The Journal of clinical endocrinology and metabolism · 2026
Integrative Machine Learning Approach for Predicting Resistance to First-generation Receptor Ligands in Acromegaly.
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Lin Wei, Shi Songchang, Zheng Yuanyuan, Laws Edward, Smith Timothy R, Min Le
Аннотация
CONTEXT: Acromegaly, caused by excess GH and 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 to evaluate 10 ML algorithms and create a predictive calculator.
SETTING: Single-center study conducted at Mass General Brigham-affiliated hospitals.
PATIENTS: One hundred eleven 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. SHapley Additive exPlanations (SHAP) analysis identified key predictors for the development of a web-based clinical calculator.
MAIN OUTCOME MEASURES: Model performance was primarily evaluated using area under the receiver operating characteristic curve (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% confidence interval: 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 magnetic resonance imaging 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.
CONCLUSION: The CatBoost-based calculator effectively predicts fgSRLs treatment response in acromegaly patients. Prospective validation is required before clinical implementation.
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 to evaluate 10 ML algorithms and create a predictive calculator.
SETTING: Single-center study conducted at Mass General Brigham-affiliated hospitals.
PATIENTS: One hundred eleven 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. SHapley Additive exPlanations (SHAP) analysis identified key predictors for the development of a web-based clinical calculator.
MAIN OUTCOME MEASURES: Model performance was primarily evaluated using area under the receiver operating characteristic curve (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% confidence interval: 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 magnetic resonance imaging 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.
CONCLUSION: The CatBoost-based calculator effectively predicts fgSRLs treatment response in acromegaly patients. Prospective validation is required before clinical implementation.