Clinical application of qualitative assessment for breast masses in shear-wave elastography

Purpose

To evaluate the interobserver agreement and the diagnostic performance of various qualitative features in shear-wave elastography (SWE) for breast masses.

Materials and methods

A total of 153 breast lesions in 152 women who underwent B-mode ultrasound and SWE before biopsy were included. Qualitative analysis in SWE was performed using two different classifications: E values (Ecol; 6-point color score, Ehomo; homogeneity score and Esha; shape score) and a four-color pattern classification. Two radiologists reviewed five data sets: B-mode ultrasound, SWE, and combination of both for E values and four-color pattern. The BI-RADS categories were assessed B-mode and combined sets. Interobserver agreement was assessed using weighted κ statistics. Areas under the receiver operating characteristic curve (AUC), sensitivity, and specificity were analyzed.

Results

Interobserver agreement was substantial for Ecol (κ = 0.79), Ehomo (κ = 0.77) and four-color pattern (κ = 0.64), and moderate for Esha (κ = 0.56). Better-performing qualitative features were Ecol and four-color pattern (AUCs, 0.932 and 0.925) compared with Ehomo and Esha (AUCs, 0.857 and 0.864; P < 0.05). The diagnostic performance of B-mode ultrasound (AUC, 0.950) was not significantly different from combined sets with E value and with four color pattern (AUCs, 0.962 and 0.954). When all qualitative values were negative, leading to downgrade the BI-RADS category, the specificity increased significantly from 16.5% to 56.1% (E value) and 57.0% (four-color pattern) (P < 0.001) without improvement in sensitivity.

Conclusion

The qualitative SWE features were highly reproducible and showed good diagnostic performance in suspicious breast masses. Adding qualitative SWE to B-mode ultrasound increased specificity in decision making for biopsy recommendation.     

弹性应变率比值与BI-RADS标准鉴别诊断乳腺肿块的对比研究

目的：探讨弹性应变率比值( SR)诊断乳腺良恶性肿块的最佳界点,对比分析SR与常规超声乳腺影像报告和数据系统( BI-RADS)标准对乳腺肿块的鉴别诊断价值。方法对94例患者共计101个乳腺肿块进行 SR 测定和 BI-RADS标准分级,以病理结果为标准,构建SR的受试者工作特征( ROC)曲线,确定其诊断乳腺肿块良恶性的最佳界点,并比较SR与BI-RADS标准的诊断价值。结果 SR的ROC曲线下面积(AUC)为0．933,最佳诊断界点为3．03,以SR≥3．03诊断为恶性, SR <3．03诊断为良性;良性组 SR 为(1．96±0．88),恶性组SR为(4．74±2．22),两组比较差异有统计学意义( P<0．01)。 SR和BI-RADS标准诊断乳腺肿块的敏感度、特异度、准确度、阳性预测值、阴性预测值分别为92．3%、88．7%、90．1%、83．7%、94．8%及71．8%、87．1%、81．2%、77．8%、83．1%, SR 的诊断敏感度高于 BI-RADS标准( P<0．05)。结论作为一种半定量检查方法, SR比BI-RADS标准具有更高的敏感度,可以提高乳腺恶性肿块的检出率。     

乳腺癌超声BI-RADS分级与病理分型及免疫组化的比较分析

目的比较分析乳腺癌超声BI-RADS分级与病理分型及免疫组化之间的关系。方法随机选取2014年12月~2016年12月间我院收治的乳腺癌患者62例作为研究对象,对62例患者的63个乳腺癌肿块的超声表现进行分析,并根据BI-RADS分级评估乳腺癌肿块,术后对标本进行病例组织学分类,分析免疫组化指标、超声表现两者间的相关性。结果 62例患者中,单侧多发1例,单侧单发61例。62例乳腺癌患者的63个肿块中3个(4.76%)3级,14个(22.22%)4级,46个(73.02%)5级;62例乳腺癌患者的63个肿块的位置,38个(60.32%)左乳、25个(39.68%)右乳。其中49个(77.78%)浸润性导管瘤,5个(7.94%)导管内癌,3个(4.76%)黏液腺癌,2个(3.17%)乳头状癌、3个(4.76%)浸润性小叶癌,1个(1.59%)鳞癌;49个浸润性导管瘤中有39个为5级,6个为4级,4个为3级,5个导管内癌中4个为5级,1个为4级,3个浸润性小叶癌中2个为5级1个为4级,2个乳头状癌和1个鳞癌均为5级,3个黏液腺癌为4级。结论 BI-RADS分级分析乳腺肿块的恶性特征准确性高,乳腺癌超声BI-RADS分级与病理分型及免疫组化间有相关性,BI-RADS分级能够对乳腺肿块的恶性特征进行比较准确的分析,能够为乳腺癌的临床治疗提供依据,确定有效的治疗方案,在乳腺癌的诊断中应用价值较高。     

