1300 Prediction of efficacy and toxicities of immune checkpoint inhibitors using real-world patient data

• Zoltan Kiss,
• Jan Wolber,
• Levente Lippenszky,
• Kathleen Mittendorf,
• Pablo Napan-Molina,
• Eszter Csernai,
• Michele LeNoue-Newton,
• Christine Micheel,
• Travis Osterman

Affiliations

Zoltan Kiss

Second Department of Medicine and Nephrology-Diabetes Centre, University of Pécs Medical School, Pécs, Hungary

Jan Wolber

GE Healthcare, Amersham, UK

Levente Lippenszky

1Science and Technology Organization – Artificial Intelligence and Machine Learning, GE HealthCare, Budapest, Budapest, Hungary

Kathleen Mittendorf

2Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA

Pablo Napan-Molina

3Science and Technology Organization – Artificial Intelligence and Machine Learning, GE HealthCare, San Ramon, CA, USA

Eszter Csernai

1Science and Technology Organization – Artificial Intelligence and Machine Learning, GE HealthCare, Budapest, Budapest, Hungary

Michele LeNoue-Newton

2Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA

Christine Micheel

2Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA

Travis Osterman

2Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA