MBDAA for Lung Neoplasia Dx

Bioprognos’ MBDAA (Multiple Biomarkers Disease Activity Algorithm) for Lung Neoplasia Dx is an innovative, non-invasive, accurate, and cost-effective already validated solution to help in Lung 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―. It also has potentially uses for screening, prognosis and recurrence monitoring.

Our MBDAA for Lung 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).

 

Click here to download the brochure in PDF format.

 

Tumor Markers

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):

  1. Tumor Markers Serum concentrations.
  2. Discard benign pathology by the exclusion of main source False Positive results.
  3. Follow-up if Tumor Markers moderate results (Grey Zone/Undetermined).
  4. 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 Lung Neoplasia Dx test ―based on the combined count of CA 15.3, CEA, CYFRA 21-1, NSE, SCC and ProGRP Tumor Markers; comorbidities; and other data from 4.296 consecutive patients, then fine-tuned by other research―, throws really interesting diagnostic capabilities: 93.5% Sensitivity and 96.2% Specificity.

MBDAA-LUNG-ROC-Curve

Besides Risk: Histologycal Type and Subtype

Our MBDAA for Lung Neoplasia Dx test also allow us to determine with that so high accuracy the Histologycal Type, distinguishing between Non-Small Cell Lung Cancer (NSCLC) or Small Cell Lung Cancer (SCLC) as well as the Subtype, discriminating between Adenocarcinoma or Squamous Cell Carcinoma (if NSCLC Type).

How does it work

As all Bioprognos’ MBDAA tests, our MBDAA for Lung 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, comorbidities, biochemistry values, CT Scan finding and lifestyle information), and click on Submit button in order to obtain the risk score of having Lung Cancer.

Final Report

After doctors entered the patient’s data, our MBDAA for Lung 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.

lung-cancer-report-sample

Click here to download the report in PDF format.

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 Lung 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 as sex, race, comorbidities or smoking habits―, 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 Lung Cancer diagnostic”, and also certified by obtaining the CE DECLARATION OF CONFORMITY (Medical Device Directive 93/42/EEC, Class I, rule 12).

CE Declaration of Conformity

Since November 22th, 2016, our MBDAA for Lung Neoplasia Dx test has the CE Declaration of Conformity that certifies it has been assessed to meet high safety, health, and environmental protection requirements.

Click here to download the CE DECLARATION OF CONFORMITY in PDF format.

This declaration also certifies that our MBDAA for Lung Neoplasia Dx test can 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 Lung Neoplasia Dx test

Our MBDAA for Lung Neoplasia Dx test has been developed for:

  • Aid in diagnostic assessments for high-risk patients (heavy smokers older than 55 years).
  • Confirm or discard malignancy from results obtained previously with other tests, such as Computed Tomography (CT) Scan 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

20/20 GeneSystems, an American biotech company, have developed the PAULA’s (Protein Assays Utilizing Lung Cancer Analytes) Test, a multiplex immunoassay blood test that incorporates both tumor antigens and autoantibodies to determine the risk that Non-Small Cell Lung Cancer (NSCLC) is present in individuals from a high-risk population.

PAULA’s Test is based in 4 biomarkers found in serum, such as CA 125, CEA, CYFRA and NY-ESO-1, and the global performance is still quite improvable, as we have demonstrated with our own Algorithm (see comparision curves below).

MBDAA-LUNG-ROC-Curve-PAULA

Superposed ROC curves for PAULA’s Test and our own MBDAA for Lung Neoplasia Dx test for comparison are as follows:

MBDAA-LUNG-ROC-Curve-VS-PAULAs-Test

Besides, our MBDAA for Lung Neoplasia Dx test is able to differentiate between Non-Small Cell Lung Cancer (NSCLC) and Small Cell Lung Cancer (SCLC) or Neuroendocrine ―while PAULA’s Test not―, and also determine the Histologycal Type and Subtype for NSCLC (Adenocarcinoma ―Large-Cell Lung Carcinoma―, and Squamous).

Based on Publications

  1. Molina, R., Marrades R. M., Auge J. M., Escudero J. M., Vinolas N., Reguart N., Ramirez J., Filella X., Molins L. and Agusti A. (2016). “Assessment of a Combined Panel of Six Serum Tumor Markers for Lung Cancer.” Am J Respir Crit Care Med 193(4): 427-437. DOI: 10.1164/rccm.201404-0603OC
  2. Molina R., Filella X., Trapé J., Augé J. M., Barco A., Cañizares F., Colomer A., Fernandez A., Gaspar M. J., Martinez-Peinado A., Pérez L., Sánchez M., Escudero J. M. (2013). “Principales causas de falsos positivos en los resultados de marcadores tumorales en suero.” Sociedad Española de Bioquímica Clínica y Patología Molecular. Comisión de Marcadores Biológicos del Cáncer. PDF

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