Pituitary adenoma classification: Tools to improve the current system
DOI:
https://doi.org/10.17879/freeneuropathology-2024-5226Keywords:
Pituitary, Classification, Machine learning, Statistical learningAbstract
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.
Metrics
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 William C. McDonald
This work is licensed under a Creative Commons Attribution 4.0 International License.
Papers are published open access under the Creative Commons BY 4.0 license. This license lets others distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Data included in the article are made available under the CC0 1.0 Public Domain Dedication waiver, unless otherwise stated, meaning that all copyrights are waived.