We address this issue by finding and establishing biomarkers based on the 3D tissue architecture that will improve the clinical diagnosis and prognosis of TC subtypes and enable a more personalized treatment and follow-up. We propose X-ray phase contrast (XPC) micro CT of solid biopsy blocks and tissue micro arrays (TMAs) combined with high-throughput image and feature analysis using machine learning to find 3D biomarkers. The clinical outcome of the project will be genuinely new 3D non-invasive tumor tissue analysis tools for clinical pathology and personalized medicine of TC.
Thyroid cancer affects approximately 300 million people worldwide and the major challenge is to more reliably stratify patients for initial therapy and follow-up measures and thus to minimize the potential harm from overtreatment in the majority of patients who are at low risk while appropriately treating and monitoring patients who are at higher risk.
