This application will read a MRI scan to locate cancer and perform a detailed quantitative analysis for follow-up and treatment. This program works with images from major MRI manufacturers as GE, Hitachi, Siemens et al.
Study Number: 07-010
Study Title: Establishing
Accuracy of Medical Imaging Analysis Software (MIAS) in Identifying Cancerous
Growth in MRI, CT Scans and PET Scans
Approval Date: June 15, 2007
June 14, 2008
1. What is done:
medical study evaluating the MIAS software was approved on June 15th,
2007. From June 15, 2007 to September 2007, research planning sessions were
held to develop a methodology for research. Based upon the research discussion,
it was decided that we would evaluate the MIAS software using cranial MRI scans
using the ?3 Plance Loc? procedure. From October 2007 to January 2008, 25 of
300 images were used to calibrate the MIAS software. From February 2008 to
present, April 2008, the first and second part of the double blind analysis of
the MRI images was performed with 150 of 275 to be completed by April 28th.
initial results of the calibration study from December 2007 are:
Two protocols for making a determination on ?3 Plane Loc? series were evaluated. In addition, the evaluation considered the accuracy of a single image determination versus a series determination. The probability of significance is set at p<=0.15, which is the naked eye misreading rate as mentioned in past meetings.
The first test was a one-way anova comparing the statistical difference between a one reading, determination and a series determination. The one reading determination, for n=169, has an accuracy rate of 97% (164/169) with a false positive rate of 3% (5/169) , and a false negative rate of 0%. The series has an accuracy rate of 70% (21/30) with a false positive rate of 30% (9/30, two studies), and false negative rate of 0%. When performing the one-way Anova, there is a statistical difference between the single image determination and a series determination at p=0.11, which is significant to p=0.15. The 70% success rate is mathematically determinable by root of 0.70 to 15th power which equals 0.976 or 97.6% success rate.
The second test, comparing two protocols 2 and 3, there was not a significance difference according to the null hypothesis of p=0.15. The value of p=0.58 is not significant to the null hypothesis. But, between images, there was a significance of p=0.07, which means that the protocols are sensitive to the images.
In test 3, a two-way Anova was done to compare if there was difference between the sample or the determination method used, as the series consensus determination of protocol 3 versus the consensus protocol 2. The analysis showed the results for both image series difference and consensus determination were not significant as their p values were 0.50 and 0.36 respectively. In the two protocols used, they are similar to each other in accuracy.
In test4, a two-way Anova was done with replications to determine if there is significance between the series id or the consensus determination versus the single MRI determination. There is a significance between the determination method where the p=3.8e-7, but there is not a significance between the series id where p=0.50. What this means is that single image determination has a greater accuracy than the consensus determination.
In the single image determination, one result of the MIAS is that it is able to separate the three positive areas, and determine correctly which is the false positive areas (2) and the true positive area (1).
current results from the ongoing double blind analysis, n(series)=25
and n(images) = 423 are:
Wheaton Medical Study 07-010
Using the MRI ?3 Plane Loc? protocol.
Summary Series Correct 1 25 Incorrect 0 Total 25 Percentage 100.00% False Pos 0.00%
Images Correct 1 414 Incorrect 9 Total 423 Percentage 97.87% False Pos 2.13%
The series accuracy is increased to
100%, n=25, and a false positive rate of 0.00%. For individual images, the
image diagnosis accuracy is 97.87%, n=423, and a false positive rate of 2.13%.
Since the initial, calibration study of December, the series diagnostic
accuracy has increased from 70% to 100%, and the individual image, diagnostic
accuracy has increased from 97.6% to 97.9% with the false positive rate reduced
from 2.40% to 2.14%.
addition, since the calibration study of December 2007, we have made the
program easier to use, and using the current DICOMM imaging program supplied
with the images, we can process 5-7 patient series per hour. When other DICOMM
imaging programs are used, the processing rate could be increased to 15-21
patients per hour based upon using a single-core computer with a 1.8 GHz
2. What is being planned:
are expecting to complete the IRB study by August 14, 2008 (Amendment for
extension is included as attachment). The reason we are asking for this extension is
so that we can prepare a report and research paper.
3. What problems we have encountered:
main problems we encountered were in the early phases. The program was modified
to make it easier for the future oncologist or oncologist technician to use.
Once the user interface was changed, it became a three step process to analyze
a patient series.
MIAS application is exceeding our initial expectations, and we have high
confidence that it can be used as primary diagnostic tool. Also, we have
learned how to improve the program and what gained new basic research insight
into the development and detection of cancer.
David Kanecki, MBA,
A.C.S., Bio. Sci.
