ArticlePNAS nexus2024
Training an automated circulating tumor cell classifier when the true classification is uncertain.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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
9 citing papers in PubMed, 9 citations in OpenAlex.
- Circulating tumour cells for guiding antibody‒drug conjugate therapy: Role of artificial intelligence.Clinical and translational medicine · 2026Review
- Flow Enabled Target Capture Halbach-based magnetic enrichment increases circulating tumor cell capture from blood in metastatic cancer patients.Molecular oncology · 2026Article
- Longitudinal Analysis of Manually and Automatically Classified Circulating Tumor Biomarkers and their Prediction of Survival in Metastatic Colorectal Cancer.Clinical pharmacology and therapeutics · 2026Article
- Artificial Intelligence for CELLSEARCH Image Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Cell surface vimentin-positive circulating tumor cells: developments, and clinical applications.Medical oncology (Northwood, London, England) · 2025Review
- The ACCEPTance of automation: refining circulating tumor cells enumeration for improved metastatic colorectal cancer prognosis.Molecular oncology · 2025Article
- Flow-based immunomagnetic enrichment of circulating tumor cells from diagnostic leukapheresis product.Molecular oncology · 2025Article
- Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy.Patterns (New York, N.Y.) · 2025Article
- Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Circulating tumor cell (CTC) and tumor-derived extracellular vesicle (tdEV) loads are prognostic factors of survival in patients with carcinoma. The current method of CTC enumeration relies on operator review and, unfortunately, has moderate interoperator agreement (Fleiss' kappa 0.60) due to difficulties in classifying CTC-like events. We compared operator review, ACCEPT automated image processing, and refined the output of a deep-learning algorithm to identify CTC and tdEV for the prediction of survival in patients with metastatic and nonmetastatic cancers. Operator review is only defined for CTC. Refinement was performed using automatic contrast maximization CM-CTC of events detected in cancer and in benign samples (CM-CTC). We used 418 samples from benign diseases, 6,293 from nonmetastatic breast, 2,408 from metastatic breast, and 698 from metastatic prostate cancer to train, test, optimize, and evaluate CTC and tdEV enumeration. For CTC identification, the CM-CTC performed best on metastatic/nonmetastatic breast cancer, respectively, with a hazard ratio (HR) for overall survival of 2.6/2.1 vs. 2.4/1.4 for operator CTC and 1.2/0.8 for ACCEPT-CTC. For tdEV identification, CM-tdEV performed best with an HR of 1.6/2.9 vs. 1.5/1.0 with ACCEPT-tdEV. In conclusion, contrast maximization is effective even though it does not utilize domain knowledge.
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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.