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

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Autor(es): dc.contributorUniversidade Federal do Rio de Janeiro (UFRJ)-
Autor(es): dc.contributorSecretaria Estadual de Saúde-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de São Paulo (UNIFESP)-
Autor(es): dc.contributorNeuroendocrinology and Neurosurgery Unit Hospital Brigadeiro-
Autor(es): dc.contributorHospital de Clinicas de Porto Alegre (UFRGS)-
Autor(es): dc.contributorInstitute of Medical Assistance to the State Public Hospital-
Autor(es): dc.contributorUniversidade Federal de Minas Gerais (UFMG)-
Autor(es): dc.contributorUniversidade Federal de Pernambuco (UFPE)-
Autor(es): dc.contributorUniversidade Federal Do Parana-
Autor(es): dc.contributorUniversidade do Estado do Rio de Janeiro (UERJ)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Londrina (UEL)-
Autor(es): dc.contributorSanta Casa de Porto Alegre-
Autor(es): dc.creatorWildemberg, Luiz Eduardo-
Autor(es): dc.creatorDa Silva Camacho, Aline Helen-
Autor(es): dc.creatorMiranda, Renan Lyra-
Autor(es): dc.creatorElias, Paula C. L-
Autor(es): dc.creatorDe Castro Musolino, Nina R-
Autor(es): dc.creatorNazato, Debora-
Autor(es): dc.creatorJallad, Raquel-
Autor(es): dc.creatorHuayllas, Martha K. P-
Autor(es): dc.creatorMota, Jose Italo S-
Autor(es): dc.creatorAlmeida, Tobias-
Autor(es): dc.creatorPortes, Evandro-
Autor(es): dc.creatorRibeiro-Oliveira, Antonio-
Autor(es): dc.creatorVilar, Lucio-
Autor(es): dc.creatorBoguszewski, Cesar Luiz-
Autor(es): dc.creatorWinter Tavares, Ana Beatriz-
Autor(es): dc.creatorNunes-Nogueira, Vania S-
Autor(es): dc.creatorMazzuco, Tânia Longo-
Autor(es): dc.creatorRech, Carolina Garcia Soares Leães-
Autor(es): dc.creatorMarques, Nelma Veronica-
Autor(es): dc.creatorChimelli, Leila-
Autor(es): dc.creatorCzepielewski, Mauro-
Autor(es): dc.creatorBronstein, Marcello D-
Autor(es): dc.creatorAbucham, Julio-
Autor(es): dc.creatorDe Castro, Margaret-
Autor(es): dc.creatorKasuki, Leandro-
Autor(es): dc.creatorGadelha, Mônica-
Data de aceite: dc.date.accessioned2025-08-21T21:51:06Z-
Data de disponibilização: dc.date.available2025-08-21T21:51:06Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1210/clinem/dgab125-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229017-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229017-
Descrição: dc.descriptionContext: 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.-
Descrição: dc.descriptionEndocrine Unit and Neuroendocrinology Research Center Med. Sch. and Hospital Universitario Clementino Fraga Filho - Universidade Federal Do Rio de Janeiro, RJ-
Descrição: dc.descriptionNeuroendocrine Unit - Instituto Estadual Do Cérebro Paulo Niemeyer Secretaria Estadual de Saúde, RJ-
Descrição: dc.descriptionNeuropathology and Molecular Genetics Laboratory Instituto Estadual Do Cérebro Paulo Niemeyer Secretaria Estadual de Saúde, RJ-
Descrição: dc.descriptionDivision of Endocrinology - Department of Internal Medicine Ribeirao Preto Medical School - University of Sao Paulo, SP-
Descrição: dc.descriptionNeuroendocrine Unit Division of Functional Neurosurgery Hospital das Clinicas Faculdade de Medicina Universidade de São Paulo, SP-
Descrição: dc.descriptionNeuroendocrine U. - Div. of Endocrinol. and Metab. - Esc. Paulista de Med. - Univ. Fed. de Sao Paulo, SP-
Descrição: dc.descriptionNeuroendocrine Unit Division of Endocrinology and Metabolism Hospital das Clínicas University of São Paulo Medical School, SP-
Descrição: dc.descriptionCellular and Molecular Endocrinology Laboratory/LIM25 Discipline of Endocrinology Hospital das Clinicas HCFMUSP Faculty of Medicine University of Sao Paulo, SP-
Descrição: dc.descriptionNeuroendocrinology and Neurosurgery Unit Hospital Brigadeiro, SP-
Descrição: dc.descriptionEndocrinology and Metabolism Unit Hospital Geral de Fortaleza Secretaria Estadual de Saúde, CE-
Descrição: dc.descriptionDivision of Endocrinology Hospital de Clinicas de Porto Alegre (UFRGS), RS,Alegre-
Descrição: dc.descriptionInstitute of Medical Assistance to the State Public Hospital-
Descrição: dc.descriptionFaculdade de Medicina Universidade Federal de Minas Gerais, MG-
Descrição: dc.descriptionNeuroendocrine Unit Division of Endocrinology and Metabolism Hospital das Clínicas Federal University of Pernambuco Medical School, PE-
Descrição: dc.descriptionEndocrine Division (SEMPR) Department of Internal Medicine Universidade Federal Do Parana, PR-
Descrição: dc.descriptionEndocrine Unit - Department of Internal Medicine Faculty of Medical Sciences Universidade Do Estado Do Rio de Janeiro-
Descrição: dc.descriptionDepartment of Internal Medicine São Paulo State University/UNESP Medical School, SP-
Descrição: dc.descriptionDivision of Endocrinology of Medical Clinical Department Universidade Estadual de Londrina (UEL), PR-
Descrição: dc.descriptionSanta Casa de Porto Alegre, RS-
Descrição: dc.descriptionDepartment of Internal Medicine São Paulo State University/UNESP Medical School, SP-
Formato: dc.format2047-2056-
Idioma: dc.languageen-
Relação: dc.relationJournal of Clinical Endocrinology and Metabolism-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectacromegaly-
Palavras-chave: dc.subjectbiomarker-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectprecision medicine-
Palavras-chave: dc.subjectprediction model-
Palavras-chave: dc.subjectsomatostatin receptor-
Palavras-chave: dc.subjectsomatostatin receptor ligands-
Título: dc.titleMachine Learning-based Prediction Model for Treatment of Acromegaly with First-generation Somatostatin Receptor Ligands-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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