About

Sandy Napel is a Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Medicine (Medical Informatics) and of Electrical Engineering at Stanford University. He is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His research focuses on artificial intelligence in medicine and imaging, integrating biomedical imaging informatics to advance healthcare technologies. As a faculty member at Stanford, he contributes to the development and application of AI-driven solutions in medical imaging, supporting education, research, and industry collaborations in this field.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Radiology
• Medicine
• Data Mining
• Cartography
• Pathology
• Internal medicine
• Nuclear medicine
• Medical physics

Selected publications

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

  BMJ · 2025-02-05 · 306 citations

  articleOpen access

  Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles-fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.

  PublisherOA PDFDOI

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

  Journal of Imaging Informatics in Medicine · 2025-08-14

  articleOpen access

  Interoperability between data sources, one of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management, can enable multi-modality research. The purpose of our study was to investigate the potential for interoperability between an imaging resource, the Medical Imaging and Data Resource Center (MIDRC), and a clinical record resource, the National COVID Cohort Collaborative (N3C). The use case was the prediction of COVID-19 severity, defined as evidence for invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice in the N3C clinical record. Patient-level matching between MIDRC and N3C was identified using Privacy Preserving Record Linking via an honest broker. We identified positive COVID-19 tests and chest radiograph procedures in N3C and used the interval between them to identify images with matching intervals in MIDRC. Of the 236 patients (306 unique images) meeting initial inclusion criteria in MIDRC, 117 patients (and 139 unique images) remained after date interval matching between repositories and exclusion of patients with multiple potential matches. The Charlson Comorbidity Index (CCI) and the minimum mean arterial pressure (MAP) on the day of the chest radiograph were used as clinical indicators. The AUC in the task of predicting severe COVID-19 was evaluated using the computer-extracted imaging index alone (MIDRC), clinical indicators alone (N3C), and both together. Our model combining imaging and clinical indicators (CCI over 2 and MAP below 70) to predict severe COVID had an AUC of 0.73 (95% CI 0.62-0.84), and the models including imaging or clinical indicators alone were 0.67 (95% CI 0.56-0.79) and 0.69 (95% CI 0.59-0.80), respectively. This study highlights the potential for cross-platform data sharing to facilitate future multi-modality research and broader collaborative studies.

  PublisherOA PDFDOI

• Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center

  Scientific Data · 2025-08-01 · 3 citations

  articleOpen access

  Interoperability (the ability of data or tools from non-cooperating resources to integrate or work together with minimal effort) is particularly important for curation of multimodal datasets from multiple data sources. The Medical Imaging and Data Resource Center (MIDRC), a multi-institutional collaborative initiative to collect, curate, and share medical imaging datasets, has made interoperability with other data commons one of its top priorities. The purpose of this study was to demonstrate the interoperability between MIDRC and two other data repositories, BioData Catalyst (BDC) and National Clinical Cohort Collaborative (N3C). Using interoperability capabilities of the data repositories, we built two cohorts for example use cases, with each containing clinical and imaging data on matched patients. The representativeness of the cohorts is characterized by comparing with CDC population statistics using the Jensen-Shannon distance. The process and methods of interoperability demonstrated in this work can be utilized by MIDRC, BDC, and N3C users to create multimodal datasets for development of artificial intelligence/machine learning models.

  PublisherOA PDFDOI

• Artificial Intelligence in Radiology: Opportunities and Challenges

  Seminars in Ultrasound CT and MRI · 2024-02-23 · 24 citations

  article
  PublisherDOI

• Abstract 3510: A radiogenomic approach for triple-negative breast cancer risk stratification

