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Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)最新文献

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Prediction of Ki-67 expression of breast cancer with a multi-parametric model using MRI parameters from ultrafast DCE-MRI and DWI 基于超快DCE-MRI和DWI MRI参数的多参数模型预测乳腺癌Ki-67表达
Akane Ohashi, M. Kataoka, M. Iima, M. Honda, Rie Ota, Y. Urushibata, Marcel Dominik Nickel, Toi Masakazu, S. Zackrisson, Y. Nakamoto
The purpose of this study is to investigate the prediction of Ki-67 expression of breast cancers using MRI parameters from ultrafast (UF) DCE-MRI, DWI, T2WI, and the lesion size. Breast MRI was performed with a 3T scanner using dedicated breast coils. UF DCE-MRI was obtained using Compressed Sensing-VIBE (prototype sequence). As a kinetic parameter of UF DCE-MRI, maximum slope (MS) was defined as percentage relative enhancement (%/s), and time to enhance (TTE) was defined as the time interval between the aorta and lesion enhancement. The apparent diffusion coefficient (ADC) was derived from DWI. Two radiologists measured each MR parameter, and inter-rater agreement was evaluated. Univariate and multivariate logistic regression analyses were perfomed to predict low Ki-67 (<; 14%) and high Ki-67 (≥ 14%) expression using MS, TTE, ADC, T2- signal intensity (SI), and lesion size. The significant parameters (p-values of < 0.05) were selected for the prediction model, and the diagnostic performance of the model was evaluated using ROC curve analysis. A total of 191 invasive carcinomas defined as mass lesions were included (72 low Ki-67/ 119 high Ki-67 lesions). The inter-rater agreements of all parameters were excellent. After univariate and multivariate logistic regression analysis, ADC and lesion size remained significant parameters. Using these significant parameters, the multi-parametric prediction model yielded an AUC of 0.77 (95%CI of 0.70-0.84) (sensitivity 72.3%, specificity 76.4%, and PPV 83.5%, and NPV 62.5%). DWI parameter (ADC) may be more valuable than UF DCE-MRI parameters (MS, TTE) to predict high Ki-67 in mass-shaped invasive breast carcinoma.
本研究旨在探讨利用超快(UF) DCE-MRI、DWI、T2WI及病变大小等MRI参数对乳腺癌Ki-67表达的预测。使用专用乳房线圈,使用3T扫描仪进行乳房MRI。使用Compressed Sensing-VIBE(原型序列)获得UF DCE-MRI。作为UF DCE-MRI的动力学参数,最大斜率(MS)定义为相对增强百分比(%/s),增强时间(TTE)定义为主动脉到病变增强的时间间隔。表观扩散系数(ADC)由DWI计算得到。两名放射科医生测量了每个MR参数,并评估了评分者之间的一致性。单因素和多因素logistic回归分析预测低Ki-67 (<;通过MS、TTE、ADC、T2信号强度(SI)和病变大小计算Ki-67高表达(≥14%)。选择p值< 0.05的显著参数作为预测模型,采用ROC曲线分析评价模型的诊断性能。共有191例浸润性癌定义为肿块病变(72例低Ki-67/ 119例高Ki-67病变)。各参数间一致性良好。经单因素和多因素logistic回归分析,ADC和病变大小仍是显著参数。使用这些显著参数,多参数预测模型的AUC为0.77 (95%CI为0.70-0.84)(敏感性72.3%,特异性76.4%,PPV 83.5%, NPV 62.5%)。DWI参数(ADC)可能比UF DCE-MRI参数(MS, TTE)更有价值预测肿块状浸润性乳腺癌的高Ki-67。
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引用次数: 0
External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening 人工智能驱动的乳腺癌风险预测模型在接受乳房x线摄影筛查的不同种族女性队列中的外部验证
A. Gastounioti, M. Eriksson, Eric A. Cohen, W. Mankowski, Lauren Pantalone, A. McCarthy, D. Kontos, P. Hall, E. Conant
The aim of this retrospective case-cohort study was to perform additional validation of an artificial intelligence (AI)-driven breast cancer risk model in a racially diverse cohort of women undergoing screening. We included 176 breast cancer cases with non-actionable mammographic screening exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4,963 controls from women with non-actionable mammographic screening exams and at least one-year of negative follow-up (Hospital University Pennsylvania, PA, USA; 9/1/2010-1/6/2015). A risk score for each woman was extracted from full-field digital mammography (FFDM) images via an AI risk prediction model, previously developed and validated in a Swedish screening cohort. The performance of the AI risk model was assessed via age-adjusted area under the ROC curve (AUC) for the entire cohort, as well as for the two largest racial subgroups (White and Black). The performance of the Gail 5-year risk model was also evaluated for comparison purposes. The AI risk model demonstrated an AUC for all women = 0.68 95% CIs [0.64, 0.72]; for White = 0.67 [0.61, 0.72]; for Black = 0.70 [0.65, 0.76]. The AI risk model significantly outperformed the Gail risk model for all women (AUC = 0.68 vs AUC = 0.55, p<0.01) and for Black women (AUC = 0.71 vs AUC = 0.48, p<0.01), but not for White women (AUC = 0.66 vs AUC = 0.61, p=0.38). Preliminary findings in an independent dataset suggest a promising performance of the AI risk prediction model in a racially diverse breast cancer screening cohort.
