ArticleScientific reports2022
Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Cellular Morphometric Analysis (CellMorph)-a comprehensive imaging-based tool for quantifying cellular phenotype heterogeneity and dynamics across biological processes.The FEBS journal · 2026Article
- Deep learning for multiclass tumor cell detection in histopathology slides of hereditary diffuse gastric cancer.iScience · 2025Article
- An Image-Based Identification of Aggressive Breast Cancer Circulating Tumor Cell Subtypes.Cancers · 2023Article
- High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.Scientific reports · 2023Article
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
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12 authors.
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Abstract
Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.
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