彩色超声与乳腺X线摄影在乳腺癌筛查中的应用对比分析

目的探讨彩色B超与乳腺X线摄影在乳腺癌筛查中的临床应用价值。方法对收治的乳腺病患者应用彩色声与乳腺X线摄影进行检查,并就这两种检查方法在发现病变及良恶性肿块鉴别方面的诊断能力进行对比研究。结果彩色多普勒超声对乳腺癌患者恶性钙化灶的检出率75.00%,明显低于乳腺X线摄影85.94%,且差异具有统计学意义。但是彩色多普勒超声对乳腺癌患者的血流信号检出率84.38%明显高于乳腺X线摄影29.69%,且差异具有统计学意义。彩色超声和乳腺X线摄影诊断乳腺癌的符合率分别为89.06%、85.94%,经统计学分析发现两种筛查方法的符合率比较无明显差异。两种检查方法联合筛查符合率为96.88%,经统计学分析发现两者联合检查的符合率与两种方法的单独诊断符合率比较存在明显差异,且差异具有统计学意义。结论彩色多普勒超声和X线摄影早期筛查乳腺癌各有优势,相互补充,可提高乳腺癌诊断准确率。     

数字乳腺X线摄影、数字乳腺断层摄影及合成X线成像技术在乳腺癌检查中的应用

<正>乳腺癌是女性常见的恶性肿瘤之一,其发病率呈逐年上升的趋势^[1]。目前,乳腺X线摄影（MG）仍是乳腺癌早期诊断的有效手段之一。其中数字乳腺X线摄影（DM）是临床上最常用的影像技术,已经成为乳腺癌筛查和诊断的首选方式。同时,数字乳腺断层摄影（DBT）及合成X线成像（SM）技术也广泛应用于临床。本文通过比较DM、DBT和SM在技术和诊断上的优势及不足,旨在选择恰当乳腺检查技术,充分发挥各自优势,有效提高乳腺癌的诊断和筛查准确率。     

数字乳腺断层摄影及其合成二维影像技术在乳腺癌筛查中的研究进展

目前乳腺X线检查仍是乳腺癌早期诊断的有效检查方法之一,主要包括全视野数字化乳腺摄影（FFDM）、数字乳腺断层摄影（DBT）、合成乳腺X线摄影（SM）以及3种技术的联合应用（FFDM联合DBT、SM联合DBT）。对DBT、SM和SM联合DBT在乳腺筛查中诊断效能、影像质量及辐射剂量等进行比较。SM联合DBT可有效平衡辐射剂量和诊断效能,但仍然在判读时间、信息的存储与传输和检查成本方面存在局限性。就以上3种检查技术在乳腺癌筛查中的研究进展予以综述。     

人工智能技术在乳腺结构扭曲检出中的研究进展

乳腺腺体结构扭曲（AD）是诊断乳腺癌的主要征象之一,但有较高的漏诊率及假阳性率。近年人工智能（AI）对医学影像处理和特征提取能力不断提升,辅助检出AD的敏感度及准确度不断提高。在临床上,乳腺影像中AI技术的应用主要集中于乳腺X线摄影。就基于乳腺X线摄影的机器学习（ML）及深度学习（DL）技术在AD检出方面的研究进展予以综述。     

屏胶乳腺X线摄影和数字化乳腺X线摄影的对比研究

数字乳腺X线摄影（DM）较屏胶乳腺X线摄影（SFM）具有更多的优势，其对于图像的获取、储存和显示都是相互分离的，每个系统都能优化产生最佳效果，而且数字图像能够由计算机处理并显示为多种形式。因此，DM系统正逐渐替代SFM系统并成为筛查及诊断乳腺癌的常规手段。就DM和SFM的成像原理、图像质量、图像处理以及临床应用进行比较。     

致密型乳腺影像筛查技术及研究进展

乳腺密度反映乳腺X 线摄影上乳腺的组成成分,乳腺密度评估是乳腺X 线筛查的常规临床需求。致密型乳腺是乳腺癌的潜在危险因素之一,也是乳腺X 线筛查乳腺癌敏感性及特异性偏倚存在的主要因素之一。就数字乳腺X 线摄影、超声、乳腺断层融合摄影及MRI 对致密型乳腺的诊断效能及研究进展予以综述。     

