ArticleNeuroradiology2022
Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study.
Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- MRI-based deep learning radiomics in predicting histological differentiation of oropharyngeal cancer: a multicenter cohort study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Leveraging vision transformer for histological grade prediction in laryngeal and hypopharyngeal squamous cell carcinoma: a large-scale multicenter study.Neuroradiology · 2026Article
- Application value of dual-sequence MRI based nomogram of radiomics and morphologic features in predicting tumor differentiation degree and lymph node metastasis of Oral squamous cell carcinoma.Frontiers in oncology · 2025Article
- Enhancing the prediction ofTranslational lung cancer research · 2024Article
- Head and Neck Squamous Cell Carcinoma: Insights from Dual-Energy Computed Tomography (DECT).Tomography (Ann Arbor, Mich.) · 2024Review
- Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied toBiomedicines · 2024Article
- Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules.Frontiers in oncology · 2023Article
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Authors and funding
13 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a dual-energy CT (DECT)-based radiomics nomogram from multicenter trials for predicting the histological differentiation of head and neck squamous cell carcinoma (HNSCC).
methodsA total of 178 patients (112 in the training and 66 in the validation cohorts) from eight institutions with histologically proven HNSCCs were included in this retrospective study. Radiomics-signature models were constructed from features extracted from virtual monoenergetic images (VMI) and iodine-based material decomposition images (IMDI), reconstructed from venous-phase DECT images. Clinical factors were also assessed to build a clinical model. Multivariate logistic regression analysis was used to develop a nomogram combining the radiomics signature models and clinical model for predicting poorly differentiated HNSCC and moderately well-differentiated HNSCC. The predictive performance of the clinical model, radiomics signature models, and nomogram was compared. The calibration degree of the nomogram was also assessed.
resultsThe tumor location, VMI-signature, and IMDI-signature were associated with the degree of HNSCC differentiation, and areas under the ROC curves (AUCs) were 0.729, 0.890, and 0.833 in the training cohort and 0.627, 0.859, and 0.843 in the validation cohort, respectively. The nomogram incorporating tumor location and two radiomics-signature models yielded the best performance in training (AUC = 0.987) and validation (AUC = 0.968) cohorts with a good calibration degree.
conclusionThe nomogram that integrated the DECT-based radiomics-signature models and tumor location showed good performance in predicting histological differentiation degree of HNSCC, providing a novel combination for predicting HNSCC differentiation.
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