共查询到20条相似文献,搜索用时 15 毫秒
1.
目的观察基于X线及超声的乳腺影像报告和数据系统(BI-RADS)选取影像学特征构建的机器学习模型预测乳腺癌分子分型的可行性。方法回顾性分析200例经病理的浸润性乳腺癌,根据免疫组织化学结果分为Luminal组(n=109)与非Luminal组(n=91),组内按7∶3比例随机分为训练亚组及测试亚组。采集11个临床信息,并提取24个影像学特征,建立4种机器学习模型,通过受试者工作特征(ROC)曲线评价各模型预测不同分子分型乳腺癌的效能,比较各模型曲线下面积(AUC)的差异。结果测试组随机森林(RF)、极端梯度提升(XGBoost)、逻辑回归(LR)及支持向量机(SVC)模型判断不同分子分型乳腺癌的敏感度分别为74.10%、74.10%、77.80%和70.40%,特异度分别为63.60%、51.50%、57.60%和60.60%,准确率分别为68.30%、61.70%、66.70%和65.00%;其中RF模型判断Luminal型与非Luminal型乳腺癌的AUC值最大(AUC=0.70,P<0.05),但与其他模型间差异均无统计学意义(P均>0.05)。结论 RF模型预测不同... 相似文献
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目的 探讨乳腺MRI特征及ADC值对乳腺影像报告和数据系统(BI-RADS)4类良恶性病变的预测能力,并尝试建立Logistic回归预测模型。方法 收集MRI诊断为BI-RADS 4类病变、并取得病理结果的79例乳腺病变患者(82个病变)。采用单因素二元Logistic回归及两独立样本t检验分析各MRI特征和ADC值鉴别良恶性乳腺病变的统计学意义,并建立多因素Logistic回归预测模型,绘制ROC曲线评价回归模型预测BI-RADS 4类病变良恶性的效能。结果 肿块型病变中,将边缘、内部强化及ADC值纳入Logistic回归预测模型中(P均<0.05,伪R2=0.62),其诊断良恶性乳腺病变的ROC曲线AUC为0.981,敏感度为87.80%,特异度为100%。非肿块型病变中,无预测变量纳入建立Logistic回归预测模型(P均>0.1)。结论 乳腺MRI特征(边缘、内部强化)及ADC值对预测肿块型BI-RADS 4类病变的良恶性具有一定意义;Logistic回归预测模型可有效鉴别BI-RADS 4类肿块型病变性质。 相似文献
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Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods. 相似文献
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目的 探讨基于改进局部三元模式(LTP)算法提取的乳腺新型纹理特征及其与常规特征融合预测乳腺癌的价值。方法 对钼靶图像进行乳腺分割,采用基于改进LTP算法提取双侧乳腺的新型纹理特征和常规特征;合并左右侧乳腺纹理特征;采用主成分分析法对提取的高维纹理特征降维;以K最近邻(KNN)和LADTree算法分别对纹理特征及融合纹理特征进行分类。结果 基于改进LTP算法提取的新型纹理特征预测乳腺癌的ROC曲线下面积(AUC)为0.732 4±0.042 8,敏感度为72.04%(67/93),特异度为74.51%(76/102),准确率为73.33%(143/195);融合常规特征后AUC为0.865 5±0.014 8,敏感度为84.95%(79/93),特异度为88.23%(90/102),准确率为86.67%(169/195)。结论 基于LTP算法提取的新型纹理特征预测乳腺癌的精度较高,与常规特征融合后可进一步提高预测效能。 相似文献
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目的 探讨MR动态增强图像纹理分析鉴别诊断乳腺结节良恶性的价值。方法 回顾性分析经手术病理证实的78例患者共80个乳腺结节的MR动态增强图像,每个结节获得63个纹理特征参数。绘制纹理参数鉴别诊断良恶性乳腺结节的ROC曲线,并与MR乳腺影像报告和数据系统(BI-RADS)的诊断效能比较。结果 78例患者的80个乳腺结节中,纹理参数中灰度游程长不均匀度判断乳腺结节良恶性的AUC值(0.836)最大且诊断准确率高,其诊断恶性乳腺结节的敏感度为82.93%(34/41)、特异度为94.87%(37/39)、准确率为88.75%(71/80)、阳性预测值为94.44%(34/36)、阴性预测值为84.09%(37/44)。MR BI-RADS分类诊断恶性乳腺结节的敏感度为95.12%(39/41)、特异度为87.18%(34/39)、准确率为91.25%(73/80)、阳性预测值为88.63%(39/44)、阴性预测值为94.44%(34/36)。MR BI-RADS分类和纹理分析判断恶性乳腺结节准确率差异无统计学意义(P=0.11)。与单独应用BI-RADS分类比较,两者联合应用可明显提高诊断恶性乳腺结节的特异度(P<0.001)。结论 MR纹理分析可作为传统诊断乳腺良恶性结节的补充。 相似文献
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Kalthum Abdullah Sofi Ali Salah Muhammed Fateh 《The Journal of international medical research》2022,50(12)
ObjectiveMammography is the gold standard screening procedure for the early diagnosis of breast cancer. This study aimed to determine the distribution of breast density among women older than 40 years in Sulaimaniyah, Iraq, and to examine the correlations between breast density and various risk factors.MethodsThis cross-sectional study included 750 women who received routine mammographic breast screening at Sulaimaniyah Breast Center. Bilateral standard two-view mammographic images (craniocaudal and mediolateral oblique projections) were acquired and reported using a picture archiving and communication system. American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) assessment categories C and D were considered as dense.ResultsA total of 54.3% of breasts were classified as dense, with ACR-BI-RADS categories C or D. Breast density was significantly associated with age, body mass index, a family history of breast cancer, and pre-menopause, and women with no history of breastfeeding were more likely to have dense breasts than those with partial or complete breastfeeding.ConclusionsThis study revealed that women from Sulaimaniyah with a distinct breast-density profile at mammographic screening may have a significantly increased risk of breast cancer. 相似文献
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目的 采用核极限学习机(KELM)方法对乳腺良恶性肿块样病变进行分类,并评估其鉴别诊断效能。方法 对93例患者103个经术后病理或长期随访确诊的乳腺肿块样病变行MR检查。由1名低年资和1名高年资乳腺影像学诊断医师参照乳腺影像报告和数据系统(BI-RADS)第2版,选取12个MRI特征及临床特征,分别独立及采用KELM方法对乳腺病变进行良恶性分类,并计算诊断效能。结果 低年资和高年资医师使用KELM方法鉴别诊断乳腺良恶性病变的敏感度、特异度、准确率分别为0.88、0.89、0.91和0.93、0.91、0.92,AUC分别为0.84和0.89。低年资和高年资医师独立诊断的敏感度、特异度、准确率分别为0.91、0.74、0.86和0.90、0.85、0.92,AUC分别为0.83和0.90。结论 基于影像学特征及临床资料特征的KELM方法可辅助临床鉴别诊断乳腺肿块样良恶性病变,具有较理想的敏感度、特异度和准确率。 相似文献
