Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

By Luke Macyszyn, H. Akbari, J. Pisapia, Xiao Da, Mark Attiah, Vadim Pigrish, Y. Bi, Sharmistha Pal, Ramana V. Davuluri, L. Roccograndi and 7 more. Neuro-Oncology, 2016

296 citations recorded by the source corpus.

Subfields: Genetics, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/744d4fbbc8e605c747a680bb6600f91ac40a59c8

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. Charles Swanton

In Neurology

1. Klaus Hermann Maier-Hein
2. Ahmed Hosny
3. Hugo J. W. L. Aerts
4. Fabian Isensee

In Radiology, Nuclear Medicine and Imaging

1. Hugo J. W. L. Aerts
2. Ahmed Hosny
3. Fabian Isensee
4. Klaus Hermann Maier-Hein
5. Maryellen Lissak Giger
6. Charles Swanton
7. Robert A. Gatenby
8. Martin Bendszus

Citations in the corpus

• Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, background): “…to distinguish each of these features with 80% to 95% sensitivity and specificity, including the prediction of glioblastoma subtypes and survival, 123 IDH mutation status in high-grade and low-grade gliomas, 95,96 the presence of chromosome 1p and 19q loss in low-grade gliomas, 95,124 MGMT…”
• Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge (2017, background): “These features may also be complemented by other measures handcrafted to the problem at hand, such as the distance of the tumor to the ventricles and critical structures in the brain [12].”
• Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017, background): “in enhanced tumors were identified to provide added value for predicting molecular subtypes of glioblastoma.(45)”