The Science of Our Name.

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DCIS Therapeutic Challenges

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Our Science

The DCISionRT test combines the latest innovations in molecular biology with artificial intelligence and machine learning to deliver the most advanced DCIS test available. DCISionRT is the first biological risk signature developed specifically for DCIS and the only test that predicts radiation therapy benefit.

The test relies on a comprehensive assessment of the proven disease progression pathways in addition to standard clinical factors. Non-linear algorithms were developed using artificial intelligence and machine learning techniques. This enables the test to calculate 10-year local recurrence risk (Total and Invasive) as well as predict the benefit of radiation therapy. DCISionRT is the only test that provides recurrence risks for DCIS patients treated with surgery alone and surgery with radiation therapy.

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As DCIS incidence increased with the rise of mammographic screening programs, researchers at University of California, San Francisco embarked on an ambitious discovery program to identify factors contributing to DCIS recurrence and progression to invasive breast cancer.

A comprehensive research effort commenced with funding from the National Cancer Institute in order to better understand the fundamental biology of DCIS. A number of biologically important progression pathways emerged from the research effort, providing for the first time, an individualized risk assessment based on DCIS tumor biology.


The PreludeDx team launched the development of DCISionRT with the vision of creating a DCIS test that not only offered women an individualized risk of recurrence but could also predict her individualized radiation therapy benefit. DCISionRT was developed with multiple studies totaling over 650 women—the largest combined study population ever used in development of a DCIS test.

A key innovation fundamental to the DCISionRT test is the use of a non-linear risk algorithm. This type of algorithm enables the test to account for highly complex interactions that drive progression and recurrence of DCIS. Although development of a non-linear algorithm requires much more data coupled with sophisticated artificial intelligence and machine learning techniques, they are better suited for intricate biologic disease processes than traditional linear algorithms.

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DCISionRT is the only DCIS test validated with Level 1b clinical evidence, meaning the test demonstrated consistent results in multiple studies including a randomized clinical trial. From development to validation, clinical evidence includes studies totaling over 2000 patients. DCISionRT was shown in the SweDCIS randomized clinical trial

to predict which patients have a significant invasive breast cancer benefit from radiation therapy. A study performed by Kaiser Permanente Northwest confirmed that DCISionRT is prognostic for local recurrence risk and that patients with Elevated DCISionRT Scores have over a 70% relative risk reduction from radiation therapy.

Global Clinical Collaboration