Machine learning-based prediction model for treatment of acromegaly with first-generation somatostatin receptor ligands.

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. AIM 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 GH < 1.0 ng/mL and normal age-adjusted 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 = 0.002), had lower GH at diagnosis (p = 0.01), had lower pretreatment GH and IGF-I (p < 0.001), and more frequently harbored tumors that were densely granulated (p = 0.014) or highly expressed SST2 (p < 0.001).The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%. CONCLUSION We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality and reduce health services costs.