目的研究动态增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)定量参数分析在早期乳腺癌诊断中的应用价值。方法选取浙江省台州医院2020年6月至2021年6月收治的乳腺癌患者70例为恶性组,另选取同期浙江...目的研究动态增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)定量参数分析在早期乳腺癌诊断中的应用价值。方法选取浙江省台州医院2020年6月至2021年6月收治的乳腺癌患者70例为恶性组,另选取同期浙江省台州医院收治的乳腺纤维腺瘤患者30例为良性组,采用DCE-MRI定量参数对乳腺癌和乳腺纤维腺瘤进行鉴别诊断分析,比较两组的DCE-MRI定量参数容量转移常数(volume transfer constant,K^(trans))、反流速率常数(reflux rate constant,K_(ep))、血管外细胞外间隙容积比(volume of the external vascular extracellular space,V_(e)),比较不同病理类型DCE-MRI定量参数K^(trans)、K_(ep)、V_(e)及DCE-MRI定量参数分析与乳腺癌患者预后的相关性及诊断敏感度、准确性。结果良性组的DCE-MRI定量参数K^(trans)、K_(ep)、V_(e)均低于恶性组(P<0.0001);导管内癌组的K^(trans)、K_(ep)、V_(e)及非特殊类型浸润性癌的K^(trans)、K_(ep)、V_(e)均低于特殊类型癌(P<0.05);雌激素受体(estrogen receptor,ER)表达阴性组的K_(ep)高于ER阳性组(P<0.05);孕激素受体(progesterone receptor,PR)阴性组的K_(ep)高于PR阳性组(P<0.05);ER、PR阴性组的K^(trans)、V_(e)高于ER、PR阳性组;人类表皮生长因子受体-2低表达和过表达组的K^(trans)、K_(ep)、V_(e)比较,差异无统计学意义(P>0.05);与K^(trans)、K_(ep)、V_(e)单一诊断比较,联合诊断对乳腺癌诊断价值较高(P<0.05)。结论DCE-MRI定量参数分析用于早期乳腺癌的诊断,能有效鉴别不同疾病的类型及预后,诊断价值较高。展开更多
The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisi...The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.展开更多
In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid ...In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve.The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them（kappa 〈0.20）. In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.展开更多
文摘目的研究动态增强磁共振成像(dynamic contrast-enhanced magnetic resonance imaging,DCE-MRI)定量参数分析在早期乳腺癌诊断中的应用价值。方法选取浙江省台州医院2020年6月至2021年6月收治的乳腺癌患者70例为恶性组,另选取同期浙江省台州医院收治的乳腺纤维腺瘤患者30例为良性组,采用DCE-MRI定量参数对乳腺癌和乳腺纤维腺瘤进行鉴别诊断分析,比较两组的DCE-MRI定量参数容量转移常数(volume transfer constant,K^(trans))、反流速率常数(reflux rate constant,K_(ep))、血管外细胞外间隙容积比(volume of the external vascular extracellular space,V_(e)),比较不同病理类型DCE-MRI定量参数K^(trans)、K_(ep)、V_(e)及DCE-MRI定量参数分析与乳腺癌患者预后的相关性及诊断敏感度、准确性。结果良性组的DCE-MRI定量参数K^(trans)、K_(ep)、V_(e)均低于恶性组(P<0.0001);导管内癌组的K^(trans)、K_(ep)、V_(e)及非特殊类型浸润性癌的K^(trans)、K_(ep)、V_(e)均低于特殊类型癌(P<0.05);雌激素受体(estrogen receptor,ER)表达阴性组的K_(ep)高于ER阳性组(P<0.05);孕激素受体(progesterone receptor,PR)阴性组的K_(ep)高于PR阳性组(P<0.05);ER、PR阴性组的K^(trans)、V_(e)高于ER、PR阳性组;人类表皮生长因子受体-2低表达和过表达组的K^(trans)、K_(ep)、V_(e)比较,差异无统计学意义(P>0.05);与K^(trans)、K_(ep)、V_(e)单一诊断比较,联合诊断对乳腺癌诊断价值较高(P<0.05)。结论DCE-MRI定量参数分析用于早期乳腺癌的诊断,能有效鉴别不同疾病的类型及预后,诊断价值较高。
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.
基金supported by the Natural Science Foundation of Jiangsu University(14KJB320003)
文摘In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve.The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them（kappa 〈0.20）. In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.
