ArticleCancers2021
Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed, 73 citations in OpenAlex.
- Spatial Omics in High-Grade Gliomas: Mapping Immune-Tumor Niches for Precision Therapy.Cancer medicine · 2026Review
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Predicting IDH Mutation in Glioma Patients Using Deep Learning Algorithms with Conformal Prediction.Journal of imaging informatics in medicine · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Radiomics and comparative neuroimaging of canine gliomas and human glioblastomas: a step toward precision medicine.Frontiers in veterinary science · 2026Article
- Review
- Immune Evasion Mechanism Mediated by ITPRIPL1 and Its Prognostic Implications in Glioma.Brain and behavior · 2025Article
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Roles and Impacts of Integrative Medical Interventions in Central Nervous System Tumor Treatment: Multi-Technology Convergence and the Paradigm Shift Toward Functional Reconstruction.CNS neuroscience & therapeutics · 2025Review
- Overdiagnosis and Overtreatment in Prostate Cancer.Diseases (Basel, Switzerland) · 2025Review
- Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.Neuro-oncology · 2025Article
- Response Assessment in Long-Term Glioblastoma Survivors Using a Multiparametric MRI-Based Prediction Model.Brain sciences · 2025Article
- Prognostic and predictive determinants in high-grade gliomas: integrating tumor-intrinsic biology with patient and system-level factors.Frontiers in neurology · 2025Review
- Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis.Frontiers in medicine · 2025Article
- Preliminary investigation on predicting postoperative glioma recurrences based on a multiparametric radiomics model.Frontiers in oncology · 2025Article
- Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.Cancers · 2023Article
- Radiogenomics: a key component of precision cancer medicine.British journal of cancer · 2023Review
- Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
Abstract
Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
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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.