Therapeutic implications of accurate classification of pituitary adenomas.

Semin Diagn Pathol

Department of Pathology, Laboratory Medicine Program, University Health Network, Ontario, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada. Electronic address:

Published: August 2013

Recent data suggest that 1 of 5 individuals in the general population is affected with a pituitary adenoma. Many of these neoplasms are clinically non-functioning adenomas that may be small and clinically undetected or may present as mass lesions; others are hormonally active and cause significant morbidity due to the metabolic effects of hormone excess (e.g., acromegaly and cushing's disease). In either case, they can grow and invade adjacent anatomic structures. Tumors with similar clinical features are morphologically heterogenous and detailed comprehensive classification of pituitary adenomas is important to predict specific clinical behaviors and genetic changes that serve as targets for therapy. We provide a practical approach to clinical diagnosis and highlight the pitfalls in the classification of these common neoplasms.

Download full-text PDF

Source
http://dx.doi.org/10.1053/j.semdp.2013.06.002DOI Listing

Publication Analysis

Top Keywords

classification pituitary
8
pituitary adenomas
8
therapeutic implications
4
implications accurate
4
accurate classification
4
adenomas data
4
data individuals
4
individuals general
4
general population
4
population pituitary
4

Similar Publications

Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.

Cancers (Basel)

January 2025

Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.

Background/objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation.

Methods: A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research.

View Article and Find Full Text PDF

Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).

View Article and Find Full Text PDF

Predictive Modeling of Non-functioning Giant Pituitary Neuroendocrine Tumor Resection: A Multi-Planar Perspective.

World Neurosurg

January 2025

Department of Neurosurgery, Emory University, Atlanta, Georgia, USA; Department of Otolaryngology, Emory University, Atlanta, Georgia, USA. Electronic address:

Background: Giant pituitary neuroendocrine tumor (GPitNET) are challenging tumors with low rates of gross total resection (GTR) and high morbidity. Previously reported machine-learning (ML) models for prediction of pituitary neuroendocrine tumor extent of resection (EOR) using preoperative imaging included a heterogenous dataset of functional and non-functional pituitary neuroendocrine tumors of various sizes leading to variability in results.

Objective: The aim of this pilot study is to construct a ML model based on the multi-dimensional geometry of tumor to accurately predict the EOR of non-functioning GPitNET.

View Article and Find Full Text PDF

A weight of evidence review on the mode of action, adversity, and the human relevance of xylene's observed thyroid effects in rats.

Crit Rev Toxicol

January 2025

Product Stewardship, Science & Regulatory, Shell Global Solutions International B.V. The Hague, the Netherlands.

Xylene substances have wide industrial and consumer uses and are currently undergoing dossier and substance evaluation under Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) for further toxicological testing including consideration of an additional neurotoxicological testing cohort to an extended one-generation reproduction toxicity (EOGRT) study. New repeated dose study data on xylenes identify the thyroid as a potential target tissue, and therefore a weight of evidence review is provided to investigate whether or not xylene-mediated changes on the hypothalamus-pituitary-thyroid (HPT) axis are secondary to liver enzymatic induction and are of a magnitude that is relevant for neurological human health concerns. Multiple published studies confirm xylene-mediated increases in liver weight, hepatocellular hypertrophy, and liver enzymatic induction the oral or inhalation routes, including an increase in uridine 5'-diphospho-glucuronosyltransferase (UDP-GT) activity, the key step in thyroid hormone metabolism in rodents.

View Article and Find Full Text PDF

Knosp and revised Knosp classifications predict non-functioning pituitary adenoma outcomes: a single tertiary center experience.

J Med Life

November 2024

Department of Endocrinology, Diabetology and Nutrition, Mohammed VI University Hospital, Medical School, Mohamed the First University, Oujda, Morocco.

Non-functioning pituitary adenomas (NFPAs) are hormonally inactive benign tumors, usually diagnosed as macro-adenoma. The aim of our research was to analyze the clinical and hormonal characteristics of NFPAs using Knosp and revised Knosp classifications. Furthermore, we aimed to assess the possibility of predicting surgical remission after surgery.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!