Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

DA Hormuth, KA Al Feghali, AM Elliott, TE Yankeelov… - Scientific reports, 2021 - nature.com
High-grade gliomas are an aggressive and invasive malignancy which are susceptible to
treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation …

Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach

R Rockne, JK Rockhill, M Mrugala… - Physics in Medicine …, 2010 - iopscience.iop.org
Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as
gliomas. They proliferate and invade extensively and yield short life expectancies despite …

Applications of radiomics and radiogenomics in high-grade gliomas in the era of precision medicine

A Fathi Kazerooni, SJ Bagley, H Akbari, S Saxena… - Cancers, 2021 - mdpi.com
Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma
biology, as well as into glioma behavior in response to standard therapies. In this article, we …

Personalized radiotherapy design for glioblastoma: integrating mathematical tumor models, multimodal scans, and Bayesian inference

J Lipková, P Angelikopoulos, S Wu… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding
normal brain tissue beyond the lesion outlines visible in the current medical scans. These …

Discriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metric

ML Neal, AD Trister, T Cloke, R Sodt, S Ahn… - PloS one, 2013 - journals.plos.org
Accurate clinical assessment of a patient's response to treatment for glioblastoma multiforme
(GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to …

Quantifying uncertainty and robustness in a biomathematical model–based patient-specific response metric for glioblastoma

A Hawkins-Daarud, SK Johnston… - JCO clinical cancer …, 2019 - ascopubs.org
Purpose Glioblastomas, lethal primary brain tumors, are known for their heterogeneity and
invasiveness. A growing body of literature has been developed demonstrating the clinical …

Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma

M Patel, J Zhan, K Natarajan, R Flintham, N Davies… - Clinical radiology, 2021 - Elsevier
AIM To investigate machine learning based models combining clinical, radiomic, and
molecular information to distinguish between early true progression (tPD) and …

Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling

DA Hormuth, AM Jarrett, TE Yankeelov - Radiation Oncology, 2020 - Springer
Background Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity
influence tumor response to radiation therapy. Quantitative imaging techniques capture …

Quantitative multiparametric MRI assessment of glioma response to radiotherapy in a rat model

X Hong, L Liu, M Wang, K Ding, Y Fan, B Ma… - Neuro …, 2014 - academic.oup.com
Background The inability of structural MRI to accurately measure tumor response to therapy
complicates care management for patients with gliomas. The purpose of this study was to …

Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models

P Kickingereder, S Burth, A Wick, M Götz, O Eidel… - Radiology, 2016 - pubs.rsna.org
Purpose To evaluate whether radiomic feature–based magnetic resonance (MR) imaging
signatures allow prediction of survival and stratification of patients with newly diagnosed …