ReviewFrontiers in radiology2023
Spatial assessments in texture analysis: what the radiologist needs to know.
Review in Frontiers in radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Positron emission tomography/computed tomography radiomics-based prediction of cervical nodal metastasis in patients with tongue squamous cell carcinoma.Oral radiology · 2026Article
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- Article
- Cone Contrast and Entropy of Adaptive Optics Images in Choroideremia.Current eye research · 2026Article
- Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence CT Texture Radiomics for Outcome Prediction After EVAR: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development and Preliminary Evaluation of an EfficientNet-Based Deep Learning System for Ultrasound Assessment of Neck Disorders: A Single-Center Study.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.Translational cancer research · 2026Article
- Article
- Advanced radiomics techniques applied on carotid duplex ultrasound data predict adverse outcomes in acute ischemic stroke.Frontiers in neurology · 2026Article
- Hippocampal subregional texture features associated with Alzheimer's disease severity and cognition.Brain communications · 2026Article
- MRI-based radiomics nomogram to predict complete response to definitive chemoradiation in patients with anal cancer.The British journal of radiology · 2025Article
- A preliminary study on predicting the stability of external Iliac vein thrombosis using CT texture analysis.BMC medical imaging · 2025Article
- Effect of reconstruction settings on radiomics feature robustness in positron emission tomography images: a clinical study.Quantitative imaging in medicine and surgery · 2025Article
- An MRI Histopathology-based Deep Learning Approach for the Classification of Prostate Cancer.Radiology. Imaging cancer · 2025Article
- Radiomic Analysis of Molecular Magnetic Resonance Imaging of Aortic Atherosclerosis in Rabbits.Current medical science · 2025Article
- Development and validation of intracranial hypertension prediction models based on radiomic features in patients with traumatic brain injury: an exploratory study based on CENTER-TBI data.Critical care (London, England) · 2025Article
- Adaptive dual-window enhancement and multi-scale texture prior fusion for robust kidney CT classification.PloS one · 2025Article
- Artificial intelligence and machine learning applications for the imaging of bone and soft tissue tumors.Frontiers in radiology · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.
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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.