ArticleCancer imaging : the official publication of the International Cancer Imaging Society2020
Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of radiomics in laryngeal cancer.The British journal of radiology · 2021Pooled it
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Review
- Review
- CT-based radiomics and deep learning models for predicting thyroid cartilage invasion and patient prognosis in laryngeal carcinoma.Scientific reports · 2025Article
- Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma.BMC cancer · 2025Article
- A prognostic model integrating radiomics and deep learning based on CT for survival prediction in laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Development and validation of a multimodal feature fusion-based model for predicting postoperative recurrence-free survival in locally advanced laryngeal squamous cell carcinoma.Frontiers in oncology · 2025Article
- Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study.The Journal of laryngology and otology · 2024Article
- The Use of Artificial Intelligence Algorithms in the Prognosis and Detection of Lymph Node Involvement in Head and Neck Cancer and Possible Impact in the Development of Personalized Therapeutic Strategy: A Systematic Review.Journal of personalized medicine · 2023Review
- MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures.Biomarker research · 2023Article
- Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology.Exploration of targeted anti-tumor therapy · 2023Review
- Clinical application of CT-based radiomics model in differentiation between laryngeal squamous cell carcinoma and squamous cell hyperplasia.Frontiers in medicine · 2023Article
- 18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization.Frontiers in oncology · 2022Article
- A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin's lymphoma of the palatine tonsil.European radiology · 2022Article
- Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT.Frontiers in medicine · 2022Article
- Incidence and predictive factors for developing vesicoureteric reflux in individuals with suprasarcral spinal cord injury: a historical cohort study.Spinal cord · 2021Article
- Development and Validation of a Radiomics Model Based onFrontiers in oncology · 2021Article
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5 authors.
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Abstract
backgroundThe aim of this study was to evaluate the prognostic value of radiomics signature and nomogram based on contrast-enhanced computed tomography (CT) in patients after surgical resection of laryngeal squamous cell carcinoma (LSCC).
methodsAll patients (n = 136) were divided into the training cohort (n = 96) and validation cohort (n = 40). The LASSO regression method was performed to construct radiomics signature from CT texture features. Then a radiomics nomogram incorporating the radiomics signature and clinicopathologic factors was established to predict overall survival (OS). The validation of nomogram was evaluated by calibration curve, concordance index (C-index) and decision curve.
resultsBased on three selected texture features, the radiomics signature showed high C-indexes of 0.782 (95%CI: 0.656-0.909) and 0.752 (95%CI, 0.614-0.891) in the two cohorts. The radiomics nomogram had significantly better discrimination capability than cancer staging in the training cohort (C-index, 0.817 vs. 0.682; P = 0.009) and validation cohort (C-index, 0.913 vs. 0.699; P = 0.019), as well as a good agreement between predicted and actual survival in calibration curves. Decision curve analysis also suggested improved clinical utility of radiomics nomogram.
conclusionsRadiomics signature and nomogram showed favorable prediction accuracy for OS, which might facilitate the individualized risk stratification and clinical decision-making in LSCC patients.
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