ArticleScientific reports2018
Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer.Scientific reports · 2019Trial
- Review
- CT Texture Patterns Reflect HPV Status but Not Histological Differentiation in Oropharyngeal Squamous Cell Carcinoma.Cancers · 2025Article
- Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics.Physics and imaging in radiation oncology · 2025Article
- Multimodal MRI-based radiomics model for predicting short-term efficacy in nasopharyngeal carcinoma.Frontiers in medicine · 2025Article
- Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments.Cancer reports (Hoboken, N.J.) · 2024Review
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Review
- Review
- New Challenges in Evaluating Outcomes after Immunotherapy in Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma.Vaccines · 2022Article
- Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature.Cancers · 2022Review
- Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad.Journal of medical Internet research · 2022Article
- Article
- Liquid Biopsies for Molecular Biology-Based Radiotherapy.International journal of molecular sciences · 2021Review
- Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology.Seminars in radiation oncology · 2021Review
- Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer.Scientific reports · 2021Article
- Article
- Article
- Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features.Scientific reports · 2021Article
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Abstract
Radiomics is one such "big data" approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between "favorable" and "unfavorable" clusters were noted.
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