Trial reportScientific reports2019
Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer.
Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics.Scientific reports · 2021Trial
- Article
- Artificial Intelligence Model for Imaging-Based Extranodal Extension Detection and Outcome Prediction in Human Papillomavirus-Positive Oropharyngeal Cancer.JAMA otolaryngology-- head & neck surgery · 2026Article
- The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients.Physics and imaging in radiation oncology · 2025Article
- Clinical advantages of incorporating predicted weekly anatomy in IMPT optimization with reduced setup error.Medical physics · 2024Article
- The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey.Journal of personalized medicine · 2024Article
- Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma.Physical and engineering sciences in medicine · 2023Article
- Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC.NPJ precision oncology · 2022Article
- Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning.European radiology experimental · 2022Article
- DIR-based models to predict weekly anatomical changes in head and neck cancer proton therapy.Physics in medicine and biology · 2022Article
- A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis.Journal of personalized medicine · 2022Article
- Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis.Scientific reports · 2022Article
- Article
- Exploratory ensemble interpretable model for predicting local failure in head and neck cancer: the additive benefit of CT and intra-treatment cone-beam computed tomography features.Quantitative imaging in medicine and surgery · 2021Article
- Article
- Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.Medical physics · 2021Article
- Article
- Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept.European radiology · 2021Article
- [Advances in head and neck squamous cell cancer research based on radiomics].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2021Review
- Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics.Translational oncology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Loco-regional control (LRC) is a major clinical endpoint after definitive radiochemotherapy (RCT) of head and neck cancer (HNC). Radiomics has been shown a promising biomarker in cancer research, however closer related to primary tumor control than composite endpoints. Radiomics studies often focus on the analysis of primary tumor (PT). We hypothesize that the combination of PT and lymph nodes (LN) radiomics better predicts LRC in HNC treated with RCT. Radiomics analysis was performed in CT images of 128 patients using Z-Rad implementation (training n = 77, validation n = 51). 285 features were extracted from PT and involved LN. Features were preselected with the maximum relevance minimum redundancy method and the multivariate Cox model was trained using least absolute shrinkage and selection operator. The mixed model was based on the combination of PT and LN radiomics, whereas the PT model included only the PT features. The mixed model showed significantly higher performance than the PT model (p < 0.01), c-index of 0.67 and 0.63, respectively; and better risk group stratification. The clinical nodal status was not a significant predictor in the combination with PT radiomics. This study shows that the LRC can be better predicted by expansion of radiomics analysis with LN features.
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