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目的 观察S-DetectTM分类技术鉴别诊断BI-RADS 4类乳腺良恶性肿块的价值。方法 对94例经二维超声诊断为BI-RADS 4类乳腺肿块患者(共104个肿块)行S-DetectTM分类技术检查,以手术或穿刺活检病理结果作为金标准,评价S-DetectTM分类技术、BI-RADS分类及二者联合应用诊断乳腺BI-RADS 4类良恶性肿块的价值。结果 104个乳腺肿块,经病理确诊为良性41个、恶性63个。S-DetectTM分类技术诊断乳腺BI-RADS 4a类乳腺肿块的敏感度(SE)66.67%,特异度(SP)89.29%、阳性预测值(PPV)57.14%、阴性预测值(NPV)92.59%;对乳腺BI-RADS 4b类肿块分别为90.91%、60.00%、88.24%及66.67%;对乳腺BI-RADS 4c类肿块分别为95.83%、66.67%、95.83%及66.67%。S-DetectTM分类技术联合BI-RADS分类诊断乳腺肿块的SE、SP、准确率明显均高于单独运用(P均<0.05)。结论 S-DetectTM分类技术判断乳腺BI-RADS 4a类良性肿块、BI-RADS 4b类及BI-RADS 4c类恶性肿块均有较高价值。S-DetectTM分类技术联合BI-RADS分类可明显提高鉴别BI-RADS 4类乳腺良恶性肿块的效能。 相似文献
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目的 评价声脉冲辐射力成像(ARFI)在乳腺影像学报告及数据系统(BI-RADS)4级乳腺肿块良、恶性诊断中的价值。 方法 用ARFI对68例共75个常规超声诊断为BI-RADS 4级的乳腺肿块进行成像,测量声触诊组织成像(VTI)模式下肿块面积与常规二维超声肿块面积比值(AR),并测量声触诊量化成像(VTQ)模式下肿块剪切波速度(SWV);以病理结果(恶性34个,良性41个)为金标准,构建ROC曲线,评价ARFI的2种成像模式对BI-RADS 4级乳腺肿块的诊断价值。 结果 良、恶性BI-RADS 4级乳腺肿块的AR值差异有统计学意义(P<0.05),ROC曲线下面积(AUC)为0.851,敏感度、特异度、准确率分别为82.40%、80.50%、81.30%。良、恶性BI-RADS 4级乳腺肿块的SWV值差异有统计学意义(P<0.05)。SWV值AUC为0.861,敏感度、特异度、准确率分别为85.30%、85.40%、85.30%。二者AUC差异无统计学意义(Z=1.47,P>0.05)。二者联合诊断的敏感度、特异度、准确率分别为88.20%、87.80%、88.00%。 结论 ARFI对鉴别BI-RADS 4级乳腺肿块的良、恶性具有较高价值;联合应用VTI和VTQ可以提高诊断效能。 相似文献
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剪切波弹性成像定性技术鉴别诊断乳腺良恶性病变 总被引:3,自引:2,他引:1
目的 探讨SWE定性技术在乳腺病灶良恶性鉴别诊断中的应用价值。方法 对236例患者共261个病灶行常规超声及SWE检查。以常规超声图像进行乳腺影像报告和数据系统(BI-RADS)分类,将SWE图像分为6种类型。以病理结果为金标准,绘制ROC曲线,评价SWE分型、BI-RADS分类及二者联合的诊断效能。结果 良性病灶100个,恶性病灶161个。以SWE分型3型为诊断界点,敏感度、特异度、准确率、阳性预测值、阴性预测值分别为85.71%(138/161)、93.00%(93/100)、88.51%(231/261)、95.17%(138/145)、80.17%(93/116);以BI-RADS 4a类为诊断界点,敏感度、特异度、准确率、阳性预测值、阴性预测值分别为98.76%(159/161)、73.00%(73/100)、88.89%(232/261)、85.48%(159/186)、97.33%(73/75);二者联合诊断的敏感度、特异度、准确率、阳性预测值、阴性预测值分别为99.38%(160/161)、70.00%(70/100)、88.12%(230/261)、84.21%(160/190)、98.59%(70/71)。SWE分型的特异度和阳性预测值均高于BI-RADS分类及联合诊断(P均<0.05),BI-RADS分类及联合诊断的敏感度和阴性预测值均高于SWE分型(P均<0.05),三者诊断准确率差异均无统计学意义(P均>0.05)。结论 SWE定性技术有助于乳腺良恶性病灶的鉴别诊断。 相似文献
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目的 分析经数字化乳腺X线引导下导丝定位钙化切除活检证实的良恶性乳腺病变的X线征象,筛选有效客观影像学因子。方法 收集接受数字化乳腺X线引导下导丝定位钙化切除活检的乳腺病变患者98例,分析并记录病变的X线征象,比较良恶性病变X线征象差异及BI-RADS4类患者A、B、C亚类中良恶性病变构成比的差异。结果 98例中,良性病变72例(72/98,73.47%),恶性26例(26/98,26.53%)。良恶性病变的钙化类型、BI-RADS分类差异有统计学意义(P均<0.05);BI-RADS类患者3三个亚类中,良恶性病变构成比差异有统计学意义(P=0.003),BI-RADS4C中恶性病变比例最高(8/11,72.73%),BI-RADS4A中良性病变比例最高(22/26,84.62%)。结论 乳腺X线引导下导丝定位钙化切除活检术能够有效发现乳腺癌。钙化类型和BI-RADS分类是恶性乳腺钙化的有效影响因子。 相似文献
12.
