Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

S Bakas, M Reyes, A Jakab, S Bauer… - arXiv preprint arXiv …, 2018 - arxiv.org
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …

A review of radiomics and deep predictive modeling in glioma characterization

S Gore, T Chougule, J Jagtap, J Saini, M Ingalhalikar - Academic radiology, 2021 - Elsevier
Recent developments in glioma categorization based on biological genotypes and
application of computational machine learning or deep learning based predictive models …

An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble

S Sahoo, S Mishra, B Panda, AK Bhoi, P Barsocchi - Sensors, 2023 - mdpi.com
Brain tumor detection in the initial stage is becoming an intricate task for clinicians
worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a …

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

JM Bhalodiya, SN Lim Choi Keung… - Digital Health, 2022 - journals.sagepub.com
Background Image segmentation is an essential step in the analysis and subsequent
characterisation of brain tumours through magnetic resonance imaging. In the literature …

A multi-parametric MRI-based radiomics signature and a practical ML model for stratifying glioblastoma patients based on survival toward precision oncology

AFI Osman - Frontiers in Computational Neuroscience, 2019 - frontiersin.org
Purpose: Predicting patients' survival outcomes is recognized of key importance to clinicians
in oncology toward determining an ideal course of treatment and patient management. This …

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

I Shahzadi, A Seidlitz, B Beuthien-Baumann… - Scientific Reports, 2024 - nature.com
Personalized treatment strategies based on non-invasive biomarkers have potential to
improve patient management in patients with newly diagnosed glioblastoma (GBM). The …

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

E Gryska, J Schneiderman, I Björkman-Burtscher… - BMJ open, 2021 - bmjopen.bmj.com
Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …

Radiogenomics model for overall survival prediction of glioblastoma

N Wijethilake, M Islam, H Ren - Medical & Biological Engineering & …, 2020 - Springer
Glioblastoma multiforme (GBM) is a very aggressive and infiltrative brain tumor with a high
mortality rate. There are radiomic models with handcrafted features to estimate glioblastoma …

RefineNet‐based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy

C Xiao, J Jin, J Yi, C Han, Y Zhou, Y Ai… - Journal of Applied …, 2022 - Wiley Online Library
Purpose An accurate and reliable target volume delineation is critical for the safe and
successful radiotherapy. The purpose of this study is to develop new 2D and 3D automatic …

MR image-based attenuation correction of brain PET imaging: review of literature on machine learning approaches for segmentation

I Mecheter, L Alic, M Abbod, A Amira, J Ji - Journal of Digital Imaging, 2020 - Springer
Recent emerging hybrid technology of positron emission tomography/magnetic resonance
(PET/MR) imaging has generated a great need for an accurate MR image-based PET …