Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients
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 in...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.799680/full |
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| author | Marco Ferrari Marco Ferrari Marco Ferrari Davide Mattavelli Alberto Schreiber Tommaso Gualtieri Vittorio Rampinelli Vittorio Rampinelli Michele Tomasoni Stefano Taboni Stefano Taboni Stefano Taboni Laura Ardighieri Simonetta Battocchio Anna Bozzola Marco Ravanelli Roberto Maroldi Cesare Piazza Paolo Bossi Alberto Deganello Piero Nicolai |
| author_facet | Marco Ferrari Marco Ferrari Marco Ferrari Davide Mattavelli Alberto Schreiber Tommaso Gualtieri Vittorio Rampinelli Vittorio Rampinelli Michele Tomasoni Stefano Taboni Stefano Taboni Stefano Taboni Laura Ardighieri Simonetta Battocchio Anna Bozzola Marco Ravanelli Roberto Maroldi Cesare Piazza Paolo Bossi Alberto Deganello Piero Nicolai |
| author_sort | Marco Ferrari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T17:30:25Z |
| format | Article |
| id | doaj.art-8f6618f449984a8eb496defcba2d5041 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T17:30:25Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-8f6618f449984a8eb496defcba2d50412022-12-22T03:23:09ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-06-011210.3389/fonc.2022.799680799680Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 PatientsMarco Ferrari0Marco Ferrari1Marco Ferrari2Davide Mattavelli3Alberto Schreiber4Tommaso Gualtieri5Vittorio Rampinelli6Vittorio Rampinelli7Michele Tomasoni8Stefano Taboni9Stefano Taboni10Stefano Taboni11Laura Ardighieri12Simonetta Battocchio13Anna Bozzola14Marco Ravanelli15Roberto Maroldi16Cesare Piazza17Paolo Bossi18Alberto Deganello19Piero Nicolai20Section of Otorhinolaryngology—Head and Neck Surgery, Department of Neurosciences, University of Padua—”Azienda Ospedale Università di Padova”, Padua, ItalyTechnology for Health (PhD program), Department of Information Engineering, University of Brescia, Brescia, ItalyGuided Therapeutics Program International Scholar, University Health Network, Toronto, CanadaUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyTechnology for Health (PhD program), Department of Information Engineering, University of Brescia, Brescia, ItalyUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalySection of Otorhinolaryngology—Head and Neck Surgery, Department of Neurosciences, University of Padua—”Azienda Ospedale Università di Padova”, Padua, ItalyGuided Therapeutics Program International Scholar, University Health Network, Toronto, CanadaArtificial Intelligence in Medicine and Innovation in Clinical Research and Methodology (PhD program), Department of Clinical and Experimental Sciences, University of Brescia, Brescia, ItalyUnit of Pathology, “ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Pathology, “ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Pathology, “ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Radiology, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Radiology, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Medical Oncology, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalyUnit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, University of Brescia—”ASST Spedali Civili di Brescia”, Brescia, ItalySection of Otorhinolaryngology—Head and Neck Surgery, Department of Neurosciences, University of Padua—”Azienda Ospedale Università di Padova”, Padua, ItalyBackgroundThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2022.799680/fullsinonasalcarcinomaskull base (head and neck)classificationmachine learningprognosis |
| spellingShingle | Marco Ferrari Marco Ferrari Marco Ferrari Davide Mattavelli Alberto Schreiber Tommaso Gualtieri Vittorio Rampinelli Vittorio Rampinelli Michele Tomasoni Stefano Taboni Stefano Taboni Stefano Taboni Laura Ardighieri Simonetta Battocchio Anna Bozzola Marco Ravanelli Roberto Maroldi Cesare Piazza Paolo Bossi Alberto Deganello Piero Nicolai Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients Frontiers in Oncology sinonasal carcinoma skull base (head and neck) classification machine learning prognosis |
| title | Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients |
| title_full | Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients |
| title_fullStr | Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients |
| title_full_unstemmed | Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients |
| title_short | Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients |
| title_sort | does reorganization of clinicopathological information improve prognostic stratification and prediction of chemoradiosensitivity in sinonasal carcinomas a retrospective study on 145 patients |
| topic | sinonasal carcinoma skull base (head and neck) classification machine learning prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.799680/full |
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