数字乳腺断层X线成像技术在乳腺癌筛查中的应用

【摘要】乳腺影像检查是降低乳腺癌死亡率的有效方法,数字乳腺断层融合X 线成像（DBT）作为近年来新出现的影像成像技术,大大提高了乳腺癌筛查的敏感度和特异度。本文就DBT与传统乳腺X线摄影检查的对比研究、在致密型与非致密型乳腺中的应用、诊断早期与浸润性乳腺癌的效能以及其目前存在的局限性等方面进行综述,旨在提高对乳腺癌的诊断,为DBT技术的发展实践提供策略。     

超声与X线摄影联合应用在乳腺癌诊断中的价值

目的：评价超声、X线摄影和两者联合应用对乳腺癌的诊断价值。方法回顾性分析我院经病理证实的81例乳腺癌患者的超声和X线摄影检查结果,并与病理结果进行对照分析。结果超声诊断乳腺癌63例,诊断准确率78％;X线摄影诊断乳腺癌65例,诊断准确率80％;两者联合诊断乳腺癌76例,诊断准确率94％,较两者单独使用诊断准确率明显提高（P＜0．05）。结论超声和X线摄影是筛查和诊断乳腺癌的首选方法,两者具有互补性,联合应用可提高乳腺癌的诊断准确率,降低误漏诊率。     

超声检查与乳腺X线摄影诊断乳腺癌临床价值分析

目的探讨在乳腺癌患者分别应用超声检查、乳腺X线摄影检查的临床价值。方法对80例患者全部患者均经穿刺活检或术后病理检查明确诊断为乳腺癌。全部患者均安排行乳腺X线摄影、超声检查。结果超声联合乳腺X线摄影检查的诊断符合率较单纯超声检查、单纯乳腺X线摄影检查显著更高,漏诊或误诊率显著更低(P0.05)。结论乳腺癌患者的临床诊断中应用超声联合乳腺X线摄影检查具有良好的应用价值。     

CT灌注和X线摄影在乳腺疾病诊断中的价值

目的：探讨CT灌注和X线摄影对乳腺疾病的诊断价值。方法：45例乳腺病变均行X线摄影和CT灌注检查,并与病理结果进行对比分析。结果：X线摄影、CT灌注诊断符合率分别为77.28%和88.89%,综合X线摄影和CT灌注诊断符合率为95.56%,三者之间差异有统计学意义（P〈0.05）。在CT灌注中,乳腺恶性病变的血流量（BF）、血容量（BV）和表面通透性（PS）的均值均明显高于良性病变（P〈0.05）。CT灌注对病变范围、乳腺癌坏死灶的判别优于X线摄影（P〈0.05）。X线摄影对钙化灶的定性诊断优于CT灌注（P〈0.05）。结论：X线摄影和CT灌注具有互补性,两者结合可提高乳腺癌诊断符合率。     

Deep Learning-Based Artificial Intelligence for Mammography

During the past decade, researchers have investigated the use of computer-aided mammography interpretation. With the application of deep learning technology, artificial intelligence (AI)-based algorithms for mammography have shown promising results in the quantitative assessment of parenchymal density, detection and diagnosis of breast cancer, and prediction of breast cancer risk, enabling more precise patient management. AI-based algorithms may also enhance the efficiency of the interpretation workflow by reducing both the workload and interpretation time. However, more in-depth investigation is required to conclusively prove the effectiveness of AI-based algorithms. This review article discusses how AI algorithms can be applied to mammography interpretation as well as the current challenges in its implementation in real-world practice.     

基于深度学习的人工智能技术在乳腺癌筛查及影像诊断中的应用进展

人工智能技术可使计算机模拟人类的思考过程和智能活动,在医学影像领域具有强大的图像处理和特征提取能力,应用基于人工智能的乳腺癌影像筛查能够在减轻放射科医生工作负担的同时提高乳腺癌筛查和诊断的准确性及敏感性。简要总结了目前常用的乳腺X线图像公共数据集和近期乳腺癌影像学人工智能辅助诊断竞赛情况,并对基于深度学习的人工智能技术在乳腺癌影像学筛查和诊断中的最新进展予以综述。     

基于深度学习的癌症计算机辅助分类诊断研究进展

近年来,基于深度学习的医学影像分类研究迅猛发展,已成为癌症计算机辅助诊断(CAD)领域的研究热点。目前,基于公开数据库(如LIDC、INbreast、DDSM公共数据库)进行的研究中,对肺结节良恶性分类研究、乳腺病灶的良恶性鉴别诊断的研究报道较为全面。对基于深度学习的肺CT图像、乳腺X线摄影图像的癌症计算机辅助分类诊断研究进展予以综述。     

