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Publications

Journal Article Preprint Epublish

PubMed: 40671945

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

ArXiv

Authors: Kevin Mallery, Nathaniel R Bristow, Nicholas Heller, Yash Travadi, Ali Arafa, Kaylee Kamalanathan, Catalina Galeano-Garces, Mahdi Ahmadi, Grant Schaap, Alexa Hesch, Olivia Hedeen, Zikora Izuora, Joel Hapke, Jeffrey Miller, Arjun Viswanathan, Ivo Babris, Songyi Bae, Tuan Le, Tony Clacko, Emmanuel S Antonarakis, Badrinath R Konety, Justin M Drake, Jiarong Hong

Affiliations

  • Astrin Biosciences, St Paul, MN, USA.
  • Department of Computer Science and Engineering, College of Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • School of Statistics, College of Liberal Arts, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Department of Pharmacology, Medical School, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Department of Medicine, Medical School, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Masonic Cancer Center, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Allina Health Cancer Institute, Minneapolis, MN, USA.
  • Department of Urology, Medical School, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Department of Mechanical Engineering, College of Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
  • Department of Electrical and Computer Engineering, College of Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.

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 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.

MeSH

Cells, Blood, Neoplastic Cells, Circulating, Neoplasms, Holography, Prostate, Microfluidics, Deep Learning, Learning, Quantitative Phase Imaging