ArticleWorld journal of radiology2016
Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer.
Article in World journal of radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Radiomic analysis for response assessment in advanced head and neck cancers, a distant dream or an inevitable reality? A systematic review of the current level of evidence.The British journal of radiology · 2020Pooled it
- Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.Visual computing for industry, biomedicine, and art · 2023Article
- Review
- Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma.Frontiers in oncology · 2022Article
- Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.Scientific reports · 2021Article
- Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma.Clinical and translational radiation oncology · 2021Article
- Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers.Frontiers in oncology · 2021Review
- Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.European radiology · 2020Article
- Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis.Cancers · 2019Review
- MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2019Article
- The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
- CT Texture Analysis-Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas.Frontiers in oncology · 2019Article
- Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer.Frontiers in oncology · 2019Article
- A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2018Article
- Observational
- Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review.Frontiers in oncology · 2018Article
- CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy.AJNR. American journal of neuroradiology · 2017Article
- "Radio-oncomics" : The potential of radiomics in radiation oncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2017Review
- Biomarkers from in vivo molecular imaging of breast cancer: pretreatmentMagma (New York, N.Y.) · 2017Article
- Texture analysis of medical images for radiotherapy applications.The British journal of radiology · 2017Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
aimTo investigate the merits of texture analysis on parametric maps derived from pharmacokinetic modeling with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as imaging biomarkers for the prediction of treatment response in patients with head and neck squamous cell carcinoma (HNSCC).
methodsIn this retrospective study, 19 HNSCC patients underwent pre- and intra-treatment DCE-MRI scans at a 1.5T MRI scanner. All patients had chemo-radiation treatment. Pharmacokinetic modeling was performed on the acquired DCE-MRI images, generating maps of volume transfer rate (K(trans)) and volume fraction of the extravascular extracellular space (ve). Image texture analysis was then employed on maps of K(trans) and ve, generating two texture measures: Energy (E) and homogeneity.
resultsNo significant changes were found for the mean and standard deviation for K(trans) and ve between pre- and intra-treatment (P > 0.09). Texture analysis revealed that the imaging biomarker E of ve was significantly higher in intra-treatment scans, relative to pretreatment scans (P < 0.04).
conclusionChemo-radiation treatment in HNSCC significantly reduces the heterogeneity of tumors.
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