Robust Image-Driven Phenotyping of Ovarian Tumor Cells using Optimized Dynamic Features in Hyperbolic Channels

2026-06-15

AI Summary

This study addresses the challenge that high-dimensional dynamic features in microfluidic imaging are often confounded by hydrodynamic artifacts induced by flow rate variations, which obscure genuine differences in cellular mechanical phenotypes. To resolve this, the authors propose a stability-guided analytical framework that simultaneously tracks morphological dynamics, kinematics, and intracellular optical density trajectories of ovarian cells within hyperbolic microchannels, thereby constructing a high-dimensional feature space. By integrating structural consistency and statistical persistence, the framework implements a cross-flow-rate feature selection mechanism that effectively decouples flow-induced noise from biological signals. This approach reduces flow-rate-associated variance in the principal components used for subtype classification from 69.9% to 9.3%, achieving high diagnostic accuracy across multiple models and under limited sampling conditions using only 20–25 robust features.

Abstract

Label-free, image-based cellular mechanophenotyping in microfluidic devices provides a high-throughput method for single-cell profiling. However, while complex microchannels (e.g., hyperbolic geometries) reveal transient deformation dynamics under continuous extensional stress, the resulting high-dimensional feature spaces are highly susceptible to hydrodynamic artifacts. Flow rate variations often distort discriminative boundaries, linking feature distributions to fluid conditions rather than intrinsic biology. To overcome this, we introduce a stability-guided analytical framework that decouples flow-induced noise from authentic mechanobiological signatures. We tracked the morphodynamic, kinematic, and intracellular optical-density trajectories of healthy and malignant ovarian cells to build a 93-dimensional feature space. Using a cross-flow screening strategy based on structural consistency and statistical persistence, we isolated robust descriptors, creating task-adapted subsets (20 features for binary classification; 25 for cancer subtyping). Variance-attribution analysis confirmed the neutralization of flow-conditioned artifacts; notably, flow-associated variance in the primary principal component fell from 69.9% to 9.3% in the subtyping task. We also found that macroscopic binary discrimination depends on bulk kinematic transitions, while clonal subtyping requires localized intracellular optical heterogeneity. These optimized subsets maintained diagnostic fidelity across multiple machine learning architectures and restricted sampling conditions. This framework establishes a robust, flow-independent foundation for continuous dynamic phenotyping.

Problem

• image-based phenotyping
• hydrodynamic artifacts
• microfluidic devices
• mechanophenotyping
• flow-induced noise

Innovation

• dynamic phenotyping
• microfluidic hyperbolic channels
• flow-invariant features
• mechanophenotyping
• variance decoupling