The 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.

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

Bossi, Paolo;
2022-01-01

Abstract

The 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.
2022
carcinoma
chemotherapy
classification
machine learning
prognosis
radiotherapy
sinonasal
skull base (head and neck)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/80711
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