Label-Free Machine Learning Prediction of Chemotherapy on Tumor Spheroids Using a Microfluidics Droplet Platform.

ABSTRACT: An integrated approach is proposed to rapidly evaluate the effects of anticancer treatments in 3D models, combining a droplet-based microfluidic platform for spheroid formation and single-spheroid chemotherapy application, label-free morphological analysis, and machine learning to assess treatment response. Morphological features of spheroids, such as size and color intensity, are extracted and selected using the multivariate information-based inductive causation algorithm, and used to train a neural network for spheroid classification into viability classes, derived from metabolic assays performed within the same platform as a benchmark. The model is tested on Ewing sarcoma cell lines and patient-derived xenograft (PDX) cells, demonstrating robust performance across datasets. It accuratel

SUBMITTER: Parent C 

PROVIDER: S-EPMC12412520 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature