Pituitary adenoma classification: Tools to improve the current system
The World Health Organization classification of pituitary tumors provides a framework for pathologists and researchers to classify pituitary adenomas. From the perspective of a practicing pathologist, this classification can be improved by pooling immunohistochemical data in a more standardized way...
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| Format: | Article |
| Language: | English |
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University of Münster / Open Journals System
2024-01-01
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| Series: | Free Neuropathology |
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| Online Access: | https://www.uni-muenster.de/Ejournals/index.php/fnp/article/view/5226 |
| _version_ | 1797356226188673024 |
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| author | William McDonald |
| author_facet | William McDonald |
| author_sort | William McDonald |
| collection | DOAJ |
| description |
The World Health Organization classification of pituitary tumors provides a framework for pathologists and researchers to classify pituitary adenomas. From the perspective of a practicing pathologist, this classification can be improved by pooling immunohistochemical data in a more standardized way, and by deliberately distinguishing features that assist in classification from those that do not. This article illustrates one general workflow to examine classification features consisting of immunohistochemical stains for anterior pituitary tumors, in order to promote debate and advance an evidence-based framework for classification.
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| first_indexed | 2024-03-08T14:22:36Z |
| format | Article |
| id | doaj.art-cb47195929714f0ea0c51153f87cc8f1 |
| institution | Directory Open Access Journal |
| issn | 2699-4445 |
| language | English |
| last_indexed | 2024-03-08T14:22:36Z |
| publishDate | 2024-01-01 |
| publisher | University of Münster / Open Journals System |
| record_format | Article |
| series | Free Neuropathology |
| spelling | doaj.art-cb47195929714f0ea0c51153f87cc8f12024-01-14T02:59:57ZengUniversity of Münster / Open Journals SystemFree Neuropathology2699-44452024-01-01510.17879/freeneuropathology-2024-5226Pituitary adenoma classification: Tools to improve the current systemWilliam McDonald0Allina Health Laboratories – Abbott Northwestern Hospital, Minneapolis, MN 55407, USA The World Health Organization classification of pituitary tumors provides a framework for pathologists and researchers to classify pituitary adenomas. From the perspective of a practicing pathologist, this classification can be improved by pooling immunohistochemical data in a more standardized way, and by deliberately distinguishing features that assist in classification from those that do not. This article illustrates one general workflow to examine classification features consisting of immunohistochemical stains for anterior pituitary tumors, in order to promote debate and advance an evidence-based framework for classification. https://www.uni-muenster.de/Ejournals/index.php/fnp/article/view/5226PituitaryClassificationMachine learningStatistical learning |
| spellingShingle | William McDonald Pituitary adenoma classification: Tools to improve the current system Free Neuropathology Pituitary Classification Machine learning Statistical learning |
| title | Pituitary adenoma classification: Tools to improve the current system |
| title_full | Pituitary adenoma classification: Tools to improve the current system |
| title_fullStr | Pituitary adenoma classification: Tools to improve the current system |
| title_full_unstemmed | Pituitary adenoma classification: Tools to improve the current system |
| title_short | Pituitary adenoma classification: Tools to improve the current system |
| title_sort | pituitary adenoma classification tools to improve the current system |
| topic | Pituitary Classification Machine learning Statistical learning |
| url | https://www.uni-muenster.de/Ejournals/index.php/fnp/article/view/5226 |
| work_keys_str_mv | AT williammcdonald pituitaryadenomaclassificationtoolstoimprovethecurrentsystem |