Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.

ABSTRACT:

Background

First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making.

Objective

To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia.

SUBMITTER: Huber M 

PROVIDER: S-EPMC8888454 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature