ArticleJournal of molecular cell biology2022
AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network.
Article in Journal of molecular cell biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Hepatic Lipid Stress Drives Metabolic Dysfunction-Associated Steatohepatitis Progression through FTO O-GlcNAcylation-Induced m6A Demethylation of HRAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cell-in-cell: a potential biomarker of prognosis and a novel mechanism of drug resistance in cancer.Frontiers in oncology · 2023Review
- Cell-in-cell structure mediates in-cell killing suppressed by CD44.Cell discovery · 2022Article
- Molecular mechanisms underlying cell-in-cell formation: core machineries and beyond.Journal of molecular cell biology · 2021Article
- Subtype-Based Analysis of Cell-in-Cell Structures in Esophageal Squamous Cell Carcinoma.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Edited by Luonan Chen Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morphological readout that can only be quantified manually at present. Moreover, the manual CIC quantification is generally over-simplified as CIC counts, which represents a major hurdle against profound mechanistic investigations. In this study, we take advantage of artificial intelligence technology to develop an automatic identification method for CICs (AIM-CICs), which performs comprehensive CIC analysis in an automated and efficient way. The AIM-CICs, developed on the algorithm of convolutional neural network, can not only differentiate between CICs and non-CICs (the area under the receiver operating characteristic curve (AUC) > 0.99), but also accurately categorize CICs into five subclasses based on CIC stages and cell number involved (AUC > 0.97 for all subclasses). The application of AIM-CICs would systemically fuel research on CIC-mediated cell death, such as high-throughput screening.
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