BI-RADS分级在临床不可触及的乳腺病变活检中的应用

目的:探讨乳腺影像报告及数据系统(BI-RADS)分级对影像学发现的亚临床乳腺病变的诊断及治疗价值.材料和方法:50例乳腺X线发现异常而临床不可触及肿块的患者,运用BI-RADS分级系统为乳腺影像评分,为所有患者行乳腺X线引导下导丝定位病灶活检术,对比影像诊断与病理结果,分析影像学对病理结果的预测价值.结果:2例BI-RADS 5级,5例BI-RADS 4级与1例BI-RADS 3级病变证实为恶性,13例BI-RADS 4级和1例BI-RADS 3级病灶诊断为癌前病变,22例BI-RADS 4级和6例BI-RADS 3级病灶最终诊断为良性病变.结论:BI-RADS 3～5级的亚临床病变,通过导丝引导下病灶定位切除活检术,能够帮助发现早期乳腺癌.     

多模态超声列线图模型鉴别BI-RADS 4类乳腺病变良恶性的临床价值

目的 建立基于临床资料、剪切波弹性成像参数和超声影像组学的列线图模型,探讨其鉴别BI-RADS 4类乳腺病变良恶性的效能。方法 回顾性收集2017年12月至2023年6月3家医院共403例BI-RADS 4类乳腺病变患者的临床资料、剪切波弹性成像及病理检查结果,以2017年12月至2019年6月南京鼓楼医院和2019年6月至2019年12月安徽医科大学第一附属医院共283个乳腺病灶为训练集,2022年4月至2023年6月北京世纪坛医院120个乳腺病灶为验证集,按病理结果,将训练集和验证集分为良性组和恶性组。通过提取病灶灰阶超声影像组学特征计算影像组学评分（Rad-score）。采用单因素及多因素Logistic回归分析鉴别BI-RADS 4类乳腺病变良恶性的影响因素,构建预测模型并绘制列线图,采用受试者工作特征（ROC）曲线、校准曲线及临床决策曲线评估该模型的效能。结果 经过特征提取及筛选,最终纳入13个影像组学特征用于计算Rad-score,验证集良、恶性组Rad-score分别为[-1.07 (-1.64, -0.37)分、0.07（-0.3,0.56）分],二者比较差异有统计学意义（Z=514,P<0.001）。多因素Logistic回归分析显示年龄（OR值：1.107,P<0.001）和最大剪切波速度（SWVmax）（OR值：3.919,P<0.001）及Rad-score（OR值：4.18,P<0.001）是预测乳腺恶性病变的独立影响因素。基于以上3个因素构建的列线图模型在训练集中及验证集中鉴别BI-RADS 4类乳腺病变良恶性的ROC曲线下面积均高于SWVmax和Rad-score（均P＜0.001）,且拟合度均良好（均P＞0.05）;在验证集中使用列线图模型预测BI-RADS 4类病变能获得更高的临床收益,将非必要穿刺活检率降低了61.16%。结论 基于患者年龄、SWVmax及Rad-score构建的列线图模型能有效预测BI-RADS 4类乳腺病变良恶性,降低非必要穿刺活检率,有一定的临床价值。     

乳腺癌不同病理特征患者超声BI-RADS分级、VTIQ技术参数、CA153的差异分析

目的 探究乳腺癌不同病理特征患者超声乳腺影像报告和数据系统(BI-RADS)分级、声触诊组织量化(VTIQ)技术参数和糖类抗原153(CA153)的差异。方法 回顾性分析我院106例经术后病理确诊的乳腺癌患者临床资料,术前均接受常规超声检查进行BI-RADS分级,接受VTIQ技术检查测量病灶剪切波速度(SWV_max),经酶联免疫吸附法检测CA153,分析上述检测结果与临床病理特征的关系。结果 乳腺癌各病理类型、组织学分级、肿瘤大小、病理分子分型间的超声BI-RADS分级分布、CA153水平比较差异均无统计学意义(P>0.05);浸润性小叶癌者SWV_max高于导管内原位癌、浸润性导管癌、其他者,浸润性导管癌、其他者SWV_max高于导管内原位癌,差异有统计学意义(P<0.05);组织学分级3级者SWV_max高于2级、1级者,2级者SWV_max高于1级者,差异有统计学意义(P<0.05);病理分子分型中Luminal B(HER2+)者SWV_max最高,且高于Luminal A、Luminal B(HER2-)、Luminal B(HER2+)、三阴型者,Luminal A者SWV_max最低,且均低于其他分子分型者,差异有统计学意义(P<0.05);乳腺癌淋巴结未转移者BI-RADS分级3~4a级分布多于转移者,SWV_max、CA153水平高于转移者,差异有统计学意义(P<0.05)。结论 超声BI-RADS分级、CA153水平与乳腺癌患者淋巴结转移状态有关,VTIQ技术参数SWVmax与病理类型、组织学分级、肿瘤大小、病理分子分型、淋巴结转移状态均有关,可为乳腺癌临床诊断和病情评估提供有效参考信息。     