FIRST RESULTS OF MEDICAL STUDY USING MIAS 60
Kanecki, MBA, A.C.S., Bio. Sci.
Associates, Inc., * P.O. Box 866 * Kenosha, WI 53141 * UNITED STATES
ABSTRACT Based upon initial
testing, the MIAS 60 program could be used as a screening program for cancer,
and as an assistant for cancer determination. As a screening program, the
program has a 95.6% accuracy rate, and as a cancer determination program, it
has a 82.4% accuracy rate. In addition, there are no
false positives given as a result. This means that a patient will not be
misdiagnosed because of false positive. The screen accuracy range is
approximately 92-100%, and the cancer detection range is 65-100 percent. In
screening, the program surpasses a human reader, and in cancer detection via a
?3 Plane Loc? analysis, it is approximately equal to a human reader. *
This study was performed at
Wheaton Franciscan Hospital of Racine, WI.
INTRODUCTION The purpose of this
study is to determine the accuracy rate of the MIAS 60 program to detect cancer
in patients using a ?3 Plane loc? analysis. The study consists of analyzing 300
patient samples, and this report gives results for the first set of 25 patient
samples. The MIAS 60 Laboratory program provides an analysis using multiple
measurements and generating a single conclusion for an individual slide and the
whole series as positive or negative. The conclusion from the whole series is
what is important in determining if someone has cancer. The first part of the
testing involved calibrating the MIAS 60 Laboratory program along with establishing
patient control sets to use a reference. These control sets were selected by
the variation of the ?3 Plane Loc? process and on comparative anatomy of the
scans to provide a close, accurate starting point. Once the control sets were
setup, the first experiments were done.
** MATERIALS AND METHODS The
first part of performing the medical analysis was to select a set of patients
as the reference set for the study. The selected set is references by a
notation of gender (f/m), nc (negative control) or pc
(positive control), and nc or pc number, i.e. 1. Thus, ?fnc1` would indicate
that the a female, negative, control one was used for
the analysis. The exam use specifies which reference set to use, based upon the
variation of the ?3 place loc? examination performed. When there is a
difference between the predicted result of cancer and the expected result of
cancer, a second review is conducted. The second review consists of comparing
the patient report for the test, and determining if the symptoms are positive
or negative, relative to the two patient samples. A positive designation is
given when a patient or patients have symptoms found in two different
anatomical areas of the brain. This method is used because all of the patient
samples we are based upon the patient having symptoms as left sided weakness,
dizziness, blurred vision, etc. Thus, the second review is used to compare
conflicting results between the predicted determination
by MIAS. Based upon the notation used, the following table shows which patient
data we used as control sets: Patient ID Gender Exam Use Cancer Present
Notation Name Number of MRI scans P010 F 1 P Fpc1 21 P001 F 1 N Fnc1 15 P004 M
1 N Mnc1 15 P005 F 3 N Fnc3 21 (2 visits) P006 F 1 N Fnc2 42 P007 F 4 N Fnc4 33
P022 M 5 N Mnc2 15 From this sample set, a series of 25 experiments are
performed. The goals of the experiments are 1) to determine the accuracy rate
of predicting cancer in a MRI test series and 2) to determine the accuracy of
recognizing significant, symptom areas that are not cancerous.
The summary of the results are:
By Cancer By Symptom Total 23, 100% 23, 100% Correct
19, 82.6% 22, 95.6% False Positive 4, 17.4% 1, 4.3% False Negative 0, 0.0% 0.
0% With the cancer analysis, the program is
approximately equal to a human reader, p=0.15 error rate, and the program error
rate is p=0.164. *In addition, the program has a false positive rate 17.4%
percent which means the reading accuracy can range from 69- 100% accuracy,
exceeding a human reader at the upper levels. By having the program issue a
false positive and not issue a false negative, this means the program`s error
will not result in a missed diagnosis. With the symptoms, the program is better
than a human reader with an accuracy rate of 95.6% and an error rate of 4.4%, p=0.044,
which is better than p=0.15, our null hypothesis for a human reader. In
addition the program has a 4.4% false positive rate which means that it range of accuracy is 91.2% to 100%, which is better than the
null hypothesis of a human reader. Therefore, this shows
the MIAS can be used for screening along with a secondary review
*** CONCLUSION The
results of this study show that the MIAS surpass a human reader for screening,
and is approximately equal to a human reader for cancer determination. Since the
program does not issue a false positive, the benefit is toward better treatment
because a lack of a false negative diagnosis means that a patient is not
misdiagnosed where a cancer exists. Based upon these results, the MIAS program
could be used as a screening program for cancer, and as an assistant for
diagnosing cancer. The patient samples used in this study were supplied by
Wheaton Franciscan Hospital of Racine, WI. The medical study is being conducted
at the same hospital.
