ArticleScientific reports2023
Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- Automated Analysis of Liquid Biopsy Using Deep Learning: Detecting Circulating Tumor Cells and Cancer-Associated Fibroblasts.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Review
- Novel CTC Detection Method in Patients with Pancreatic Cancer Using High-Resolution Image Scanning.Cancers · 2025Article
- Cancer-Associated Fibroblasts: Origin, Classification, Tumorigenicity, and Targeting for Cancer Therapy.MedComm · 2025Review
- Representation learning enables robust single cell phenotyping in whole slide liquid biopsy imaging.Scientific reports · 2025Article
- Cancer‑associated fibroblasts in human malignancies, with a particular emphasis on sarcomas (Review).International journal of oncology · 2025Review
- Circulating tumor cells: Blood-based detection, molecular biology, and clinical applications.Cancer cell · 2025Review
- Contrastive Representation Learning for Single Cell Phenotyping in Whole Slide Imaging of Enrichment-free Liquid Biopsy.bioRxiv : the preprint server for biology · 2025Article
- Review
- Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics.Sensors (Basel, Switzerland) · 2024Article
- Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.Scientific reports · 2024Article
- Artificial intelligence: illuminating the depths of the tumor microenvironment.Journal of translational medicine · 2024Review
- Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2024Article
- Review
- Training an automated circulating tumor cell classifier when the true classification is uncertain.PNAS nexus · 2024Article
- Clinical application of circulating tumor cells.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2023Article
- A Hybrid Intelligence Approach for Circulating Tumor Cell Enumeration in Digital Pathology by Using CNN and Weak Annotations.IEEE access : practical innovations, open solutions · 2023Article
- The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs. Our deep learning method achieved precision and recall of 94% (± 0.2%) and 96% (± 0.2%) for mCTC detection, and 93% (± 1.7%) and 84% (± 3.1%) for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% (± 0.2%) and 78% (± 0.3%) for mCTC and 58% (± 3.9%) and 56% (± 3.5%) for CAF. Our custom imaging system combined with deep learning cell identification method represents an important advance on CTC and CAF analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.