Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials.

By B. Ellingson, Martin Bendszus, J. Boxerman, D. Barboriak, Bradley James Erickson, Marion Smits, S. Nelson, E. Gerstner, B. Alexander, G. Goldmacher and 18 more. Neuro-Oncology, 2015

646 citations recorded by the source corpus.

Subfields: Genetics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/532637ea1b1a783cfe9067023208da92223bf821

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Andriy Myronenko

In Radiology, Nuclear Medicine and Imaging

1. Mariano Cabezas
2. Daniel S. Marcus
3. Victor Alves
4. Tal Arbel
5. Marcel Prastawa
6. Nematollah Kayhan Batmanghelich
7. Benedikt Wiestler
8. Martin Bendszus

Citations in the corpus

• Understanding and Interpreting Machine Learning in Medical Image Computing Applications (2018, background): “Still, perfusion MRI is not widely acquired in clinical practice [3]; in fact, perfusion MRI is seen as a plus, while structural MRI is part of the current consensus recommendations for standardized brain tumor imaging [2].”
• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2018, background): “Alternative research directions include the enhancement of the flexibility of DL systems that might lack a given set of input images [139], as a transition measure towards worldwide adoption of the standardization initiatives for GBM imaging [140].”