| Autor: |
Dibson D. Gondim, Khaleel I. Al-Obaidy, Muhammad T. Idrees, John N. Eble, Liang Cheng |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Journal of Pathology Informatics, Vol 14, Iss , Pp 100299- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2153-3539 |
| DOI: |
10.1016/j.jpi.2023.100299 |
| Popis: |
Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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