Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools,
capable of classifying cancer cells based on their functions, may substantially enhance …

The malignancy potential is correlated with the mechanical deformability of the cancer cells.
However, mechanical tests for clinical applications are limited. We present here a Triangular …

The recurrence of cancer following chemotherapy treatment is a major cause of death
across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL) …

The physical properties of cells are promising biomarkers for cancer diagnosis and
prognosis. Here we determine the physical phenotypes that best distinguish human cancer …

Cancer phenotypes are often associated with changes in the mechanical states of cells and
their microenvironments. Numerous studies have established correlations between cancer …

Cancer mortality is mostly related to metastasis. Metastasis is currently prognosed via
histopathology, disease-statistics, or genetics; those are potentially inaccurate, not rapidly …

This chapter shows important implications for the combined use of imaging analysis with
nanomechanical measurements as a novel biomarker for evaluating and sensing changes …

In the pursuit of advancing cancer therapy, this study explores the predictive power of
machine learning in analyzing tumor characteristics, specifically focusing on the effects of …

Objective: The ability of cells to collectively move is essential in various biological contexts
including cancer metastasis. In this paper, we propose an automatic video analysis tool to …

We provide an innovative, bioengineering, mechanobiology-based approach to rapidly (2-h)
establish the in vivo metastatic likelihood of patient tumor-samples, where results are in …