Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32.
Article
PubMed
Google Scholar
Schorn VJ, Miles BA. Laryngeal Squamous Cell Carcinoma. New York: Springer; 2014.
Book
Google Scholar
Muller P, Belot A, Morris M, Rachet B, Cancer Research UK Cancer survival group, London School of Hygiene and Tropical Medicine. Net survival and the probability of cancer death from rare cancers Available from http://csg.lshtm.ac.uk/rare-cancers/. Accessed 20 Sept 2016.
Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA Cancer J Clin. 2014;64(1):9–29.
Article
PubMed
Google Scholar
Hoffman HT, Porter K, Karnell LH, et al. Laryngeal cancer in the United States: changes in demographics, patterns of care, and survival. Laryngoscope. 2006;116(9 Pt 2 Suppl 111):1–13.
Article
PubMed
Google Scholar
Balch CM, Soong SJ, Gershenwald JE, et al. Prognostic factors analysis of 17,600 melanoma patients: validation of the American joint committee on Cancer melanoma staging system. J Clin Oncol. 2001;19(16):3622–34.
Article
CAS
PubMed
Google Scholar
Michor F, Polyak K. The origins and implications of intratumor heterogeneity. Cancer Prev Res (Phila). 2010;3(11):1361–4.
Article
Google Scholar
Ahn SY, Park CM, Park SJ, et al. Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy. Investig Radiol. 2015;50(10):719–25.
Article
CAS
Google Scholar
Cozzi L, Dinapoli N, Fogliata A, et al. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer. 2017;17(1):829.
Article
PubMed
PubMed Central
Google Scholar
Tang X. Texture information in run-length matrices. IEEE Trans Image Process. 1998;7(11):1602–9.
Article
CAS
PubMed
Google Scholar
Nardone V, Tini P, Nioche C, et al. Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med. 2018;123(6):415–23.
Article
PubMed
Google Scholar
Buvat I, Orlhac F, Soussan M. Tumor texture analysis in PET: where do we stand? J Nucl Med. 2015;56(11):1642–4.
Article
CAS
PubMed
Google Scholar
Huang YQ, Liang CH, He L, et al. Development and validation of a Radiomics Nomogram for preoperative prediction of lymph node metastasis in colorectal Cancer. J Clin Oncol. 2016;34(18):2157–64.
Article
PubMed
Google Scholar
Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung Cancer. Radiology. 2016;281(3):947–57.
Article
PubMed
Google Scholar
Zhang H, Graham CM, Elci O, et al. Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. Radiology. 2013;269(3):801–9.
Article
PubMed
Google Scholar
Kuno H, Qureshi MM, Chapman MN, et al. CT texture analysis potentially predicts local failure in head and neck squamous cell carcinoma treated with Chemoradiotherapy. AJNR Am J Neuroradiol. 2017;38(12):2334–40.
Article
CAS
PubMed
Google Scholar
Lydiatt WM, Patel SG, O'Sullivan B, et al. Head and neck cancers-major changes in the American joint committee on cancer eighth edition cancer staging manual. CA Cancer J Clin. 2017;67(2):122–37.
Article
PubMed
Google Scholar
Nioche C, Orlhac F, Boughdad S, et al. LIFEx: a freeware for Radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9.
Article
CAS
PubMed
Google Scholar
Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007;26(30):5512–28.
Article
PubMed
Google Scholar
Tibshirani R. The lasso method for variable selection in the cox model. StatMed. 1997;16(4):385–95.
CAS
Google Scholar
Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23.
Article
PubMed
Google Scholar
Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med. 2007;35:2052–6.
Article
PubMed
Google Scholar
Vickers AJ, Cronin AM, Elkin EB, et al. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8:53.
Article
PubMed
PubMed Central
Google Scholar
Ganeshan B, Goh V, Mandeville HC, et al. Non-small cell lung Cancer: Histopathologic correlates for texture parameters at CT. Radiology. 2013;266(1):326–36.
Article
PubMed
Google Scholar
Sun J, Yu XR, Shi BB, Zheng J, Wu JT. CT features of retroperitoneal solitary fibrous tumor: report of three cases and review of the literature. World J Surg Oncol. 2014;12:324.
Nordsmark M, Overgaard M, Overgaard J. Pretreatment oxygenation predicts radiation response in advanced squamous cell carcinoma of the head and neck. Radiother Oncol. 1996;41(1):31–9.
Article
CAS
PubMed
Google Scholar
Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E. Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev. 2004;18(17):2095–107.
Article
CAS
PubMed
PubMed Central
Google Scholar
Skogen K, Ganeshan B, Good C, Critchley G, Miles K. Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade. J Neuro-Oncol. 2013;111(2):213–9.
Article
Google Scholar
Swinson DE, O'Byrne KJ. Interactions between hypoxia and epidermal growth factor receptor in non-small-cell lung cancer. Clin Lung Cancer. 2006;7(4):250–6.
Article
CAS
PubMed
Google Scholar
Goh V, Sanghera B, Wellsted DM, Sundin J, Halligan S. Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis. Eur Radiol. 2009;19(6):1358–65.
Article
PubMed
Google Scholar
Yun G, Kim YH, Lee YJ, Kim B, Hwang JH, Choi DJ. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep. 2018;8(1):7226.
Article
PubMed
PubMed Central
Google Scholar
Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol. 2012;67(2):157–64.
Article
CAS
PubMed
Google Scholar
Fujima N, Hirata K, Shiga T, et al. Integrating quantitative morphological and intratumoural textural characteristics in FDG-PET for the prediction of prognosis in pharynx squamous cell carcinoma patients. Clin Radiol. 2018;73(12):1059 e1–1059.e8.
Article
PubMed
Google Scholar
Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck Cancer. Front Oncol. 2015;5:272.
Article
PubMed
PubMed Central
Google Scholar
Yuan Y, Ren J, Shi Y, Tao X. MRI-based radiomic signature as predictive marker for patients with head and neck squamous cell carcinoma. Eur J Radiol. 2019;117:193–98.
Raitiola H, Pukander J, Laippala P. Glottic and supraglottic laryngeal carcinoma: differences in epidemiology, clinical characteristics and prognosis. Acta Otolaryngol. 1999;119(7):847–51.
Article
CAS
PubMed
Google Scholar
Jin T, Hu WH, Guo LB, et al. Treatment results and prognostic factors of patients undergoing postoperative radiotherapy for laryngeal squamous cell carcinoma. Chin J Cancer. 2011;30(7):482–9.
Article
PubMed
PubMed Central
Google Scholar
Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23(15):4259–69.
Article
PubMed
Google Scholar