ReviewClinical radiology2020
Machine learning and glioma imaging biomarkers.
Review in Clinical radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 4 of them syntheses that pooled it.
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
54 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled it
- Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.Journal of neurointerventional surgery · 2023Pooled it
- Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment.AJNR. American journal of neuroradiology · 2022Pooled it
- Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies.Frontiers in oncology · 2022Pooled it
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- The Neurogenic Niche: Interactions Among Vessels, Glia, and Neural Stem Cells.Stem cells international · 2026Review
- Integrating single-cell RNA-Seq and machine learning to dissect a novel Palmitoylation-related prognostic signature of glioblastoma.BMC neurology · 2025Article
- 2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET/CT.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Neuroplasticity in Diffuse Low-grade Gliomas: Backward Modelling of Brain-tumor Interactions Prior to Diagnosis is Needed to Better Predict Recovery after Treatment.Current neurology and neuroscience reports · 2025Review
- Radiotherapy for glioma in the AI era: current applications and future prospects.Frontiers in oncology · 2025Review
- Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.Briefings in bioinformatics · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Article
- Construction and validation of a machine learning-based immune-related prognostic model for glioma.Journal of cancer research and clinical oncology · 2024Article
- Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.Radiology. Artificial intelligence · 2024Article
- Morphological MRI features as prognostic indicators in brain metastases.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimTo review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. MATERIALS AND
methodsThe PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction.
resultsMagnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).
conclusionAlthough pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.
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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.