Sandy Napel

Affiliations: 
Stanford University, Palo Alto, CA 
Area:
Computer Science, Biomedical Engineering, Radiology, Medical Biophysics
Google:
"Sandy Napel"

Children

Sign in to add trainee
David S. Paik grad student 2002 Stanford
Shaohua Sun grad student 2007 Stanford
Padmavathi Sundaram grad student 2007 Stanford
Feng Zhuge grad student 2007 Stanford
Tejas S. Rakshe grad student 2008 Stanford
BETA: Related publications

Publications

You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect.

Jaggi A, Mastrodicasa D, Charville GW, et al. (2021) Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma. Journal of Medical Imaging (Bellingham, Wash.). 8: 054501
Balagurunathan Y, Beers A, McNitt-Gray M, et al. (2021) Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge. Ieee Transactions On Medical Imaging
Zhang M, Tong E, Hamrick F, et al. (2021) Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study. Neurosurgery
Tam LT, Yeom KW, Wright JN, et al. (2021) MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study. Neuro-Oncology Advances. 3: vdab042
Cetin I, Raisi-Estabragh Z, Petersen SE, et al. (2020) Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Frontiers in Cardiovascular Medicine. 7: 591368
Ogunleye AA, Deptula PL, Inchauste SM, et al. (2020) The utility of three-dimensional models in complex microsurgical reconstruction. Archives of Plastic Surgery. 47: 428-434
Bakr S, Gevaert O, Patel B, et al. (2020) Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images. Radiology. Imaging Cancer. 2: e190062
Jaggi A, Mattonen SA, McNitt-Gray M, et al. (2020) Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. Tomography (Ann Arbor, Mich.). 6: 111-117
Mattonen SA, Gude D, Echegaray S, et al. (2020) Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines. Journal of Medical Imaging (Bellingham, Wash.). 7: 042803
Zwanenburg A, Vallières M, Abdalah MA, et al. (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 191145
See more...