本回顾性病例队列研究的目的是在接受筛查的不同种族女性队列中对人工智能(AI)驱动的乳腺癌风险模型进行额外验证。我们纳入了176例乳腺癌患者,在癌症诊断前3个月至2年进行了不可操作的乳房x光检查,并从进行不可操作的乳房x光检查和至少1年阴性随访的妇女中随机抽取4,963例对照(宾夕法尼亚医院大学,PA, USA;9/1/2010-1/6/2015)。通过人工智能风险预测模型从全视野数字乳房x线摄影(FFDM)图像中提取每位女性的风险评分,该模型先前在瑞典筛查队列中开发并验证。AI风险模型的性能通过整个队列以及两个最大的种族亚组(白人和黑人)的ROC曲线下年龄调整面积(AUC)进行评估。Gail 5年风险模型的表现也进行了评估以进行比较。AI风险模型显示,所有女性的AUC = 0.68 95% ci [0.64, 0.72];White = 0.67 [0.61, 0.72];为Black = 0.70[0.65, 0.76]。AI风险模型在所有女性(AUC = 0.68 vs AUC = 0.55, p<0.01)和黑人女性(AUC = 0.71 vs AUC = 0.48, p<0.01)中均显著优于Gail风险模型,但在白人女性(AUC = 0.66 vs AUC = 0.61, p=0.38)中表现不佳。一个独立数据集的初步发现表明,人工智能风险预测模型在种族多样化的乳腺癌筛查队列中表现良好。
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引用次数: 1
Development of a 3D model of clinically relevant microcalcifications 临床相关微钙化三维模型的建立
A. Carton, C. Jailin, Raoul De Silva, Rubèn Sanchez De la Rosa, S. Muller
A realistic 3D anthropomorphic software model of microcalcifications may serve as a useful tool to assess the performance of breast imaging applications through simulations. We present a method allowing to simulate visually realistic microcalcifications with large morphological variability. Principal component analysis (PCA) was used to analyze the shape of 281 biopsied microcalcifications imaged with a micro-CT. The PCA analysis requires the same number of shape components for each input microcalcification. Therefore, the voxel-based microcalcifications were converted to a surface mesh with same number of vertices using a marching cube algorithm. The vertices were registered using an iterative closest point algorithm and a simulated annealing algorithm. To evaluate the approach, input microcalcifications were reconstructed by progressively adding principal components. Input and reconstructed microcalcifications were visually and quantitatively compared. New microcalcifications were simulated using randomly sampled principal components determined from the PCA applied to the input microcalcifications, and their realism was appreciated through visual assessment. Preliminary results have shown that input microcalcifications can be reconstructed with high visual fidelity when using 62 principal components, representing 99.5% variance. For that condition, the average L2 norm and dice coefficient were respectively 10.5 μm and 0.93. Newly generated microcalcifications with 62 principal components were found to be visually similar, while not identical, to input microcalcifications. The proposed PCA model of microcalcification shapes allows to successfully reconstruct input microcalcifications and to generate new visually realistic microcalcifications with various morphologies.
一个真实的三维拟人化微钙化软件模型可以作为一个有用的工具,通过模拟来评估乳房成像应用的性能。我们提出了一种方法,允许模拟具有大形态变异性的视觉逼真的微钙化。采用主成分分析(PCA)对281例显微ct活检微钙化灶的形态进行分析。PCA分析要求每个输入微钙化的形状分量数量相同。因此,使用行进立方体算法将基于体素的微钙化转换为具有相同顶点数的表面网格。使用迭代最近点算法和模拟退火算法对顶点进行配准。为了评估该方法,通过逐步添加主成分来重建输入微钙化。对输入和重建的微钙化进行视觉和定量比较。新的微钙化使用随机抽样的主成分来模拟,这些主成分由应用于输入微钙化的PCA确定,并通过视觉评估来评价它们的真实感。初步结果表明,当使用62个主成分,方差为99.5%时,可以以较高的视觉保真度重建输入的微钙化。在该条件下,平均L2范数和dice系数分别为10.5 μm和0.93。新生成的62个主成分的微钙化在视觉上与输入的微钙化相似,但不完全相同。提出的微钙化形状PCA模型可以成功地重建输入的微钙化,并生成具有各种形态的新的视觉逼真的微钙化。
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引用次数: 0
Normalized image noise as an absolute quantity for performance assessment in 2D mammography 归一化图像噪声作为二维乳房x线摄影性能评估的绝对数量
N. Paruccini, R. Villa, N. Oberhofer
A method is proposed to equate the measured noise to a thickness of Aluminium by exposing a simple test object which includes an Aluminium step-wedge sandwiched between PMMA slabs. The scaling process turns the Image Noise expressed in mmAl into an absolute quantity which reflects exposure conditions. A second quantity, the dose independent Normalized Image Noise is defined. It is a characteristic value for every mammography unit/model and phantom setup and represents a measure of a system’s overall detection efficiency in clinical conditions. 8 mammography units of different manufacturers and detector technologies have been evaluated for PMMA thickness 3, 5 and 7 cm over a wide average glandular dose (AGD) range. The calculated Normalised Image Noise values were reproducible (uncertainty 3-5%) and coherent with known physical characteristics of the detector-grid combinations. Image Noise resulted sensitive to radiation spectra and scatter amount. Starting from threshold contrast detection with the CDMAM ver. 3.4 phantom, it was possible to identify Image Noiseacceptable/achievable thresholds which correspond to adequate image quality. Signal difference to noise (SDNR) analysis based on the proposed test object was in good agreement with SDNR evaluation according to the EUREF guideline (difference 1- 7%). Conversion coefficient, Image Noise and Normalised Image Noise could all be derived from a single exposure without having to determine the detector's response curve beforehand which is particularly advantageous for non-linear response. Given the sensitivity of Image Noise to radiation quality and dose, it is a suitable metric for optimization.
提出了一种方法,通过暴露一个简单的测试对象,其中包括夹在PMMA板之间的铝阶梯楔,将测量到的噪声等同于铝的厚度。缩放过程将用mmAl表示的图像噪声转化为反映曝光条件的绝对量。定义了第二个量,即与剂量无关的归一化图像噪声。它是每个乳房x光检查单元/模型和幻影设置的特征值,代表了系统在临床条件下的整体检测效率的度量。在较宽的平均腺剂量(AGD)范围内,对不同制造商和检测技术的8台乳房x线照相术单元进行了PMMA厚度3,5和7cm的评估。计算的归一化图像噪声值是可重复的(不确定性为3-5%),并且与已知的探测器-网格组合的物理特性一致。图像噪声对辐射光谱和散射量非常敏感。从阈值对比度检测开始用CDMAM方法。3.4幻影,可以识别符合足够图像质量的图像噪声可接受/可实现阈值。基于拟议测试对象的信号噪声差(SDNR)分析与根据EUREF指南进行的SDNR评估结果吻合良好(差1- 7%)。转换系数、图像噪声和归一化图像噪声都可以从单次曝光中得出,而无需事先确定探测器的响应曲线,这对于非线性响应特别有利。考虑到图像噪声对辐射质量和剂量的敏感性,它是一个合适的优化度量。
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引用次数: 0
Tumor growth rate estimations in a breast cancer screening population 乳腺癌筛查人群中肿瘤生长速率的估计
Hanna Tomic, Akane Ohashi, Victor Dahlblom, Anna Bjerkén, D. Förnvik, M. Dustler, S. Zackrisson, A. Tingberg, P. Bakic
Tumor growth rate estimations can provide useful information about tumor progression and aggressiveness. Understanding the breast cancer progression and aggressiveness could aid with personalized screening/follow-up, treatment options, and prognosis. This paper reports a preliminary estimation of the tumor volume doubling time (TVDT) for cancers detected during the Malmö Breast Tomosynthesis Screening Trial (MBTST). The trial included 14 848 women in whom 139 cancers were detected. Out of those, 101 spiculated or circumscribed masses, had prior images available, making them suitable for tumor growth evaluation. In the preliminary analysis of images from 30 women, tumor size was measured in mammograms from MBTST and prior images. The analyzed cases were selected among women with visible tumors in two consecutive screening exams. The tumor size was measured in two orthogonal directions. The average of the two measurements was used in the analysis. The mean time and the corresponding standard deviation (SD) between the two consecutive mammograms were 744 ± 73 days. The mean TVDT and SD were 637 ± 428 days (range 159-2373 days). Future work will include the analysis of a larger number of women and a stratification of TVDT related to screening intervals.
肿瘤生长速率估计可以提供关于肿瘤进展和侵袭性的有用信息。了解乳腺癌的进展和侵袭性有助于个性化的筛查/随访、治疗选择和预后。本文报道了在Malmö乳腺断层合成筛查试验(MBTST)中检测到的癌症的肿瘤体积倍增时间(TVDT)的初步估计。该试验包括14848名妇女,其中139人被检测出癌症。其中,101个针状或有边界的肿块有可用的先验图像,使它们适合肿瘤生长评估。在对来自30名妇女的图像进行初步分析时,肿瘤大小是通过MBTST和先前图像的乳房x线照片来测量的。所分析的病例是在连续两次筛查检查中有可见肿瘤的妇女中选择的。在两个正交方向上测量肿瘤大小。在分析中使用了两次测量的平均值。两次连续乳房x光检查的平均时间和相应的标准差(SD)为744±73天。平均TVDT和SD为637±428天(159 ~ 2373天)。今后的工作将包括对更多妇女的分析和与筛查间隔有关的TVDT分层。
{"title":"Tumor growth rate estimations in a breast cancer screening population","authors":"Hanna Tomic, Akane Ohashi, Victor Dahlblom, Anna Bjerkén, D. Förnvik, M. Dustler, S. Zackrisson, A. Tingberg, P. Bakic","doi":"10.1117/12.2625730","DOIUrl":"https://doi.org/10.1117/12.2625730","url":null,"abstract":"Tumor growth rate estimations can provide useful information about tumor progression and aggressiveness. Understanding the breast cancer progression and aggressiveness could aid with personalized screening/follow-up, treatment options, and prognosis. This paper reports a preliminary estimation of the tumor volume doubling time (TVDT) for cancers detected during the Malmö Breast Tomosynthesis Screening Trial (MBTST). The trial included 14 848 women in whom 139 cancers were detected. Out of those, 101 spiculated or circumscribed masses, had prior images available, making them suitable for tumor growth evaluation. In the preliminary analysis of images from 30 women, tumor size was measured in mammograms from MBTST and prior images. The analyzed cases were selected among women with visible tumors in two consecutive screening exams. The tumor size was measured in two orthogonal directions. The average of the two measurements was used in the analysis. The mean time and the corresponding standard deviation (SD) between the two consecutive mammograms were 744 ± 73 days. The mean TVDT and SD were 637 ± 428 days (range 159-2373 days). Future work will include the analysis of a larger number of women and a stratification of TVDT related to screening intervals.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"10 1","pages":"1228613 - 1228613-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80431312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Lesion detection in contrast enhanced spectral mammography 增强x光造影中的病变检测
C. Jailin, Pablo Milioni de Carvalho, Zhijin Li, R. Iordache, S. Muller
Background and purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spectral Mammography (CESM) where access to large databases is complex. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases. Material and methods: A large CESM diagnostic dataset with biopsy-proven lesions was collected from various hospitals and different acquisition systems. The annotated data were split on a patient level for the training (55%), validation (15%) and test (30%) of a deep neural network with a state-of-the-art detection architecture. Free Receiver Operating Characteristic (FROC) was used to evaluate the model for the detection of 1) all lesions, 2) biopsied lesions and 3) malignant lesions. ROC curve was used to evaluate breast cancer classification. The metrics were finally compared to clinical results. Results: For the evaluation of the malignant lesion detection, at high sensitivity (Se<0.95), the false positive rate was at 0.61 per image. For the classification of malignant cases, the model reached an Area Under the Curve (AUC) in the range of clinical CESM diagnostic results. Conclusion: This CAD is the first development of a lesion detection and classification model for CESM images. Trained on a large dataset, it has the potential to be used for helping the management of biopsy decision and for helping the radiologist detecting complex lesions that could modify the clinical treatment.
背景与目的:近年来出现的用于乳腺图像分析的神经网络模型是计算机辅助诊断的一个突破。这种方法尚未在对比增强光谱乳房x线照相术(CESM)中发展起来,因为访问大型数据库是复杂的。这项工作提出了一种基于深度学习的计算机辅助诊断开发,用于能够检测病变和分类病例的CESM重组图像。材料和方法:从不同的医院和不同的采集系统收集了具有活检证实病变的大型CESM诊断数据集。标注的数据在患者层面上进行分割,用于具有最先进检测架构的深度神经网络的训练(55%)、验证(15%)和测试(30%)。利用自由受者工作特征(FROC)评价该模型对1)所有病变、2)活检病变和3)恶性病变的检测能力。采用ROC曲线评价乳腺癌的分型。最后将这些指标与临床结果进行比较。结果:对于恶性病变检测的评价,在高灵敏度(Se<0.95)下,假阳性率为0.61 /张。对于恶性病例的分类,该模型在临床CESM诊断结果范围内达到曲线下面积(Area Under the Curve, AUC)。结论:该CAD是CESM图像病变检测和分类模型的首次发展。在一个大数据集上训练,它有可能被用于帮助活检决策的管理,并帮助放射科医生检测复杂的病变,从而改变临床治疗。
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引用次数: 2
Mammographic image metadata learning for model pretraining and explainable predictions 用于模型预训练和可解释预测的乳房x线图像元数据学习
Lester Litchfield, M. Hill, N. Khan, R. Highnam
Purpose: To introduce a novel technique for pretraining deep neural networks on mammographic images, where the network learns to predict multiple metadata attributes and simultaneously to match images from the same patient and study. Further to demonstrate how this network can be used to produce explainable predictions. Methods: We trained a neural network on a dataset of 85,558 raw mammographic images and seven types of metadata, using a combination of supervised and self-supervised learning techniques. We evaluated the performance of our model on a dataset of 4,678 raw mammographic images using classification accuracy and correlation. We also designed an ablation study to demonstrate how the model can produce explainable predictions. Results: The model learned to predict all but one of the seven metadata fields with classification accuracy ranging from 78-99% on the validation dataset. The model was able to predict which images were from the same patient with over 93% accuracy on a balanced dataset. Using a simple X-ray system classifier built on top of the first model, representations learned on the initial X-ray system classification task showed by far the largest effect size on ablation, illustrating a method for producing explainable predictions. Conclusions: It is possible to train a neural network to predict several kinds of mammogram metadata simultaneously. The representations learned by the model for these tasks can be summed to produce an image representation that captures features unique to a patient and study. With such a model, ablation offers a promising method to enhance the explainability of deep learning predictions.
