Characterisation of indeterminate focal breast lesions on grey-scale ultrasound: role of ultrasound elastography

Purpose

This study was undertaken to evaluate the role of ultrasound (US) elastography in characterising focal breast lesions classified as indeterminate on B-mode US.

Materials and methods

Eighty-four focal breast lesions, 64 benign and 20 malignant (mean diameter, 15.1 mm), detected but not characterised on B-mode US in 72 women, Breast Imaging Reporting and Data System (BI-RADS) US category 3 (n=56) or category 4 (n=28), were studied with US elastography and classified in consensus by two radiologists according to a five-point colour scale. Sensitivity, specificity and positive and negative predictive values (PPV and NPV) of US elastography compared with conventional US were calculated in relation to microhistology (n=67) and cytology (n=17), which were used as the reference standard.

Results

A total of 65/84 (77.4%) lesions were correctly classified as benign or malignant using US elastography, whereas the remaining 19/84 (22.6%) were incorrectly assessed. There were no statistically significant differences between US elastography and B-mode US with regard to sensitivity (70% vs. 68.4%), specificity (79.6% vs. 78.5%), PPV (51.8% vs. 48.1%) and NPV 89% vs. 89.5% (p>0.5). By contrast, a statistically significant difference was noted in the evaluation of BI-RADS 3 lesions, in which US elastography had 50% sensitivity, 86% specificity, 30% PPV and 93.5% NPV compared with BI-RADS 4 lesions (78.6%, 57.1%, 64.7% and 72.7%) (p<0.5).

Conclusions

The high NPV of US elastography may help reduce the use of biopsy in BI-RADS 3 lesions, but its low PPV in BI-RADS 4 lesions does not allow avoidance of biopsy on the basis of the US elastographic score alone in this group of lesions.     

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging

PurposeThere are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions. This study aimed to establish an AI diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging and to compare its performance with that of radiologists.MethodsThe ultrasound images of 1311 lesions were evaluated by radiologists according to the BI-RADS categories, using pathology results as reference. Two classification standards (standards 1 and 2) for benign and malignant lesions were defined and used to calculate the diagnostic performance of radiologists, altogether and individually. The breast lesion images were also used to develop an AI diagnostic model.ResultsThe diagnostic performance of AI and that of the radiologists were compared using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). All parameters of diagnostic performance, except for sensitivity and NPV, improved with standard 2. For the 202 lesions in the test set, the diagnostic performance of the AI model had 77.0% accuracy, 82.0% sensitivity, 71.7% specificity, 79.3% PPV, 75.1% NPV, and an AUC of 0.846. When the AI model was used to analyze category 4A lesions, the PPV was 9.3%, which was better than that of the radiologists, although not significantly.ConclusionsDeep learning technology shows a good performance in classifying benign and malignant breast lesions. It may be potentially used in practice to improve diagnostic accuracy and reduce unnecessary biopsies of breast lesions.     

乳腺影像报告和数据系统在国人女性乳腺癌筛查中的应用价值

目的 探讨乳腺影像报告和数据系统(BI-RADS)评估分类在国人女性乳腺癌筛查中的应用价值.方法 搜集2009年8月至12月参加乳腺癌筛查项目中行乳腺X线摄影的3483名妇女资料,参照BI-RADS标准对乳腺评估分类,对于疾病的诊断最终以组织病理结果为金标准,计算BI-RADS评估分类的准确度、敏感度、特异度及BI-RADS各类的阳性预测值(PPV)和阴性预测值(NPV).结果 3483名受检妇女乳腺组成中脂肪型、散在腺体型、不均匀致密型和高度致密型分别有267、1245、1890和81名.进行BI-RADS评估分类,0～5类分别为273(7.8%)、1011(29.0%)、1741(50.0%)、383(11.0%)、59(1.7%)和16(0.5%)名.71例受检者的77个乳腺病变经病理证实,包括恶性病变29例,良性病变48例.BI-RADS评估分类的准确度为63.6%(49/77),敏感度为93.1%(27/29),特异度为45.8%(22/48),BI-RADS总体PPV为50.9%(27/53),0类、4类和5类的PPV分别为25.0%(1/4)、36.4%(12/33)和87.5%(14/16),2类、3类的NPV分别为90.9%(10/11)和100.0%(12/12).结论 乳腺X线摄影应用BI-RADS评估分类可以有效地预测乳腺恶性病变,在国人女性乳腺癌筛查应用中有一定价值.

