RT Journal Article
SR Electronic
T1 Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy
JF In Vivo
JO In Vivo
FD International Institute of Anticancer Research
SP 3355
OP 3360
DO 10.21873/invivo.12633
VO 35
IS 6
A1 FRANCESCA DE FELICE
A1 VALENTINO VALENTINI
A1 MARCO DE VINCENTIIS
A1 CIRA ROSARIA TIZIANA DI GIOIA
A1 DANIELA MUSIO
A1 AIDA ANGELA TUMMOLO
A1 LUDOVICA ISABELLA RICCI
A1 VALERIA CONVERTI
A1 SILVIA MEZI
A1 DANIELA MESSINEO
A1 GIANLUCA TENORE
A1 MARCO DELLA MONACA
A1 MASSIMO RALLI
A1 FRANCESCO VULLO
A1 ANDREA BOTTICELLI
A1 EDOARDO BRAUNER
A1 PAOLO PRIORE
A1 ROMEO UMBERTO
A1 PAOLO MARCHETTI
A1 CARLO DELLA ROCCA
A1 ANTONELLA POLIMENI
A1 VINCENZO TOMBOLINI
YR 2021
UL http://iv.iiarjournals.org/content/35/6/3355.abstract
AB Background/Aim: To investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT). Patients and Methods: 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. Results: 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. Conclusion: 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.