An AI-powered blood test to detect cancer using nanoDSF
This work addresses the problem of non-invasive cancer detection for patients, offering a potential pan-cancer diagnostic tool from a simple blood test, though it appears incremental as it applies an existing technique in a new way.
The researchers developed a novel cancer diagnostic method using plasma denaturation profiles from Differential Scanning Fluorimetry, achieving 92% accuracy in classifying 84 glioma patients and 63 healthy controls with machine learning.
We describe a novel cancer diagnostic method based on plasma denaturation profiles obtained by a non-conventional use of Differential Scanning Fluorimetry. We show that 84 glioma patients and 63 healthy controls can be automatically classified using denaturation profiles with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool from a simple blood test.