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Related Experiment Video

Updated: Sep 18, 2025

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Integrative Machine Learning Approach for Predicting Resistance to First-generation Receptor Ligands in Acromegaly.

Wei Lin1,2, Songchang Shi3, Yuanyuan Zheng1,4

  • 1Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

The Journal of Clinical Endocrinology and Metabolism
|June 26, 2025
PubMed
Summary

This study developed a machine learning calculator to predict treatment response in acromegaly patients, aiding personalized therapy. The tool accurately identifies individuals likely to benefit from first-generation somatostatin receptor ligands (fgSRLs).

Keywords:
acromegalyfirst-generation somatostatin receptor ligandmachine learningpersonalized medicineweb-based clinical calculator

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Area of Science:

  • Endocrinology
  • Machine Learning in Medicine
  • Computational Biology

Background:

  • Acromegaly, driven by excess growth hormone (GH) and insulin-like growth factor-1 (IGF-1), often requires first-generation somatostatin receptor ligands (fgSRLs) therapy post-surgery.
  • Patient responses to fgSRLs therapy exhibit significant variability, necessitating personalized treatment strategies.

Purpose of the Study:

  • To create a machine learning (ML)-based calculator for predicting individual patient responses to fgSRLs therapy.
  • To support evidence-based management of acromegaly by enabling prediction of treatment efficacy.

Main Methods:

  • A retrospective analysis of 111 acromegaly patients treated between January 2010 and July 2024 at Mass General Brigham-affiliated hospitals.
  • Evaluation of ten ML algorithms to predict fgSRLs resistance, with the CatBoost model selected for optimal performance (AUROC 0.896).
  • Identification of key predictors of resistance, including pre-treatment GH, Knosp grade, IGF-1 index, MRI density, and comorbidity burden, using SHAP analysis.

Main Results:

  • The CatBoost model achieved high predictive accuracy (82.4%) and specificity (88.2%) in identifying fgSRLs resistance.
  • Key predictors for treatment resistance were identified, including pre-treatment GH and IGF-1 levels, Knosp grade, MRI characteristics, and overall comorbidity burden.
  • A web-based clinical calculator was developed, demonstrating excellent calibration and clinical utility.

Conclusions:

  • The developed CatBoost-based calculator demonstrates effectiveness in predicting fgSRLs treatment response in acromegaly.
  • Further prospective validation is recommended prior to widespread clinical implementation of the predictive tool.