Niha Beig

Employment

• Case Western Reserve University PhD Candidate
  2015 - Present

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (15)

• Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas
  Radiology 2018 DOI: https://doi.org/10.1148/radiol.2018180910
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• Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study
  Marwa Ismail, Virginia B. Hill, Volodymyr Statsevych +10 more
  American Journal of Neuroradiology 2018 DOI: 10.3174/ajnr.a5858
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• Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas
  Scientific Reports 2018 DOI: 10.1038/s41598-018-33473-0
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• Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography
  Journal of Medical Imaging 2018 DOI: 10.1117/1.jmi.5.2.024501
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• Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma
  Niha G. Beig, Jay Patel, Prateek Prasanna +10 more
  Scientific Reports 2018 DOI: 10.1038/s41598-017-18310-0
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• Radiomics and radiogenomics in lung cancer: A review for the clinician
  Lung Cancer 2018 DOI: 10.1016/j.lungcan.2017.10.015
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• Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response
  Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 2018 DOI: 10.1007/978-3-030-00934-2_89
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• NIMG-17. STRUCTURAL DEFORMATION FIELD ON T1W MRI IN HEALTHY BRAIN PARENCHYMA IS ASSOCIATED WITH OVERALL SURVIVAL IN TREATMENT-NAÏVE GLIOBLASTOMA MULTIFORME
  Neuro-Oncology 2017 DOI: 10.1093/neuonc/nox168.595
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• MEDU-48. MRI TEXTURAL FEATURES CAN DIFFERENTIATE PEDIATRIC POSTERIOR FOSSA TUMORS
  Neuro-Oncology 2017 DOI: 10.1093/neuonc/nox083.197
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• Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma
  Medical Imaging 2017: Computer-Aided Diagnosis 2017 DOI: 10.1117/12.2255694
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• An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT:
  Medical Physics 2017 DOI: 10.1002/mp.12208
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• Radiographic-Deformation and Textural Heterogeneity (r-DepTH): An Integrated Descriptor for Brain Tumor Prognosis
  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017 2017 DOI: 10.1007/978-3-319-66185-8_52
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• Computerized textural analysis of lung CT to enable quantification of tumor infiltrating lymphocytes in NSCLC.
  Journal of Clinical Oncology 2016 DOI: 10.1200/jco.2016.34.15_suppl.11584
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• Evaluation of radiomic features on baseline CT scan to predict clinical benefit for pemetrexed based chemotherapy in metastatic lung adenocarcinoma.
  Journal of Clinical Oncology 2016 DOI: 10.1200/jco.2016.34.15_suppl.11582
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• Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models
  Medical Imaging 2014: Computer-Aided Diagnosis 2014 DOI: 10.1117/12.2044333
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