[HTML][HTML] Improved prediction of surgical resectability in patients with glioblastoma using an artificial neural network

AP Marcus, HJ Marcus, SJ Camp, D Nandi… - Scientific Reports, 2020 - nature.com
In managing a patient with glioblastoma (GBM), a surgeon must carefully consider whether
sufficient tumour can be removed so that the patient can enjoy the benefits of …

Extent of resection in patients with glioblastoma: limiting factors, perception of resectability, and effect on survival

D Orringer, D Lau, S Khatri, GJ Zamora-Berridi… - Journal of …, 2012 - thejns.org
Object The extent of resection (EOR) is a known prognostic factor in patients with
glioblastoma. However, gross-total resection (GTR) is not always achieved. Understanding …

[HTML][HTML] Predicting surgical outcome in patients with glioblastoma multiforme using pre-operative magnetic resonance imaging: development and preliminary …

HJ Marcus, S Williams, A Hughes-Hallett, SJ Camp… - Neurosurgical …, 2017 - Springer
The lack of a simple, objective and reproducible system to describe glioblastoma multiforme
(GBM) represents a major limitation in comparative effectiveness research. The objectives of …

[HTML][HTML] Accuracy of the neurosurgeons estimation of extent of resection in glioblastoma

S Sezer, MJ van Amerongen, HHK Delye… - Acta …, 2020 - Springer
Background The surgeons' estimate of the extent of resection (EOR) shows little accuracy in
previous literature. Considering the developments in surgical techniques of glioblastoma …

Defining glioblastoma resectability through the wisdom of the crowd: a proof-of-principle study

AM Sonabend, BE Zacharia, MB Cloney… - …, 2017 - journals.lww.com
BACKGROUND Extent of resection (EOR) correlates with glioblastoma outcomes.
Resectability and EOR depend on anatomical, clinical, and surgeon factors. Resectability …

[HTML][HTML] Predicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

S Cepeda, A Pérez-Nuñez, S García-García… - Cancers, 2021 - mdpi.com
Simple Summary Identifying GBM patients with very short survival could contribute to
adapting the therapeutic approach. According to our results, high-precision models can be …

Automatic brain tumor segmentation and overall survival prediction using machine learning algorithms

E Carver, C Liu, W Zong, Z Dai, JM Snyder… - … Sclerosis, Stroke and …, 2019 - Springer
Purpose: This study was designed to evaluate the ability of a U-net neural net-work to
properly identify three regions of a brain tumor and an ELM for the prediction of patient …

[HTML][HTML] Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

Y Wan, R Rahmat, SJ Price - Acta neurochirurgica, 2020 - Springer
Background Measurement of volumetric features is challenging in glioblastoma. We
investigate whether volumetric features derived from preoperative MRI using a convolutional …

[HTML][HTML] Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated …

I Kommers, D Bouget, A Pedersen, RS Eijgelaar… - Cancers, 2021 - mdpi.com
Simple Summary Neurosurgical decisions for patients with glioblastoma depend on tumor
characteristics in the preoperative MR scan. Currently, this is based on subjective estimates …

[HTML][HTML] Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

RH Helland, A Ferles, A Pedersen, I Kommers… - Scientific reports, 2023 - nature.com
Extent of resection after surgery is one of the main prognostic factors for patients diagnosed
with glioblastoma. To achieve this, accurate segmentation and classification of residual …