Circulating tumor cell detection in cancer patients using in-flow deep learning holography

TL;DR

This paper introduces a novel, integrated digital holographic microscopy system combined with deep learning and immunofluorescence for improved detection of circulating tumor cells in blood, demonstrating higher sensitivity and specificity in a prostate cancer pilot study.

Contribution

The study presents a new DHM-based platform that combines microfluidic enrichment, dual-modality imaging, and real-time deep learning analysis for enhanced CTC detection and characterization.

Findings

Higher CTC counts in late-stage prostate cancer patients compared to controls

Nearly two-thirds of CTCs were EpCAM-negative but PSMA-positive

Low false positive rate of 1 cell/mL in healthy controls

Abstract

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve the sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate significantly higher CTC counts in patients with late-stage prostate cancer (n=13) compared to healthy controls (n=8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.