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
crossNN demonstrates the potential of AI-driven, minimally invasive tumor diagnostics, which could complement imaging in cases where biopsy is too risky or impractical. Such models can improve diagnostic precision, reduce patient morbidity, and streamline selection of personalized treatments, impacting radiology workflows and multidisciplinary cancer care.
Source
EurekAlert
Related News
•EurekAlert
Study Warns: AI Alone Is Not Enough in Critical Healthcare Decisions
Evaluating both AI algorithms and human users is key for safe adoption in high-stakes healthcare settings, according to an Ohio State study.
•EurekAlert
AI Dramatically Improves Prediction of Delivery Timing from Ultrasound Images
Ultrasound AI's study validates advanced AI for predicting delivery timing using standard ultrasound images.
•EurekAlert
AI Voice Analysis Shows Promise for Early Laryngeal Cancer Detection
Researchers demonstrated AI could detect early laryngeal cancer from voice recordings, distinguishing it from benign conditions.