Breast MRI BI-RADS 3: Impact of Patient-Level Factors on Compliance With Short-Term Follow-Up

PurposeA BI-RADS 3 assessment on breast MRI is given when a finding is estimated to have less than 2% chance of breast cancer. Patients in this category are typically recommended to return for a 6-month follow-up MRI. Compliance with this recommendation is low, and we aim to understand which factors are associated with compliance.Materials and MethodsAll patients with an MRI examination given a BI-RADS category 3 between February 1, 2011, and June 30, 2016, were retrospectively reviewed. Patient demographics and breast-related medical history were extracted from the electronic medical record. Patients presenting for follow-up MRI between 3 and 10 months were considered compliant. Univariate and multivariate analysis was performed to identify which patient-level factors were associated with compliance with follow-up MRI.ResultsOverall, 190 women with a BI-RADS 3 assessment on MRI were included in the study. Of these women, 106 were compliant with the recommended follow-up MRI (57.3%), 34 had delayed follow-up (18.4%), and 45 were noncompliant (24.3%). Reason for examination, personal history of breast cancer, and family history of breast cancer were significantly associated with compliance.ConclusionsWe found that 75.7% of patients had a follow-up MRI after a BI-RADS 3 assessment, but only 57.3% were timely in their follow-up. Our data suggest that there may be subsets of patients who would benefit from additional support and resources to help increase overall compliance and timely compliance.     

Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use

Objectives

With much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist’s opinion into an AI algorithm would make the algorithm achieve better accuracy than an algorithm trained on imaging parameters alone. Open-source BI-RADS data sets were evaluated to see whether inclusion of a radiologist’s opinion (in the form of BI-RADS classification) in addition to image parameters improved the accuracy of prediction of histology using three machine learning algorithms vis-à-vis algorithms using image parameters alone.

BI-RADS3级乳腺病变的回顾分析

目的 遵循美国放射协会制定的乳腺影像学报告和数据系统(ACR BI-RADS)对乳腺病变的分级标准,观察分为3级病变组中满足此标准的例数,同时评价几种临床因素对BI-RADS分级的影响.方法 对超声检查最初分为BI-RADS 3级病变的487例乳腺肿块患者的声像图特征进行回顾分析.总结最初的超声表现,且对几种临床中可能影响乳腺病变分级的因素,包括患者年龄、病灶的多发性、是否可以扪及、超声医生的经验以及病灶的大小进行评价.结果 487例病例中,479例(98.36%)为良性,8例(1.64%)为恶性.203例(41.68%)(包括8例恶性病灶)经回颐分析,按BI-RADS分级标准再评估为4级.如在最初超声检查分析时,严格按照分级标准,活检阳性率仅3.94%(8/203),而96.06%(195/203)不需要进行活检.年龄40岁以上和病灶多发性再评估为4级的频率更高(分别P=0.008,P=0.006).而病灶是否可以扪及、病灶大小及医生的经验对再评估结果影响的差异则无显著统计学意义.结论 病灶的多发性和年龄对于分为3级病变再分级有明显的影响.

Abstract：

Objective To investigate how many probably benign lesions on ultrasound(US) fulfilled the published criteria and to evaluate how clinical and personal factors influenced the categorization of breast lesions.Methods A total of 487 lesions in 487 women with more than 12 months follow-up after the initial category 3 assessment on US were included.The initial US images were retrospectively reviewed according to previously published criteria,and evaluated several factors that could influence the characterization of breast lesions in clinical practice such as age,multiplicity,palpability,radiologist 's experience,and lesion size.Results Of 487 lesions,479 (98.36%) were benign and 8 (1.64%) were malignant.Of 487 lesions,203(41.68%) including 8 malignancies were reassessed as category 4.If strict criteria had been applied at initial US examination,the positive biopsy rate would have been only 3.94% (8/203) and 96.06% (195 of 203) biopsies would have been unnecessary.Lesions in women 40 years or older and multiple lesions were more frequently reassessed as category 4 (P = 0.008 and P = 0.006,respectively).The presence of palpability,lesion size,and the radiologist 's experience did not significantly influence the categorization of breast lesions on US.Of 487 probably benign lesions,41.68 % could be classified as category 4 lesions when strict criteria were applied in initial practice.Conclusions The multiplicity of the lesion and the patient 's age were found to have a significant influence on the classifcation of probably benign solid masses.