Abstract：

Objective To study the value of breast imaging reporting and data system (BI-RADS)in Chinese breast cancer screening. Methods A total number of 3483 women participated in breast cancer screening with mammography in Hexi district in Tianjin from August to December 2009, which was organized by ministry of public health. BI-RADS assessment categories and recommendations were compared with histological findings. The precision, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Results Among 3483 screening mammography cases, 267 were almost entirely fat breast, 1245 were scauered fibroglandular, 1890 were dense and 81 extremely dense.There were 1011 patients(29.0%) with category 1, 1741 (50.0%) with category 2, 383 (11.0%) with category 3, 59 patients(1. 7%) with category 4 and 16 (0. 5%) with category 5 according to BI-RADS assessment categories. Totally, 71 women with 77 lesions were confirmed by histological examinations. There were 29 malignant and 48 benign lesions. The diagnostic precision, sensitivity, specificity of BI-RADS were 63. 6% (49/77) , 93. 1% (27/29) and 45.8% (22/48) . The general PPV of BI-RADS was 50. 9%(27/53). The PPV of categories 0, 4, 5 were 25.0% (1/4), 36. 4% (12/33) and 87. 5% (14/16). The NPV of categories 2 and3 were90.9% (10/11), 100.0% (12/12). Conclusions B1-RADS is of much value in assessing the breast malignancy. It is applicable in Chinese breast cancer screening.     

乳腺影像报告和数据系统在国人女性乳腺癌筛查中的应用价值

目的 探讨乳腺影像报告和数据系统(BI-RADS)评估分类在国人女性乳腺癌筛查中的应用价值.方法 搜集2009年8月至12月参加乳腺癌筛查项目中行乳腺X线摄影的3483名妇女资料,参照BI-RADS标准对乳腺评估分类,对于疾病的诊断最终以组织病理结果为金标准,计算BI-RADS评估分类的准确度、敏感度、特异度及BI-RADS各类的阳性预测值(PPV)和阴性预测值(NPV).结果 3483名受检妇女乳腺组成中脂肪型、散在腺体型、不均匀致密型和高度致密型分别有267、1245、1890和81名.进行BI-RADS评估分类,0～5类分别为273(7.8%)、1011(29.0%)、1741(50.0%)、383(11.0%)、59(1.7%)和16(0.5%)名.71例受检者的77个乳腺病变经病理证实,包括恶性病变29例,良性病变48例.BI-RADS评估分类的准确度为63.6%(49/77),敏感度为93.1%(27/29),特异度为45.8%(22/48),BI-RADS总体PPV为50.9%(27/53),0类、4类和5类的PPV分别为25.0%(1/4)、36.4%(12/33)和87.5%(14/16),2类、3类的NPV分别为90.9%(10/11)和100.0%(12/12).结论 乳腺X线摄影应用BI-RADS评估分类可以有效地预测乳腺恶性病变,在国人女性乳腺癌筛查应用中有一定价值.     

