M
Michael Rutherford
Instructor
faculty
COM | Biomedical Informatics
12 h-index
49 pubs
393 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Michael Rutherford's research centers on the application of computational methods and data analysis in healthcare and biomedical fields. He has contributed to the development of tools for medical image synthesis, such as the Python library 'medigan,' and has investigated methods for de-identifying clinical and imaging data for AI projects. His work also includes evaluating the implementation of standards like HL7 FHIR to facilitate data exchange in clinical research. Rutherford has explored the analysis of caregiver burden through social media discussions and contributed to health economic analyses for tobacco dependency treatment services. His publication record also includes research into the molecular mechanisms of multiple myeloma, examining transcriptional profiles and gene expression in plasma cells across different disease stages.
Metrics
- h-index: 12
- Publications: 49
- Citations: 393
Selected Publications
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Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies (2026)
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Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images (2025)
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Evaluation of Electronic Health Record to Hl7® Fhir® Mappings in Pediatric Research Studies (2025)
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New implementation of data standards for AI in oncology: Experience from the EuCanImage project (2024)
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Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp> (2024)
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Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification (2024)
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Documenting the de-identification process of clinical and imaging data for AI for health imaging projects (2024)
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New implementation of data standards for AI in oncology. Experience from the EuCanImage project (2024)
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Abstract 6579: Accelerating de-identification of images with cloud services to support data sharing in cancer research (2023)
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medigan: a Python library of pretrained generative models for medical image synthesis (2023)
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Analysis of Caregiver Burden Expressed in Social Media Discussions (2023)
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Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups (2021)
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High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse (2021)
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A DICOM dataset for evaluation of medical image de-identification (2021)
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Evaluating Site-Level Implementations of the HL7 FHIR Standard to Support eSource Data Exchange in Clinical Research (2021)
Grants & Funding
- TO4 Moonshot BioBank – Support to IROC NIH/Nat. Cancer Institute - Pass Through: Leidos Co-Investigator
Collaboration Network
Top Collaborators
Fred Prior
10 shared publications
In database
- medigan: a Python library of pretrained generative models for medical image synthesis
- A DICOM dataset for evaluation of medical image de-identification
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
- Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 1: Report of the MIDI Task Group - Best Practices and Recommendations, Tools for Conventional Approaches to De-identification, International Approaches to De-identification, and Industry Panel on Image De-identification
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project
Showing 5 of 10 shared publications
Christopher P. Wardell
University of Arkansas for Medical Sciences
7 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 7 shared publications
Eileen M. Boyle
Centre National de la Recherche Scientifique (FR)
6 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Cody Ashby
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Michael Bauer
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Antje Hoering
Cancer Research And Biostatistics (US)
6 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Carolina Schinke
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Sharmilan Thanendrarajan
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Maurizio Zangari
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Bart Barlogie
Hôpital Claude Huriez
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Faith E. Davies
NYU Langone Health (US)
6 shared publications
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Frits van Rhee
6 shared publications
In database
- High‐risk transcriptional profiles in multiple myeloma are an acquired feature that can occur in any subtype and more frequently with each subsequent relapse
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Supplementary Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
- Data from <i>BRAF</i> and <i>DIS3</i> Mutations Associate with Adverse Outcome in a Long-term Follow-up of Patients with Multiple Myeloma
Showing 5 of 6 shared publications
Phillip Farmer
5 shared publications
In database
- A DICOM dataset for evaluation of medical image de-identification
- Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Medical Image De-Identification Benchmark Challenge
- Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
Karim Lekadir
Artificial Intelligence in Medicine (Canada) (CA)
5 shared publications
- medigan: a Python library of pretrained generative models for medical image synthesis
- Documenting the de-identification process of clinical and imaging data for AI for health imaging projects
- New implementation of data standards for AI in oncology. Experience from the EuCanImage project
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project
- medigan: a Python library of pretrained generative models for medical image synthesis
Kirk Smith
4 shared publications
- A DICOM dataset for evaluation of medical image de-identification
- Linking <i>The Cancer Imaging Archive</i> and <scp>GenBank</scp> to the <scp>National Clinical Cohort Collaborative</scp>
- Medical Image De-Identification Benchmark Challenge
- Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
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