Deep Learning Classification of Pancreatic Cancer Cells Over a Progression of Epithelial to Mesenchymal States by Deformability Cytometry Under Microfluidic Viscoelastic Flows

Cancer cell classification over intermediate or hybrid states during the epithelial‐to‐mesenchymal transition (EMT) provides information on their heterogeneity, plasticity, and invasiveness. While accomplished by immunofluorescence imaging of protein markers, it is low‐throughput and limited by sample heterogeneity. Since intracellular redistribution of filamentous proteins during EMT alters cellular biomechanics, we present single‐cell imaging over a continuum of deformation and recovery regions under microfluidic viscoelastic flows coupled to a multi‐region deep learning framework for high‐throughput EMT classification on morphometric shape descriptors, binary masks of cell geometry, and brightfield images retaining intracellular texture. We infer that despite nuclear enlargement during EMT that enhances stiffness, vimentin redistribution around the cytoskeleton likely allows progressively EMT‐induced pancreatic cancer cells to support greater deformation and faster relaxation to isotropic shapes. Using an architecture combining convolutional feature extraction with attention‐based regional aggregation, the contributions of cell deformation and relaxation toward classification of intermediate EMT states are captured using global geometric information and intracellular texture related to cytoskeletal remodeling. In comparison to single‐region imaging flow cytometry or scalar deformability metrics, the reported multi‐region attention‐based measurement of the deformation and relaxation dynamics improves the classification of intermediate EMT states that present the greatest plasticity for metastasis.