乳腺影像报告和数据系统在国人女性乳腺癌筛查中的应用价值

目的 探讨乳腺影像报告和数据系统(BI-RADS)评估分类在国人女性乳腺癌筛查中的应用价值.方法 搜集2009年8月至12月参加乳腺癌筛查项目中行乳腺X线摄影的3483名妇女资料,参照BI-RADS标准对乳腺评估分类,对于疾病的诊断最终以组织病理结果为金标准,计算BI-RADS评估分类的准确度、敏感度、特异度及BI-RADS各类的阳性预测值(PPV)和阴性预测值(NPV).结果 3483名受检妇女乳腺组成中脂肪型、散在腺体型、不均匀致密型和高度致密型分别有267、1245、1890和81名.进行BI-RADS评估分类,0～5类分别为273(7.8%)、1011(29.0%)、1741(50.0%)、383(11.0%)、59(1.7%)和16(0.5%)名.71例受检者的77个乳腺病变经病理证实,包括恶性病变29例,良性病变48例.BI-RADS评估分类的准确度为63.6%(49/77),敏感度为93.1%(27/29),特异度为45.8%(22/48),BI-RADS总体PPV为50.9%(27/53),0类、4类和5类的PPV分别为25.0%(1/4)、36.4%(12/33)和87.5%(14/16),2类、3类的NPV分别为90.9%(10/11)和100.0%(12/12).结论 乳腺X线摄影应用BI-RADS评估分类可以有效地预测乳腺恶性病变,在国人女性乳腺癌筛查应用中有一定价值.     

Power Doppler sonography: Anything to add to BI-RADS US in solid breast masses?

Objective

To evaluate the contribution of power Doppler ultrasonography (PDUS) to breast imaging reporting and data system ultrasonography (BI-RADS US) categorization of solid breast masses.

Materials and methods

Totally 94 solid lesions with histopathological results in 49 patients were included in the study. US features of the lesions were classified according to American College of Radiologists (ACR) BI-RADS US lexicon. Lesions were evaluated qualitatively according to their PDUS properties and quantitatively with spectral analysis. Hypervascularity, penetration of vessels into the mass or branching-disordered course and resistivity index values higher than 0.85 were accepted as probable malignant criteria.

Results

Fifty-five of 94 lesions were benign (58.5%), while 39 (41.5%) were malignant histopathologically. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of US and PDUS in the diagnosis of malignant lesions were 100%, 58.2%, 62.9%, 100% and 71.8%, 81.8%, 73.7%, 80.4%, respectively. Criteria used for the distinction of malignant and benign lesions like number of vessels (p < 0.05), distribution of tumoral vessels, morphology of vessels and resistivity index values higher than 0.85 showed statistically significant difference (p < 0.001). When sonographic findings were combined with PDUS and spectral analysis findings, sensitivity, specificity, PPV and NPV were 100%, 52.7%, 60% and 100%, respectively.

Conclusion

PDUS and spectral analysis have no contribution to BI-RADS US. For the spectral analysis, when RI value is one or greater, malignancy risk significantly increases.     

Contrast-enhanced MR imaging in patients with BI-RADS 3-5 microcalcifications

PURPOSE: The aim of this study was to evaluate the role of magnetic resonance imaging (MRI) in patients with microcalcifications classed as Breast Imaging Reporting and Data Systems (BI-RADS) 3-5. MATERIALS AND METHODS: Fifty-five patients with mammographic microcalcifications classified as BI-RADS categories 3, 4 or 5 underwent MRI and biopsy with stereotactic vacuum-assisted biopsy (VAB). Our gold standard was microhistology in all cases and histology with histological grading in patients who underwent surgery. Patients with a microhistological diagnosis of benign lesions underwent mammographic follow-up for at least 12 months. MRI was performed with a 1.5-Tesla (T) unit, and T1 coronal three-dimensional (3D) fast low-angle shot sequences were acquired before and after injection of paramagnetic contrast agent (0.1 mmol/kg). MRI findings, according to the Fisher score, were classified into BI-RADS classes. In patients with cancer who underwent surgery, we retrospectively compared the extension of the mammographic and MRI findings with histological extension. RESULTS: Histology revealed 26 ductal in situ cancers (DCIS) and ductal microinvasive cancers (DCmic), three atypical ductal hyperplasias (ADH) and 26 benign conditions. Histological grading of the 26 patients with cancer revealed four cases of G1, 11 cases of G2 and 11 cases of G3. If we consider mammographic BI-RADS category 3 as benign and BI-RADS 4 and 5 as malignant, mammography had 77% sensitivity, 59% specificity, 63% positive predictive value (PPV), 74% negative predictive value (NPV) and 67.2% diagnostic accuracy. If we consider MRI BI-RADS categories 1, 2 and 3 as benign and 4 and 5 as malignant, MRI had 73% sensitivity, 76% specificity, 73% PPV, 76% NPV and 74.5% diagnostic accuracy. As regards disease extension, mammography had 45% sensitivity and MRI had 84.6% sensitivity. CONCLUSION: Mammography and stereotactic biopsy still remain the only techniques for characterising microcalcifications. MRI cannot be considered a diagnostic tool for evaluating microcalcifications. It is, however, useful for identifying DCIS with more aggressive histological grades. An important application of MRI in patients with DCIS associated with suspicious microcalcifications could be to evaluate disease extension after a microhistological diagnosis of malignancy, as it allows a more accurate presurgical planning.     

US, colour-Doppler US and fine-needle aspiration biopsy in the diagnosis of thyroid nodules

PURPOSE: The purpose of this study was to correlate the diagnosis of benign or malignant thyroid nodules obtained with grey-scale ultrasound (US) and colour-Doppler US with the cytological findings after US-guided fine-needle aspiration (FNA). MATERIALS AND METHODS: Between January 2004 and June 2005, 516 thyroid nodules in 420 patients (181 solitary thyroid nodules and 239 multiple nodules) were prospectively evaluated with US, colour-Doppler US and US-guided FNA. The nodules were classified as sonographically benign, suspicious or malignant in accordance with established US criteria. Cytological findings were classified as inadequate, benign, indeterminate, suspicious or malignant. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of US and colour-Doppler US were evaluated using FNA as the reference procedure. RESULTS: The sensitivity, specificity, PPV, NPV and overall accuracy values of grey-scale US were 46%, 73%, 34%, 82% and 67%, respectively, for solitary thyroid nodules and 35%, 72%, 14%, 90% and 68%, respectively, for multiple nodules. The evaluation of nodule vascularity with colour-Doppler US produced a slight increase in sensitivity but a slight reduction in specificity. CONCLUSIONS: Thyroid nodules cannot be accurately characterised using grey-scale US or colour-Doppler US.     

乳腺影像报告与数据系统在鉴别诊断乳腺良恶性肿瘤中的应用价值

目的评价乳腺影像报告与数据系统(BI-RADS)在乳腺良恶性肿瘤鉴别诊断中的应用价值.资料与方法根据 BI-RADS 规范,分析202例患者共234个肿瘤的形态、方向、边缘、边界、内部回声、后方回声及钙化情况,并将病灶定义为3级(可能良性)、4级(可能恶性)和5级(高度可疑恶性).计算每个病灶特征的阳性预测值与阴性预测值,分析影响诊断的因素.结果?49个(20.9%)为 BI-RADS 3级,47个(20.1%)为 BI-RADS 4级,138个(59.0%)为 BI-RADS 5级,BI-RADS 3级对乳腺肿瘤的阴性预测值为98.0%(48/49),BI-RADS 4级与5级对乳腺肿瘤的阳性预测值分别为34.0%(16/47)和92.8%(128/138).BI-RADS 对乳腺恶性肿瘤的诊断敏感性为99.3%,特异性为53.9%,阳性预测值为77.8%,阴性预测值为98.0%.对恶性肿瘤的诊断有较高阳性预测值的超声特征包括不完整的边缘(100.0%)、微钙化(100.0%)、不清晰的边界(97.3%)、后方回声衰减(97.0%);对良性肿瘤的诊断有较高阳性预测值的超声特征包括后方回声增强(100.0%)、形态规则(98.0%)、病灶方向平行于皮肤长轴(98.0%)、边缘完整(98.0%)、边界清晰(98.0%)与无钙化(98.0%).结论 BI-RADS 可帮助分析影响诊断的因素,有助于乳腺良恶性肿瘤的鉴别诊断.     

