| Autor: |
Hasic Telalovic J; Computer Science Department, School of Science and Technology, University Sarajevo, 71210 Sarajevo, Bosnia and Herzegovina., Pillozzi S; Medical Oncology Unit, Careggi University Hospital, Largo Brambilla 4, 50134 Florence, Italy., Fabbri R; Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy., Laffi A; Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, IEO, IRCCS, Via Ripamonti 435, 20141 Milan, Italy., Lavacchi D; Medical Oncology Unit, Careggi University Hospital, Largo Brambilla 4, 50134 Florence, Italy., Rossi V; Medical Oncology Unit, Careggi University Hospital, Largo Brambilla 4, 50134 Florence, Italy., Dreoni L; Medical Oncology Unit, Careggi University Hospital, Largo Brambilla 4, 50134 Florence, Italy., Spada F; Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, IEO, IRCCS, Via Ripamonti 435, 20141 Milan, Italy., Fazio N; Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, IEO, IRCCS, Via Ripamonti 435, 20141 Milan, Italy., Amedei A; Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy., Iadanza E; Department of Information Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy., Antonuzzo L; Medical Oncology Unit, Careggi University Hospital, Largo Brambilla 4, 50134 Florence, Italy. |
| Abstrakt: |
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. |
