ArticleEuropean radiology2020
Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06854107 (Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma), which is not on this map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Intratumoral and Peritumoral Habitat Radiomics of MRI Predict Pathologic Complete Response to Neoadjuvant Chemoimmunotherapy in Oral Squamous Cell Carcinoma
Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 93 citations in OpenAlex.
- Systematic review of prognostic models for predicting recurrence and survival in patients with treated oropharyngeal cancer.BMJ open · 2024Pooled it
- Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.Radiation oncology (London, England) · 2022Trial
- CT imaging of thyroid cartilage invasion in glottic laryngeal cancer: correlation of traditional semiotics and texture analysis with histopathology.La Radiologia medica · 2026Article
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Machine learning-based treatment outcome prediction in head and neck cancer using integrated noninvasive diagnostics.International journal of computer assisted radiology and surgery · 2026Article
- Human Papillomavirus-Positive Oropharyngeal Cancer: Clinical and Radiologic Characteristics.Journal of the Korean Society of Radiology · 2026Review
- From Radical Resection to Precision Surgery: Integrating Diagnostic Biomarkers, Radiomics-Based Predictive Models, and Perioperative Systemic Therapy in Head and Neck Oncology.Diagnostics (Basel, Switzerland) · 2025Review
- Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis.Medical physics · 2025Article
- Can MRI radiomics predict neck metastasis at initial diagnosis in patients with squamous cell carcinoma of the tongue?Oral radiology · 2025Article
- Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics.Physics and imaging in radiation oncology · 2025Article
- Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Head and Neck Squamous Cell Carcinoma: Insights from Dual-Energy Computed Tomography (DECT).Tomography (Ann Arbor, Mich.) · 2024Review
- Prognosis Prediction in Head and Neck Squamous Cell Carcinoma by Radiomics and Clinical Information.Biomedicines · 2024Article
- Using T2-weighted magnetic resonance imaging-derived radiomics to classify cervical lymphadenopathy in children.Pediatric radiology · 2024Article
- Population-Based Prognostic Models for Head and Neck Cancers Using National Cancer Registry Data from Taiwan.Journal of epidemiology and global health · 2024Article
- The [Cancers · 2024Article
- Response to the letter to the editor on the article: radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma.La Radiologia medica · 2024Article
- Article
- Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma.Radiology. Imaging cancer · 2024Observational
Corrections and comments
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Authors and funding
16 authors at 7 institutions in 2 countries.
Funding
Abstract
objectivesHead and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND
methodsNative T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC).
resultsIn oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001).
conclusionsMRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.
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