Search

Purpose: To develop a predictive index model, integrating both clinical and high-resolution anoscopy (HRA) features to further personalize the decision making process in anal canal carcinoma in COVID19 era., Methods and Materials: We assess HRA parameters after definitive chemoradiotherapy in patients with anal canal malignant lesions., Results: HRA features could be important to assess the effect of CRT and a risk stratification system should be introduced in clinical practice to better allocate therapeutic interventions., Conclusion: To our knowledge this is the first proposal for HRA findings in anal canal cancer after definitive CRT. We believe that a risk score can be useful to estimate the risk of treatment failure (in term of persistence disease and/or recurrence) and its clinical relevance should not to be underestimated., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

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., (Copyright © 2021 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)