乳腺摄片普查过程中漏误诊的相关因素

乳腺摄片为乳腺癌普查的重要手段，但部分患者乳腺片存在着假阴性与假阳性的不足，为临床漏误诊埋下了隐患。为减少漏误诊，本文搜集近年来国内外部分有关乳腺摄片普查方而的资料进行分析总结认为，乳腺摄片技术方面导致乳腺癌普查摄片假阴性与假阳性的因素可为：乳腺摄影器械质量性能及其相关因素、乳腺摄片投照位置的选择与胶片质量方面的因素以及受检者乳腺密度与年龄方而的因素等。     

Tomosynthesis as a screening tool for breast cancer: A systematic review

BackgroundMammography is an important screening tool for reducing breast cancer mortality. Digital breast tomosynthesis (DBT) can potentially be integrated with mammography to aid in cancer detection.MethodUsing the PRISMA guidelines, a systematic review of current literature was conducted to identify issues relating to the use of tomosynthesis as a screening tool together with mammography.FindingsUsing tomosynthesis with digital mammography (DM) increases breast cancer detection, reduces recall rates and increases the positive predictive value of those cases recalled. Invasive cancer detection is significantly improved in tomosynthesis compared to mammography, and has improved success for women with heterogeneous or extremely dense breasts.ConclusionTomosynthesis reduces some limitations of mammography at the time of screening that until recently were most often addressed by ultrasound at later work-up. Tomosynthesis can potentially be adopted alongside mammography as a screening tool.     

F-18 fluorodeoxyglucose-positron emission tomography imaging for primary breast cancer and loco-regional staging

Breast cancer is the most common female malignancy in Western countries. The limitations of mammography, ultrasound and MRI do not allow reliable identification of primary breast cancer at early stages. Functional breast imaging with positron emission tomography (PET) and F-18 fluorodeoxyglucose (FDG) enables the visualization of increased glucose metabolism of breast cancer. However, despite the successful identification of primary breast cancer, FDG-PET provides a low sensitivity to detect small tumors. Therefore, FDG-PET does not allow screening of asymptomatic women and cannot be used to exclude breast cancer in patients with suspicious breast masses or abnormal mammography. FDG-PET is a powerful tool for staging of breast cancer patients, but does not detect micrometastases and small tumor infiltrated lymph nodes. Nevertheless, in patients with locally advanced breast cancer, PET accurately determines the extent of disease, particularly the loco-regional lymph node status. Advances in technology, for example the development of dedicated breast imaging devices such as positron emission mammography, hold promise to improve the detection of primary tumors in the future.     

Radiological diagnosis of mammary carcinomas. I: pathology and x-ray mammography

Breast cancer is the most common malignant tumor in women: almost 10 % will suffer from breast cancer during their life and almost half of these will die of it. The spectrum of radiologic methods for diagnosing breast cancer is wide, including X-ray mammography, ultrasound, magnetic resonance mammography, and minimally invasive biopsies. After long-lasting controversies, breast cancer screening using X-ray mammography has now been introduced in Germany, following the projects in the Netherlands and Sweden. However, assessing mammographic films under screening conditions requires skills distinctly different from those needed under clinical conditions. This first part of two covers the histopathological basics and X-ray mammography; the second will deal with ultrasound of the breast and magnetic resonance mammography.     

Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts

Purpose

Mammography, the standard method of breast cancer screening, misses many cancers, especially in dense-breasted women. We compared the performance and diagnostic yield of mammography alone versus an automated whole breast ultrasound (AWBU) plus mammography in women with dense breasts and/or at elevated risk of breast cancer.

Methods

AWBU screening was tested in 4,419 women having routine mammography (Trial Registration: ClinicalTrials.gov Identifier: NCT00649337). Cancers occurring during the study and subsequent 1-year follow-up were evaluated. Sensitivity, specificity and positive predictive value (PPV) of biopsy recommendation for mammography alone, AWBU and mammography with AWBU were calculated.

Results

Breast cancer detection doubled from 23 to 46 in 6,425 studies using AWBU with mammography, resulting in an increase in diagnostic yield from 3.6 per 1,000 with mammography alone to 7.2 per 1,000 by adding AWBU. PPV for biopsy based on mammography findings was 39.0% and for AWBU 38.4%. The number of detected invasive cancers 10 mm or less in size tripled from 7 to 21 when AWBU findings were added to mammography.

Conclusion

AWBU resulted in significant cancer detection improvement compared with mammography alone. Additional detection and the smaller size of invasive cancers may justify this technology’s expense for women with dense breasts and/or at high risk for breast cancer.