ArticleQuantitative imaging in medicine and surgery2019
The value of the computer-aided diagnosis system for thyroid lesions based on computed tomography images.
Article in Quantitative imaging in medicine and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Performance Evaluation of Deep Learning for the Detection and Segmentation of Thyroid Nodules: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Development and validation of an Automated computed tomography segmentation model for thyroid nodules using a channel attention high-resolution network.Gland surgery · 2026Article
- Can Whole-Thyroid-Based CT Radiomics Model Achieve the Performance of Lesion-Based Model in Predicting the Thyroid Nodules Malignancy? - A Comparative Study.Journal of imaging informatics in medicine · 2026Article
- The application of computer-aided diagnosis in Breast Imaging Reporting and Data System ultrasound training for residents-a randomized controlled study.Translational cancer research · 2024Article
- Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma.BMC endocrine disorders · 2023Article
- Clinical diagnostic value of American College of Radiology thyroid imaging report and data system in different kinds of thyroid nodules.BMC endocrine disorders · 2022Article
- Dual-energy computed tomography could reliably differentiate metastatic from non-metastatic lymph nodes of less than 0.5 cm in patients with papillary thyroid carcinoma.Quantitative imaging in medicine and surgery · 2021Article
- An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images.Quantitative imaging in medicine and surgery · 2021Article
- Automatic Recognition and Classification System of Thyroid Nodules in CT Images Based on CNN.Computational intelligence and neuroscience · 2021Article
- Can contrast-enhancement computed tomography texture and histogram analyses help to differentiate malignant from benign thyroid nodules?Japanese journal of radiology · 2020Article
- Diagnostic accuracy of single-source dual-energy computed tomography and ultrasonography for detection of lateral cervical lymph node metastases of papillary thyroid carcinoma.Journal of thoracic disease · 2019Article
- Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT.Computational intelligence and neuroscience · 2019Article
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8 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThyroid nodules are commonly found at palpation amounting to 4-7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions.
methodsIn this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition.
resultsIn thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively.
conclusionsThe proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.
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