Model integrating CT-based radiomics and genomics for survival prediction in esophageal cancer patients receiving definitive chemoradiotherapy

[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..