ReviewThe British journal of radiology2017
Texture analysis of medical images for radiotherapy applications.
Review in The British journal of radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Repeatability and Reproducibility of Radiomic Features: A Systematic Review.International journal of radiation oncology, biology, physics · 2018Pooled it
- Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.Journal of cancer research and clinical oncology · 2023Trial
- A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy.BMC gastroenterology · 2023Trial
- Radiomics-Based Prediction of Treatment Response in Non-Small Cell Lung Cancer Using Pre-Treatment CT Imaging Features.Diagnostics (Basel, Switzerland) · 2026Article
- MRI-Derived Biomarkers and Radiomic Signatures for Early, Dose-Dependent Evaluation of Prostate Cancer Radiotherapy: An Exploratory Study.Journal of imaging · 2026Article
- Radiomics and clinical data predict pseudoprogression after radiotherapy in high-grade glioma.Frontiers in oncology · 2026Article
- Multimodality and temporal analysis of cervical cancer treatment response.Quantitative imaging in medicine and surgery · 2026Article
- Variations in radiomic features of the femoral head and neck during helical tomotherapy in prostate and rectal cancer patients.BMC cancer · 2025Article
- Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy.Scientific reports · 2025Article
- The prognostic value of [Clinical physiology and functional imaging · 2025Article
- Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.Health science reports · 2025Article
- Characterizing Muscle Tissue Quality Post-Stroke: Echovariation as a Clinical Indicator.Journal of clinical medicine · 2024Article
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- A prognostic nomogram for T3N0M0 esophageal squamous cell carcinoma patients undergoing radical surgery based on computed tomography radiomics and inflammatory nutritional biomarkers.Journal of applied clinical medical physics · 2024Article
- Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.Breast cancer research and treatment · 2024Article
- Article
- MRI-based radiomic features of the urinary bladder wall identify patients with moderate-to-severe international prostate symptom score.World journal of urology · 2024Article
- Article
- Prognostic Value of Radiomic Analysis Using Pre- and Post-TreatmentJournal of personalized medicine · 2024Article
- Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.Frontiers in computational neuroscience · 2024Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
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Abstract
The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.
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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.