Sandy Napel

Sandy Napel

Stanford University

H-index: 73

North America-United States

About Sandy Napel

Sandy Napel, With an exceptional h-index of 73 and a recent h-index of 42 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Imaging.

His recent articles reflect a diverse array of research interests and contributions to the field:

A radiogenomic approach for triple-negative breast cancer risk stratification

AI in Radiology: Opportunities and Challenges

Predicting treatment response for the safe non-operative management of patients with rectal cancer using an MRI-based deep-learning model.

Early Detection of Lung Cancer in the NLST Dataset

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Machine learning with multimodal data for COVID-19

Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules

Radiomic features quantifying pixel-level characteristics of breast tumors from magnetic resonance imaging predict risk factors in triple-negative breast cancer.

Sandy Napel Information

University

Position

Professor of Radiology

Citations(all)

24896

Citations(since 2020)

10215

Cited By

17671

hIndex(all)

73

hIndex(since 2020)

42

i10Index(all)

203

i10Index(since 2020)

93

Email

University Profile Page

Stanford University

Google Scholar

View Google Scholar Profile

Sandy Napel Skills & Research Interests

Imaging

Top articles of Sandy Napel

Title

Journal

Author(s)

Publication Date

A radiogenomic approach for triple-negative breast cancer risk stratification

Cancer Research

Humaira Noor

Yuanning Zheng

Adam Mantz

Ryle Zhou

Andrew Kozlov

...

2024/3/22

AI in Radiology: Opportunities and Challenges

Marta N Flory

Sandy Napel

Emily B Tsai

2024/2/23

Predicting treatment response for the safe non-operative management of patients with rectal cancer using an MRI-based deep-learning model.

Heather M Selby

Charles Liu

Vipul Sheth

Sandy Napel

Todd Wagner

...

2023/6/1

Early Detection of Lung Cancer in the NLST Dataset

medRxiv

Pritam Mukherjee

Anna Brezhneva

Sandy Napel

Olivier Gevaert

2023/3/2

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv preprint arXiv:2309.12325

Karim Lekadir

Aasa Feragen

Abdul Joseph Fofanah

Alejandro F Frangi

Alena Buyx

...

2023/8/11

Machine learning with multimodal data for COVID-19

Weijie Chen

Rui C Sá

Yuntong Bai

Sandy Napel

Olivier Gevaert

...

2023/7/5

Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules

Journal of Medical Imaging

Heather M Selby

Pritam Mukherjee

Christopher Parham

Sachin B Malik

Olivier Gevaert

...

2023/7/1

Radiomic features quantifying pixel-level characteristics of breast tumors from magnetic resonance imaging predict risk factors in triple-negative breast cancer.

Adam B Mantz

Ryle Zhou

Andrew Kozlov

Wendy DeMartini

Shu-Tian Chen

...

2022/6/1

Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: a multi-center study

Neuro-oncology

Michael Zhang

Elizabeth Tong

Sam Wong

Forrest Hamrick

Maryam Mohammadzadeh

...

2022/4/1

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

Journal of Medical Imaging

Jaryd R Christie

Omar Daher

Mohamed Abdelrazek

Perrin E Romine

Richard A Malthaner

...

2022/11/1

Artificial intelligence and machine learning in cancer imaging

Dow-Mu Koh

Nickolas Papanikolaou

Ulrich Bick

Rowland Illing

Charles E Kahn Jr

...

2022/10/27

The medical segmentation decathlon

Nature communications

Michela Antonelli

Annika Reinke

Spyridon Bakas

Keyvan Farahani

Annette Kopp-Schneider

...

2022/7/15

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study

Neuro-oncology advances

Lydia T Tam

Kristen W Yeom

Jason N Wright

Alok Jaju

Alireza Radmanesh

...

2021/1/1

Machine-learning approach to differentiation of benign and malignant peripheral nerve sheath tumors: a multicenter study

Neurosurgery

Michael Zhang

Elizabeth Tong

Forrest Hamrick

Edward H Lee

Lydia T Tam

...

2021/9/1

Machine learning radiomics model for early identification of small-cell lung cancer on computed tomography scans

JCO Clinical Cancer Informatics

Rajesh P Shah

Heather M Selby

Pritam Mukherjee

Shefali Verma

Peiyi Xie

...

2021/7

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

IEEE Transactions on Medical Imaging

Lubomir Hadjiiski

Sandy Napel

Dmitry Goldgof

Gustavo Perez

Pablo Arbelaez

...

2021/12/1

The Journal of Medical Imaging would like to sincerely thank the following individuals who served as Associate Editors in 2020. The success of our publication hinges on the …

Journal of Medical Imaging

Katherine P Andriole

Susan M Astley

Stephen Aylward

François O Bochud

Jovan G Brankov

...

2021/2

Pipelines in image analysis

Sandy Napel

Ashish Sharma

Annie Gu

2021/9/30

Neuro-Oncology Advances

Masafumi Miyai

Tomohiro Kanayama

Fuminori Hyodo

Takamasa Kinoshita

Takuma Ishihara

...

2020

Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma

Journal of Medical Imaging

Akshay Jaggi

Domenico Mastrodicasa

Gregory W Charville

R Brooke Jeffrey Jr

Sandy Napel

...

2021/9/1

See List of Professors in Sandy Napel University(Stanford University)

Co-Authors

H-index: 99
Mark Musen

Mark Musen

Stanford University

H-index: 59
Dominik Fleischmann

Dominik Fleischmann

Stanford University

H-index: 58
Christopher F. Beaulieu

Christopher F. Beaulieu

Stanford University

H-index: 58
Olivier Gevaert

Olivier Gevaert

Stanford University

H-index: 57
Daniel Spielman

Daniel Spielman

Yale University

H-index: 27
Burak Acar

Burak Acar

Bogaziçi Üniversitesi

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