depicted in the accompanying
Figures can be implemented in software, the actual connections
between the system components (or the flow of the process steps)
may differ depending upon the manner in which the application is
programmed. Given the teachings herein, one of ordinary skill in
the related art will be able to contemplate these and similar
implementations or configurations of the present invention.
[0021] FIG. 1 is a high-level block diagram of a system for
providing automated diagnostic support and physician workflow
assistance for breast imaging, according to an exemplary embodiment
of the invention. More specifically, FIG. 1 illustrates a CAD
(computer-aided diagnosis) system (10) that implements methods for
analyzing various types of patient information (1) and (2) of a
subject patient to provide diagnostic assessments and
recommendations and other decision support to assist a physician in
various aspects of physician workflow with respect to the subject
patient. The CAD system (10) uses machine learning methods that
enable the CAD system (10) to continually learn to analyze the
patient information (1, 2) and continually provide more accurate
diagnostic assessments and/or decisions to assist physician
workflow.
[0022] The input to the CAD system (10) comprises various sources
of patient information including image data (1) in one or more
imaging modalities (e.g., ultrasound image data, X-ray mammography
image data, MRI etc.) and non-image data (2) from various
structured and/or unstructured data sources, including clinical
data which is collected over the course of a patient's treatment
and other information such as patient history, family history,
demographic information, financial information, and any other
relevant patient information. The CAD system (10) implements
methods for automatically extracting information (features) from
the image data (1) and non-image data (2) and combining the
extracted information in a manner that is suitable for analysis by
the CAD system (10). Depending on the diagnostic and decision
support function(s) supported by the CAD system (10), the CAD
system (10) can generate one or more outputs (11), (12), and/or
(13). As explained below, in the field of breast care, these
outputs can provide physician workflow assistance in the areas of
screening, diagnosing and/or staging for breast cancer.
[0023] In another exemplary embodiment of the invention, the CAD
system (10) can extract and analyze information from image data (1)
and (optionally) non-image data (2) to automatically generate and
output a probability of diagnosis and (optionally) a measure of
confidence of the diagnosis (11) or alternatively output a
suggested therapy with a probability and (optional) measure of
confidence as to the impact of the suggested therapy, e.g., the
probability that the suggested therapy will have the desired
(beneficial) impact. Collectively, the output (11) can be referred
to herein as "Probability and Confidence of Suggestion".
[0024] More specifically, by way of example, for purposes of
diagnosing breast cancer, the CAD system (10) may comprise methods
for automatically detecting and diagnosing (or otherwise
characterizing) suspect breast lesions in breast tissue and
outputting, for example, a probability of malignancy of such
lesions, together with an optional measure of confidence in such
diagnosis. In this example, the CAD system (10) could extract and
analyze relevant features from a screening X-ray mammogram (image
data) and clinical history information (non-image data) of a
patient and provide a current estimate and confidence of
malignancy.
[0025] Alternatively, for patients with known breast cancer for
example, the CAD system (10) could suggest an course of therapy, in
which case, the probability and confidence (11) would refer to the
likelihood that the therapy would have the desired (presumably
beneficial) impact, which could range from curing the patient from
breast cancer, to a purely palliative treatment whose sole aim
would be to improve the quality of life of a patient with terminal
breast cancer. More specifically, the CAD system (10) could in
addition to suggesting a therapy, automatically provide a
probability and/or measure of confidence that the therapy will have
a determined outcome and possible provide a probability and/or
measure of confidence that the therapy will not have a determined
detrimental impact such as side effects. The probability can be
specified as a distribution over possible outcomes both beneficial
and detrimental, or a set of distributions over possible outcomes
both beneficial and detrimental at one or more time points in the
future, or a time-varying distribution over possible outcomes at
different times in the future, etc.
[0026] In another exemplary embodiment of the invention, the CAD
system (10) can automatically determine and specify one or more
additional tests, measurements, or features which, if
made/obtained, could increase the confidence of diagnosis (i.e.,
sensitivity analysis). For example, the CAD system (10) can
determine and output a "score" (12) for each additional test,
measurement or feature, which provides some measure or indication
as to the potential usefulness of the particular imaging modality
or feature(s) that would improve the confidence of an assessment or
diagnosis determined by the CAD system (10). For example, assuming
the CAD system (10) extracts and analyzes relevant features from a
screening X-ray mammogram (image data) and clinical history
information (non-image data) of a patient and provides a current
estimate and confidence of malignancy of a detected lesion, the CAD
system (10) could further indicate which imaging modality or
modalities would most likely provide the maximum amount of
additional information that would be useful in determining whether
the lesion is malignant or benign, or determining the extent of
cancer ("staging"), or would be most useful in deciding on a course
of therapy for a patient with known breast cancer--for instance,
deciding between surgery, radiotherapy, chemotherapy, hormone
therapy or some combination thereof (the so called "cocktail"
therapy).
[0027] In another exemplary embodiment of the invention, the CAD
system (10) can identify and output (via display or list) one or
more exemplary case studies that are similar to a current case
(13). For example, as noted above and explained in further detail
below, the CAD system (10) may comprise a database (or library) of
previously labeled (diagnosed) cases, and based on features
extracted from patient information input to the CAD system (10) for
the subject patient, the CAD system (10) can search and display the
n-most relevant cases from the library for diagnostic assistance.
In other words, the CAD system (10) can provide a set of similar
cases from the training set using the automatically extracted
features.
[0028] It is to be appreciated that the CAD system (10) function of
displaying similar cases in the context of physician workflow can
provide significant assistance to the physician. For instance,
displaying similar cases can provide training for inexperienced
users. Indeed, novice users can review other cases to determine or
otherwise understand the basis or reasons why the case interpreted
in the way that it was. Moreover, display of similar cases can
provide a means for experienced users to confirm the diagnostic
results of the CAD system (10). Indeed, in addition to probability
of diagnosis for a given condition, the CAD system (10) could
display similar cases to justify its assessment. Moreover,
displaying similar cases enables assessment of prognosis and
treatment. More specifically, by studying similar cases to see how
other patients responded to different treatment options, a
physician can begin to assess the efficacy of these options for the
current patient.
[0029] In view of the above, the CAD system (10) can be generally
viewed as an automated system that can assist physician workflow by
providing an assessment of the current state of a patient (e.g.
probability of likelihood of a particular disease) and determining
next best health care or diagnostic paths for the subject patient
(e.g., identifying additional tests (or features) that can be
obtained, which would likely reduce any ambiguity of the
assessment). As noted above, it is to be appreciated that the CAD
system (10) implements one or more machine-learning methods whereby
the information is learned, and the decisions driven, by data that
is collected in a training set of the CAD system (10). In
particular, as noted above, the CAD system (10) could include a
library of exemplary diagnosed cases from which training data is
obtained to teach the CAD system (10). In contrast to "expert
systems" which are developed and derived from a set of rules
dictated by an expert and translated into code, the CAD system (10)
learns to provide accurate diagnostic decisions and provide
decision support based on training data that is learned from
diagnosed cases or learned from expert knowledge.
[0030] It is to be appreciated that various machine learning
methods may be implemented by the CAD system (10). For example, the
systems and methods described in U.S. patent application Ser. No.
10/702,984, filed on Nov. 6, 2003, by Zhou et al, entitled "System
and Method for Real-Time Feature Sensitivity Analysis Based on
Contextual Information," which is commonly assigned and
incorporated herein by reference, can be used in the CAD system
(10) for determining which tests or features may be most relevant
for reducing ambiguity of a diagnosis. Essentially, the Zhou
approach is to create a model of the process, and determine the
relative importance of each feature in reducing ambiguity. Such
method can be implemented herein whereby each imaging modality, or
diagnostic path, could be described as a set of one or more
features. Then, the methods described by Zhou would be used to
determine which feature(s) would likely provide the greatest
improvement in confidence in a diagnosis or assessment. Other
machine learning techniques which learn from a large training set
of cases can be implemented in the CAD system (10). For example,
various machine learning techniques, such as decision trees, SVM,
neural networks, or Bayesian networks, or ensemble methods which
combine multiple such methods, for example, may be used.
Alternately, model-based algorithms which would be defined or
trained specifically to detect some kind of lesion, for instance,
based on causal knowledge of the various factors that are related
to a particular kind of lesion, for example.
[0031] It is to be appreciated that the CAD system (10) can provide
proper decision support even in the absence of various features or
information that can be used for rendering such decisions. For
example, if the medical records of a patient only contain a
screening mammogram and basic demographic information about the
patient (for example, age and race), but no clinical or family
information, the CAD system (10) is able to provide a probability
and confidence of diagnosis, along with a best estimation of what
test or procedure should be performed next. In this case, the
recommended procedure might even be to collect the family
information for the patient. Of course, the confidence of the
system will improve with more information as it is provided. As an
extreme, consider the situation where there no information at all
for a given patient. In this instance, the CAD system (10) should
be able to provide a physician with some guidance as to an initial
step to take with respect to the patient. Various methods for
learning and/or performing inference with missing/noisy data may be
used in the decision support system.
[0032] It is to be appreciated that the above methods can be
extended to provide automatic screening for medical conditions such
as breast cancer. In the United States, current recommendations
provide that all women over the age of 40 are to be screened yearly
using X-ray mammography. As in the case of breast cancer diagnosis,
there have been studies in the literature to automatically assess
the risk associated with mammograms. In this regard, it may be that
MRI or ultrasound may be better screening tools for a particular
subset of the population or for women with some particular finding
in their diagnostic mammogram. Furthermore, for some women, the
risk of developing breast cancer at the current point in their life
may be so small that it may not be worth the risk of being
subjected to ionizing radiation, or the cost, to even perform a
screening.
[0033] Accordingly, the CAD system (10) can be configured to make a
determination, in view of a patient's clinical and family history,
as to the likelihood that the patient has (or can develop) breast
cancer, and what screening test (if any) should be given to the
patient to best detect suspicious lesions or risk of cancer for
further diagnosis. This likelihood could also be inferred at any
point in time during the patient history, e.g., after the first
screening exam, or after multiple screens and MRI tests. Such
determinations can be made using a training set as described above
and using machine-learning techniques. Moreover, for screening, the
CAD system (10) can generate and output decisions as discussed
above, including likelihood of disease, exemplar cases from a
training set, and the screening test that would be optimal for the
given patient. In this case, a determination as to the screening
test may be of most interest. Indeed, for such determination, a
screening mammogram would not be available for the classification.
Moreover, the comparison would not necessarily be made to correct a
diagnosis of the patient, but rather to correct identification of
either suspicious lesions in the breast, or sufficient risk of
breast cancer to warrant further diagnostic tests.
[0034] In another exemplary embodiment of the invention, the CAD
system (10) can provide assistance in breast imaging with regard to
staging of tumors for therapy. In general, a staging process
involves precisely locating a lesion and determining if a lesion is
single- or multi-focal. In according the an exemplary embodiment of
the invention, the CAD system (10) can learn to determine which
test should be used to stage a lesion, given information about the
lesion obtained from screening and/or diagnosis test. For example,
in a training set, the results of the staging from different
modalities could be compared to those results actually found during
therapy or follow-up visits. Accordingly, machine-learning methods
as described above can be used to enable the CAD system (10) to
"learn" a proper approach to staging for a given patient. Exemplar
cases from the training set can also potentially show what the
results of the staging, and perhaps even the outcomes after therapy
for patients with "similar" cases.
[0035] The exemplary CAD systems and methods discussed above with
reference to FIG. 1 provide a general framework for developing CAD
systems that can support one or more imaging modalities and provide
one or more functionalities for providing assistance in various
aspects of physician workflow. Exemplary embodiments of CAD systems
and methods according to the invention, which are based on the
framework of FIG. 1, will be discussed with reference to FIGS. 2, 3
and 4, for example, for providing assistance to physician workflow
in breast imaging. The exemplary embodiments of FIGS. 2 and 3
depict CAD systems and methods for breast imaging for one or more
ultrasound imaging modalities. FIG. 4 is an exemplary embodiment of
a CAD system which incorporates the systems of FIGS. 2 and 3 and
provides further functionality for enabling a multi-modal CAD
system that can be used for various for breast imaging in multiple
imaging modalities.
[0036] Referring now to FIG. 2, a block diagram illustrates a
system for providing automatic diagnostic and decision support for
breast imaging according to another exemplary embodiment of the
invention. In particular, the CAD system (20) of FIG. 2 illustrates
one or more exemplary frameworks for implementing the CAD system
(10) of FIG. 1 to support ultrasound (B-mode analysis) breast
imaging. In general, the CAD system (20) comprises a data
processing system (21) which comprises a feature extraction module
(22), a feature combination module (23), a classification module
(24), a diagnostic/workflow assistance module (25) and an automated
detection module (29).
[0037] The automated detection module (29) implements methods for
processing ultrasound image data (3) of breast tissue to detect and
segment potential lesions in the imaged breast tissue. More
specifically, the automated detection module (29) implements one or
more conventional methods for processing ultrasound image data (3)
to automatically detect lesions and other abnormal anatomical
structures such as micro calcifications or masses in breast tissue,
etc. The automated detection module (29) automatically detects and
mark regions of features of interest in the image data, which are
identified as being potential lesions, abnormalities, disease
states, etc.
[0038] The feature extraction module (22) implements various
methods (22-1, 22-2, 22-3, 22-4) for extracting relevant parameters
from ultrasound image data (3) and other sources of non-image
patient data (4) such as clinical, family, history data, etc, such
as described in further detail below, which can be used for
providing automated diagnosis and decision support functions. The
feature combination module (23) combines the extracted features in
a manner that is suitable for input to the classification module
(24) for analysis.
[0039] The classification module (24) comprises a classification
method (24-1) (or classification engine) that analyzes the combined
extracted parameters using one or more classification models, which
are trained/dynamically adapted via model builder (24-2), to
generate information that is used to provide diagnostic and
decision support. The diagnostic/workflow assistance module (25)
includes one or more methods for implementing functions such as
described above with reference to FIG. 1 (e.g., providing a
diagnosis, providing a set of cases similar to a current case,
providing a score showing the likely benefit of additional tests or
features that would improving the confidence of diagnosis,
etc.).