目的:介绍一种用于乳房x线摄影图像预训练深度神经网络的新技术,其中网络学习预测多个元数据属性,同时匹配来自同一患者和研究的图像。进一步证明这个网络可以用来产生可解释的预测。方法:采用监督学习和自监督学习相结合的方法,在包含85,558张原始乳房x线照片和七种元数据的数据集上训练神经网络。我们使用分类精度和相关性评估了我们的模型在4,678张原始乳房x线照片数据集上的性能。我们还设计了一个消融研究,以证明该模型如何产生可解释的预测。结果:该模型学会了在验证数据集上预测除一个字段外的所有元数据字段,分类准确率在78-99%之间。该模型能够在平衡数据集上预测哪些图像来自同一患者,准确率超过93%。使用建立在第一个模型之上的简单x射线系统分类器,从初始x射线系统分类任务中学习到的表示显示了迄今为止对消融的最大影响大小,说明了一种产生可解释预测的方法。结论:训练神经网络同时预测多种乳房x线照片元数据是可能的。模型为这些任务学习的表征可以被总结成一个图像表征,该图像表征捕获了患者和研究的独特特征。有了这样一个模型,消融提供了一个有前途的方法来增强深度学习预测的可解释性。
{"title":"Mammographic image metadata learning for model pretraining and explainable predictions","authors":"Lester Litchfield, M. Hill, N. Khan, R. Highnam","doi":"10.1117/12.2626199","DOIUrl":"https://doi.org/10.1117/12.2626199","url":null,"abstract":"Purpose: To introduce a novel technique for pretraining deep neural networks on mammographic images, where the network learns to predict multiple metadata attributes and simultaneously to match images from the same patient and study. Further to demonstrate how this network can be used to produce explainable predictions. Methods: We trained a neural network on a dataset of 85,558 raw mammographic images and seven types of metadata, using a combination of supervised and self-supervised learning techniques. We evaluated the performance of our model on a dataset of 4,678 raw mammographic images using classification accuracy and correlation. We also designed an ablation study to demonstrate how the model can produce explainable predictions. Results: The model learned to predict all but one of the seven metadata fields with classification accuracy ranging from 78-99% on the validation dataset. The model was able to predict which images were from the same patient with over 93% accuracy on a balanced dataset. Using a simple X-ray system classifier built on top of the first model, representations learned on the initial X-ray system classification task showed by far the largest effect size on ablation, illustrating a method for producing explainable predictions. Conclusions: It is possible to train a neural network to predict several kinds of mammogram metadata simultaneously. The representations learned by the model for these tasks can be summed to produce an image representation that captures features unique to a patient and study. With such a model, ablation offers a promising method to enhance the explainability of deep learning predictions.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"23 1","pages":"1228616 - 1228616-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81698560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Refraction-contrast CT measurement system based on x-ray dark field imaging for μm-order scale imaging of breast tissue specimens 基于x射线暗场成像的折射对比CT测量系统用于μm级乳腺组织标本成像
N. Sunaguchi, Z. Huang, K. Taniguchi, D. Shimao, T. Yuasa, R. Nishimura, A. Iwakoshi, M. Ando, S. Ichihara
The importance of the three-dimensional (3D) pathological observation of biological soft tissues has increased in recent year, and various visualization tools to obtain 3D information easily and analysis methods focusing on the 3D micro structures have been developed. Refraction-contrast computed tomography based on x-ray dark-field imaging technique (XDFI) is one of the powerful methods with a high contrast and spatial resolution. In this study, in order to apply XDFI as new pathology tool, we will develop the x-ray optics and the x-ray camera, which are important components of the XDFI imaging system, to achieve a spatial resolution of 5 μm and evaluate the spatial resolution by experiments of the x-ray micro chart and the breast tissue specimen.
