MIAS80j-r-1-1 (Computer Aided Diagnosis) Medical Diagnostics
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Ultra High Precision Computation Module / Kbasic 6

MIAS80j-R-1-1 (Computer Aided Diagnosis)


Software Licensing


Part I – Basic Information


  1. Title: MIAS (Medical Image Analysis System) 80j – r-1-1
  2. Purpose:  To provide analysis and computer aided diagnosis of MRI scans in the cranial and abdominal areas of the body.
  3. Benefit: This program can be used as a “second reader” or to aid a physician in analysis MRI scans for the cranial and abdominal area. The software has been calibrated through a medical study performed from 2007-2008.
  4. Licensing Summary 20 Client Use Only. Two weeks training included with each purchase and subscription renewal. Unlimited email support.

Part II – Program Information about use in medical study






Status Report


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

IRB Expiration Date: June 14, 2008



1. What is done:


            The 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.

            The 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).


            The 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%.

            In 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 processor.



2. What is being planned:

            We 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:



            The 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.


4. Conclusion:


            The 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. 

Kanecki Associates, Inc., * P.O. Box 866 * Kenosha, WI 53141 * UNITED STATES 





David Kanecki, MBA, A.C.S., Bio. Sci. 

Kanecki 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 machines.