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Practical approaches to diagnosing PitNETs/adenomas based on cell lineage
Abhijit Goyal-Honavar1, Geeta Chacko2
1Department of Neurosurgery, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
Brain Pathology (Zurich, Switzerland)
|August 25, 2024
Summary
The current classification of pituitary tumors (PitNETs) uses transcription factor (TF) immunohistochemistry (IHC), but challenges remain. This review examines diagnostic approaches and limitations in the existing PitNET classification system.
Area of Science:
- Endocrinology
- Pathology
- Oncology
Background:
- Pituitary adenomas (PitNETs) classification evolved to transcription factor (TF) immunohistochemistry (IHC), establishing a cell lineage-based system.
- Current diagnostic protocols face challenges including financial and logistical burdens of extensive IHC testing.
- The emergence of "multilineage" PitNETs, like PIT1-SF1 co-expressing tumors, challenges the existing classification framework.
Purpose of the Study:
- To review practical diagnostic approaches for PitNETs.
- To examine PitNET subtypes that challenge the current classification system and their clinical implications.
- To assess limitations within the existing PitNET classification system.
Main Methods:
- Literature review of diagnostic algorithms for PitNETs.
- Analysis of reported PitNET types with multiple TF expression.
- Assessment of limitations in current IHC interpretation standardization.
Main Results:
- Algorithms have been proposed to reduce the number of IHC tests, with variable diagnostic accuracy.
- The use of TFs has decreased the reported proportion of null cell tumors.
- Multilineage PitNETs, particularly those co-expressing PIT1 and SF1, are recognized but lack a defined place in the current classification.
Conclusions:
- The current cell lineage-based classification of PitNETs requires refinement to accommodate newly identified tumor subtypes.
- Standardization of IHC interpretation is crucial for accurate and consistent PitNET diagnosis.
- Future classification systems must address multilineage PitNETs and optimize diagnostic workflows.

