It is well established that the earlier cancer is detected, the higher the chances of successfully treating a patient. A methodology using a simple blood sample combined with Artificial Intelligence to achieve this is now proven but will need to be tested in clinical trials and will probably take some years till it can be widely introduced as a diagnostic tool.
A team headed by G. V. Shivashankar, (Mechano-Genomic Group, Division of Biology and Chemistry, Paul-Scherrer Institute, Villigen, Switzerland / Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland) have shown that using a simple blood sample combined with Artificial Intelligence can detect cancerous tumours as well as distinguish between three tumour groups with a combined precision of about 85%:
"....chromatin imaging of PBMCs combined with machine learning methods provides such robust and predictive chromatin biomarkers. We show that such chromatin biomarkers enable the classification of 10 healthy and 10 pan-tumor patients. Furthermore, we extended our pipeline to assess the tumor types and states of 30 tumor patients undergoing (proton) radiation therapy. We show that our pipeline can thereby accurately distinguish between three tumor groups with up to 89% accuracy and enables the monitoring of the treatment effects. Collectively, we show the potential of chromatin biomarkers for cancer diagnostics and therapy evaluation."
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