First demonstration of in vivo estimation of Young’s modulus in cancers using poroelastography with experimental validation

This study developed and experimentally validated a non-invasive ultrasound poroelastography approach for estimating Young’s modulus in cancers in vivo without assuming that tumor tissue is mechanically incompressible. The method combines steady-state axial and corrected lateral strain measurements with externally applied stress, tumor geometry, poroelastic theory, and Eshelby’s inclusion model to reconstruct both Young’s modulus and Poisson’s ratio. The framework was first evaluated using finite element and ultrasound simulations representing tumors with different stiffness contrasts and compressibility values. The authors then tested the method in an orthotopic murine model of triple-negative human breast cancer generated with MDA-MB-231 cells. Fifteen tumors were divided into small, medium, and large volume groups and imaged in vivo during creep compression ultrasound poroelastography. Following imaging, corresponding tumor samples were excised and mechanically tested ex vivo to provide independent Young’s modulus measurements for validation. UniVert-derived stress-strain curves showed nonlinear behavior over the full 0-50% strain range but an approximately linear response between 5% and 15%, the same strain range used for the imaging analysis. Tumor stiffness generally increased with tumor size, although differences among size groups were not statistically significant. Young’s modulus estimates from in vivo poroelastography correlated strongly with ex vivo mechanical testing, with an r² of 0.75 and a Bland-Altman mean difference of 0.12 kPa with 95% limits of agreement of approximately -38 to 38 kPa. The authors report less than 15% relative error for most experimental cases and an average error below 20% overall. The study provides experimental support for quantitative ultrasound-based assessment of tumor stiffness and may help connect non-invasive cancer imaging with tumor mechanobiology, diagnosis, and treatment monitoring.