Objective： Early assessment of response to neoadjuvant chemotherapy （NAC） for breast cancer allows therapy to be individualized. The optimal assessment method has not been established. We investigated the accuracy ...Objective： Early assessment of response to neoadjuvant chemotherapy （NAC） for breast cancer allows therapy to be individualized. The optimal assessment method has not been established. We investigated the accuracy of automated breast ultrasound （ABUS） to predict pathological outcomes after NAC. Methods： A total of 290 breast cancer patients were eligible for this study. Tumor response after 2 cycles of chemotherapy was assessed using the product change of two largest perpendicular diameters （PC） or the longest diameter change （LDC）. PC and LDC were analyzed on the axial and the coronal planes respectively. Receiver operating characteristic （ROC） curves were used to evaluate overall performance of the prediction methods. Youden＇s indexes were calculated to select the optimal cut-off value for each method. Sensitivity, specificity, positive and negative predictive values （PPV and NPV） and the area under the ROC curve （AUC） were calculated accordingly.Results： ypT0/is was achieved in 42 patients （14.5%） while ypT0 was achieved in 30 patients （10.3%） after NAC. All four prediction methods （PC on axial planes, LDC on axial planes, PC on coronal planes and LDC on coronal planes） displayed high AUCs （all〉0.82）, with the highest of 0.89 [95% confidence interval （95% CI）, 0.83-0.95] when mid-treatment ＆BUS was used to predict final pathological complete remission （pCR）. High sensitivities （85.7%-88.1%） were observed across all four prediction methods while high specificities （81.5%-85.1%） were observed in two methods used PC. The optimal cut-off values defined by our data replicate the WHO and the RECIST criteria. Lower AUCs were observed when mid-treatment ABUS was used to predict poor pathological outcomes. Conclusions：ABUS is a useful tool in early evaluation of pCR after NAC while less reliable when predicting poor pathological outcomes.展开更多
Objective： The automated breast ultrasound system（ABUS） is a potential method for breast cancer detection;however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic st...Objective： The automated breast ultrasound system（ABUS） is a potential method for breast cancer detection;however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic study to evaluate the clinical performance of the ABUS for breast cancer detection by comparing it to handheld ultrasound（HHUS） and mammography（MG）.Methods： Eligible participants underwent HHUS and ABUS testing; women aged 40–69 years additionally underwent MG. Images were interpreted using the Breast Imaging Reporting and Data System（BI-RADS）.Women in the BI-RADS categories 1–2 were considered negative. Women classified as BI-RADS 3 underwent magnetic resonance imaging to distinguish true-and false-negative results. Core aspiration or surgical biopsy was performed in women classified as BI-RADS 4–5, followed by a pathological diagnosis. Kappa values and agreement rates were calculated between ABUS, HHUS and MG.Results： A total of 1,973 women were included in the final analysis. Of these, 1,353（68.6%） and 620（31.4%）were classified as BI-RADS categories 1–3 and 4–5, respectively. In the older age group, the agreement rate and Kappa value between the ABUS and HHUS were 94.0% and 0.860（P〈0.001）, respectively; they were 89.2% and0.735（P〈0.001） between the ABUS and MG, respectively. Regarding consistency between imaging and pathology results, 78.6% of women classified as BI-RADS 4–5 based on the ABUS were diagnosed with precancerous lesions or cancer; which was 7.2% higher than that of women based on HHUS. For BI-RADS 1–2, the false-negative rates of the ABUS and HHUS were almost identical and were much lower than those of MG.Conclusions： We observed a good diagnostic reliability for the ABUS. Considering its performance for breast cancer detection in women with high-density breasts and its lower operator dependence, the ABUS is a promising option for breast cancer detection in China.展开更多
目的探讨超声自动乳腺全容积扫描（ABVS）技术在乳腺肿块BI-RADS分类（3~5类）的价值。方法回顾性分析235例（共250个结节）手持超声（HUS）诊断为BI-RAD S 3~5类、同时接受ABVS检查的患者。分别应用HUS和HUS＋ABVS对乳腺病变进行BI-RAD...目的探讨超声自动乳腺全容积扫描（ABVS）技术在乳腺肿块BI-RADS分类（3~5类）的价值。方法回顾性分析235例（共250个结节）手持超声（HUS）诊断为BI-RAD S 3~5类、同时接受ABVS检查的患者。分别应用HUS和HUS＋ABVS对乳腺病变进行BI-RADS分类,以病理结果为金标准,分别计算HUS和HUS＋ABVS诊断乳腺肿块的敏感度、特异度和准确率,ROC曲线分析并比较两种方法的诊断效能。结果 250个结节中,HUS诊断3~5类乳腺病变的敏感度100%（103/103）,特异度69.39%（102/147）,准确率82.00%（205/250）;HUS＋ABVS的敏感度100%（103/103）,特异度80.95%（119/147）,准确率88.80%（222/250）。ABVS＋HUS诊断BI-RADS 3~5类病变的ROC曲线下面积为0.973,大于HUS的0.940（P=0.032）。通过＂汇聚征＂诊断乳腺恶性肿瘤的敏感度、特异度及准确率分别为65.05%（67/103）、95.92%（141/147）、83.20%（208/250）。两种方法对乳腺病变卫星灶的检出率差异有统计学意义（χ^2=30.78,P〈0.05）,但对于乳腺肿块内钙化及周围导管扩张的检出率差异无统计学意义（X^2=2.56、1.22,P均〉0.05）。结论 HUS＋ABVS技术在准确判断乳腺占位病变BI-RADS分类、鉴别肿瘤良恶性方面优于HUS。ABVS对于乳腺肿块的钙化、导管扩张及卫星灶的发现具有重要补充作用。展开更多
目的：运用Meta分析评价自动乳腺全容积成像系统（automated breast volume scanning,ABVS）与手持探头超声（hand-held ultrasound,HHUS）对乳腺良、恶性肿瘤的诊断价值。方法 ：采用计算机自动配合手工检索COCHRANE、WEB OF SCIENCE、...目的：运用Meta分析评价自动乳腺全容积成像系统（automated breast volume scanning,ABVS）与手持探头超声（hand-held ultrasound,HHUS）对乳腺良、恶性肿瘤的诊断价值。方法 ：采用计算机自动配合手工检索COCHRANE、WEB OF SCIENCE、PUBMED、EMBASE、中文科技期刊全文数据库、中国生物医学文献数据库、中国知网（CNKI）、万方数字化期刊全文数据库（检索时间：建库至2015年3月7日）,搜集并筛选出HHUS与ABVS鉴别诊断女性良恶性乳腺肿瘤的文献,应用STATA12.0软件对入选文献的试验数据进行分析。结果 ：17篇文献符合纳入标准。ABVS和HHUS合并诊断比值比分别为95.21（53.85-168.36）、28.88（16.15-51.66）,合并特异度分别为0.90（0.86-0.93）、0.84（0.78-0.89）,合并敏感度分别为0.91（0.88-0.93）、0.84（0.79-0.88）。结论 ：ABVS在乳腺良、恶性肿瘤鉴别诊断方面较HHUS具有更高的临床价值。展开更多
文摘Objective： Early assessment of response to neoadjuvant chemotherapy （NAC） for breast cancer allows therapy to be individualized. The optimal assessment method has not been established. We investigated the accuracy of automated breast ultrasound （ABUS） to predict pathological outcomes after NAC. Methods： A total of 290 breast cancer patients were eligible for this study. Tumor response after 2 cycles of chemotherapy was assessed using the product change of two largest perpendicular diameters （PC） or the longest diameter change （LDC）. PC and LDC were analyzed on the axial and the coronal planes respectively. Receiver operating characteristic （ROC） curves were used to evaluate overall performance of the prediction methods. Youden＇s indexes were calculated to select the optimal cut-off value for each method. Sensitivity, specificity, positive and negative predictive values （PPV and NPV） and the area under the ROC curve （AUC） were calculated accordingly.Results： ypT0/is was achieved in 42 patients （14.5%） while ypT0 was achieved in 30 patients （10.3%） after NAC. All four prediction methods （PC on axial planes, LDC on axial planes, PC on coronal planes and LDC on coronal planes） displayed high AUCs （all〉0.82）, with the highest of 0.89 [95% confidence interval （95% CI）, 0.83-0.95] when mid-treatment ＆BUS was used to predict final pathological complete remission （pCR）. High sensitivities （85.7%-88.1%） were observed across all four prediction methods while high specificities （81.5%-85.1%） were observed in two methods used PC. The optimal cut-off values defined by our data replicate the WHO and the RECIST criteria. Lower AUCs were observed when mid-treatment ABUS was used to predict poor pathological outcomes. Conclusions：ABUS is a useful tool in early evaluation of pCR after NAC while less reliable when predicting poor pathological outcomes.
