MBDAA for Ovarian Neoplasia Dx
Bioprognos’ MBDAA (Multiple Biomarkers Disease Activity Algorithm) for Ovarian Neoplasia Dx is an innovative, non-invasive, accurate, and cost-effective already validated test to help in Ovarian Cancer diagnosis as well as confirmatory diagnostic ―as an adjunct to suspicious image procedures findings, in order to reduce the number of unnecessary tissue biopsies that patients have to undergo―, with potentially uses for screening, prognosis and recurrence monitoring.
Our MBDAA for Ovarian Cancer Dx is based on a score calculation that it is obtained from several Biomarkers of the patient (mainly Tumor Markers but also patient’s clinical information).
Tumor Markers are parameters released by tumor cells, which enter the bloodstream or other biological fluids and are useful for the diagnosis, prognosis and treatment monitoring.
Most Tumor Markers are not specific to any type of cancer and the differences between benign and malignant diseases are quantitative (for example, patients with epithelial tumors tend to have significantly higher levels of these Tumor Markers than patients without malignancy).
There are now more than 20 well known parameters that are widely regarded as Tumor Markers such as PSA ―related to Prostate Cancer―, CA 15.3 ―related to Breast Cancer―, CA 125 and HE4 ―both related to Ovarian Cancer―, CEA and CA 19.9 ―both related to different gastrointestinal cancers (Colorectal, Gastric and Pancreatic Cancer)―, or NSE and ProGRP ―both related to in Lung Cancer―.
However, there are a variety of factors that can affect the accuracy of Tumor Markers by increasing its levels without malignancy presence. The main reason are benign diseases, among others, such as technical interferences.
In this sense, the Spanish Society of Clinical Biochemistry and Molecular Pathology, Cancer Biomarker Commission established the Barcelona Criteria, 4 criteria that help to correctly distinguish and value Tumor Markers results and reduce False Positives (FP):
- Tumor Markers Serum concentrations.
- Discard benign pathology by the exclusion of main source False Positive results.
- Follow-up if Tumor Markers moderate results (Grey Zone/Undetermined).
- Technical interference.
Statistical measurements in diagnostic tests
Unfortunately, the use of Tumor Markers in routine presents also other problems such as low Sensitivity in early stages, or nonexistence of any specific Tumor Marker for each malignant tumor. However, the combination of 2 or more Tumor Markers has a better outcome, especially in advanced stages.
In this regard, the combination of several Tumor Markers ―as well as the inclusion of patient history information in the equations―, using complex algorithms with multiple variables, results in higher Sensitivity and Specificity: that is what we have christened Multi-Biomarker Disease Activity Algorithm (MBDAA).
The Sensitivity of a diagnostic test is the percentage of actual positives that are correctly identified, and Specificity is the proportion of true negatives that are correctly classified. Both variables are closely linked together and give an idea of the accuracy of a test.
A test that correctly identifies all true positive as positive, but has many false negatives would have a Sensitivity of 100%, but low Specificity. For example, Sensitivity measures the number of cancerous tumors that are correctly identified as cancerous, whereas Specificity measures how many benign tumors are correctly identified as benign. A high Sensitivity means fewer cancers diagnosed as benign and high Specificity means fewer benign tumors diagnosed as cancerous.
Besides, the positive predictive value (PPV) is the number of true positives correctly identified on total real positive. A test with many false positives will have a low VPP. Moreover, the negative predictive value (NPV) is the number of true negatives correctly identified on the total actual negative. A high NPV value means that very few true positives were incorrectly identified as negative.
All these different values can be plotted together in a graphic that it is known as Receiving Operator Curve (ROC), where better results are displayed with curves that tends to come near to the upper left corner of the image (where 100% Sensitivity and 100% Specificity are reached).
Receiving Operator Curves (ROC)
The ROC curve of our MBDAA for Ovarian Neoplasia Dx test ―based on the combined count of AFP, β-hCG, CA 19.9, CA 125, CEA and HE4 Tumor Markers; FSH serum levels; comorbidities; and other data from 4.520 consecutive patients, then fine-tuned by other research―, throws really interesting diagnostic capabilities: 93.8% Sensitivity and 94.4% Specificity.
How does it work
As all Bioprognos’ MBDAA tests, our MBDAA for Ovarian Neoplasia Dx test is available online once access is granted through our secure Cloud Platform. As a Cloud solution, it is designed to be used in a Software as a Service (SaaS) basis, that means, no installation, no periodically patch upgrades, low TCO (Total Cost of Ownership) and no maintenance.
In this way, doctors or lab technicians only should fill the form with values obtained previously from patients (personal data, family background, menopausal status, comorbidities, biochemistry values, CT Scan/US finding, medications and prior procedures information), and click on Submit button in order to obtain the risk score of having Ovarian Cancer.