目的表征乳腺图像中肿块部分纹理特征,通过纹理分析实现乳腺图像中肿块部分与正常腺体部分的分类。方法应用分形特征值表征乳腺图像纹理特征,利用多级分形特征提取法将乳腺图像分解成一系列细节图像,提取出多个分形特征值;利用分类精度、ROC曲线及曲线下面积(AUC)进行特征选择构建分形特征序列,最后应用支持向量机(SVM)方法进行分类。结果对60幅图像的可疑病变区域进行分形特征序列提取分析,SVM交叉验证分类精度达84.50%。结论基于分形维数的乳腺图像分类方法不仅能对肿块与正常腺体进行图像分类,还可有效表征乳腺图像的纹理信息,有助于提高乳腺肿块诊断的准确率。 相似文献
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数字乳腺断层摄影诊断致密型乳腺无钙化肿块 总被引:3,自引:3,他引:0
目的 通过与常规乳腺X线摄影(DM)和超声进行对比,分析数字乳腺断层摄影(DBT)对致密型乳腺内无钙化肿块的诊断价值。方法 参照乳腺影像报告和数据系统(BI-RADS)标准,回顾性分析DBT、DM及超声表现为无钙化肿块的致密型乳腺的1 144例患者资料,以组织病理结果为金标准,评估DBT、DM及超声对乳腺无钙化肿块的检出率、诊断符合率、敏感度、特异度、假阴性率及BI-RADS分类,并进行统计学分析。结果 DBT、DM及超声检查对致密型乳腺无钙化肿块的检出率和诊断符合率分别为86.62%(991/1 144)、77.80%(890/1 144)、99.65%(1 140/1 144)和83.92%(960/1 144)、75.00%(858/1 144)、94.67%(1 083/1 144),差异均有统计学意义(P均< 0.05)。DBT、DM及超声对致密型乳腺肿块恶性病变的诊断敏感度、特异度和假阴性率分别为89.39%(312/349)、79.93%(231/289)、92.70%(432/466),81.51%(648/795)、73.33%(627/855)、96.02%(651/678)和10.60%(37/349)、20.07%(58/289)、7.30%(34/466)。3种检查对乳腺良性肿块病变的BI-RADS分类评估差异无统计学意义(P=0.75),对乳腺恶性肿块的BI-RADS分类差异有统计学意义(P<0.01),其中超声与DM和DBT、DBT与DM对乳腺恶性肿块的BI-RADS分类评估差异均有统计学意义(P均< 0.016 7)。结论 DBT对致密型乳腺无钙化肿块的检出及诊断较DM具有更大优势;DBT和超声对致密型乳腺无钙化肿块的检出及诊断价值相近。 相似文献
14.
We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting regions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tissue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the ‘neighbourhood’ for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise constant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system. 相似文献
15.
目的 观察乳腺假血管瘤样间质增生(PASH)超声和乳房X线片表现.方法 纳入51例经病理证实PASH患者,共54个结节,观察其超声及X线表现.采用Newcombe法评价超声及X线结节检出率的差异.结果 54个乳腺PASH结节中,26个(26/54,48.15%)经超声评价为乳腺影像报告和数据系统(BI-RADS)3类、... 相似文献
16.
