Model integrating CT-based radiomics and genomics for survival prediction in esophageal cancer patients receiving definitive chemoradiotherapy
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J. Cui | Fengchang Yang | Ning Liu | W. Hou | S. Yuan | Yinjun Dong | Shou-hui Zhu | Jun Li | Li Li
[1] J. Cui,et al. Clinical Outcome-Related Cancer Pathways and Mutational Signatures in Patients With Unresectable Esophageal Squamous Cell Carcinoma Treated With Chemoradiotherapy. , 2022, International journal of radiation oncology, biology, physics.
[2] Wencheng Zhang,et al. CT-based radiomics nomogram may predict local recurrence-free survival in esophageal cancer patients receiving definitive chemoradiation or radiotherapy: a multicenter study. , 2022, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[3] E. D. Geijsen,et al. Randomized Study on Dose Escalation in Definitive Chemoradiation for Patients With Locally Advanced Esophageal Cancer (ARTDECO Study) , 2021, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[4] K. Lam,et al. Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study , 2021, Cancers.
[5] Feng-jun Wang,et al. Identification of four genes and biological characteristics of esophageal squamous cell carcinoma by integrated bioinformatics analysis , 2021, Cancer cell international.
[6] I. Castiglioni,et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer , 2021, European Journal of Nuclear Medicine and Molecular Imaging.
[7] A. Rana,et al. Frequency and prognostic value of mutations associated with the homologous recombination DNA repair pathway in a large pan cancer cohort , 2020, Scientific Reports.
[8] Yong Yin,et al. Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery , 2020, Frontiers in Oncology.
[9] Wanqing Chen,et al. Cancer burden of major cancers in China: A need for sustainable actions , 2020, Cancer communications.
[10] M. Hatt,et al. Radiogenomics-based cancer prognosis in colorectal cancer , 2019, Scientific Reports.
[11] T. Niu,et al. Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy , 2019, EBioMedicine.
[12] Xiaoying Gao,et al. CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy , 2019, Journal of radiation research.
[13] Xiance Jin,et al. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics , 2019, European Radiology.
[14] Raymond Y Huang,et al. Artificial intelligence in cancer imaging: Clinical challenges and applications , 2019, CA: a cancer journal for clinicians.
[15] F. Zhu,et al. Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes. , 2019, Radiology.
[16] Philippe Lambin,et al. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer , 2018, Acta oncologica.
[17] Elaine R. Mardis,et al. The emerging clinical relevance of genomics in cancer medicine , 2018, Nature Reviews Clinical Oncology.
[18] Z. Hou,et al. Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma , 2017, Oncotarget.
[19] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[20] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[21] Jinzhong Yang,et al. The Rise of Radiomics and Implications for Oncologic Management. , 2017, Journal of the National Cancer Institute.
[22] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[23] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[24] K. Miles,et al. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.
[25] D. Hanahan,et al. Hallmarks of Cancer: The Next Generation , 2011, Cell.
[26] T. Helleday. Homologous recombination in cancer development, treatment and development of drug resistance. , 2010, Carcinogenesis.
[27] A. Giordano,et al. Cell Cycle Genes in Ovarian Cancer , 2004, Clinical Cancer Research.
[28] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..