Differential Diagnosis of Malignant Thyroid Calcification Nodule Based on Computed Tomography Image Texture
| Autor: | Yingying Shi, Yijia Qian, Shuyun Chen, Wenxian Peng, Kexin Chen, Han Xiao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
medicine.diagnostic_test business.industry Thyroid Health Informatics Computed tomography Nodule (medicine) medicine.disease 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Image texture 030220 oncology & carcinogenesis medicine Radiology Nuclear Medicine and imaging Radiology medicine.symptom Differential diagnosis business Calcification |
| Zdroj: | Journal of Medical Imaging and Health Informatics. 11:767-772 |
| ISSN: | 2156-7018 |
| DOI: | 10.1166/jmihi.2021.3366 |
| Popis: | Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning, support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were selected after feature optimization by logistic regression analysis (P |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|