MIAS, Medical Image Analysis
System, uses a two dimensional approach to spot cancer. The reason this
approach is more accurate and efficient than the three-dimensional approach
used today is due to the computers software ability for spotting the cancers
location by pixel rating as opposed to someone having to determine an area from
a visual contrast. After the individual determines which areas that the
fluorescent dyes highlight an assumption is made which runs a risk of human
error. The ability to spot the cancer by computer software pixel ratings
increases location accuracy and reduced errors in reading radiological CT, MRI,
and PET scans. Combined the two test run consistently could solve the problems
that exist in radiological diagnosis!
** The Medical Imaging Analysis
Software uses a special patented Dicomm File Converter to extract the bipmap
files used by all Medical Scanners. Then, a pixel is assigned a value of 0
(black) ? 24 MILLION (bright white). A cancer usually is observed on a scan
with a value of 150 or over Based upon the statistical value and pixel value a
cancer can be detected to an exact location on our grid of 5000 by 5000 pixels
The MIAS provides an image of the cancer shape, location, intensity, and many
other features to detect cancer smaller than 1mm in size!
*** Imaging Enhancements have the
ability for earlier cancer detection less than 1mm sq from modifying the pixel
rating from 0-255 to 0-24 million range. This has been
done when the program applies ability while reading jpeg files or bitmap files
with 24-bit color. Currently, the program works on the 2006 Dicomm standard of
a bitmap file of 8-bit grayscale and 24-bit gray scale files that is run on all
Comparative Analysis of the Medical Image Analysis System 60 (MIAS)
Various Computer Aided Detection (CAD) Applications
A.C.S., Bio. Sci.
Status of Computer Aided Detection, Version 1.0
have as weakness that they cannot they cannot make a diagnosis. The current
system can only provide viewing capability. The benefit of the ?MIAS? program
is that the computer can provide diagnosis. The result of this is that it can
be used more hospital areas, including smaller community hospitals. Thus, the
?Second Opinion? allows for an evaluation and diagnosis by computer software
which is based on using specific criteria similar to what an a physician may
Current systems are CAD 1.0 systems meaning
that they still rely on the physician`s evaluation completely. In essence, they
are computer viewer`s of MRI slides similar to the
light viewer used to view X-Ray slides. Our MIAS is a complete system where the computer
actually reads, evaluates, charts, and makes a diagnosis of the MRI for a
patient being evaluated for cancer. In addition, it provides quantitative
information so that the it can be charted as compared
to evaluating a MRI by using the ?naked? eye and guessing changes in cancer.
Our program is that only program that makes a firm diagnosis and provides a
reliability index of that diagnosis. The disadvantage of CAD 1.0 systems is that they are
still viewers. Although, they use computer graphics, they are still viewers not
analyzers and cannot give a diagnosis. To perform better the MIAS is needed to
use the ability of the computer and software to help make a decision and make a
diagnosis which is a CAD 2.0 system. Our MIAS system is a CAD 2.0 system. The
benefit of a CAD 2.0 system is that it the computer makes a diagnosis so it
aids the physician in verifying what they have determined, and in certain cases
where an expert physician is not available, it can help to make a screening
determination for further examination. Thus, the CAD 2.0 system is an active
tool that makes a diagnosis as compared to showing an image and leaving the
evaluation to the physician viewing it. With CAD 2.0, the physician`s expertise
and time are utilized better because it can the
physician in providing detailed analysis along with a specific diagnosis.
In order to
facilitate many cost-effective cancer screening stations, it will be
advantageous to do the screening on a remote computer. The reason why a
screening on a remote computer is needed is because it can be setup in a non
dedicated MRI area. The benefit of this is that a special room is not needed as
for the MRI setup used currently. For example, when an MRI is performed on a
patient, the MRI scans the patient. Then, the physician must evaluate the scan
on the computer that is directly used by the MRI instrument. The result of this
is that you can only scan a patient or evaluate a patient scan at one time.
Thus, the MRI cost increase because you using a specialized area to evaluate a
scan. With screening on a remote computer, the cost are
lower, because a standard hospital area can be used as opposed to a specialized
MRI area. In addition, the remote screening can be done in more areas of the
hospital than compared the one fixed area of the hospital used for MRI
evaluation. The MIAS program is designed to work with or without a database.
