An AI-powered blood test to detect cancer using nanoDSF
Philipp O. Tsvetkov, R\'emi Eyraud, St\'ephane Ayache, Anton A., Bougaev, Soazig Malesinski, Hamed Benazha, Svetlana Gorokhova, Christophe, Buffat, Caroline Dehais, Marc Sanson, Franck Bielle, Dominique, Figarella-Branger, Olivier Chinot, Emeline Tabouret, Fran\c{c}ois Devred
TL;DR
This paper introduces an AI-powered blood test leveraging nanoDSF to detect cancer by analyzing plasma denaturation profiles, achieving high accuracy and potential as a universal diagnostic tool.
Contribution
It presents a novel use of Differential Scanning Fluorimetry combined with machine learning for non-invasive, pan-cancer detection from blood samples.
Findings
92% classification accuracy for glioma detection
Applicable to various cancer types
High throughput workflow suitable for clinical use
Abstract
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.
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