@incollection{YOGANANDA202257,
title = {5 - Simultaneous brain tumor segmentation and molecular profiling using deep learning and T2w magnetic resonance images},
editor = {Jyotismita Chaki},
booktitle = {Brain Tumor MRI Image Segmentation Using Deep Learning Techniques},
publisher = {Academic Press},
pages = {57-79},
year = {2022},
isbn = {978-0-323-91171-9},
doi = {https://doi.org/10.1016/B978-0-323-91171-9.00005-3},
url = {https://www.sciencedirect.com/science/article/pii/B9780323911719000053},
author = {Chandan Ganesh Bangalore Yogananda and Bhavya R. Shah and Fang F. Yu and Sahil S. Nalawade and James Holcomb and Divya Reddy and Benjamin C. Wagner and Marco C. Pinho and Bruce Mickey and Toral R. Patel and Baowei Fei and Ananth J. Madhuranthakam and Joseph A. Maldjian},
keywords = {Isocitrate dehydrogenase, 1p/19q, Methyl guanine-DNA methyltransferase, Deep learning, Convolutional Neural Networks (CNN), Magnetic resonance imaging, Glioma, Dense-U-net},
abstract = {Gliomas demonstrate diverse imaging features, variable response to therapy, and differences in prognosis. This is largely a function of genetic heterogeneity. Several key mutations serve as therapeutic and prognostic markers such as isocitrate dehydrogenase (IDH) mutation status, O6-methyl guanine-DNA methyltransferase (MGMT) promoter status, and 1p/19q co-deletion status. Currently, the gold standard for molecular marker determination requires tissue from either an invasive brain biopsy or surgical resection. Here we describe our work in developing highly accurate simultaneous deep learning segmentation and classification approaches for noninvasive profiling of molecular markers using T2-weighted magnetic resonance images only.}
}