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DE Felice, Francesca; Valentini, Valentino; DE Vincentiis, Marco; DI Gioia, Cira Rosaria Tiziana; Musio, Daniela; Tummulo, Aida Angela; Ricci, Ludovica Isabella; Converti, Valeria; Mezi, Silvia; Messineo, Daniela; Tenore, Gianluca; Della Monaca, Marco; Ralli, Massimo; Vullo, Francesco; Botticelli, Andrea; Brauner, Edoardo; Priore, Paolo; Umberto, Romeo; Marchetti, Paolo; Della Rocca, Carlo; Polimeni, Antonella; Tombolini, Vincenzo
In vivo (Athens), 11/2021, Volume: 35, Issue: 6Journal Article
To investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT). Consecutive SGMT patients were identified, and a data set included nine predictor variables and a dependent variable disease-free survival (DFS) event was standardized. The open-source R software was used. Survival outcomes were estimated by the Kaplan-Meier method. The random forest approach was used to select the important explanatory variables. A classification tree that optimally partitioned SGMT patients with different DFS rates was built. In total, 54 SGMT patients were included in the final analysis. Five-year DFS was 62.1%. The top two important variables identified were pathologic node (pN) and pathologic tumor (pT). Based on these explanatory variables, patients were partitioned in three groups, including pN0, pT1-2 pN+ and pT3-4 pN+ with 26%, 38% and 75% probability of recurrence, respectively. Accordingly, 5-year DFS rates were 73.7%, 57.1% and 34.3%, respectively. The proposed decision tree algorithm is an appropriate tool to partition SGMT patients. It can guide decision-making and future research in the SGMT field.
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