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Volume 11
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10.3390/diagnostics11050804
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Article

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

by
Jasminka Hasic Telalovic
1,†,
Serena Pillozzi
2,†,
Rachele Fabbri
3,
Alice Laffi
4,
Daniele Lavacchi
2,
Virginia Rossi
2,
Lorenzo Dreoni
2,
Francesca Spada
4,
Nicola Fazio
4,
Amedeo Amedei
5,*,
Ernesto Iadanza
3,‡ and
Lorenzo Antonuzzo
2,‡
1
Computer Science Department, University Sarajevo School of Science and Technology, 71210 Sarajevo, Bosnia and Herzegovina
2
Medical Oncology Unit, Careggi University Hospital, Largo Brambilla 4, 50134 Florence, Italy
3
Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy
4
Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, IEO, IRCCS, Via Ripamonti 435, 20141 Milan, Italy
5
Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this paper.
‡
These authors contributed equally to this paper.
Diagnostics 2021, 11(5), 804; https://doi.org/10.3390/diagnostics11050804
Submission received: 16 March 2021 / Revised: 23 April 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

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.
Keywords: neuroendocrine tumors; machine learning; prognostic factors; predictive biomarkers; somatostatin analogs; random forest classifier neuroendocrine tumors; machine learning; prognostic factors; predictive biomarkers; somatostatin analogs; random forest classifier

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MDPI and ACS Style

Hasic Telalovic, J.; Pillozzi, S.; Fabbri, R.; Laffi, A.; Lavacchi, D.; Rossi, V.; Dreoni, L.; Spada, F.; Fazio, N.; Amedei, A.; et al. A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy. Diagnostics 2021, 11, 804. https://doi.org/10.3390/diagnostics11050804

AMA Style

Hasic Telalovic J, Pillozzi S, Fabbri R, Laffi A, Lavacchi D, Rossi V, Dreoni L, Spada F, Fazio N, Amedei A, et al. A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy. Diagnostics. 2021; 11(5):804. https://doi.org/10.3390/diagnostics11050804

Chicago/Turabian Style

Hasic Telalovic, Jasminka, Serena Pillozzi, Rachele Fabbri, Alice Laffi, Daniele Lavacchi, Virginia Rossi, Lorenzo Dreoni, Francesca Spada, Nicola Fazio, Amedeo Amedei, and et al. 2021. "A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy" Diagnostics 11, no. 5: 804. https://doi.org/10.3390/diagnostics11050804

APA Style

Hasic Telalovic, J., Pillozzi, S., Fabbri, R., Laffi, A., Lavacchi, D., Rossi, V., Dreoni, L., Spada, F., Fazio, N., Amedei, A., Iadanza, E., & Antonuzzo, L. (2021). A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy. Diagnostics, 11(5), 804. https://doi.org/10.3390/diagnostics11050804

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