🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Sandy Napel

University of Chicago Medical Center

ORCID: 0000-0002-6876-5507

Also affiliated with Stanford University

MedicineComputer Science

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 1%percentile
9.4Author DataRank

Indexed papers

2in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology
Why this DataRank?

An author's DataRank is the sum of the DataRanks of all 2 indexed papers attributed to them. A prolific author with many moderate-impact papers can outrank one with a single high-impact paper.

Author scores recompute whenever paper DataRanks are refreshed, so this number lags the underlying paper scores by at most one batch run.

Read the full methodology →

Top data-sharing exemplar

The highest-impact dataset this researcher has shared, ranked by DataRank — the single contribution doing the most to lift their data-sharing standing.

The Medical Segmentation Decathlon

Nature Communications(2022)10.1038/s41467-022-30695-9
Top 3%
6.9DataRank
Top 3%1226 citations

Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp‐Schneider, Bennett A. Landman +53 more

Papers

Driven by 14 papers — median percentile 94. Top paper: “FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare”.

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

BMJ(2025)10.1136/bmj-2024-081554
N/A
7.0DataRank · unranked
386 citations

Alejandro F. Frangi, Antonio R. Porras, Ben Glocker, Celia Cintas, Curtis P. Langlotz +44 more

The Medical Segmentation Decathlon

Nature Communications(2022)10.1038/s41467-022-30695-9
Top 3%
6.9DataRank
Top 3%1226 citations

Annika Reinke, Spyridon Bakas, Keyvan Farahani, Annette Kopp‐Schneider, Bennett A. Landman +53 more

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Tomography(2020)10.18383/j.tom.2019.00031
Top 9%
2.4DataRank
Top 9%75 citations

Sandy Napel, Arjun Jaggi, Sarah A. Mattonen, Lubomir M. Hadjiiski, Mark Muzi +24 more

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Radiology(2020)10.1148/radiol.2020191145
N/A
1.2DataRank · unranked
3690 citations

Martin Vallières, Mahmoud A. Abdalah, Hugo J.W.L. Aerts, Vincent Andrearczyk, Aditya Apte +58 more

Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines

Journal of Medical Imaging(2020)10.1117/1.jmi.7.4.042803
N/A
0DataRank · unranked
26 citations

Dev Gude, Sebastian Echegaray, Shaimaa Bakr, Daniel L. Rubin, Sandy Napel +1 more

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study

Neurosurgery(2021)10.1093/neuros/nyab212
N/A
0DataRank · unranked
20 citations

Elizabeth Tong, Forrest Hamrick, Edward H. Lee, Lydia Tam, Courtney Pendleton +12 more

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

Neuro-Oncology(2021)10.1093/neuonc/noab211
N/A
0DataRank · unranked
20 citations

Elizabeth Tong, Sam Wong, Forrest Hamrick, Maryam Mohammadzadeh, Vaishnavi Rao +11 more

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

Journal of Medical Imaging(2021)10.1117/1.jmi.8.5.054501
N/A
0DataRank · unranked
5 citations

Domenico Mastrodicasa, Gregory W. Charville, R. Brooke Jeffrey, Sandy Napel, Bhavik N. Patel +1 more

Machine learning with multimodal data for COVID-19

Heliyon(2023)10.1016/j.heliyon.2023.e17934
N/A
0DataRank · unranked
25 citations

Rui Carlos Sá, Yuntong Bai, Sandy Napel, Olivier Gevaert, DIANE S. LAUDERDALE +2 more

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

Journal of Medical Imaging(2023)10.1117/1.jmi.10.4.044006
N/A
0DataRank · unranked
1 citations

Pritam Mukherjee, Christopher Parham, Sachin B. Malik, Olivier Gevaert, Sandy Napel +2 more

Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case

Journal of Imaging Informatics in Medicine(2025)10.1007/s10278-025-01605-4
N/A
0DataRank · unranked
0 citations

Rachel Baccile, Hui Li, Karen Drukker, Christopher Meyer, Nicholas P. Gruszauskas +95 more

A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets

Nature Machine Intelligence(2020)10.1038/s42256-020-0173-6
N/A
0DataRank · unranked
102 citations

Mu Zhou, Edward Lee, Anne Schicht, Yoganand Balagurunathan, Sandy Napel +6 more

Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank

Frontiers in Cardiovascular Medicine(2020)10.3389/fcvm.2020.591368
N/A
0DataRank · unranked
61 citations

Zahra Raisi‐Estabragh, Steffen E. Petersen, Sandy Napel, Stefan K. Piechnik, Stefan Neubauer +4 more

Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images

Radiology Imaging Cancer(2020)10.1148/rycan.2020190062
N/A
0DataRank · unranked
14 citations

Olivier Gevaert, Bhavik N. Patel, Andrew Kesselman, Rajesh P. Shah, Sandy Napel +2 more