The database feature is added to allow results to be charted quickly and reduce
the amount of paper needed as the results are directly entered into a hospital
patient records system. The MIAS can run on a PC for the evaluation of the MRI
scan, but the MIAS does not have the capability to access the control mechanism
of various MRI instruments. The MIAS is an evaluation tool. By using the MIAS,
the gaps filled are the ability to determine if treatment is needed
sooner, ability to have a second
computer diagnosis which increases the physician`s productivity and quality
because the MIAS will specifically show and provide a report where the cancer
areas are located in a easy format,
ability to provide greater treatment with the current facilities
available because the MIAS can evaluate the scan in a standard hospital area as
opposed to a specialized area like the MRI area which means that all parts of
the hospital can operate at a greater efficiency and quality to the patient and
on timely basis
of Claims of Using MIAS
reviewing various studies of using computer aided detection for mammography and
MR, I have found that the MIAS program in MR use and in mammography use is
better individual and series diagnosis in terms of sensitivities and false
positions per image. The thesis is supported by comparing various research
studies. (Roman et al, Obenauer et
et al, van Engeland et al, and Gan et al) The sensitivity levels for a series
detection ranges from 50.3% to 73% compared with our initial studies for the
MIAS of 82% to 100%, and with an individual series sensitivity of others of 71%
to 91% compared to 93% to 100% for our MIAS. The significant issue found is
that positive detection has a high linkage to false positive areas in 53% of
the cases (van Engeland et al) compared to 20% of the case with our MIAS. The
number of false positive per image in various studies (Roman et al, Obenauer et al, Shiraishi et
al, van Engeland et al, and Gan et al) for MRI range from 0.5 per image
(Obenauer et al) to 1.02 with mammography (Obenaurer et al). On the MRI, 3
Plane LOC our false positive per image is approximately 0.26 per image,
significant compared to 0.5 per image. With the MIAS Mammography, the initial
evaluation will be completed soon. In
all of the studies examined (Roman et al, Obenauer et al, Shiraishi et
al, van Engeland et al, and Gan et al), the individual slide sensitivity rate
for MR and mammogram is higher than the sensitivity rate for a series, which is
consistent with our findings from September 2007 to May 2008.
the difficulties encountered in computer aided detection are the diagnostic
determination of white matter spaces (Lasbury et al, Groeschel et al). The result of white matter decreases
the sensitivity rate for an individual slide to 80% (Groeschel et al). In our
MIAS, we have been able to detect white and our MIAS can detect and accurately
diagnose at a higher rate.94.1% and 73.3% for individual and series
respectively. In individual series, our MIAS is less
susceptible to white matter issues in individual scans, but it relative close
to accuracy of other systems in series scans. (Groeschel et al)
Therefore, the MIAS for MR and mammography is better in most areas. Please see the notes in the
annotated references for additional discussion.
Discussion of Evaluation of Current Computer Aided Detection
et al (Lassbury, 2001) report that MRI with computer aided detection (CAD) can
recognize white matter deficiencies in patients. With our MIAS program for CAD
we have observed that the white matter is related to ischemia and the medical
imaging analysis system (MIAS) was able to correct diagnose the effects of the
finding. Another finding by Lasbury et al is that 26% of the white matter in a
MRI is related to significant findings. With the MIAS, the correlation to
significance was observed in the MR experiments using the MIAS. The MIAS includes
processes that address issues of white matter in CAD, and can correlate them.
et al (Obenaurer, 2006) review the false positive rates of various CAD programs
to MRI image evaluation. One of the findings is that the False
positive marker rate is 205/412 or 49.76% and further improvements are
necessary for computer aid detection, or, a 50.24% accuracy rate compared to a
97% accuracy rate with our MIAS. The 97% single image accuracy rate is due to
initial testing performed in the period of Sept 2007 to May 2008. In a
comparison of false positives per image, Obenauer et al, found 0.5 false
positives per image with computer aided detection with MRI or 7.0 false
positives per ?3 Plane LOC? series compared with 2.0 false positives per image
with our MIAS. In comparing current CAD results to a series MR evaluation,
Obenauer et al, found that 77/226
or 34.1% missed or 65.9% accurate
compared with our Mias is 92% accuracy for a series.
When comparing CAD system to
detecting specific masses Groeschel et al
(Groeschel, 2006) , found that VRS (Virchow-Robin Spaces) can be viewed in 80% of MRI
cases. The MIAS was able to detect cases of ischemia or other hard to
determine findings in specific cases and to classify correctly as positive or
negative. The finding detecting specific masses is from the study performed
Sept 2007 to May 2008. The result is that the MIAS is
consistent with other applications in identifying specific mass areas.