[0040] The CAD system (20) further comprises a user interface (26)
(e.g., graphical user interface displayed on computer monitor with
keyboard and mouse input devices) which enables a user to select
one or more functions supported by the diagnostic/workflow
assistance module (25) and which enables the system to render and
present processing results to the user. The processing results can
be rendered and presented to a user in one or more of various ways
according to exemplary embodiments of the invention as described
below.
[0041] The CAD system (20) further comprises a repository (27) that
maintains a clinical domain knowledge base of information that is
derived from various sources. For instance, the clinical domain
knowledge (27) may include knowledge that is learned or
automatically extracted from a large database of analyzed/labeled
cases (28) related to the clinical domain(s) supported by the CAD
system (20). The clinical domain knowledge (27) may include expert
clinical knowledge that is input directly by an expert from
analyzing previous claims, or information related to
rules/regulations/guidelines associated with medical bodies or
insurance companies, with regard to the supported clinical
domain(s). As explained in detail below, the clinical domain
knowledge in repository (27) can be used by the various methods
(22, 23, 24, and 25) of the data processing system (21).
[0042] The feature extraction module (22) includes various methods
to extract image parameters associated with the "detected" regions
of the ultrasound image data, which can be used diagnosing
potential cancerous tissue. Such features include parameters
associated with spiculation (22-1), acoustic shadowing (22-2),
height/depth ratio (22-3) and/or other possible image parameters
that can be used to automatically classify lesions or abnormalities
in breast tissue.
[0043] In other exemplary embodiments of the invention, the data
processing system (21) extracts and analyzes relevant parameters
from non-image patient data records (4) of a subject patient, which
can used in conjunction with the extracted image parameters (22-1,
22-3, 22-3) to provide automated diagnosis. The patient data (4)
can include patient information from a plurality of structured and
unstructured data sources, which is collected over the course of a
patient's treatment. In general, the structured data sources
include, for example, financial (billing), laboratory, and pharmacy
databases, wherein patient information in typically maintained in
database tables. The unstructured data sources include for example,
waveform data, free-text based documents of laboratory test
results, doctor progress notes, details about medical procedures,
prescription drug information, radiological reports, and other
specialist reports.
[0044] The non-image patient data (4) can include a significant
amount of useful data indicative of a person having breast cancer
or a history that indicates that the person has a high potential
for developing breast cancer. By way of example, such clinic
information may be found in history and physical notes, wherein a
physician notes that a person has been previously diagnosed with
breast cancer. Other indications, such as family history of breast
cancer, history of smoking, age, gender, etc., can also be used to
assess the risk of developing or having breast cancer. Accordingly,
the feature extraction module (22) includes one or more data
extraction methods (22-4) for extracting relevant patient data from
the non-image patient data (4), which may be relevant for assessing
or diagnosing a medical condition.
[0045] It is to be appreciated than any suitable data analysis/data
mining methods may be implemented by the extraction module(s)
(22-4) for extracting relevant parameters from the patient data
(4). In one exemplary embodiment of the invention, patient data
extraction methods (22-4) and feature combination method (23) may
be implemented using the data mining methods and feature
combination methods as described in commonly assigned and copending
U.S. patent application U.S. Ser. No. 10/287,055, filed on Nov. 4,
2002, entitled "Patient Data Mining", which claims priority to U.S.
Provisional Application Ser. No. 60/335,542, filed on Nov. 2, 2001,
which are both fully incorporated herein by reference. Briefly,
U.S. Ser. No. 10/287,055 describes data mining methods for
extracting relevant information from clinical data records using
domain-specific knowledge contained in a knowledge base (e.g., in
repository (27)), which are represented as probabilistic assertions
about the patient at a particular time (referred to as elements)
and combining all elements that refer to the same variable
(domain-specific criteria) at a given time period to form a single
unified probabilistic assertion regarding that variable.
[0046] In the exemplary embodiment of FIG. 2, as noted above, the
data processing system (21) uses clinical domain knowledge data
maintained in the repository (27) to perform the various methods of
feature extraction (22), feature combination (23) and model
building (24-2). The domain-specific knowledge base (27) may
include disease-specific domain knowledge. For example, the
disease-specific domain knowledge may include various factors that
influence risk of a disease, disease progression information,
complications information, outcomes and variables related to a
disease, measurements related to a disease, and policies and
guidelines established by medical bodies such as the American
College of Radiology (ACR). The domain-specific knowledge base (27)
may also include institution-specific domain knowledge. For
example, this may include information about the data available at a
particular hospital, document structures at a hospital, policies of
a hospital, guidelines of a hospital, and any variations of a
hospital.
[0047] The clinical domain knowledge base (27) may be derived from
various sources. For instance, the clinical domain knowledge base
(27) may include knowledge that is learned from a large database of
analyzed/labeled cases (28). In addition, the clinical domain
knowledge base (27) may include knowledge that is input by an
expert from analyzing previous claims, or from rules and
regulations published by an insurance company, for example. The
data in the domain knowledge base (27) can be encoded as an input
or as programs that produce information that can be understood by
the system. As noted above, the domain expert data may be obtained
by manual input from a domain expert using an appropriate user
interface or the domain expert data may be automatically or
programmatically input.