After doctors entered the patient’s data, our MBDAA for Ovarian Neoplasia Dx test presents the results in a separate screen that can be converted to a PDF document in order to be downloaded or sent by email.
The report includes two main sections: Patient Data and Outcome. In the first one, all patient information entered previously is showed as record. The second one includes: Results, with the risk assessment calculated and a score bar showing the probability of having Ovarian Cancer; Comments, that are created dynamically, such as levels of blood markers that would suspect the presence of Cancer, but when considering other variables together ―such menopausal status, comorbidities or current medications―, do not correspond with malignant diseases; and finally, Conclusions, with recommendations suggesting to retest patient in 1 year (for Low Risk), or in 4 weeks (for Moderate Risk, that is, these cases in which Tumor Marker levels are higher than normality but there is not quite clear to be High Risk.
Please note that final report is oriented to healthcare professionals only ―not to patients―, because it was designed as “a tool to help healthcare professionals in Ovarian Cancer diagnostic”, and in this way it is been certified by obtaining the CE DECLARATION OF CONFORMITY (Medical Device Directive 93/42/EEC, Class I, rule 12).
CE Declaration of Conformity
In the same way the CE Declaration of Conformity we obtained on past November 22th, 2016 for our MBDAA for Lung Neoplasia Dx test, we have started the process to obtain the CE Declaration of Conformity for our MBDAA for Ovarian Neoplasia Dx test. This will certify it has been assessed to meet high safety, health, and environmental protection requirements.
This declaration also will certify that our MBDAA for Ovarian Neoplasia Dx test could be sold throughout the European Economic Area (EEA) without restrictions.
Besides, there are two main benefits CE marking brings to businesses and consumers within the EEA:
- Businesses know that products bearing the CE marking can be traded in the EEA.
- Consumers enjoy the same level of health, safety, and environmental protection throughout the entire EEA.
Uses and purposes for our MBDAA for Ovarian Neoplasia Dx test
Our MBDAA for Ovarian Neoplasia Dx test has been developed for:
- Aid in diagnostic assessments for high-risk patients (women older than 55 years with Ovarian or Breast Cancer family history).
- Confirm or discard malignancy from results obtained previously with other tests, such as Computed Tomography (CT) Scan or Ultrasound (US) findings thanks higher Sensitivity and Specificity than imaging procedures.
- Help doctors predict the cancer’s behaviour and response to treatment, as well as a person’s chance of recovery.
- Guide treatment decisions (such as decide whether to add or immunotherapy after surgery and/or radiation therapy), therapy monitoring (doctors may use changes in the presence or amount of one or more Tumor Markers to assess how the cancer is responding to treatment) and predict or monitor for recurrence (looking for changes in the amount of a Tumor Marker may be part of their follow-up care plan and may help detect a recurrence sooner than other methods).
Other tests in the market
Different methods for early diagnosis have been developed, including the use of the CA 125 Tumor Marker, but the results are not satisfactory, since this marker has a low Sensitivity in the early stages while offering a high proportion of false positives (FP) in premenopausal women.
For some years, the valuation levels of HE4 ―a novel Tumor Marker―, offers greater sensitivity for these early stages, and greater specificity.
A combination of the values of CA125, HE4 and menopausal status of the patients, the ROMA test (Risk of Ovarian Malignancy Algorithm) ―a 3 variables and low complexity to help physicians in the Ovarian Cancer diagnosis―.
Another different approach sought to improve early diagnosis by combining the CA125 and characteristics of the ovarian mass obtained by Ultrasound, which led to the RMI (Risk of Malignancy Index for Ovarian Cancer) test.
Both test are useful to help in the diagnosis of abdominal masses ―as well as the monitoring of treatment―, but both can be clearly improved because ROME does not consider Ultrasound or age of the patient and RMI does not use the best Tumor Marker to date for the diagnosis of Ovarian Cancer ―the HE4―, besides other variables that we have found as valuable to this disease in our own algorithm MBDAA for Ovarian Cancer.
Superposed ROC curves for the markers CA125, HE4, ROMA, RMI and our own MBDAA Ovarian Test for comparison are as follows:
Finally, Vermillion (NasdaqCM: VRML), an American biotech company, have developed the OVA1 Test to also help physicians make the right treatment decisions for patients with pelvic masses and avoid unnecessary surgeries.
OVA1 is based in 5 biomarkers found in serum, such as Apolipoprotein A1, β2 Microglobulin, CA 125, Prealbumin and Transferrin, and the global performance is greater than ROMA and RMI, but still not significant, as we have demonstrated with our own Algorithm (see comparision curves below).
Superposed ROC curves for OVA1 and our own MBDAA for Ovarian Neoplasia Dx test for comparison are as follows:
Based on Publications
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