  Cancer Research · 2024-03-22

  article

  Abstract Background: Triple-negative breast cancer (TNBC) is an aggressive disease that accounts for 15-20% of all breast cancers. Expressions of ER, PR and HER2 receptors are lacking in this disease, and thus targeted therapies are not effective. TNBC has a shorter relapse-free survival, higher metastasis rate and decreased overall survival compared with other breast cancers. However, when undergoing standard treatment, some patients respond well, while others have poor outcome, suggesting TNBC heterogeneity. Early stratification of patients with long versus short survival could identify the subgroup of patients who would not benefit from exposure to toxicity of chemotherapy treatment. Here, we developed a non-invasive radiogenomic approach for TNBC risk stratification. Methods: A transcriptomic-based prognostic gene signature was previously developed using the TCGA-BRCA cohort (n=860). Briefly, LASSO Cox regression model analysis with the ‘glmnet’ R package was used to identify the transcriptomic signature gene-set consisting of 50 genes. We tested this signature to prognosticate overall survival in a Stanford cohort (n=63) and a previously published SCANB cohort (n=604). The patients were stratified into high- and low-risk groups based on the median risk-score. Next, we developed a machine learning model that identified a radiomic feature set to predict the prognostic transcriptomic risk-groups. Radiomic features were extracted from pre-treatment breast MRI. Radiomics features were extracted using PyRadiomics. The model utilized Decision Tree Classifier and LeaveOneOut method was used for cross-validation. Results: The transcriptomic signature low-risk group was significantly associated with improved overall survival in the two TNBC cohorts, with hazard ratios of 0.11 [95% CI: 0.01-0.88] for the Stanford cohort and 0.71 [95% CI: 0.52-0.97] for the SCANB cohort (log-rank p-values p=0.012 and p=0.032, respectively). Including this transcriptomic signature in a multivariate analysis, which adjusted for clinical features (patient age, grade, stage and Ki67%), the transcriptomic prognostic signature remained a significant prognostic factor (p<0.05). The radiomic feature set (consisting of 20 features) predicted the high- and low-risk transcriptomic groups with a mean accuracy of 72.2% and a mean AUROC of 71%. The precision, F1 and recall scores were 67%, 74% and 82%, respectively. In an independent dataset consisting of 116 Stanford TNBC patients, we used this model to predict risk groups based on the MRI radiomics features, and evaluated the prognostic effects of predicted risk groups. The overall survival of the predicted high-risk group was significantly poorer than the predicted low-risk group (p=0.013). Conclusions: We present a prognostic model that can non-invasively stratify TNBC patients for low versus high mortality risk using radiomic features derived from pre-treatment patient MRI data. Citation Format: Humaira Noor, Yuanning Zheng, Adam Mantz, Ryle Zhou, Andrew Kozlov, Wendy B. DeMartini, Shu-tian Chen, Satoko Okamoto, Debra Ikeda, Sarah Mattonen, Sandy Napel, Melinda L. Telli, George Sledge, Allison Kurian, Mina Satoyoshi, Olivier Gevaert, Haruka Itakura. A radiogenomic approach for triple-negative breast cancer risk stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3510.

  PublisherDOI

• Supplementary Figures 1-8 from Prognostic PET <sup>18</sup>F-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non–Small Cell Lung Cancer

  2023-03-30

  preprintOpen access

  <p>PDF file - 1.4MB, Supplement 1: External data set cohort characteristics Supplement 2: Study cohort metagenes significantly associated with FDG uptake features Supplement 3: DAVID and GSEA enrichment analysis for metagenes in study cohort Supplement 4: Predicted FDG uptake features gene enrichment analysis Supplement 5: Kaplan Meier curves for single genes associated with FDG uptake features and survival Supplement 6: Survival analysis incorporating imaging features with known variables of prognosis in non-small cell lung cancer Supplement 7: IPA networks for SUVmax and the compound model Supplement 8: SUVmax associations with selected glycolytic genes by SAM!</p>

  PublisherDOI

• Data from Prognostic PET <sup>18</sup>F-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non–Small Cell Lung Cancer

  2023-03-30

  preprintOpen access

  <div>Abstract<p>Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non–small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (<i>LY6E</i>, <i>RNF149</i>, <i>MCM6</i>, and <i>FAP</i>) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUV<sub>max</sub>), SUV<sub>variance</sub>, and SUV<sub>PCA2</sub>], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49–13.8] and validation (HR, 6.12; CI, 1.08–34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis. <i>Cancer Res; 72(15); 3725–34. ©2012 AACR</i>.</p></div>

  PublisherDOI

• De-identified ID of the patients included in the RG cohort from <i>GFPT2</i>-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma

  2023-03-31

  preprintOpen access

  <p>This supplement file contains a list of de-identified IDs of the patients included in the RG cohort data available online.</p>

  PublisherDOI

• Supplement material descriptions and Figure S1-S7 and Table S1-S3 from <i>GFPT2</i>-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma

  2023-03-31

  preprintOpen access

  <p>This supplement file provides additional information and analysis descriptions to the main manuscript ordered as in which they appear in the main text. This supplement file also provides general descriptions and overview to all the supplement materials included. This supplement file also includes Figure S1-S7 and Table S1-S3.</p>

  PublisherDOI

• List of SCC SUVmax correlated genes from <i>GFPT2</i>-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma

  2023-03-31

  preprintOpen access

  <p>This supplement file contains the identified SUVmax correlated genes in the lung squamous cell carcinoma patients in the RG cohort.</p>

  PublisherDOI

Recent grants

• NIH Grant R01CA160251

  NIH · $2.9M · 2017

• NIH Grant R01HL067194

  NIH · $1.9M · 2007

• NIH Grant U01CA187947

  NIH · $2.9M · 2020

• NIH Grant R01CA109089

  NIH · $2.1M · 2011

• NIH Grant R01HL050305

  NIH · $728k · 1998

Frequent coauthors

• Daniel T. Chang

  401 shared

• Olivier Gevaert

  343 shared

• Sylvia K. Plevritis

  337 shared

• Gary K. Steinberg

  Stanford Medicine

  301 shared

• Erik P. Sulman

  New York University

  300 shared

• S. Cheshier

  University Children's Hospital Zurich

  300 shared

• Tali Mazor

  University of California, San Francisco

  300 shared

• Robert H. Bell

  Durham University

  300 shared