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Related Experiment Video

Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly.

Yuki Shinya^1,2, Abdul Karim Ghaith¹, Sukwoo Hong^1,2

¹1Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota.

Neurosurgical Focus

|July 1, 2025

Summary

A machine learning model can predict long-term outcomes for patients with growth hormone (GH)-secreting pituitary adenomas after surgery. This tool helps identify patients needing further treatment, improving personalized care.

Keywords:

acromegaly long-term outcomes machine learning outcome prediction pituitary adenomas

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Last Updated: Sep 17, 2025

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Endoscopic Endonasal Trans-sphenoidal Approach: Minimally Invasive Surgery for Pituitary Adenomas

Endoscopic Endonasal Trans-sphenoidal Approach: Minimally Invasive Surgery for Pituitary Adenomas

Published on: January 17, 2018

Area of Science:

Endocrinology
Neurosurgery
Data Science

Background:

Growth hormone (GH)-secreting pituitary adenomas (PAs) cause significant morbidity and mortality.
Endonasal transsphenoidal surgery (ETS) is a primary treatment, but recurrence is common.
Predicting long-term outcomes after ETS is crucial for patient management.

Purpose of the Study:

To develop and validate a machine learning (ML) model for predicting long-term outcomes in patients with GH-secreting PAs post-ETS.
To identify key predictors of intervention-free rates (IFR) after primary ETS.

Main Methods:

Retrospective cohort study of 100 patients with GH-secreting PAs treated with ETS.
Collected clinical, radiological, and biochemical data.
Developed and evaluated supervised ML models (decision trees, random forests) using AUROC and SHAP values to predict IFR.

Main Results:

The 3-year and 5-year intervention-free rates (IFR) after primary ETS were 70% and 67%, respectively.
A decision tree model achieved 81% accuracy, highlighting gross-total resection (GTR) and patient age as key predictors.
SHAP analysis identified tumor size < 9 mm, GTR, age > 65 years, and Knosp grade 0 as factors associated with a longer IFR.

Conclusions:

The developed ML model provides a nuanced prediction of patients likely to experience recurrent or persistent acromegaly post-ETS.
This model can aid in personalized treatment planning and follow-up strategies.
External validation of the ML model is recommended to enhance clinical utility and resource allocation.