GE HealthCare Strategic research collaboration, 2019-2024

Five-year strategic research collaboration on predicting immune checkpoint inhibitor efficacy and toxicity from real-world EHR data. Dr. Osterman served as principal investigator on the flagship Digital Precision Oncology study.

Vanderbilt and GE HealthCare announced a five-year strategic research collaboration in January 2019 (dotmed.com) centered on applying machine learning to predict immune checkpoint inhibitor (ICI) effectiveness and toxicity from real-world EHR data. Dr. Osterman served as principal investigator from Vanderbilt; Dr. Jan Wolber led on the GE HealthCare side. Multi-disciplinary teams on both sides spanned medical oncology, radiochemistry, biostatistics, bioinformatics, and machine learning. The collaboration ran through 2024 and produced four peer-reviewed manuscripts, twelve conference presentations, multiple abstracts, and a U.S. patent on the underlying model-generation framework.

The flagship paper - Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data (Lippenszky et al., JCO Clinical Cancer Informatics, 2024) - demonstrated that routinely-collected EHR data is enough to build clinically useful prediction models across both efficacy and toxicity, without bespoke biomarker panels or unstructured-data extraction pipelines. The imaging companion paper extended the framework to pre-treatment thorax CT (Smith et al., JCO CCI, 2025) using radiomic features and deep learning to predict ICI-induced pneumonitis from 2,700 CT volumes.

Beyond Vanderbilt the work was externally validated on multi-center German pan-cancer cohorts in collaboration with the Essen-Bochum team (Kiss, Lippenszky et al., SITC 2023). The full narrative arc - what the team bet against the prevailing assumptions of the field (real-world data over curated cohorts, pan-cancer over single-cancer), what shipped, and what's next - lives in the Digital Precision Oncology case study.

Named programs

Case study

Long-form narrative: Digital Precision Oncology with GE HealthCare.

Peer-reviewed publications (3)

  1. David S. Smith et al. Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography. JCO Clinical Cancer Informatics Feb 20, 2025
  2. Levente Lippenszky et al. Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data. JCO Clinical Cancer Informatics Mar 21, 2024
  3. Protiva Rahman et al. Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records. JAMIA Open Apr 1, 2023

Selected talks (1)

  1. Society for Immunotherapy of Cancer Annual Meeting (San Diego, CA): "EHR-based Models for Predicting Efficacy and Toxicities Prior to ICI Treatment". Nov 4, 2023

Abstracts (10)

  1. Pablo Napan Molina et al. 1228 Machine learning models can predict efficacy and toxicities using short medical history prior to ICI therapy | Journal for ImmunoTherapy of Cancer. Nov 5, 2024
  2. David Smith et al. 1246 Prediction of pneumonitis in immunotherapy patients from prior thorax CT. Journal for ImmunoTherapy of Cancer Nov 1, 2024
  3. Pablo Napan Molina et al. 1228 Machine learning models can predict efficacy and toxicities using short medical history prior to ICI therapy. Journal for ImmunoTherapy of Cancer Nov 1, 2024
  4. Zoltan Kiss et al. 1294 External validation of machine learning models to predict efficacy and toxicity of immune checkpoint inhibitors using real-world pan cancer cohorts. Journal for ImmunoTherapy of Cancer Nov 1, 2023
  5. Levente Lippenszky et al. 1300 Prediction of efficacy and toxicities of immune checkpoint inhibitors using real-world patient data. Journal for ImmunoTherapy of Cancer Nov 1, 2023
  6. Eszter Csernai et al. Rolling window-based hepatitis toxicity prediction from routine bloodwork in patients undergoing immune checkpoint inhibitor therapy.. Journal of Clinical Oncology Jun 2022
  7. Gergely Horváth et al. Predicting immune checkpoint inhibitor-related hepatitis using electronic health records of patients.. Journal of Clinical Oncology Jun 2022
  8. Levente Lippenszky et al. Predicting immune checkpoint inhibitor-related pneumonitis using patient medical information.. Journal of Clinical Oncology Jun 2022
  9. Neha M Jain, Philip Edward Lammers, Michael R. Savona, Travis John Osterman, Salil Goorha. Using a standard implementation science framework to improve clinical trial enrollment for a community Tennessee oncology center.. Journal of Clinical Oncology Jun 2022
  10. Kathleen F. Mittendorf et al. Overcoming barriers in academic-industry partnerships to improve predictive modeling in immuno-oncology.. Journal of Clinical Oncology Jun 2022

Patents (1)

  1. Jan Wolber, Eszter Katalin Csernai, Zoltán Kiss, Levente Lippenszky, Gergely Horváth, Travis Osterman, Ben Ho Park, David Samuel Smith, Daniel Fabbri, Michele LeNoue-Newton, Kathleen Mittendorf. Model generation apparatus for therapeutic prediction and associated methods and models (2025).

In the news (1)

  1. GE Healthcare, Vanderbilt Explore Use of AI to Predict Immunotherapy Toxicity, Efficacy · Precision Medicine Online. Nov 27, 2023

Disclosures

This collaboration is publicly disclosed through two independent records: ASCO COI and CMS Open Payments.

Related: all collaborations · Epic · Microsoft · Tempus AI · nference · NCCN · ai in oncology · lung cancer.

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