Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

[1] Nóbrega TD(2018)Clinicopathological evaluation and survival of patients with squamous cell carcinoma of the tongue Med Oral Patol Oral Cir Bucal 23 579-587

[2] Queiroz SI(2008)Poor prognosis in patients with stage I and II oral tongue squamous cell carcinoma Cancer 112 345-351

[3] Santos EM(2011)Stromal features are predictive of disease mortality in oral cancer patients J Pathol 223 470-481

[4] Rusthoven K(2002)Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis Lancet 360 1131-1135

[5] Ballonoff A(2003)Tumour thickness predicts cervical nodal metastases and survival in early oral tongue cancer Oral Oncol 39 386-390

[6] Raben D(2020)Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas Cancers Head Neck 5 6-3930

[7] Marsh D(2019)Radiomics and machine learning for radiotherapy in head and neck cancers Front Oncol 9 174-5

[8] Suchak K(2017)Somatic mutations drive distinct imaging phenotypes in lung cancer Cancer Res 77 3922-497

[9] Moutasim KA(2016)Accuracy of MRI in prediction of tumour thickness and nodal stage in oral tongue and gingivobuccal cancer with clinical correlation and staging J Clin Diagn Res 10 1-586

[10] Brenner H(2019)Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a web-based prognostic tool Virchows Arch 475 489-527

[1] Nóbrega TD(2018)Clinicopathological evaluation and survival of patients with squamous cell carcinoma of the tongue Med Oral Patol Oral Cir Bucal 23 579-587

[2] Queiroz SI(2008)Poor prognosis in patients with stage I and II oral tongue squamous cell carcinoma Cancer 112 345-351

[3] Santos EM(2011)Stromal features are predictive of disease mortality in oral cancer patients J Pathol 223 470-481

[4] Rusthoven K(2002)Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis Lancet 360 1131-1135

[5] Ballonoff A(2003)Tumour thickness predicts cervical nodal metastases and survival in early oral tongue cancer Oral Oncol 39 386-390

[6] Raben D(2020)Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas Cancers Head Neck 5 6-3930

[7] Marsh D(2019)Radiomics and machine learning for radiotherapy in head and neck cancers Front Oncol 9 174-5

[8] Suchak K(2017)Somatic mutations drive distinct imaging phenotypes in lung cancer Cancer Res 77 3922-497

[9] Moutasim KA(2016)Accuracy of MRI in prediction of tumour thickness and nodal stage in oral tongue and gingivobuccal cancer with clinical correlation and staging J Clin Diagn Res 10 1-586

[10] Brenner H(2019)Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a web-based prognostic tool Virchows Arch 475 489-527