Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-03984-4 |
| _version_ | 1798033449016098816 |
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| author | Qian Da Shijie Deng Jiahui Li Hongmei Yi Xiaodi Huang Xiaoqun Yang Teng Yu Xuan Wang Jiangshu Liu Qi Duan Dimitris Metaxas Chaofu Wang |
| author_facet | Qian Da Shijie Deng Jiahui Li Hongmei Yi Xiaodi Huang Xiaoqun Yang Teng Yu Xuan Wang Jiangshu Liu Qi Duan Dimitris Metaxas Chaofu Wang |
| author_sort | Qian Da |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:30:34Z |
| format | Article |
| id | doaj.art-aada59cf8df34c61831adf2535d720fe |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T20:30:34Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-aada59cf8df34c61831adf2535d720fe2022-12-22T04:04:31ZengNature PortfolioScientific Reports2045-23222022-01-011211810.1038/s41598-021-03984-4Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learningQian Da0Shijie Deng1Jiahui Li2Hongmei Yi3Xiaodi Huang4Xiaoqun Yang5Teng Yu6Xuan Wang7Jiangshu Liu8Qi Duan9Dimitris Metaxas10Chaofu Wang11Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineDepartment of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineSensetime ResearchDepartment of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineSensetime ResearchDepartment of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineDepartment of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineShanghai Pulmonary Hospital, Tongji University School of MedicineDepartment of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineSensetime ResearchDepartment of Computer Science, Rutgers The State University of New JerseyDepartment of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of MedicineAbstract 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.https://doi.org/10.1038/s41598-021-03984-4 |
| spellingShingle | Qian Da Shijie Deng Jiahui Li Hongmei Yi Xiaodi Huang Xiaoqun Yang Teng Yu Xuan Wang Jiangshu Liu Qi Duan Dimitris Metaxas Chaofu Wang Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning Scientific Reports |
| title | Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning |
| title_full | Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning |
| title_fullStr | Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning |
| title_full_unstemmed | Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning |
| title_short | Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning |
| title_sort | quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning |
| url | https://doi.org/10.1038/s41598-021-03984-4 |
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