近年来,生物软组织三维病理观察的重要性日益增加,各种方便获取三维信息的可视化工具和以三维微观结构为重点的分析方法应运而生。基于x射线暗场成像技术(XDFI)的折射对比计算机断层扫描是一种具有高对比度和空间分辨率的有效方法。在本研究中,为了将XDFI作为一种新的病理工具,我们将开发XDFI成像系统的重要组成部分x射线光学元件和x射线相机,以实现5 μm的空间分辨率,并通过x射线显微图和乳腺组织标本的实验来评估空间分辨率。
{"title":"Refraction-contrast CT measurement system based on x-ray dark field imaging for μm-order scale imaging of breast tissue specimens","authors":"N. Sunaguchi, Z. Huang, K. Taniguchi, D. Shimao, T. Yuasa, R. Nishimura, A. Iwakoshi, M. Ando, S. Ichihara","doi":"10.1117/12.2624056","DOIUrl":"https://doi.org/10.1117/12.2624056","url":null,"abstract":"The importance of the three-dimensional (3D) pathological observation of biological soft tissues has increased in recent year, and various visualization tools to obtain 3D information easily and analysis methods focusing on the 3D micro structures have been developed. Refraction-contrast computed tomography based on x-ray dark-field imaging technique (XDFI) is one of the powerful methods with a high contrast and spatial resolution. In this study, in order to apply XDFI as new pathology tool, we will develop the x-ray optics and the x-ray camera, which are important components of the XDFI imaging system, to achieve a spatial resolution of 5 μm and evaluate the spatial resolution by experiments of the x-ray micro chart and the breast tissue specimen.","PeriodicalId":92005,"journal":{"name":"Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)","volume":"19 1","pages":"122860D - 122860D-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91299288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Classification of intrinsic subtypes and histological grade for breast cancers by multimodality images 多模态影像对乳腺癌内在亚型和组织学分级的分类
C. Muramatsu, Takumi Iwasaki, M. Oiwa, T. Kawasaki, H. Fujita
Success of breast cancer treatment is subject to various factors, including cancer stage and cancer grade. The best treatment is selected based on the characteristic of cancer. It is desirable to predict the cancer characteristics and prognostic factors accurately and promptly by diagnostic imaging. The purpose of the study is to investigate the use of multimodality diagnostic images in predicting breast cancer subtypes to assist diagnosis and treatment planning. In this study, we classify lesions into molecular subtypes and simultaneously predict histological grades and invasiveness of the cancers by mammography and breast ultrasound images. Models with different architectures including single input and multi-input layers with single head and multiple head models are compared. The results indicate that use of multimodality images is more predictive than using single modalities. The automatic subtype classification using multimodality images may support a prompt treatment planning and proper patient care.
乳腺癌治疗的成功取决于多种因素,包括癌症分期和癌症等级。根据癌症的特点选择最佳的治疗方法。通过影像学诊断准确、及时地预测肿瘤特征和预后因素是十分必要的。本研究的目的是探讨多模态诊断图像在预测乳腺癌亚型中的应用,以辅助诊断和治疗计划。在这项研究中,我们将病变分为分子亚型,同时通过乳房x线摄影和乳房超声图像预测肿瘤的组织学分级和侵袭性。对单输入层和多输入层、单头模型和多头模型的不同结构模型进行了比较。结果表明,使用多模态图像比使用单一模态更具预测性。使用多模态图像的自动亚型分类可以支持及时的治疗计划和适当的患者护理。
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引用次数: 0
Iodine quantification in dual-energy mammography: linearity, bias and variability 双能乳房x光检查中的碘定量:线性、偏倚和可变性
G. Pacheco-Guevara, J. Castillo-Lopez, Y. Villaseñor-Navarro, M. Brandan
Contrast-enhanced digital mammography (CEDM) is used to detect iodine uptake in breast lesions. Iodine concentrations inside or around breast lesions could be used as a biomarker, provided a properly characterized quantification method is implemented. In this work, we have evaluated a method to quantify iodine concentrations in CEDM in terms of its intrinsic linearity, bias and variability. This evaluation was performed in a virtual clinical trial (VCT) environment, simulating anthropomorphic breast phantoms containing solid and liquid lesions with different iodine concentrations. Our results showed that anatomical variables such as breast size and lesion size and composition have a considerable effect on the iodine quantification. The method was linear in the clinical iodine concentration range, and showed an approximately constant 1 mg/cm2 bias in the 0 – 2 mg/cm2 range for both solid and liquid lesions. Corrections were proposed that reduced the variability due to breast size, lesion size, and composition.
对比增强数字乳房x线摄影(CEDM)用于检测乳腺病变中的碘摄取。乳腺病变内部或周围的碘浓度可作为一种生物标志物,前提是实施一种适当的表征定量方法。在这项工作中,我们评估了一种定量碘浓度的方法,根据其固有的线性,偏差和可变性。该评估是在虚拟临床试验(VCT)环境中进行的,模拟含有不同碘浓度的固体和液体病变的拟人化乳房幻象。我们的结果表明,解剖变量,如乳房大小和病变的大小和组成有相当大的影响碘定量。该方法在临床碘浓度范围内呈线性,并且在0 - 2 mg/cm2范围内显示出近似恒定的1 mg/cm2偏置。修正建议减少由于乳房大小,病变大小和组成的可变性。
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引用次数: 1
期刊
Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.)
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