目的 探讨CEUS对乳腺X线摄影(MG)诊断为BI-RADS 3~5类病变的诊断价值。方法 对120例乳腺摄影诊断为BI-RADS 3~5类病变的患者行CEUS检查,所有患者均于影像学检查后接受病理检查。以病理结果为金标准,计算MG与CEUS的诊断敏感度、特异度、阳性预测值(PPV)和阴性预测值(NPV),并进行统计学分析,比较其诊断效能。结果 120例中,MG诊断为BI-RADS 3类病变37例,其中CEUS诊断2例真阳性,4例假阳性;BI-RADS 4类病变60例,CEUS诊断14例真阳性,33例真阴性;BI-RADS 5类病变23例,CEUS诊断18例真阳性,2例假阳性,3例CEUS诊断阴性者均为假阴性。MG与CEUS对BI-RADS 3~5类病变的诊断敏感度差异无统计学意义(95.12% vs 82.93%, P=0.366),CEUS的诊断特异度明显高于MG(81.01% vs 44.30%, P<0.001);MG与CEUS的PPV和NPV差异均有统计学意义(P均<0.05)。结论 CEUS应用于乳腺摄影BI-RADS 3、4、5类病变中,可提高其诊断特异度;对于BI-RADS 3类病变,CEUS阴性诊断可增强诊断信心。 相似文献
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目的 探讨声触诊组织成像量化(VTIQ)技术、钼靶X线及二者联合诊断乳腺良恶性病灶的价值。方法 对99例患者(110个乳腺病灶)行术前VTIQ成像和钼靶X线检查,获得病灶的剪切波速度平均值(SWVmean),并进行乳腺影像报告与数据系统(BI-RADS)分类。以病理结果为金标准,分别绘制SWVmean、钼靶X线及二者联合诊断乳腺病灶良恶性的ROC曲线,评价其诊断效能。比较VTIQ技术、钼靶X线及二者联合诊断乳腺良恶性病灶的AUC的差异。结果 乳腺良性病灶SWVmean为(3.03±0.78)m/s,恶性为(5.61±2.11)m/s,差异有统计学意义(P<0.001)。SWVmean诊断乳腺良恶性病灶的截断值为3.93 m/s,钼靶X线为BI-RADS 4B类。VTIQ技术、钼靶X线及二者联合诊断乳腺良恶性病灶的AUC分别为0.870 、0.749 和0.873,VTIQ技术与钼靶X线、二者联合与钼靶X线的AUC差异均有统计学意义(P=0.036、0.015),二者联合与VTIQ技术AUC差异无统计学意义(P=0.908)。结论 VTIQ技术与钼靶X线联合诊断乳腺良恶性病灶具有较高价值。 相似文献
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《Ultrasound in medicine & biology》2022,48(11):2267-2275
The aim of the work described here was to develop an ultrasound (US) image–based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US. 相似文献
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目的探讨常规超声与S-Detect技术在乳腺病灶良恶性鉴别诊断中的效能比较。 方法选取2018年6月至7月在中国医科大学附属第一医院经手术病理证实的367例乳腺病灶患者,共468个病灶。所有病灶分别由3名不同年资(1、4、7年)乳腺超声医师进行二维超声成像(静态图像及动态图像)的两次乳腺超声影像报告与数据系统(BI-RADS)分类以及计算机S-Detect分类,通过绘制不同BI-RADS分类诊断组的ROC曲线,确定最佳诊断界值,以进行各组BI-RADS分类的良恶性统计,以病理结果为"金标准",应用诊断试验四格表分别计算不同BI-RADS分类诊断组及S-Detect分类组对乳腺病灶良恶性诊断的敏感度、特异度、准确性、阳性预测值及阴性预测值,采用χ2检验分别将各BI-RADS分类组诊断效能与S-Detect分类组进行比较。绘制各组的ROC曲线,应用Z检验分别将各BI-RADS分类组ROC曲线下面积与S-Detect分类组进行比较。 结果468个乳腺病灶术后病理诊断良性313个,恶性155个。通过绘制不同BI-RADS分类诊断组的ROC曲线,确定最佳诊断界值为BI-RADS 4a类。S-Detect分类诊断敏感度93.5%明显高于低年资医师静态图像BI-RADS分类诊断69.0%及低年资医师动态录像BI-RADS分类诊断72.3%,差异有统计学意义(χ2=30.627、24.785,P均<0.001),S-Detect分类诊断特异度83.7%,明显低于中年资医师动态图像BI-RADS分类诊断92.0%,差异有统计学意义(χ2=10.124,P=0.001),其余各诊断效能差异均无统计学意义(P均>0.05)。S-Detect分类诊断曲线下面积0.917高于低年资医师两次(静态图像及动态图像)BI-RADS分类0.790、0.803,差异均有统计学意义(Z=5.271、4.693,P均<0.0001);S-Detect分类诊断曲线下面积与中年资医师静态BI-RADS分类0.917比较,差异无统计学意义(P>0.05),低于中年资医师动态BI-RADS分类0.941,差异有统计学意义(Z=4.327,P<0.0001);S-Detect分类诊断曲线下面积均低于高年资医师两次BI-RADS分类0.946、0.959,差异均有统计学意义(Z=4.225、5.477,P均<0.0001)。 结论S-Detect分类技术可以达到中年资医师静态图像BI-RADS分类的诊断水平,但低于其动态图像的诊断水平。 相似文献