文摘Objective： The automated breast ultrasound system（ABUS） is a potential method for breast cancer detection;however, its diagnostic performance remains unclear. We conducted a hospital-based multicenter diagnostic study to evaluate the clinical performance of the ABUS for breast cancer detection by comparing it to handheld ultrasound（HHUS） and mammography（MG）.Methods： Eligible participants underwent HHUS and ABUS testing; women aged 40–69 years additionally underwent MG. Images were interpreted using the Breast Imaging Reporting and Data System（BI-RADS）.Women in the BI-RADS categories 1–2 were considered negative. Women classified as BI-RADS 3 underwent magnetic resonance imaging to distinguish true-and false-negative results. Core aspiration or surgical biopsy was performed in women classified as BI-RADS 4–5, followed by a pathological diagnosis. Kappa values and agreement rates were calculated between ABUS, HHUS and MG.Results： A total of 1,973 women were included in the final analysis. Of these, 1,353（68.6%） and 620（31.4%）were classified as BI-RADS categories 1–3 and 4–5, respectively. In the older age group, the agreement rate and Kappa value between the ABUS and HHUS were 94.0% and 0.860（P〈0.001）, respectively; they were 89.2% and0.735（P〈0.001） between the ABUS and MG, respectively. Regarding consistency between imaging and pathology results, 78.6% of women classified as BI-RADS 4–5 based on the ABUS were diagnosed with precancerous lesions or cancer; which was 7.2% higher than that of women based on HHUS. For BI-RADS 1–2, the false-negative rates of the ABUS and HHUS were almost identical and were much lower than those of MG.Conclusions： We observed a good diagnostic reliability for the ABUS. Considering its performance for breast cancer detection in women with high-density breasts and its lower operator dependence, the ABUS is a promising option for breast cancer detection in China.
文摘目的：运用Meta分析评价自动乳腺全容积成像系统（automated breast volume scanning,ABVS）与手持探头超声（hand-held ultrasound,HHUS）对乳腺良、恶性肿瘤的诊断价值。方法 ：采用计算机自动配合手工检索COCHRANE、WEB OF SCIENCE、PUBMED、EMBASE、中文科技期刊全文数据库、中国生物医学文献数据库、中国知网（CNKI）、万方数字化期刊全文数据库（检索时间：建库至2015年3月7日）,搜集并筛选出HHUS与ABVS鉴别诊断女性良恶性乳腺肿瘤的文献,应用STATA12.0软件对入选文献的试验数据进行分析。结果 ：17篇文献符合纳入标准。ABVS和HHUS合并诊断比值比分别为95.21（53.85-168.36）、28.88（16.15-51.66）,合并特异度分别为0.90（0.86-0.93）、0.84（0.78-0.89）,合并敏感度分别为0.91（0.88-0.93）、0.84（0.79-0.88）。结论 ：ABVS在乳腺良、恶性肿瘤鉴别诊断方面较HHUS具有更高的临床价值。
