ReviewSovremennye tekhnologii v meditsine2021
Radiomics and Digital Image Texture Analysis in Oncology (Review).
Review in Sovremennye tekhnologii v meditsine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 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
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Comparative diagnostic accuracy of radiomics, deep learning, and hybrid AI for invasiveness stratification of pulmonary ground-glass nodules: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.Neuroradiology · 2022Pooled it
- Pembrolizumab ± paricalcitol in metastatic pancreatic cancer postmaximal cytoreduction.The oncologist · 2025Trial
- CT imaging of thyroid cartilage invasion in glottic laryngeal cancer: correlation of traditional semiotics and texture analysis with histopathology.La Radiologia medica · 2026Article
- Preliminary exploration of radiomic mammographic analysis in triple negative breast cancer related to BRCA profile.Scientific reports · 2026Article
- Differentiating the differentiation degree of gastric cancer based on MRI radiomics.BMC medical imaging · 2025Article
- Ultrasound based radiomics nomogram combined with clinical parameters to predict lymphovascular space invasion in endometrioid adenocarcinoma.Scientific reports · 2025Article
- MRI-based diffusion weighted imaging and diffusion kurtosis imaging grading of clear cell renal cell carcinoma using a deep learning classifier.Oncology letters · 2025Article
- A preliminary study on predicting the stability of external Iliac vein thrombosis using CT texture analysis.BMC medical imaging · 2025Article
- Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.BMC medical imaging · 2025Article
- LinkingLearning health systems · 2025Article
- Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions.Journal of imaging · 2024Review
- The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.Cancers · 2024Review
- Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging.International journal of molecular sciences · 2024Article
- Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study.Frontiers in oncology · 2024Article
- Development and Validation of Radiomics-Based Models for Predicting the Parametrial Invasion in Stage IB1 to IIA2 Cervical Cancer.International journal of general medicine · 2024Article
- Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics.Journal of asthma and allergy · 2024Article
- Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features.Frontiers in oncology · 2024Article
- Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning.Journal of clinical medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of "virtual biopsy" is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
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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.