Computer-aided classification of BI-RADS category 3 breast lesions

PURPOSE: To evaluate a system for computer-aided classification (CAC) of lesions assigned to Breast Imaging Reporting and Data System (BI-RADS) category 3 at conventional mammographic interpretation. MATERIALS AND METHODS: A CAC system was used to analyze 106 cases of lesions (42 malignant) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least two of four radiologists. The CAC system automatically extracted from the digitized mammograms quantitative features that characterized the lesions. The system then used a classification scheme to score the lesions by the likelihood of their malignancy on the basis of these features. The classification scheme was trained with 646 pathologically proved cases (323 malignant), and the results were tested with receiver operating characteristic (ROC) analysis by using the jackknife method. Sensitivity, specificity, positive predictive value, and accuracy were calculated. Category 3 lesions were stratified among BI-RADS categories 2-5 according to CAC-assigned lesion score, and this classification was compared with the results of pathologic analysis. RESULTS: Jackknife analysis of CAC results in the training data set yielded a sensitivity of 94%, specificity of 78%, positive predictive value of 81%, and area under the ROC curve of 0.90. Of the 42 malignant lesions that had been classified at conventional interpretation as probably benign, nine were assigned by the CAC system to BI-RADS category 4, and 29 were assigned to category 5. The CAC system correctly upgraded the BI-RADS classification of these 38 lesions (sensitivity, 90%) and incorrectly upgraded the classification of only 20 benign lesions (specificity, 69%). CONCLUSION: The CAC system scored 38 of the 42 malignant lesions initially assigned to BI-RADS category 3 as BI-RADS category 4 or 5, and thus correctly upgraded the category in 90% of these lesions.     

Role of magnetic resonance imaging in probably benign (BI-RADS category 3) microcalcifications of the breast

Purpose

This study was undertaken to evaluate whether magnetic resonance (MR) imaging is able to rule out malignancy in the case of BI-RADS 3 microcalcifications, providing a sufficient negative predictive value (NPV) for early work-up, and to reduce unnecessary stereotactically guided vacuum-assisted biopsy (SVAB) procedures.

Materials and methods

We prospectively enrolled consecutive women with BI-RADS 3 microcalcifications, who subsequently underwent MR imaging and SVAB. The MR studies were reviewed according to the MR-BI-RADS classification system; lesions assessed as MR-BI-RADS 1 and 2 were considered negative for malignancy, categories MR-BI-RADS 3, 4 and 5 indicated malignant lesions. The presence of additional findings was recorded. Histologic analysis and follow-up were the reference standard. MR sensitivity, specificity, positive predictive value (PPV) and NPV were calculated.

Results

The final population consisted of 71 lesions. Histologic analysis showed malignancy in six cases (malignancy rate 8 %). At MR analysis, 60 (85 %) lesions were considered negative for malignancy and 11 (15 %) malignant. Additional MR imaging findings were identified in 19 (27 %) patients, all corresponding to nonmalignant lesions. MR sensitivity was 33 %, specificity 86 %, PPV 18 % and NPV 93 %.

Conclusions

Because of its relatively low NPV, MR imaging is not able to safely exclude malignancy in the case of BI-RADS 3 microcalcifications. The relatively high malignancy rate found in this study might support SVAB in the case of BI-RADS 3 microcalcifications.     

LI-RADS CT分类标准对肝细胞癌的诊断效能评价

目的：探讨肝脏影像报告和数据管理系统（LI-RADS）CT分级诊断标准对肝细胞癌（HCC）的临床诊断价值。方法：回顾性分析158例肝癌高危患者肝脏病变患者的上腹部CT资料,并根据LI-RADS分类标准对病变进行分析评估,并与临床客观诊断结果进行比较。结果：158例患者的 CT 图像共发现179个肝内病灶,其中 LI-RADS 1～5类病灶共167个：1类和2类48个,临床客观诊断结果均为良性（阴性预测值为100％）;3类4个;4类6个,其中2个病灶的术后病理结果为 HCC（阳性预测值为33．3％）;5类109个,其中103例为 HCC（阳性预测值为94．5％）。受试者工作特征（ROC）曲线下面积为0．89（P＜0．001）。若将LI-RADS 1～2类病灶归为阴性,3～5类病灶归为阳性,LI-RADS对诊断肝癌的总符合率为91．6％（153／167）,检出 HCC 的敏感度为100％（105／105）,特异度为77．4％（48／62）,阳性预测值为88．2％（105／119）,阴性预测值为100％（48／48）。若将LI-RADS 3类病灶排除,1～2类病灶归为阴性,4～5类病灶归为阳性,LI-RADS对肝内已检出病灶的诊断符合率为93．9％（153／163）,检出 HCC 的敏感度为100％（105／105）,特异度为82．8％（48／58）,阳性预测值为91．3％（105／115）,阴性预测值为100％（48／48）。结论：LI-RADS分类标准对 HCC 的CT诊断具有很好的诊断效果,有利于提高CT诊断报告的准确性。     

