ArticleFrontiers in oncology2018
Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges.
Article in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.Neuroradiology · 2022Pooled it
- [Contrast-enhanced CT-based habitat radiomics for analyzing the predictive capability for oral squamous cell carcinoma].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2026Article
- Addressing the current challenges in the clinical application of AI-based Radiomics for cancer imaging.Frontiers in medicine · 2025Review
- Super-resolution ultrasound and microvasculomics: a consensus statement.European radiology · 2024Review
- Machine Learning Approaches for Integrating Clinical and Radiographic Data in the Early Detection of Osteonecrosis of the Jaw : Machine Learning Approaches for Integrating Clinical Data in Osteonecrosis of the Jaw.Galen medical journal · 2024Review
- Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey.European radiology · 2023Article
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- Updates on LI-RADS Treatment Response Criteria for Hepatocellular Carcinoma: Focusing on MRI.Journal of magnetic resonance imaging : JMRI · 2023Review
- Artificial Intelligence for Radiation Oncology Applications Using Public Datasets.Seminars in radiation oncology · 2022Review
- Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET/CT Radiomics.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- 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
- CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease.Frontiers in neuroscience · 2022Article
- Radiomics in radiation oncology for gynecological malignancies: a review of literature.The British journal of radiology · 2021Review
- Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021Article
- Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.European radiology · 2021Article
- Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features.Scientific reports · 2021Article
- Radiomics and radiogenomics in ovarian cancer: a literature review.Abdominal radiology (New York) · 2021Review
- Roadmap: proton therapy physics and biology.Physics in medicine and biology · 2021Review
- CEUS-Based Radiomics Can Show Changes in Protein Levels in Liver Metastases After Incomplete Thermal Ablation.Frontiers in oncology · 2021Article
- Real-world applications of deep convolutional neural networks in diagnostic cancer imaging.Chinese clinical oncology · 2020Article
Corrections and comments
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Authors and funding
48 authors.
Funding
Abstract
Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (
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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.