AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network

Abstract

Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis relays 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 (AI) 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 (CNN), 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.

Cell-in-cell: a potential biomarker of prognosis and a novel mechanism of drug resistance in cancer

“…Tang et al. ( 70 ) proposed an automated identification method for specific CICs. Uniform high-throughput counting methods such as this are needed to improve the reliability of research results and provide more precise data support for clinical treatment.…”

“…Finally, due to the lack of a unified and specific counting method, CIC counting currently relies on manual counting, which may cause bias in the results. Tang et al (70) proposed an automated identification method for specific CICs. Uniform high-throughput counting methods such as this are needed to improve the reliability of research results and provide more precise data support for clinical treatment.…”