The Mathematical Neuro-oncology Lab had a strong showing at the 2017 Society for Neuro-Oncology annual meeting. Eight members presented a variety of research ranging from the evaluation of response metrics in GBM treatment to novel methods for applying machine learning methods to radiomics.
- Gustavo De Leon, BS – “Identifying Early Indicators of Immunotherapeutic Response: CAR T-Cell Therapy”
- Susan Christine Massey, PhD – “Extent of glioblastoma invasion predicts overall survival following upfront radiotherapy concurrent with temozolomide”
- Kyle W. Singleton, PhD – “Discrimination of clinically impactful treatment response in recurrent glioblastoma patients receiving bevacizumab treatment”
- Kyle W. Singleton, PhD – “Role of pretreatment tumor dynamics and imaging response in discriminating glioblastoma survival following gamma knife”
- Michael Vogelbaum, MD, PhD – “Impact of post-surgical enhancing tumor volume and T2/FLAIR volume on the survival impact of bevacizumab in NRG Oncology/ RTOG 0825”
- Pamela R Jackson, PhD – “P53 amplification modifies the glioblastoma microenvironment: Differentiating the contribution of cells vs edema in the T2 weighted MRI signal”
- Leland Hu, MD – “Accurate patient-specific machine learning models of glioblastoma invasion using transfer learning”
- Kristin R Swanson, PhD – “Radiomics of tumor invasion 2.0: combining mechanistic tumor invasion models with machine learning models to accurately predict tumor invasion in human glioblastoma patients”