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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8145352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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