ArticleScientific data2023
Label-free tumor cells classification using deep learning and high-content imaging.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Circulating tumour cells for guiding antibody‒drug conjugate therapy: Role of artificial intelligence.Clinical and translational medicine · 2026Review
- An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.Lab on a chip · 2026Article
- OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.Computational and structural biotechnology journal · 2026Article
- Minimal Residual Disease Detection: Bridging Molecular and Clinical Strategies for Recurrence Prevention in Gynecologic Cancers.International journal of molecular sciences · 2025Review
- Iteration of Tumor Organoids in Drug Development: Simplification and Integration.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments.Biosensors · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Label-free optical microscopy with artificial intelligence: a new paradigm in pathology.Biophotonics discovery · 2025Review
- MIML: multiplex image machine learning for high precision cell classification via mechanical traits within microfluidic systems.Microsystems & nanoengineering · 2025Article
- Article
- Applications of 3D models in cholangiocarcinoma.Frontiers in oncology · 2025Review
- Evaluating Circulating Biomarkers for Diagnosis, Prognosis, and Tumor Monitoring in Pediatric Sarcomas: Recent Advances and Future Directions.Biomolecules · 2024Review
- Artificial intelligence: illuminating the depths of the tumor microenvironment.Journal of translational medicine · 2024Review
- Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation.APL bioengineering · 2024Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells.Open life sciences · 2024Article
- Editorial: Experts' opinion in medicine 2022.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Furthermore, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a dataset of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.
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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.