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

Context: Acromegaly, caused by excess growth hormone (GH) and insulin-like growth factor-1 (IGF-1) due to pituitary adenomas, often necessitates first-generation somatostatin receptor ligands (fgSRLs) therapy when surgery fails. However, responses to fgSRLs therapy vary widely.

Objective: To develop a machine learning (ML)-based calculator that predicts individual responses to fgSRLs therapy, enabling evidence-based acromegaly management.

Design: A retrospective study (January 2010-July 2024) utilizing the Research Patient Data Registry (RPDR) to evaluate ten ML algorithms and create a predictive calculator.

Setting: Single-center study conducted at Mass General Brigham-affiliated hospitals.

Patients: 111 acromegaly patients met inclusion criteria, classified as fgSRLs-responsive (n = 64) or fgSRLs-resistant (n = 47).

Interventions: IGF-1 trajectories were analyzed using linear mixed-effects modeling. Ten ML algorithms were assessed to predict fgSRLs resistance. SHAP analysis identified key predictors for the development of a web-based clinical calculator.

Main Outcome Measures: Model performance was primarily evaluated using AUROC, along with accuracy, precision, recall, specificity, F1 score, and decision curve analysis (DCA).

Results: The CatBoost model exhibited optimal performance based on AUROC 0.896 (95% CI: 0.751-0.990), with accuracy 82.4%, precision 86.7%, specificity 88.2%, and F1 score 81.2%. Key predictors of fgSRLs resistance identified via SHAP analysis included pre-fgSRLs treatment GH, Knosp grade, pre-fgSRLs treatment IGF-1 index, T2-weighted MRI density, and comorbidity burden. The model demonstrated excellent calibration (Brier score 0.131) and clinical utility via DCA. A web-based calculator was developed for clinical use.

Conclusions: The CatBoost-based calculator effectively predicts fgSRLs treatment response in acromegaly patients. Prospective validation is required before clinical implementation.