ArticleScientific reports2017
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 241 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
241 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients.Cancer medicine · 2023Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma.AJNR. American journal of neuroradiology · 2022Trial
- Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics.Scientific reports · 2021Trial
- Radiomics Using Fused PET and CT Data for Prognostic and Diagnostic Modeling in Head and Neck Cancer: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Radiomics as a spatial context for treatment decision-making in head and neck cancer.British journal of cancer · 2026Review
- Inside from outside: head-to-skull-and-muscles translation with statistical shape relation and multiple region-of-interest learning.Medical & biological engineering & computing · 2026Article
- Survival outcomes of radiotherapy alone versus concurrent chemoradiotherapy in T1-2 head and neck cancer with low-volume disease: A multicenter cohort study.Clinical and translational radiation oncology · 2026Article
- Computed tomography and magnetic resonance imaging radiomics for predicting non-complete response after chemoradiotherapy in unresectable locally advanced head and neck squamous cell carcinoma.Physics and imaging in radiation oncology · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Impact of Radiomics Parameters and Clinical Integration on Prognostication in Head and Neck Squamous Cell Carcinoma: A Multicenter Study.Life (Basel, Switzerland) · 2026Article
- Review
- Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care.JMIR research protocols · 2026Article
- Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.medRxiv : the preprint server for health sciences · 2026Article
- Preliminary treatment planning strategy for blood dose reduction in head and neck radiotherapy.Journal of applied clinical medical physics · 2026Article
- Review
- Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions.Frontiers in oncology · 2026Article
- MRI-based deep learning and radiomics pipeline for myxoid liposarcoma: a feasibility study in a rare sarcoma.Scientific reports · 2025Article
- Exploring the Role of CT-Based Delta-Radiomics in Unresectable Vulvar Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Skeletal Muscle Radiation Attenuation at C3 Predicts Survival in Head and Neck Cancer.Current oncology (Toronto, Ont.) · 2025Article
181 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.
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