LI-RADS MRI分类标准对肝细胞癌的诊断效能

目的：探讨肝脏影像报告和数据管理系统（LI-RADS）MRI 分级诊断标准对肝细胞癌（HCC）的诊断价值。方法：回顾性分析88例肝脏病变患者的上腹部MRI资料,并根据LI-RADS分类标准对病变进行分析评估,并与临床客观诊断结果进行比较。结果：88例患者MRI共发现117个病灶,其中LI-RADS 1～5类病灶99个：1类和2类病灶25个,临床客观诊断结果均为良性（阴性预测值为100％）;3类病灶3个,其中1个为HCC（阳性预测值为33．3％）;4类病灶8个,其中5个为 HCC（阳性预测值为62．5％）;63个5类病灶中61个为 HCC（阳性预测值为96．8％）。受试者工作特征（ROC）曲线下面积为0．96（P＜0．001）。若将LI-RADS 1～2类病灶归为阴性,3～5类归为阳性,LI-RADS对诊断 HCC的总符合率为92．9％（92／99）,敏感度为100％（67／67）,特异度为78．1％（25／32）,阳性预测值为90．5％（67／74）,阴性预测值为100％（25／25）。若将LI-RADS 3类病灶排除,1～2类病灶归为阴性,4～5类病灶归为阳性,LI-RADS对 HCC的诊断符合率为94．8％（91／96）,敏感度为100％（66／66）,特异度为83．3％（25／30）,阳性预测值为93．0％（66／71）,阴性预测值为100％（25／25）。结论：LI-RADS分类标准对HCC的MRI诊断具有很好的诊断效果,有利于提高MRI诊断报告的准确性。     

初探超声造影基于LI-RADS分类标准诊断肝细胞癌的可行性

目的：探讨基于肝脏影像报告和数据管理系统（LI-RADS）的超声造影检查诊断肝细胞癌（HCC）的可行性。方法：回顾性分析108例有肝癌高风险的单发肝脏病变患者的超声造影资料,并基于LI-RADS分类标准对肝脏病变进行分析评估,并与病理或临床诊断结果相对照。结果：108个病灶中LI-RADS 1～5类病灶共106个：1类和2类病灶19个,临床客观诊断结果均为良性（阴性预测值为100％）;3类病灶3个;22个4类病灶中17个为 HCC（阳性预测值为77．3％）;62个5类病灶中有54个为 HCC（阳性预测值为87．1％）。受试者工作特征（ROC）曲线下面积为0．83（P＜0．001）。若将LI-RADS 3～5类病灶归为阳性,基于LI-RADS的超声造影检查对HCC的诊断符合率为83．9％（92／106）,敏感度为100％（73／73）,特异度为57．6％（19／33）,阳性预测值为83．9％（73／87）,阴性预测值为100％（19／19）;若将 LI-RADS 3类病灶排除、1～2类病灶归为阴性、4～5类病灶归为阳性,则诊断符合率为87．4％（90／103）,敏感度为100％（71／71）,特异度为59．4％（19／32）,阳性预测值为84．5％（71／84）,阴性预测值为100％（19／19）。结论：超声造影检查中应用LI-RADS分类标准诊断 HCC具有一定的可行性。     

BI-RADS categorization as a predictor of malignancy.

