| Autor: |
Ya-Li Wang, Song Gao, Qian Xiao, Chen Li, Marcin Grzegorzek, Ying-Ying Zhang, Xiao-Han Li, Ye Kang, Fang-Hua Liu, Dong-Hui Huang, Ting-Ting Gong, Qi-Jun Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Computational and Structural Biotechnology Journal, Vol 24, Iss , Pp 205-212 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2001-0370 |
| DOI: |
10.1016/j.csbj.2024.03.007 |
| Popis: |
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
