ReviewCancers2023
Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 3 of them syntheses that pooled it.
What it found
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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
50 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence for predicting pituitary neuroendocrine tumor consistency: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data.American journal of rhinology & allergy · 2025Pooled it
- The potential of MRI radiomics based on extrapulmonary metastases in predicting EGFR mutations: a systematic review and meta-analysis.Biomedical engineering online · 2025Pooled it
- Integrating Multimodal MRI Habitat and Transformer-Based Pathomics to Predict High-Risk Molecular Subtypes and Explore Biological Mechanisms in Adult Diffuse Gliomas.CNS neuroscience & therapeutics · 2026Article
- Application advances of habitat imaging based on CT or PET/CT: a systematic review.La Radiologia medica · 2026Review
- Multi-habitat radiomics on T2-FS MRI for identifying spatial patterns of axial spondyloarthritis-associated bone marrow edema.Clinical rheumatology · 2026Article
- The future is not always uniform: rethinking radiotherapy through spatial fractionation.Nature reviews. Clinical oncology · 2026Review
- PET-based Radiomics Analysis for Predicting Prognosis and Differentiation Treatment-Related Changes in Glioma: A Systematic Review.Journal of biomedical physics & engineering · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.Journal of imaging · 2026Review
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.Journal of translational medicine · 2026Article
- Multimodal MRI-based nomogram integrating clinical-radiological, radiomic, and habitat features to discriminate solitary fibrous tumors from atypical meningiomas.Scientific reports · 2026Article
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.BMC medical imaging · 2026Article
- Development and Validation of an Explainable MRI-Based Habitat Radiomics Model for Predicting p53-Abnormal Endometrial Cancer: A Multicentre Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- Habitat-based MRI radiomics for enhanced parkinson's diagnosis.Scientific reports · 2026Article
- Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.Frontiers in oncology · 2026Article
- Habitat Radiomics Analysis Based on Non-contrast CT in Differentiation of Parotid Pleomorphic Adenoma and AdenolymphomaCurrent medical imaging · 2026Article
- Computational Pipeline in Neuroradiomics.Methods in molecular biology (Clifton, N.J.) · 2026Article
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
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.
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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.