ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
Prediction of extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.The Laryngoscope · 2026Pooled it
- Systematic review of artificial intelligence and radiomics for preoperative prediction of extranodal extension and lymph node metastasis in oropharyngeal cancer.Frontiers in oncology · 2025Pooled it
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- An explainable multimodal 2.5D deep learning-radiomics model for predicting extranodal extension in lung adenocarcinoma using preoperative CT: a multicenter retrospective cohort study.BMC medical imaging · 2026Article
- CT-based Extranodal Extension for Nodal Staging and Prognostic Stratification in Surgically Resectable Esophageal Squamous Cell Carcinoma.Radiology. Imaging cancer · 2026Observational
- Imaging-Detected Extranodal Extension in Head and Neck Cancer: Current Evidence, Standardized Grading, and Clinical Implications.Korean journal of radiology · 2026Review
- CT-based texture analysis for preoperative prediction of key histopathological features in laryngeal cancer.Polish journal of radiology · 2026Article
- Multimodal imaging of cervical lymphadenopathy: diagnostic value and clinical applications.Medicine and pharmacy reports · 2025Review
- Artificial Intelligence in the Diagnosis of Tongue Cancer: A Systematic Review with Meta-Analysis.Biomedicines · 2025Review
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Radiological extranodal extension in head and neck cancers: current evidence and challenges in imaging detection and prognostic impact.BJR open · 2025Review
- [Advances in the diagnosis and prognosis of extranodal extension in head and neck squamous cell carcinoma].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Review
- A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas.Cancers · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundExtranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC) correlates to poor prognoses and influences treatment strategies. Deep learning may yield promising performance of predicting ENE in HNSCC but lack of transparency and interpretability. This work proposes an evolutionary learning method, called EL-ENE, to establish a more interpretable ENE prediction model for aiding clinical diagnosis.
methodsThere were 364 HNSCC patients who underwent neck lymph node (LN) dissection with pre-operative contrast-enhanced computerized tomography images. All the 778 LNs were divided into training and test sets with the ratio 8:2. EL-ENE uses an inheritable bi-objective combinatorial genetic algorithm for optimal feature selection and parameter setting of support vector machine. The diagnostic performances of the ENE prediction model and radiologists were compared using independent test datasets.
resultsThe EL-ENE model achieved the test accuracy of 80.00%, sensitivity of 81.13%, and specificity of 79.44% for ENE detection. The three radiologists achieved the mean diagnostic accuracy of 70.4%, sensitivity of 75.6%, and specificity of 67.9%. The features of gray-level texture and 3D morphology of LNs played essential roles in predicting ENE.
conclusionsThe EL-ENE method provided an accurate, comprehensible, and robust model to predict ENE in HNSCC with interpretable radiomic features for expanding clinical knowledge. The proposed transparent prediction models are more trustworthy and may increase their acceptance in daily clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.