[0048] The extraction modules (22-4) can use relevant data in the
domain knowledge base (27) to extract relevant parameters and
produce probabilistic assertions (elements) about the patient that
are relevant to an instant in time or time period. The domain
knowledge required for extraction is generally specific to each
source. For example, extraction from a text source may be carried
out by phrase spotting, wherein a list of rules are provided that
specify the phrases of interest and the inferences that can be
drawn therefrom. For example, if there is a statement in a doctor's
note with the words--"There is evidence of lesions in the left
breast"--then, in order to infer from this sentence that the
patient has or may have breast cancer, a rule can be specified that
directs the system to look for the phrase "lesion," and, if it is
found, to assert that the patient may have breast cancer with a
some degree of confidence. Extraction from a database source may be
carried out by querying a table in the source, in which case, the
domain knowledge needs to encode what information is present in
which fields in the database. On the other hand, the extraction
process may involve computing a complicated function of the
information contained in the database, in which case, the domain
knowledge may be provided in the form of a program that performs
this computation whose output may be fed to the rest of the
system.
[0049] The methods implemented by the feature combination module
(23) can be those described in the above-incorporated patent
application. For example, a feature combination method can be a
process of producing a unified view of each variable at a given
point in time from potentially conflicting assertions from the
same/different sources. In various embodiments of the present
invention, this is performed using domain knowledge regarding the
statistics of the variables represented by the elements.
[0050] The model builder (24-2) builds classification models
implemented by the classification method (24-1), which are trained
(and possibly dynamically optimized) to analyze various extracted
features and provide diagnostic assistance and assessment on
various levels, depending on the implementation. It is to be
appreciated that the classification models may be "black boxes"
that are unable to explain their prediction to a user (which is the
case if classifiers are built using neural networks, example). The
classification models may be "white boxes" that are in a human
readable form (which is the case if classifiers are built using
decision trees, for example). In other embodiments, the
classification models may be "gray boxes" that can partially
explain how solutions are derived (e.g., a combination of "white
box" and "black box" type classifiers). The type of classification
models that are implemented will depend on the domain knowledge
data and model building process (24-2). The type of model building
process will vary depending on the classification scheme
implemented, which may include decision trees, support vector
machines, Bayesian networks, probabilistic reasoning, etc., and
other classification methods that are known to those of ordinary
skill in the art.
[0051] The model builder/update process (24-2) uses data in the
clinical domain knowledge base (27) to train classification models,
and possibly dynamically update previously trained classification
models that are implemented by the classification process (24-1).
In one exemplary embodiment of the invention, the model
builder/update process (24-2) is implemented "off-line" for
building/training a classification model that learns to provide
proper diagnostic assessments and decisions for workflow
assistance. In another exemplary embodiment of the invention, the
model builder/update process (24-2) employs "continuous" learning
methods that can use the domain knowledge data in repository (27)
which is updated with additional learned data derived from newly
analyzed patient data or otherwise optimize the classification
model(s) associated with the relevant condition. Advantageously, a
continuous learning functionality adds to the robustness of the CAD
system (20) by enabling the classification process (24-1) to
continually improve over time without costly human
intervention.
[0052] The diagnostic/workflow assistance module (26) can provide
one or more diagnostic and decision support functions as described
above with reference to FIG. 1. For instance, the
diagnostic/workflow assistance module (26) can command the
classification module (24) to classify one or more breast lesions
detected in ultrasound image data (4) as malignant or benign and
provide a probability of such diagnosis and (optionally) a measure
of confidence in the diagnosis, based on a set of features
extracted from ultrasound image data (3) and/or non-image patient
data records (4). The classification engine (25-1) could perform
such classification using one or more classification models that
are trained to analyze the combined features output from module
(23). In another exemplary embodiment, the diagnostic/workflow
assistance module (25) can command the classification module (24)
to determine what additional image parameter or features (e.g.,
from B-mode ultrasound image data, other image mode, and/or
non-image data) can be obtained and further analyzed to increase
the confidence in the diagnosis. Moreover, the diagnostic/workflow
assistance module (25) can command the classification module (23)
to obtain and display (via user interface) one or more similar
patient cases in repository (27) based on the current set of
extracted features.
[0053] Referring now to FIG. 3, a block diagram illustrates a
system for providing automated diagnostic and decision support for
breast imaging according to another exemplary embodiment of the
invention. More specifically, FIG. 3 illustrates a CAD system (30)
that supports additional ultrasound imaging methods (in addition to
B-mode analysis) for providing automated diagnosis of breast
lesions in breast tissue, for example, and other decision support
function to assist physician workflow. In one exemplary embodiment,
the CAD system (30) of FIG. 3 incorporates an automated B-mode
analysis of the CAD system (20) discussed above with reference to
FIG. 2. The CAD system (30) of FIG. 3 illustrates one or more
exemplary frameworks for the CAD system (10) of FIG. 1 to support
one or more ultrasound imaging methods including, for example,
B-mode, contrast imaging, and/or strain imaging, etc.
