ArticleComputational and structural biotechnology journal2019
Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy.
Article in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Current status and quality of radiomics studies in lymphoma: a systematic review.European radiology · 2020Pooled it
- Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.Journal of cancer research and clinical oncology · 2022Trial
- Is the prediction of one or two ipsilateral positive lymph nodes by computerized tomography and ultrasound reliable enough to restrict therapeutic neck dissection in oral squamous cell carcinoma (OSCC) patients?Journal of cancer research and clinical oncology · 2021Trial
- Role of artificial intelligence in head and neck surgery - A narrative review.Turkish journal of surgery · 2026Article
- Article
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Application of multimodal ultrasound radiomics in the diagnosis of superficial lymph node tuberculosis.BMC medical imaging · 2026Article
- Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model.Diagnostics (Basel, Switzerland) · 2026Article
- CT-based texture analysis for preoperative prediction of key histopathological features in laryngeal cancer.Polish journal of radiology · 2026Article
- Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.Journal of imaging informatics in medicine · 2024Article
- Survival prediction in diffuse large B-cell lymphoma patients: multimodal PET/CT deep features radiomic model utilizing automated machine learning.Journal of cancer research and clinical oncology · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients.Physics and imaging in radiation oncology · 2024Article
- Radiomics Features on Enhanced Computed Tomography Predict FOXP3 Expression and Clinical Prognosis in Patients with Head and Neck Squamous Cell Carcinoma.Journal of imaging informatics in medicine · 2024Article
- The value of radiomics features of the spleen as surrogates for differentiating subtypes of common pediatric lymphomas.Quantitative imaging in medicine and surgery · 2024Article
- Using T2-weighted magnetic resonance imaging-derived radiomics to classify cervical lymphadenopathy in children.Pediatric radiology · 2024Article
- Advances and residual knowledge gaps in the neck management of head and neck squamous cell carcinoma patients with advanced nodal disease undergoing definitive (chemo)radiotherapy for their primary.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2024Review
- Distinguishing diffuse large B-cell lymphoma from Hodgkin's lymphoma in children using an enhanced computed tomography radiomics approach.Translational pediatrics · 2024Article
- Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or normal lymph nodes. MATERIALS AND
methodsA retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC.
resultsIn the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively.
conclusionMachine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.
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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.