Artificial intelligence-driven morphology-based enrichment of malignant cells from body fluid
Cell morphology is a fundamental feature used to evaluate patient specimens in pathological analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with high background of non-malignant cells, restricting the ability for downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multi-dimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low-levels or undetectable in pre-sort patient samples. Combined, our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multi-dimensional morphology analysis, and microfluidic sorting.