[0054] More specifically, referring to FIG. 3, the CAD system (30)
comprises a data processing system (31) which implements methods
for automatic classification (diagnosis) of breast cancer based on
various parameters are extracted from one or more types of
ultrasound image data (5) and/or non-image patient data (6), as
well as other methods to assist a physician to decide an a care or
diagnosis path for a particular patient. In general, the data
processing system (31) comprises a feature extraction module (32),
a feature combination module (33), a classification module (34) and
a diagnostic/workflow assistance module (35). Moreover, the CAD
system (30) comprises a user interface (36) which enables user
interaction with the CAD system (30) to select one or more
functions supported by the diagnostic/workflow assistance module
(35) (e.g., providing automated diagnosis and confidence of
diagnosis for breast cancer, determine what additional ultrasound
imaging modalities or features (e.g., from B-mode ultrasound image
data, other image mode, and/or non-image data) can be obtained and
further analyzed to increase the confidence in diagnosis, obtain
and display one or more similar patient cases in a repository (38)
based on the current set of extracted features.)
[0055] The feature extraction module (32) implements various
methods (32-1.about.32-5) for extracting relevant parameters from
one or more of various modes of ultrasound image data (5) and
non-image patient data (6), which can be analyzed to provided
automated diagnosis and other types of decision support as
discussed herein. For instance, the feature extraction module (32)
includes an automated B-mode analysis module (32-1) which
implements, for example, the automated detection (29), spiculation
(23-1), acoustic shadowing (23-2), and H/D ratio (23-3) methods as
described above in the system (20) of FIG. 2. In addition, the
feature extraction module (32) includes methods for extracting
relevant parameters from ultrasound measurements including strain
and elastography (32-2), motion of fluid using techniques such as
acoustic streaming (32-3), 3D ultrasound imaging (32-4) and motion
of blood using techniques such as contrast perfusion (32-5).
[0056] The various feature extraction modules can be implemented
using methods that are well known to those of ordinary skill in the
art. For example, for ultrasound strain/elastography imaging, the
systems and method described in the following patents: Hall et al,
"Ultrasonic elasticity imaging", U.S. Pat. No. 6,508,768, issued
Jan. 21, 2003; Nightingale et al, "Method and apparatus for the
identification and characterization of regions of altered
stiffness", U.S. Pat. No. 6,371,912, issued Apr. 16, 2002; and Von
Behren et al, "System and method for strain image display", U.S.
Pat. No. 6,558,424, issued May 6, 2003, which are all incorporated
herein by reference, can be implemented for extracting relevant
parameters from ultrasound measurements including strain and
elastography. Moreover, the systems and methods for acoustic
streaming as described in Trahey et al, "Method and apparatus for
distinguishing between solid masses and fluid-filled cysts", U.S.
Pat. No. 5,487,387, issued Jan. 30, 1996, which is incorporated
herein by reference, can be used for extracting features related to
motion of fluid. In addition, the systems and methods for contrast
perfusion as described in Philips et al, "Dual process ultrasound
contrast agent imaging", U.S. Pat. No. 6,632,177, issued Oct. 14,
2003, which is incorporated herein by reference, may be used for
extracting features related to motion of blood. It is to be
understood that other known techniques may be implemented.
[0057] The feature combination module (33) combines a set of
extracted features in a manner that is suitable for input and
analysis by the classification module (34). The classification
module (34) comprises classification methods (34-1) to analyze the
combined extracted parameters using one or more classification
models, which are trained/dynamically adapted via model builder
(34-2), to provide automatic diagnosis of breast cancer and other
decisions support functions. The CAD system (30) further comprises
a repository (37) that maintains a clinical domain knowledge base
of information which provides training data used by the model
builder (34-2) to build/train classification models used by the
classification methods (34-1). A large database of analyzed/labeled
cases (38) related to the clinical domain or domains supported by
the CAD system (30) can be used to obtain training data in
repository (37). The clinical domain knowledge in repository (37)
can be used by the various methods (32, 33, 34 and 35) of the data
processing system (31).
[0058] In general, the various components of the CAD system (30) of
FIG. 3 are essentially similar to those of the CAD system (20) of
FIG. 2 as discussed above, except that the CAD system (30) of FIG.
3 provides a more diverse framework that supports various
ultrasound imaging methods in addition to B-mode ultrasound to
enable a more complete CAD system for ultrasound breast imaging. It
is to be appreciated that the various modules (32, 33, 34 and 35)
in FIG. 3 can implement the same or similar methods as those
corresponding modules (22, 23, 24 and 25) of the CAD system (20) of
FIG. 2 as described above. However, the various methods, such as
the classification and model building methods in classification
modules (24) and (34) will vary depending on the types of decision
support functions, feature extraction methods and/or image
modalities supported by the respective CAD systems (20) and (30).
Moreover, the clinical domain knowledge base (37) is similar to the
knowledge base (27) of FIG. 2, except that the training data in
knowledge bases (27) and (37) will vary depending on the types of
decision support functions, feature extraction methods and/or image
modalities supported by the respective CAD systems (20) and
(30).
[0059] Referring now to FIG. 4, a block diagram illustrates a
system for providing automated diagnostic and decision support for
breast imaging according to another exemplary embodiment of the
invention. More specifically, in one exemplary embodiment of the
invention, FIG. 4 illustrates a CAD system (40) that is an
extension of the exemplary CAD systems (20) and (30), wherein the
CAD system (40) incorporates the functions and methods of CAD
systems (20) and (30) for ultrasound breast imaging, and further
incorporated methods and functions for enabling a multi-modal CAD
for breast imaging in multiple imaging modalities.
[0060] Referring to FIG. 4, the CAD system (40) comprises a data
processing system (41) which implements methods for providing
automated diagnosis of breast lesions in breast tissue and
providing decision support for diagnostic and/or care paths to
assist physician workflow, by extracting and analyzing parameters
from various sources of patient information (7), including, for
example, one or more different types of image data (e.g., MRI image
data (7a), ultrasound image data (7b), X-Ray mammography image data
(7c)) and non-image data such as genetics and/or proteomics data
(7d) and clinical, history and/or physical data (7e) of the subject
patient.
[0061] In general, the data processing system (41) comprises a
feature extraction module (42), a feature combination module (43),
a classification module (44) and a diagnostic/workflow assistance
module (45). Moreover, the CAD system (40) comprises a user
interface (46) which enables user interaction with the CAD system
(40) to select one or more functions supported by the
diagnostic/workflow assistance module (45) (e.g., providing
automated diagnosis and confidence of diagnosis for breast cancer,
determining what additional imaging modalities or features could be
obtained and further analyzed to increase the confidence in
diagnosis, obtaining and displaying one or more similar patient
cases in a repository based on a current set of extracted features,
etc.)
[0062] The feature extraction module (42) implements "n" feature
extraction methods for extracting image parameters
(42-1.about.42-2) from the supported imaging modalities, and other
feature or text extraction methods (42-3, 42-4) for extracting
parameters from non-image data sources. For instance, the feature
extraction module (42) can include methods for extracting and
analyzing image parameter from various types of ultrasound data (as
discussed above with reference to FIGS. 2 and 3) and other imaging
modalities. The feature combination module (43) combines a set of
extracted features in a manner that is suitable for input and
analysis by the classification module (44). The classification
module (44) comprises classification methods (44-1) to analyze the
combined extracted parameters using one or more classification
models, which are trained/dynamically adapted via model builder
(44-2), to provide the various decision support functions. The CAD
system (40) further comprises a repository (47) that maintains a
clinical domain knowledge base of information which provides
training data used by the model builder (44-2) to build/train
classification models used by the classification methods (44-1). A
large database of analyzed/labeled cases (48) related to the
clinical domain or domains supported by the CAD system (40) can be
used to obtain training data that is stored in the repository (47).
The clinical domain knowledge in repository (47) can be used by the
various methods (42, 43, 44 and 45) of the data processing system
(41).
[0063] It is to be appreciated that the various modules (42, 43, 44
and 45) in FIG. 4 can implement the same or similar methods as
those corresponding modules (22, 23, 24 and 25) of the CAD system
(20) of FIG. 2 and/or corresponding modules (32, 33, 34 and 35) of
the CAD system (30) of FIG. 3, as described above. However, the
various methods, such as the classification and model building
methods of the classification module (44) will vary depending on
the types of decision support functions, feature extraction methods
and/or image modalities supported by the CAD system (40). Moreover,
the clinical domain knowledge base (47) is similar to the knowledge
bases (27) and (37) of FIGS. 2 and 3, except that the training data
in knowledge bases (47) will vary depending on the types of
decision support functions, feature extraction methods and/or image
modalities supported by the CAD system (40).
[0064] Various machine learning methods according to exemplary
embodiments of the invention for assessing the likely value of
additional tests for diagnosis of breast cancer will now be
described with reference to the exemplary node diagram of FIG. 5.
For these exemplary embodiments, it is assumed that a training set
consists of m cases and each case consists of n features extracted
from previously performed tests. Each case C.sub.i, (i=1, . . . ,
m) can be represented as a vector of features (f.sub.1, f.sub.2, .
. . , f.sub.n).