PURPOSE: To determine the positive predictive value (PPV) of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories 0, 2, 3, 4, and 5 by using BI-RADS terminology and by auditing data on needle localizations. MATERIALS AND METHODS: Between April 1991 and December 1996, 1,400 mammographically guided needle localizations were performed in 1,109 patients. Information entered into the mammographic database included where the initial mammography was performed (inside vs outside the institution), BI-RADS category, mammographic finding, and histopathologic findings. A recorded recommendation was available for 1,312 localizations in 1,097 patients, who composed the study population. RESULTS: The 1,312 localizations yielded 449 (34%) cancers (139 [31%] were ductal carcinoma in situ [DCIS]; 310 [69%] were invasive cancers) and 863 (66%) benign lesions. There were 15 (1%) category 0 lesions; the PPV was 13% (two of 15 lesions). There were 50 (4%) category 2 lesions; the PPV was 0% (0 of 40 lesions). There were 141 (11%) category 3 lesions; the PPV was 2% (three of 141 lesions). The three cancers in this group were all non-comedotype DCIS. There were 936 (71%) category 4 lesions; the PPV was 30% (279 of 936 lesions). There were 170 (13%) category 5 lesions; the PPV was 97% (165 of 170 lesions). CONCLUSION: Placing mammographic lesions into BI-RADS categories is useful for predicting the presence of malignancy. Perhaps, most important, a lesion placed into BI-RADS category 3 is highly predictive of benignity, and short-term interval follow-up as an alternative to biopsy would decrease the number of biopsies performed in benign lesions.     

弹性成像对超声引导乳腺肿块穿刺活检指导价值研究

目的探讨超声弹性成像对超声引导BI-RADS 4级乳腺肿块穿刺活检的指导价值。方法回顾性分析141例经超声引导下BI-RADS 4级乳腺肿块穿刺活检患者的临床资料。所有患者穿刺前均进行常规超声及弹性成像检查,以BI-RADS分级及弹性评分评价乳腺肿块的良恶性。结果 BI-RADS分级为4a级的阴性预测值(NPV)为89.6%,弹性评分≤3分的NPV为95.5%,两者比较,差异无统计学意义(P>0.05);BI-RADS分级为4b级的阳性预测值(PPV)为59.3%,弹性评分≥4分的PPV为85.3%,两者比较,差异有统计学意义(P<0.05);BI-RADS分级为4c级的PPV为89.7%,弹性评分≥4分的PPV为96.9%,两者比较,差异无统计学意义(P>0.05);BI-RADS分级≥4b级的PPV为72.0%,弹性评分≥4分的PPV为90.9%,两者比较,差异有统计学意义(P<0.05)。结论弹性评分<3分的4a级乳腺肿块可以短期随访观察;弹性评分为5分的4c级乳腺肿块建议直接手术治疗;对于良恶性难以鉴别的4b级乳腺肿块,超声弹性成像可以进一步提高其PPV,建议穿刺活检明确诊断。     

Differentiation of mammographically suspicious lesions: evaluation of breast ultrasound, MRI mammography and electrical impedance scanning as adjunctive technologies in breast cancer detection

AIM: Various modalities are used as an adjunct to mammography for differentiation of potentially suspicious breast lesions. Electrical impedance scanning (EIS) is a new technique based upon the principle that cancer cells exhibit altered local dielectric properties and thus show measurably higher conductivity values. The accuracy of differentiation of benign and malignant breast lesions was evaluated to determine whether EIS duplicates or supplements the results obtainable from ultrasound (US) or magnetic resonance imaging (MRI). MATERIALS AND METHODS: One hundred mammographically suspicious lesions were examined using US, MRI and EIS. Definitive histology was acquired through either lesion biopsy or surgical excision. RESULTS: Fifty of 62 malignant lesions were correctly identified using EIS (81% overall sensitivity), 24/38 benign lesions were correctly identified as benign (63% specificity). Negative predictive value and positive predictive value of 67 and 78% were observed, respectively. kappa-factor evaluation revealed a value of 0.82 between MRI and EIS and 0.62 between US and EIS. CONCLUSIONS: EIS may be a valuable adjunct for differentiation of suspicious mammographic lesions. Based upon the calculated kappa-factor, EIS results supplement US examinations. Artifacts (superficial skin lesions, poor contact, air bubbles) currently result in the high false-positive rate of EIS.