Details

Autor(en) / Beteiligte

• Wildemberg, Luiz Eduardo
• da Silva Camacho, Aline Helen
• Miranda, Renan Lyra
• Elias, Paula C L
• de Castro Musolino, Nina R
• Nazato, Debora
• Jallad, Raquel
• Huayllas, Martha K P
• Mota, Jose Italo S
• Almeida, Tobias
• Portes, Evandro
• Ribeiro-Oliveira, Antonio
• Vilar, Lucio
• Boguszewski, Cesar Luiz
• Winter Tavares, Ana Beatriz
• Nunes-Nogueira, Vania S
• Mazzuco, Tânia Longo
• Rech, Carolina Garcia Soares Leães
• Marques, Nelma Veronica
• Chimelli, Leila
• Czepielewski, Mauro
• Bronstein, Marcello D
• Abucham, Julio
• de Castro, Margaret
• Kasuki, Leandro
• Gadelha, Mônica

Titel

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

Ist Teil von

• The journal of clinical endocrinology and metabolism, 2021-07, Vol.106 (7), p.2047-2056

Ort / Verlag

US: Oxford University Press

Erscheinungsjahr

2021

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• 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 < .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.