ArticleFrontiers in oncology2020
Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Quantitative analysis of fibroblast migration reveals migratory states characterized by force generation, cell shape and motion.bioRxiv : the preprint server for biology · 2026Article
- Colloid-patterned surfaces distinguish malignant mechanophenotypes.Materials today. Bio · 2026Article
- Artificial intelligence based platform for the automatic and simultaneous explainable detection of apnoea, oxygen desaturation, and artefacts in paediatric polygraphy exams (REST).Scientific reports · 2025Article
- Review
- Development of label-free cell tracking for discrimination of the heterogeneous mesenchymal migration.PloS one · 2025Article
- ODEP-Based Robotic System for Micromanipulation and In-Flow Analysis of Primary Cells.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Patch-Based Far-Infrared Radiation (FIR) Therapy Does Not Impact Cell Tracking or Motility of Human Melanoma Cells In Vitro.Current issues in molecular biology · 2024Article
- Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes.Membranes · 2024Article
- Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics.Scientific reports · 2024Article
- Machine learning approach for recognition and morphological analysis of isolated astrocytes in phase contrast microscopy.Scientific reports · 2024Article
- Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer.Scientific reports · 2024Article
- Prostate cancer detection using e-nose and AI for high probability assessment.BMC medical informatics and decision making · 2023Article
- Changes in expression of mesothelial BBS genes in 2D and 3D after lithium chloride and ammonium sulphate induction of primary cilium disturbance: a pilot study.Pharmacological reports : PR · 2023Article
- Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations
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