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- Title
Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands.
- Authors
Wildemberg, Luiz Eduardo; da SilvaCamacho, 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 Jr., Antonio; Vilar, Lucio; Boguszewski, Cesar Luiz; Tavares, Ana Beatriz Winter; Nunes-Nogueira, Vania S.; Mazzuco, Tânia Longo; Rech, Carolina Garcia Soares Leães; Marques, Nelma Veronica; Chimelli, Leila
- Abstract
<bold>Context: </bold>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.<bold>Objective: </bold>To develop a prediction model of therapeutic response of acromegaly to fg-SRL.<bold>Methods: </bold>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).<bold>Results: </bold>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%.<bold>Conclusion: </bold>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.
- Subjects
SOMATOSTATIN receptors; DOPAMINE receptors; ACROMEGALY; PREDICTION models; SOMATOMEDIN; PROTEINS; RESEARCH; PREDICTIVE tests; RESEARCH methodology; CELL receptors; MEDICAL cooperation; EVALUATION research; HUMAN growth hormone; TREATMENT effectiveness; COMPARATIVE studies; DRUG monitoring; LOGISTIC regression analysis; LIGANDS (Biochemistry); BLOOD
- Publication
Journal of Clinical Endocrinology & Metabolism, 2021, Vol 106, Issue 7, p2047
- ISSN
0021-972X
- Publication type
journal article
- DOI
10.1210/clinem/dgab125