Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients

Abstract

BackgroundThe classification of sinonasal carcinomas (SNCs) is a conundrum. Consequently, prognosis and prediction of response to non-surgical treatment are often unreliable. The availability of prognostic and predictive measures is an unmet need, and the first logical source of information to be investigated is represented by the clinicopathological features of the disease. The hypothesis of the study was that clinicopathological information on SNC could be exploited to better predict prognosis and chemoradiosensitivity.MethodsAll patients affected by SNC who received curative treatment, including surgery, at the Unit of Otorhinolaryngology—Head and Neck Surgery of the University of Brescia between October 1998 and February 2019 were included in the analysis. The institutional series was reviewed and a survival analysis was performed. Machine learning and multivariable statistical methods were employed to develop, analyze, and test 3 experimental classifications (classification #1, based on cytomorphological, histomorphological, and differentiation information; classification #2, based on differentiation information; and classification #3, based on locoregional extension) of SNC, based on the inherent clinicopathological information. The association of experimental classifications with prognosis and chemoradiosensitivity was tested.ResultsThe study included 145 patients. From a prognostic standpoint, the machine learning-generated classification of SNC provided better prediction than the current World Health Organization classification. However, the prediction of the chemoradiosensitivity of SNC was not achievable.ConclusionsReorganization of clinicopathological information, with special reference to those related to tumor differentiation, can improve the reliability of prognosis of SNC. Prediction of chemoradiosensitivity remains an unmet need and further research is required.