CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification

[1] Kann BH(2018)Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks Sci Rep. 8 14036-1781

[2] Aneja S(2015)Accuracy of preoperative imaging in detecting nodal extracapsular spread in oral cavity squamous cell carcinoma AJNR Am J Neuroradiol. 36 1776-1288

[3] Loganadane GV(2010)Lymph node central necrosis on computed tomography as predictor of extracapsular spread in metastatic head and neck squamous cell carcinoma: pilot study J Laryngol Otol. 124 1284-1618

[4] Kelly JR(2015)Accuracy of computed tomography to predict extracapsular spread in p16-positive squamous cell carcinoma Laryngoscope. 125 1613-1194

[5] Smith SM(2013)Accuracy of computed tomography in the prediction of extracapsular spread of lymph node metastases in squamous cell carcinoma of the head and neck JAMA Otolaryngol Head Neck Surg. 139 1187-382

[6] Decker RH(2014)Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach Nat Commun. 5 4006-444

[7] Aiken AH(2016)Radiomics in head and neck cancer: from exploration to application Transl Cancer Res. 5 371-338

[8] Poliashenko S(2015)Deep learning Nature 521 436-1081

[9] Beitler JJ(2012)Multi-column deep neural network for traffic sign classification Neural Netw. 32 333-359

[10] Chen AY(2018)Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging Clin Cancer Res. 24 1073-489

[1] Kann BH(2018)Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks Sci Rep. 8 14036-1781

[2] Aneja S(2015)Accuracy of preoperative imaging in detecting nodal extracapsular spread in oral cavity squamous cell carcinoma AJNR Am J Neuroradiol. 36 1776-1288

[3] Loganadane GV(2010)Lymph node central necrosis on computed tomography as predictor of extracapsular spread in metastatic head and neck squamous cell carcinoma: pilot study J Laryngol Otol. 124 1284-1618

[4] Kelly JR(2015)Accuracy of computed tomography to predict extracapsular spread in p16-positive squamous cell carcinoma Laryngoscope. 125 1613-1194

[5] Smith SM(2013)Accuracy of computed tomography in the prediction of extracapsular spread of lymph node metastases in squamous cell carcinoma of the head and neck JAMA Otolaryngol Head Neck Surg. 139 1187-382

[6] Decker RH(2014)Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach Nat Commun. 5 4006-444

[7] Aiken AH(2016)Radiomics in head and neck cancer: from exploration to application Transl Cancer Res. 5 371-338

[8] Poliashenko S(2015)Deep learning Nature 521 436-1081

[9] Beitler JJ(2012)Multi-column deep neural network for traffic sign classification Neural Netw. 32 333-359

[10] Chen AY(2018)Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging Clin Cancer Res. 24 1073-489