ReviewRadiology. Imaging cancer2020
Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors.
Review in Radiology. Imaging cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 49 citations in OpenAlex.
- Impact of different pre-processing methods combined with combat harmonization on the reproducibility of radiomics features in healthy human brain MRI.Magma (New York, N.Y.) · 2026Article
- Breast Cancer Brain Metastases: Current Understanding and Future Directions.Current oncology reports · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Radiomic analysis of the peritumoral zone identifies imaging signatures of glioma invasion associated with HSP70 expression.Frontiers in oncology · 2026Article
- The pitfalls of fixed-ratio data splitting in radiomics model performance evaluation.Abdominal radiology (New York) · 2025Review
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Prediction of neoadjuvant therapy efficacy in gastric cancer: the interplay between biomarkers and radiomics and its potential for clinical translation.Frontiers in oncology · 2025Review
- Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.European radiology · 2024Article
- Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review.Cureus · 2024Review
- A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.Abdominal radiology (New York) · 2024Article
- Artificial Intelligence in the Future Landscape of Pediatric Neuroradiology: Opportunities and Challenges.AJNR. American journal of neuroradiology · 2024Review
- Article
- Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses.Journal of cancer research and clinical oncology · 2024Article
- Theranostics and artificial intelligence: new frontiers in personalized medicine.Theranostics · 2024Review
- Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.Cancers · 2023Article
- ComBat Harmonization of Myocardial Radiomic Features Sensitive to Cardiac MRI Acquisition Parameters.Radiology. Cardiothoracic imaging · 2023Article
- Review
- Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives.Biomarker research · 2023Review
- Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases.Frontiers in neurology · 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
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has become apparent that radiologic images can serve as mineable databases that contain large amounts of quantitative features with potential clinical significance. Extraction and analysis of these quantitative features is commonly referred to as texture or radiomic analysis. Numerous studies have demonstrated applications for texture and radiomic characterization methods for assessing brain tumors to improve noninvasive predictions of tumor histologic characteristics, molecular profile, distinction of treatment-related changes, and prediction of patient survival. In this review, the current use and future potential of texture or radiomic-based approaches with machine learning for brain tumor image analysis and prediction algorithm construction will be discussed. This technology has the potential to advance the value of diagnostic imaging by extracting currently unused information on medical scans that enables more precise, personalized therapy; however, significant barriers must be overcome if this technology is to be successfully implemented on a wide scale for routine use in the clinical setting.
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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.