Treffer: PROLACTINOMA SIZE CLASSIFICATION USING A NOVEL PROLACTIN THRESHOLD.

Title:
PROLACTINOMA SIZE CLASSIFICATION USING A NOVEL PROLACTIN THRESHOLD.
Authors:
Ariwodo, Ogechukwu1 (AUTHOR), Iyer, Harshavrdhan G.1 (AUTHOR), Zermano, Jorge Rios1 (AUTHOR), Median, Yetzali Claudio1 (AUTHOR), Safa, Adrian1 (AUTHOR), Nieto-Gutierrez, Astrid1 (AUTHOR), Mckevitt, Chase1 (AUTHOR), Sanchez-Garavito, Emiliano J.1 (AUTHOR), de Llano, Juan Pablo Navarro Garcia1 (AUTHOR), Donaldson, Angela1 (AUTHOR), Olomu, Osarenoma U.1 (AUTHOR), Hinojosa, Alfredo Quinones-1 (AUTHOR), Almeida, Joao Paulo1 (AUTHOR), Samson, Susan L.1 (AUTHOR), Chaichana, Kaisorn L.1 (AUTHOR)
Source:
Journal of Neurological Surgery. Part B. Skull Base. 2025 Supplement 1, Vol. 86, pS1-S576. 576p.
Subject Terms:
*PYTHON programming language, *SUPPORT vector machines, *RANDOM forest algorithms, *RECEIVER operating characteristic curves, *PROLACTINOMA, *PEARSON correlation (Statistics)
Database:
Academic Search Index

Weitere Informationen

This article in the Journal of Neurological Surgery explores the relationship between serum prolactin levels and tumor size in prolactinoma patients. By analyzing data from 251 patients, researchers established a serum prolactin threshold of 149 ng/mL for categorizing tumor size. Predictive models using logistic regression, support vector machine, and random forest classifier showed promising accuracies. The study highlights the potential of using serum prolactin levels to classify prolactinomas by size, especially in settings with limited access to advanced imaging techniques. [Extracted from the article]