
Researchers developed the crossNN AI model that classifies over 170 cancer types from DNA methylation data, achieving over 97% accuracy and enabling non-invasive diagnosis from liquid biopsies and tissue samples.
Key Details
- 1crossNN is a simple, explainable neural network AI trained on 8,000+ reference tumors and tested on 5,000+ tumors.
- 2Achieved 99.1% accuracy for brain tumor diagnosis; 97.8% accuracy across more than 170 tumor types from all organs.
- 3Uses DNA methylation profiles obtained from tissue or body fluids (e.g., cerebrospinal fluid), enabling some diagnoses to avoid surgical biopsies.
- 4Proven more accurate than previous AI solutions for tumor classification.
- 5The method is being prepared for clinical trials at all eight sites of the German Cancer Consortium.
- 6crossNN's workflow is fully explainable, meeting a key regulatory requirement for clinical adoption.
Why It Matters

Source
EurekAlert
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