Machine Learning-based Prediction Model for Treatment of Acromegaly with First-generation Somatostatin Receptor Ligands

Abstract

Context: Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly. Objective: To develop a prediction model of therapeutic response of acromegaly to fg-SRL. Methods: Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP). Results: A total of 153 patients were analyzed. Controlled patients were older (P=.002), had lower GH at diagnosis (P=.01), had lower pretreatment GH and IGF-I (P