Hugo J.W.L. Aerts
Brigham and Women's Hospital
244 Papers
793 Citations
Hugo J.W.L. Aerts is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Medicine & Lung cancer. The author has an hindex of 62, co-authored 204 publications. Previous affiliations of Hugo J.W.L. Aerts include Harvard University & Stanford University.
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Papers
Corrigendum: Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC.
Hugo J.W.L. Aerts,Patrick Grossmann,Yongqiang Tan,Geoffrey R. Oxnard,Naiyer A. Rizvi,Lawrence H. Schwartz,Binsheng Zhao +6 more
TL;DR: This paper presents a meta-analyses of the determinants of infectious disease in eight operation rooms of the immune system and shows clear patterns in response to antibiotics and in particular the presence of E.coli A and B.
Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma.
William D. Dunn,Hugo J.W.L. Aerts,Lee Cooper,Chad A. Holder,Scott N. Hwang,Carle C. Jaffe,Daniel J. Brat,Rajan Jain,Adam E. Flanders,Pascal O. Zinn,Rivka R. Colen,David A. Gutman +11 more
- 01 Jan 2016
TL;DR: As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.
TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features
E Rios Velazquez,Raphael Meier,William D. Dunn,Brian M. Alexander,Roland Wiest,Stefan Bauer,David A. Gutman,Mauricio Reyes,Hugo J.W.L. Aerts +8 more
TL;DR: A fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features can enable more reproducible definition and quantification of imaging based biomarkers.
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SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data
Thibaud P. Coroller,Wenya Bi,Malak Abedalthagafi,Ayal A. Aizer,W Wu,Noah F. Greenwald,Rameen Beroukhim,Ossama Al-Mefty,Sandro Santagata,Ian F. Dunn,Brian M. Alexander,Raymond Y. Huang,Hugo J.W.L. Aerts +12 more
TL;DR: A strong association between radiologic features and meningioma grade is demonstrated and pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.
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