Sandy Napel

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

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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
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Publications

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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
Mukherjee P, Zhou M, Lee E, et al. (2020) A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets Nature Machine Intelligence. 2: 274-282
Mattonen SA, Davidzon GA, Benson J, et al. (2019) Bone Marrow and Tumor Radiomics at F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology. 190357
Tunali I, Hall LO, Napel S, et al. (2019) Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Medical Physics
Napel S, Mu W, Jardim-Perassi BV, et al. (2018) Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer
Bakr S, Gevaert O, Echegaray S, et al. (2018) A radiogenomic dataset of non-small cell lung cancer. Scientific Data. 5: 180202
Bernard O, Lalande A, Zotti C, et al. (2018) Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved? Ieee Transactions On Medical Imaging
Wu J, Cao G, Sun X, et al. (2018) Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology. 172462
Balagurunathan Y, Beers A, Kalpathy-Cramer J, et al. (2018) Semi-Automated Pulmonary Nodule Interval Segmentation using the NLST data. Medical Physics
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