Microvascularity detection and quantification in glioma: a novel deep-learning-based framework
| Autor: | Zhifeng Shi, Jinhua Yu, Qisheng Tang, Yuanyuan Wang, Xieli Li |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Adult Male medicine.medical_specialty China medicine.medical_treatment H&E stain Pathology and Forensic Medicine Targeted therapy 03 medical and health sciences 0302 clinical medicine Deep Learning Glioma Medicine Humans Molecular Biology Grading (tumors) Survival analysis Aged business.industry Brain Neoplasms Deep learning Hazard ratio Cell Biology Middle Aged medicine.disease Subtyping 030104 developmental biology 030220 oncology & carcinogenesis Microvessels Female Artificial intelligence Radiology business |
| Zdroj: | Laboratory investigation; a journal of technical methods and pathology. 99(10) |
| ISSN: | 1530-0307 |
| Popis: | Microvascularity is highly correlated with the grading and subtyping of gliomas, making this one of its most important histological features. Accurate quantitative analysis of microvessels is helpful for the development of a targeted therapy for antiangiogenesis. The deep-learning algorithm is by far the most effective segmentation and detection model and enables location and recognition of complex microvascular networks in large images obtained from hematoxylin and eosin (HE) stained specimens. We proposed an automated deep-learning-based method to detect and quantify the microvascularity in glioma and applied it to comprehensive clinical analyses. A total of 350 glioma patients were enrolled in our study, for which digitalized imaging of HE stained slides were reviewed, molecular diagnosis was performed and follow-up was investigated. The microvascular features were compared according to their histologic types, molecular types, and patients' prognosis. The results show that the proposed method can quantify microvascular characteristics automatically and effectively. Significant increases of microvascular density and microvascular area were observed in glioblastomas (95% p 0.001 in density, 170% p 0.001 in area) in comparison with other histologic types; increases were also observed in cases with TERT-mut only (68% p 0.001 in density, 54% p 0.001 in area) compared with other molecular types. Survival analysis showed that microvascular features can be used to cluster cases into two groups with different survival periods (hazard ratio [HR] 2.843, log-rank 0.001), which indicates the quantified microvascular features may potentially be alternative signatures for revealing patients' prognosis. This deep-learning-based method may be a useful tool in routine clinical practice for precise diagnosis and antiangiogenic treatment. |
| Databáze: | OpenAIRE |
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