Produção Nacional
Revisado por pares
Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands
2021; Oxford University Press; Volume: 106; Issue: 7 Linguagem: Inglês
10.1210/clinem/dgab125
ISSN1945-7197
AutoresLuiz Eduardo Wildemberg, Aline Helen da Silva Camacho, Renan Lyra Miranda, Paula Condé Lamparelli Elias, Nina Rosa de Castro Musolino, Debora Nazato, Raquel S. Jallad, Martha Katherine Paniagua Huayllas, José Ítalo Soares Mota, Tobias Skrebsky de Almeida, Evandro Portes, Antônio Ribeiro‐Oliveira, Lúcio Vilar, César Luiz Boguszewski, Ana Beatriz Winter Tavares, Vânia dos Santos Nunes Nogueira, Tânia Longo Mazzuco, Carolina Garcia Soares Leães, Nelma Verônica Marques, Leila Chimelli, Mauro Antônio Czepielewski, Marcello D. Bronstein, Júlio Abucham, Margaret de Castro, Leandro Kasuki, Mônica R. Gadelha,
Tópico(s)Growth Hormone and Insulin-like Growth Factors
ResumoAbstract 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 < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .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.
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