[0065] It is further assumed that for each case C.sub.i, the real
diagnosis (d.sub.i) given by a biopsy result is:
d i = { 1 If a lesion is malignant 0 Otherwise ##EQU00001##
and that there are k variables corresponding to the different tests
that were performed on the patients (T.sub.i1, T.sub.i2, T.sub.i3,
. . . , T.sub.ik), wherein each one of the k variables can take
values in the set {0,1}, and wherein k=1 if the corresponding test
predicted correctly with respect to the real diagnosis d, or where
k=0 otherwise.
[0066] Further assuming that such previous information is extracted
from the training data, the exemplary machine learning based
methods described hereafter can be used to predict which test will
provide an accurate diagnosis based on a feature vector extracted
from a patient medical history.
[0067] In one exemplary embodiment, one method is as follows.
First, a mapping M is determined from the feature space to
{(P.sub.1, P.sub.2, P.sub.3, P.sub.4)/P.sub.i.epsilon.{0,1}} such
that for every C.sub.i, M(C.sub.i)=M(f.sub.1, f.sub.2, . . . ,
f.sub.n)=(T.sub.i1, T.sub.i2, T.sub.i3, T.sub.i4). This process can
be achieved using artificial neural network techniques as
illustrated in FIG. 5. For each new patient, the mapping M will
provide a corresponding binary output that describes which tests
are recommended for this patient.
[0068] This problem also can be viewed as a multi-class
classification problem where for each case C, its label is defined
according to which test gave the correct diagnosis. For example,
one possible approach is as follows. For each test, all the
training cases are labeled according to the accuracy of that test
for that case. Then, four classifiers are trained (one for each
test) using any binary classification algorithm (e.g., SVMs,
Decision Trees, Bayesian networks, etc.). When a new patient is
considered, the patient data is tested in the four classifiers to
predict which tests will give the correct diagnosis. It is to be
noted that with the above two approaches, the outcome of the
process can be more than one test.
[0069] Another exemplary approach is as follows. Assume that there
are m cases in a training set. A new case will be compared to these
m cases using the n features described above. Based on this
comparison, p cases are selected as being most "similar" to the
current case, wherein similarity can be defined in one of various
ways. For instance, one approach is to consider the Euclidean
distance in the n-dimensional feature space. Other well-known
distance measures can also be employed. It is to be appreciated
that the above process can also be used to select exemplar cases
from a library of cases for display as well.
[0070] One the similarity measures are determined and the most
`similar" cases are identified, classifiers can be constructed for
each of the k tests in the training set. In particular, by way of
example, a classifier would be constructed to test whether a lesion
is benign or malignant using, for example, each of the following
sets of information: (i) current information and a diagnostic
mammogram; (ii) current information and ultrasound; (iii) current
information and MRI, etc.
[0071] Each classifier would be constructed without learning from
one of the p cases (i.e. leave-one-out approach), and then the
withheld case would be classified using this classifier. This would
be repeated for each of the p cases, and the entire process for
each of the k tests. An average likelihood would then be computed
for each of the k tests, which would serve as the score of which
test would be most useful.
[0072] It is to be appreciated that in accordance with other
exemplary embodiments of the invention, CAD systems may be
implemented in a distributed model, wherein various
modules/components of the CAD are distributed over a communications
network. For example, a CAD system can be offered by an ASP
(application service provider) to provide remote access serving of
CAD functions via an application server. For example, a database of
cases used to identify similar cases could be located in a central
location. The advantage is that large databases of cases, which
occupy a lot of memory, do not have to reside at every system. In
addition, updates to the cases can be made very easily. This
central location could be within a hospital, for example, or it
could be one central database accessed by everyone using such a
system. Another possibility is to use a distributed database, where
cases are located in multiple locations but are searched and
accessed as if they are in one place. That way, cases located at
different locations can be searched to find similar cases. In
addition to the database, the other parts of the CAD system, such
as the classifier, could be centrally located.
[0073] Moreover, in view of above, it is to be appreciated that a
CAD system according to the invention can be implemented as a
service (e.g., Web service) that is offered by a third-party
service provider pursuant to service contract or SLA (service level
agreement) to provide diagnostic support and other decision support
functions as described herein based one of various service/payment
schemes. For example, the third-party service provider can be
contractually obligated to train, maintain, and update
classification models for various clinical domains, and a physician
or healthcare organization can access the CAD system "on-line" on a
pay-per use basis, yearly subscription fee, etc. In such instance,
various methods known to those of ordinary skill in the art can be
implemented to maintain patient confidentiality and otherwise
transmit patient data over communication channels using secured
encryption, compression schemes, etc. Those of ordinary skill in
the art can readily envision various architectures and
implementation for CAD systems according to the invention and
nothing herein shall be construed as a limitation of the scope of
the invention.
[0074] Although exemplary embodiments of the present invention have
been described herein with reference to the accompanying drawings,
it is to be understood that the invention is not limited to those
precise embodiments, and that various other changes and
modifications may be affected therein by one skilled in the art
without departing from the scope or spirit of the invention. All
such changes and modifications are intended to be included within
the scope of the invention as defined by the appended claims.
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