Patient-Specific Microfluidic Elasto-Filtration (psMEF) CTC Technology for Cancer Diagnosis via Synergy between Nonlinear Dynamic System Engineering and Clinical Data

Early cancer diagnosis, critical to the increase survival rate of cancer patients, is still challenging although to date more than 745,000 relevant published research articles (Google Scholar database) and more than 346,000 articles (PubMed database). In recent years, the detection of Circulating Tumor Cells (CTCs) from patients’ blood samples using microfluidics is a popular non-invasive method for diagnosis and therapy monitoring of cancer. However, the translation from the research of microfluidic CTC devices to a practical CTC detection system in clinics is still challenging because of the significant variation of biological properties among different patients. From the viewpoint of nonlinear dynamic system engineering, we propose a novel methodology: new nonlinear Microfluidic Elasto-Filtration (MEF) model for patients using hyperelasticity for prediction of large deformation of CTC and biophysical measurements of blood samples from each patient for the estimation of equivalent energy dissipation parameter. In this approach, we demonstrate the patient-specific Microfluidic Elasto-Filtration (psMEF) CTC technology can overcome the barrier of translation research. The pre-clinical study of psMEF CTC technology has shown the excellent clinical sensitivity and clinical specificity according to US FDA regulation. Increasing number of clinical data can enable the continuous improvement of psMEF methodology for early cancer diagnosis.