Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience
Abstract Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84238-x |
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| author | Mingrong Zuo Xiang Xing Linmao Zheng Hao Wang Yunbo Yuan Siliang Chen Tianping Yu ShuXin Zhang Yuan Yang Qing Mao Yongbin Yu Ni Chen Yanhui Liu |
| author_facet | Mingrong Zuo Xiang Xing Linmao Zheng Hao Wang Yunbo Yuan Siliang Chen Tianping Yu ShuXin Zhang Yuan Yang Qing Mao Yongbin Yu Ni Chen Yanhui Liu |
| author_sort | Mingrong Zuo |
| collection | DOAJ |
| description | Abstract Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. A weakly supervised deep learning model was developed using ResNet-50 combined with an attention mechanism. We investigated the performance of the model by calculating area under the curve (AUC) in a ten-fold cross-validation setting. Heatmap visualizations showed the prediction mechanism of the model. The results were promising, with high AUC values for differentiating grades of astrocytomas, oligodendrogliomas, all gliomas, and glioma types in the TCGA dataset (0.9419, 0.8659, 0.9904, and 0.9298, respectively), and in the WCH cohort (0.9048, 0.7423, 0.9510, and 0.7098, respectively). The model demonstrated a strong ability to infer IDH status in the TCGA dataset (AUC = 0.9488). The weakly supervised deep learning model proved to be an effective and reliable tool for neuropathological diagnosis, making it an attractive auxiliary tool. |
| format | Article |
| id | doaj-art-f42211cea2254c3aacafad198fd4f1fa |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f42211cea2254c3aacafad198fd4f1fa2025-01-05T12:14:02ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-84238-xWeakly supervised deep learning-based classification for histopathology of gliomas: a single center experienceMingrong Zuo0Xiang Xing1Linmao Zheng2Hao Wang3Yunbo Yuan4Siliang Chen5Tianping Yu6ShuXin Zhang7Yuan Yang8Qing Mao9Yongbin Yu10Ni Chen11Yanhui Liu12Department of Neurosurgery, West China Hospital, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan UniversityDepartment of Pathology, West China Hospital, Sichuan UniversitySchool of Information and Software Engineering, University of Electronic Science and Technology of ChinaDepartment of Neurosurgery, West China Hospital, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan UniversityDepartment of Pathology, West China Hospital, Sichuan UniversityDepartment of Critical Care Medicine, West China Hospital, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan UniversitySchool of Information and Software Engineering, University of Electronic Science and Technology of ChinaDepartment of Pathology, West China Hospital, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan UniversityAbstract Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA). We utilized the OpenSlide library to load WSIs, segmented them into small patches using the DeepZoom module, and then normalized the color using the Reinhard method. A weakly supervised deep learning model was developed using ResNet-50 combined with an attention mechanism. We investigated the performance of the model by calculating area under the curve (AUC) in a ten-fold cross-validation setting. Heatmap visualizations showed the prediction mechanism of the model. The results were promising, with high AUC values for differentiating grades of astrocytomas, oligodendrogliomas, all gliomas, and glioma types in the TCGA dataset (0.9419, 0.8659, 0.9904, and 0.9298, respectively), and in the WCH cohort (0.9048, 0.7423, 0.9510, and 0.7098, respectively). The model demonstrated a strong ability to infer IDH status in the TCGA dataset (AUC = 0.9488). The weakly supervised deep learning model proved to be an effective and reliable tool for neuropathological diagnosis, making it an attractive auxiliary tool.https://doi.org/10.1038/s41598-024-84238-xGliomaWeakly supervised deep learningHematoxylin-eosin stainingDiagnosis |
| spellingShingle | Mingrong Zuo Xiang Xing Linmao Zheng Hao Wang Yunbo Yuan Siliang Chen Tianping Yu ShuXin Zhang Yuan Yang Qing Mao Yongbin Yu Ni Chen Yanhui Liu Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience Scientific Reports Glioma Weakly supervised deep learning Hematoxylin-eosin staining Diagnosis |
| title | Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience |
| title_full | Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience |
| title_fullStr | Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience |
| title_full_unstemmed | Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience |
| title_short | Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience |
| title_sort | weakly supervised deep learning based classification for histopathology of gliomas a single center experience |
| topic | Glioma Weakly supervised deep learning Hematoxylin-eosin staining Diagnosis |
| url | https://doi.org/10.1038/s41598-024-84238-x |
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