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Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma
To identify magnetic resonance imaging (MRI) features in the prediction of tumor aggressiveness in patients with papillary thyroid carcinoma (PTC). In this prospective study, 105 patients with 122 PTCs underwent MRI with T1-weighted, T2-weighted, diffusion-weighted imaging and contrast-enhanced sequ...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039645/ https://www.ncbi.nlm.nih.gov/pubmed/29953007 http://dx.doi.org/10.1097/MD.0000000000011279 |
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author | Song, Bin Wang, Hao Chen, Yongqi Liu, Weiyan Wei, Ran Dai, Zedong Hu, Wenjuan Ding, Yi Wang, Lanyun |
author_facet | Song, Bin Wang, Hao Chen, Yongqi Liu, Weiyan Wei, Ran Dai, Zedong Hu, Wenjuan Ding, Yi Wang, Lanyun |
author_sort | Song, Bin |
collection | PubMed |
description | To identify magnetic resonance imaging (MRI) features in the prediction of tumor aggressiveness in patients with papillary thyroid carcinoma (PTC). In this prospective study, 105 patients with 122 PTCs underwent MRI with T1-weighted, T2-weighted, diffusion-weighted imaging and contrast-enhanced sequences prior to thyroidectomy. Based on exclusion criteria, 62 patients with 62 PTCs were finally suitable for further analysis. Tumor aggressiveness was defined according to the surgical histopathology. Tumor size, apparent diffusion coefficients (ADC) value and MRI features on images were obtained for each patient. Descriptive statistics for tumor aggressiveness, sensitivity, specificity, and accuracy of individual features were determined. A multivariate logistic regression model was developed to identify features that were independently predictive for tumor aggressiveness. Analyses of receiver-operating characteristic (ROC) curve were performed. High aggressive PTC significantly differed from low aggressive PTC in size (P = .016), size classification (P < .001), ADC value (P = .01), angulation on the lateral surface of the lesion (P = .009), signal intensity heterogeneity on ADC maps (P = .003), early enhancement degree (P < .001), tumor margin on delayed contrast-enhanced images (P < .001), and inner lining of delayed ring enhancement (P = .028). The interobserver agreement between the 2 readers was satisfactory with Cohen k ranging from 0.83 to 1.00 (P < .001). Logistic regression model showed lesion size classification and tumor margin on delayed contrast-enhanced images as strongest independent predictors of high aggressive PTC (P = .009 and P = .047), with an accuracy of 83.9%. The area under ROC curve for ADC value and lesion size were 0.68 and 0.81, respectively. These findings suggest that MRI before surgery has the potential to discriminate tumor aggressiveness in patients with PTC. |
format | Online Article Text |
id | pubmed-6039645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-60396452018-07-16 Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma Song, Bin Wang, Hao Chen, Yongqi Liu, Weiyan Wei, Ran Dai, Zedong Hu, Wenjuan Ding, Yi Wang, Lanyun Medicine (Baltimore) Research Article To identify magnetic resonance imaging (MRI) features in the prediction of tumor aggressiveness in patients with papillary thyroid carcinoma (PTC). In this prospective study, 105 patients with 122 PTCs underwent MRI with T1-weighted, T2-weighted, diffusion-weighted imaging and contrast-enhanced sequences prior to thyroidectomy. Based on exclusion criteria, 62 patients with 62 PTCs were finally suitable for further analysis. Tumor aggressiveness was defined according to the surgical histopathology. Tumor size, apparent diffusion coefficients (ADC) value and MRI features on images were obtained for each patient. Descriptive statistics for tumor aggressiveness, sensitivity, specificity, and accuracy of individual features were determined. A multivariate logistic regression model was developed to identify features that were independently predictive for tumor aggressiveness. Analyses of receiver-operating characteristic (ROC) curve were performed. High aggressive PTC significantly differed from low aggressive PTC in size (P = .016), size classification (P < .001), ADC value (P = .01), angulation on the lateral surface of the lesion (P = .009), signal intensity heterogeneity on ADC maps (P = .003), early enhancement degree (P < .001), tumor margin on delayed contrast-enhanced images (P < .001), and inner lining of delayed ring enhancement (P = .028). The interobserver agreement between the 2 readers was satisfactory with Cohen k ranging from 0.83 to 1.00 (P < .001). Logistic regression model showed lesion size classification and tumor margin on delayed contrast-enhanced images as strongest independent predictors of high aggressive PTC (P = .009 and P = .047), with an accuracy of 83.9%. The area under ROC curve for ADC value and lesion size were 0.68 and 0.81, respectively. These findings suggest that MRI before surgery has the potential to discriminate tumor aggressiveness in patients with PTC. Wolters Kluwer Health 2018-06-29 /pmc/articles/PMC6039645/ /pubmed/29953007 http://dx.doi.org/10.1097/MD.0000000000011279 Text en Copyright © 2018 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0 |
spellingShingle | Research Article Song, Bin Wang, Hao Chen, Yongqi Liu, Weiyan Wei, Ran Dai, Zedong Hu, Wenjuan Ding, Yi Wang, Lanyun Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
title | Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
title_full | Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
title_fullStr | Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
title_full_unstemmed | Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
title_short | Magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
title_sort | magnetic resonance imaging in the prediction of aggressive histological features in papillary thyroid carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039645/ https://www.ncbi.nlm.nih.gov/pubmed/29953007 http://dx.doi.org/10.1097/MD.0000000000011279 |
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