Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma.
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Summary
Radiomic biomarkers appear to classify DM risk for patients with nonmetastatic HPV-related OPC and could be used either alone or with other clinical characteristics in the assignment of DM risk in future HPV- related OPC clinical trials.
- Type
- article
- Published
- 2018-11-01
- Cited by
- 72
- References
- 53
- OpenAlex
- https://openalex.org/W2791051851
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3748344
Keywords
Medicine, Concordance, Stage (stratigraphy), Proportional hazards model, Oncology
References
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- Outcomes of HPV-related oropharyngeal cancer patients treated by radiotherapy alone using altered fractionation.
- CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
- Lack of influence of intravenous contrast on head and neck IMRT dose distributions
- Intratumoral heterogeneity: Clonal cooperation in epithelial-to-mesenchymal transition and metastasis
- Quantifying tumour heterogeneity with CT
- Open Access Research
- The influence of HPV-associated p16-expression on accelerated fractionated radiotherapy in head and neck cancer: evaluation of the randomised DAHANCA 6&7 trial.
- Survival of squamous cell carcinoma of the head and neck in relation to human papillomavirus infection: Review and meta‐analysis
- Point-of-care outcome assessment in the cancer clinic: audit of data quality.
- Natural course of distant metastases following radiotherapy or chemoradiotherapy in HPV-related oropharyngeal cancer.
- Epidemiology of HPV-associated oropharyngeal cancer
- Comparative prognostic value of HPV16 E6 mRNA compared with in situ hybridization for human oropharyngeal squamous carcinoma.
- A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Cited by
- The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis
- Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study
- Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers
- Integrating tumor and nodal imaging characteristics at baseline and mid-treatment CT scans to predict distant metastasis in oropharyngeal cancer treated with concurrent chemoradiotherapy
- A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy
- Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging
- Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer
- Artificial Intelligence in Radiation Oncology.
- Imaging-Based Individualized Response Prediction Of Carbon Ion Radiotherapy For Prostate Cancer Patients
- Application of radiomics for prediction of HPV status for patients with head and neck cancers.
- The prognostic value of CT radiomic features from primary tumours and pathological lymph nodes in head and neck cancer patients
- Možnosti deintenzifikacije zdravljenja HPV pozitivnih ploščatoceličnih karcinomov orofarinksa
- Deintensification of treatment for human papillomavirus-related oropharyngeal cancer: Current state and future directions
- Comprehensive Analysis of Radiomic Datasets by RadAR
- Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest
- CT and FDG-PET radiologic biomarkers in p16+ oropharyngeal squamous cell carcinoma patients treated with definitive chemoradiotherapy
- Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients
- Quantitative ultrasound radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma treated with radical radiotherapy
- Application of radiomics and machine learning in head and neck cancers
- Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature
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