ReviewComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2021
Analyzing magnetic resonance imaging data from glioma patients using deep learning.
Review in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Optimization of deep learning models for inference in low resource environments.Computers in biology and medicine · 2025Article
- A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.Frontiers in oncology · 2025Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.Medical physics · 2023Article
- ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- Evaluating automated longitudinal tumor measurements for glioblastoma response assessment.Frontiers in radiology · 2023Article
- The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation.Scientific data · 2022Article
- Application of Deep Learning Technology in Glioma.Journal of healthcare engineering · 2022Article
- Brain Tumor Segmentation From Multi-Modal MR Images via Ensembling UNets.Frontiers in radiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.