ArticleQuantitative imaging in medicine and surgery2022
Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 21 citations in OpenAlex.
- Differentiation of bilateral BI-RADS 4 breast lesions with one benign and one malignant finding using dual-layer detector spectral CT: a case report.Frontiers in oncology · 2026Article
- Prediction of thyroid function based on CT radiomics: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- Machine learning model incorporating spectral CT quantitative parameters and clinical characteristics for differentiation of benign and malignant thyroid nodules.BMC medical imaging · 2025Article
- Article
- Electron Density and Effective Atomic Number as Quantitative Biomarkers for Differentiating Malignant Brain Tumors: An Exploratory Study with Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules.BMC cancer · 2025Article
- Prediction models for differentiating benign from malignant liver lesions based on multiparametric dual-energy non-contrast CT.European radiology · 2025Article
- Improved image quality and micronodule detection in thyroid spectral computed tomography using modified swimmer's position.Quantitative imaging in medicine and surgery · 2025Article
- The predictive value of the ultrasound grayscale ratio for identifying malignant thyroid nodules.Frontiers in endocrinology · 2025Article
- A nomogram for risk stratification of central cervical lymph node metastasis in patients with papillary thyroid carcinoma.Quantitative imaging in medicine and surgery · 2024Article
- Advancing Differentiation of Hepatic Metastases in Malignant Melanoma through Dual-Energy Computed Tomography Rho/Z Maps.Diagnostics (Basel, Switzerland) · 2024Article
- Diagnostic accuracy of dual-energy computed tomography-based nomogram for differentiating papillary thyroid microcarcinomas from micronodular goiters.Quantitative imaging in medicine and surgery · 2023Article
- Nomogram based on spectral CT quantitative parameters and typical radiological features for distinguishing benign from malignant thyroid micro-nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Diagnosis of benign and malignant thyroid nodules by a dual-layer spectral detector CT-based nomogram.Frontiers in oncology · 2023Article
- Article
- Diagnostic Effectiveness of Dual Source Dual Energy Computed Tomography for Benign and Malignant Thyroid Nodules.Evidence-based complementary and alternative medicine : eCAM · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to investigate the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules.
methodsPatients with thyroid nodules and pathological surgical results who underwent preoperative DS-DECT were selected. Quantitative parameter-derived DS-DECT was applied to classify benign and malignant nodules. Then, machine learning and binary logistic regression analysis models were constructed using the DS-DECT quantitative parameters to distinguish between benign and malignant nodules. The receiver operating characteristic curve was used to assess the diagnostic performance. The DeLong test was used to compare the diagnostic efficacy.
resultsOne hundred and thirty patients with 139 confirmed thyroid nodules were involved in the study. The malignant group had a significantly higher iodine concentration
conclusionsMultiple quantitative parameters of DS-DECT combined with machine learning could differentiate between benign and malignant thyroid nodules.
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