Cargando…
Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI
BACKGROUND: Traditional subjective method for the analysis of time-intensity curves (TICs) from breast dynamic contrast enhanced MRI (DCE-MRI) presented a low specificity. Hence, a semi-automatic quantitative method was proposed and evaluated for distinguishing invasive ductal carcinomas from benign...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354764/ https://www.ncbi.nlm.nih.gov/pubmed/25887917 http://dx.doi.org/10.1186/s13046-015-0140-y |
_version_ | 1782360795012661248 |
---|---|
author | Yin, Jiandong Yang, Jiawen Han, Lu Guo, Qiyong Zhang, Wei |
author_facet | Yin, Jiandong Yang, Jiawen Han, Lu Guo, Qiyong Zhang, Wei |
author_sort | Yin, Jiandong |
collection | PubMed |
description | BACKGROUND: Traditional subjective method for the analysis of time-intensity curves (TICs) from breast dynamic contrast enhanced MRI (DCE-MRI) presented a low specificity. Hence, a semi-automatic quantitative method was proposed and evaluated for distinguishing invasive ductal carcinomas from benign lesions. MATERIALS AND METHODS: In the traditional method, the lesion was extracted by placing a region of interest (ROI) manually. The mean curve of the TICs from the ROI was subjectively classified as one of three patterns. Only one quantitative parameter, the mean value of maximum slope of increase (MSI), was provided. In the new method, the lesion was identified semi-automatically, and the mean curve was classified quantitatively. Some additional parameters, the signal intensity slope (SI(slope)), initial percentage of enhancement (E(initial)), percentage of peak enhancement (E(peak)), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) were derived from the mean curves as well as the lesion areas. Wilcoxon’s test and receiver operating characteristic (ROC) analyses were performed, and P < 0.05 was considered significant. RESULTS: According to the TIC classification results, the accuracies were 59.16% for the traditional manual method and 76.05% for the new method (P < 0.05). For the mean MSI values from the manual method, the accuracy was 63.35%. For the mean TICs derived from the semi-automatic method, the accuracies were 77.47% for SI(slope), 65.24% for MSI, 58.45% for E(initial), 66.20% for E(peak), 71.83% for ESER, and 54.93% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the accuracies were 73.24%, 72.54%, 58.45%, 62.68%, 64.09%, and 55.64%, respectively. CONCLUSION: Compared with traditional subjective method, the semi-automatic quantitative method proposed in this study showed a higher performance, and should be used as a supplementary tool to aid radiologist's subjective interpretation. |
format | Online Article Text |
id | pubmed-4354764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43547642015-03-11 Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI Yin, Jiandong Yang, Jiawen Han, Lu Guo, Qiyong Zhang, Wei J Exp Clin Cancer Res Research Article BACKGROUND: Traditional subjective method for the analysis of time-intensity curves (TICs) from breast dynamic contrast enhanced MRI (DCE-MRI) presented a low specificity. Hence, a semi-automatic quantitative method was proposed and evaluated for distinguishing invasive ductal carcinomas from benign lesions. MATERIALS AND METHODS: In the traditional method, the lesion was extracted by placing a region of interest (ROI) manually. The mean curve of the TICs from the ROI was subjectively classified as one of three patterns. Only one quantitative parameter, the mean value of maximum slope of increase (MSI), was provided. In the new method, the lesion was identified semi-automatically, and the mean curve was classified quantitatively. Some additional parameters, the signal intensity slope (SI(slope)), initial percentage of enhancement (E(initial)), percentage of peak enhancement (E(peak)), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) were derived from the mean curves as well as the lesion areas. Wilcoxon’s test and receiver operating characteristic (ROC) analyses were performed, and P < 0.05 was considered significant. RESULTS: According to the TIC classification results, the accuracies were 59.16% for the traditional manual method and 76.05% for the new method (P < 0.05). For the mean MSI values from the manual method, the accuracy was 63.35%. For the mean TICs derived from the semi-automatic method, the accuracies were 77.47% for SI(slope), 65.24% for MSI, 58.45% for E(initial), 66.20% for E(peak), 71.83% for ESER, and 54.93% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the accuracies were 73.24%, 72.54%, 58.45%, 62.68%, 64.09%, and 55.64%, respectively. CONCLUSION: Compared with traditional subjective method, the semi-automatic quantitative method proposed in this study showed a higher performance, and should be used as a supplementary tool to aid radiologist's subjective interpretation. BioMed Central 2015-03-04 /pmc/articles/PMC4354764/ /pubmed/25887917 http://dx.doi.org/10.1186/s13046-015-0140-y Text en © Yin et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Yin, Jiandong Yang, Jiawen Han, Lu Guo, Qiyong Zhang, Wei Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI |
title | Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI |
title_full | Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI |
title_fullStr | Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI |
title_full_unstemmed | Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI |
title_short | Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI |
title_sort | quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced mri |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354764/ https://www.ncbi.nlm.nih.gov/pubmed/25887917 http://dx.doi.org/10.1186/s13046-015-0140-y |
work_keys_str_mv | AT yinjiandong quantitativediscriminationbetweeninvasiveductalcarcinomasandbenignlesionsbasedonsemiautomaticanalysisoftimeintensitycurvesfrombreastdynamiccontrastenhancedmri AT yangjiawen quantitativediscriminationbetweeninvasiveductalcarcinomasandbenignlesionsbasedonsemiautomaticanalysisoftimeintensitycurvesfrombreastdynamiccontrastenhancedmri AT hanlu quantitativediscriminationbetweeninvasiveductalcarcinomasandbenignlesionsbasedonsemiautomaticanalysisoftimeintensitycurvesfrombreastdynamiccontrastenhancedmri AT guoqiyong quantitativediscriminationbetweeninvasiveductalcarcinomasandbenignlesionsbasedonsemiautomaticanalysisoftimeintensitycurvesfrombreastdynamiccontrastenhancedmri AT zhangwei quantitativediscriminationbetweeninvasiveductalcarcinomasandbenignlesionsbasedonsemiautomaticanalysisoftimeintensitycurvesfrombreastdynamiccontrastenhancedmri |