Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging
| Autor: | Fang Jing Li, Shih Neng Yang, Yen Hsiu Liao, Geoffrey Zhang, Jun Ming Chen, Tzung Chi Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Physiology
Radiography lcsh:Medicine Pathology and Laboratory Medicine 030218 nuclear medicine & medical imaging Diagnostic Radiology 0302 clinical medicine Nuclear magnetic resonance Blood Flow Breast Tumors Medicine and Health Sciences Image Processing Computer-Assisted Medicine lcsh:Science Multidisciplinary medicine.diagnostic_test Radiology and Imaging food and beverages Hematology Middle Aged Magnetic Resonance Imaging Body Fluids Professions Blood Oncology 030220 oncology & carcinogenesis Kinetic curve Benign Breast Conditions Female Radiology Anatomy Research Article Adult medicine.medical_specialty Imaging Techniques Image processing Breast Neoplasms Research and Analysis Methods 03 medical and health sciences Breast cancer Signs and Symptoms Diagnostic Medicine Predictive Value of Tests Radiologists Breast Cancer Cancer Detection and Diagnosis Humans Aged Kinetic model business.industry lcsh:R Biology and Life Sciences Cancers and Neoplasms Magnetic resonance imaging Models Theoretical medicine.disease Mr imaging Dynamic contrast People and Places Lesions Women's Health Population Groupings lcsh:Q business |
| Zdroj: | PLoS ONE, Vol 11, Iss 4, p e0152827 (2016) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Objective The aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions. Materials and Methods A total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared. Results An average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p |
| Databáze: | OpenAIRE |
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