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A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first...

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Autores principales: Hasic Telalovic, Jasminka, Pillozzi, Serena, Fabbri, Rachele, Laffi, Alice, Lavacchi, Daniele, Rossi, Virginia, Dreoni, Lorenzo, Spada, Francesca, Fazio, Nicola, Amedei, Amedeo, Iadanza, Ernesto, Antonuzzo, Lorenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145352/
https://www.ncbi.nlm.nih.gov/pubmed/33925256
http://dx.doi.org/10.3390/diagnostics11050804
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author Hasic Telalovic, Jasminka
Pillozzi, Serena
Fabbri, Rachele
Laffi, Alice
Lavacchi, Daniele
Rossi, Virginia
Dreoni, Lorenzo
Spada, Francesca
Fazio, Nicola
Amedei, Amedeo
Iadanza, Ernesto
Antonuzzo, Lorenzo
author_facet Hasic Telalovic, Jasminka
Pillozzi, Serena
Fabbri, Rachele
Laffi, Alice
Lavacchi, Daniele
Rossi, Virginia
Dreoni, Lorenzo
Spada, Francesca
Fazio, Nicola
Amedei, Amedeo
Iadanza, Ernesto
Antonuzzo, Lorenzo
author_sort Hasic Telalovic, Jasminka
collection PubMed
description The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS.
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spelling pubmed-81453522021-05-26 A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy Hasic Telalovic, Jasminka Pillozzi, Serena Fabbri, Rachele Laffi, Alice Lavacchi, Daniele Rossi, Virginia Dreoni, Lorenzo Spada, Francesca Fazio, Nicola Amedei, Amedeo Iadanza, Ernesto Antonuzzo, Lorenzo Diagnostics (Basel) Article The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS. MDPI 2021-04-28 /pmc/articles/PMC8145352/ /pubmed/33925256 http://dx.doi.org/10.3390/diagnostics11050804 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hasic Telalovic, Jasminka
Pillozzi, Serena
Fabbri, Rachele
Laffi, Alice
Lavacchi, Daniele
Rossi, Virginia
Dreoni, Lorenzo
Spada, Francesca
Fazio, Nicola
Amedei, Amedeo
Iadanza, Ernesto
Antonuzzo, Lorenzo
A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
title A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
title_full A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
title_fullStr A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
title_full_unstemmed A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
title_short A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy
title_sort machine learning decision support system (dss) for neuroendocrine tumor patients treated with somatostatin analog (ssa) therapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145352/
https://www.ncbi.nlm.nih.gov/pubmed/33925256
http://dx.doi.org/10.3390/diagnostics11050804
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