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_第4页

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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2625747

Akane Ohashi, M. Kataoka, M. Iima, M. Honda, Rie Ota, Y. Urushibata, Marcel Dominik Nickel, Toi Masakazu, S. Zackrisson, Y. Nakamoto

{"title":"Prediction of Ki-67 expression of breast cancer with a multi-parametric model using MRI parameters from ultrafast DCE-MRI and DWI","authors":"Akane Ohashi, M. Kataoka, M. Iima, M. Honda, Rie Ota, Y. Urushibata, Marcel Dominik Nickel, Toi Masakazu, S. Zackrisson, Y. Nakamoto","doi":"10.1117/12.2625747","DOIUrl":"https://doi.org/10.1117/12.2625747","url":null,"abstract":"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.","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":"1 1","pages":"122860B - 122860B-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83096081","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

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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2627140

A. Gastounioti, M. Eriksson, Eric A. Cohen, W. Mankowski, Lauren Pantalone, A. McCarthy, D. Kontos, P. Hall, E. Conant

{"title":"External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening","authors":"A. Gastounioti, M. Eriksson, Eric A. Cohen, W. Mankowski, Lauren Pantalone, A. McCarthy, D. Kontos, P. Hall, E. Conant","doi":"10.1117/12.2627140","DOIUrl":"https://doi.org/10.1117/12.2627140","url":null,"abstract":"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.","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":"1 1","pages":"1228617 - 1228617-4"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89413140","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

Development of a 3D model of clinically relevant microcalcifications 临床相关微钙化三维模型的建立

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2625771

A. Carton, C. Jailin, Raoul De Silva, Rubèn Sanchez De la Rosa, S. Muller

{"title":"Development of a 3D model of clinically relevant microcalcifications","authors":"A. Carton, C. Jailin, Raoul De Silva, Rubèn Sanchez De la Rosa, S. Muller","doi":"10.1117/12.2625771","DOIUrl":"https://doi.org/10.1117/12.2625771","url":null,"abstract":"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.","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":"74 1","pages":"1228602 - 1228602-7"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85201137","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

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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2624538

N. Paruccini, R. Villa, N. Oberhofer

{"title":"Normalized image noise as an absolute quantity for performance assessment in 2D mammography","authors":"N. Paruccini, R. Villa, N. Oberhofer","doi":"10.1117/12.2624538","DOIUrl":"https://doi.org/10.1117/12.2624538","url":null,"abstract":"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.","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":"29 1","pages":"122861A - 122861A-9"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77973495","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

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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2625730

Hanna Tomic, Akane Ohashi, Victor Dahlblom, Anna Bjerkén, D. Förnvik, M. Dustler, S. Zackrisson, A. Tingberg, P. Bakic

{"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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2624577

C. Jailin, Pablo Milioni de Carvalho, Zhijin Li, R. Iordache, S. Muller

{"title":"Lesion detection in contrast enhanced spectral mammography","authors":"C. Jailin, Pablo Milioni de Carvalho, Zhijin Li, R. Iordache, S. Muller","doi":"10.1117/12.2624577","DOIUrl":"https://doi.org/10.1117/12.2624577","url":null,"abstract":"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.","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":"21 1","pages":"122860A - 122860A-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73430151","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: 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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2626199

Lester Litchfield, M. Hill, N. Khan, R. Highnam

{"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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2624056

N. Sunaguchi, Z. Huang, K. Taniguchi, D. Shimao, T. Yuasa, R. Nishimura, A. Iwakoshi, M. Ando, S. Ichihara

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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2625871

C. Muramatsu, Takumi Iwasaki, M. Oiwa, T. Kawasaki, H. Fujita

{"title":"Classification of intrinsic subtypes and histological grade for breast cancers by multimodality images","authors":"C. Muramatsu, Takumi Iwasaki, M. Oiwa, T. Kawasaki, H. Fujita","doi":"10.1117/12.2625871","DOIUrl":"https://doi.org/10.1117/12.2625871","url":null,"abstract":"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.","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":"35 1","pages":"122860Y - 122860Y-6"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86608249","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

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

Breast imaging : 11th International Workshop, IWDM 2012, Philadelphia, PA, USA, July 8-11, 2012 : proceedings. International Workshop on Breast Imaging (11th : 2012 : Philadelphia, Pa.) Pub Date : 2022-07-13 DOI: 10.1117/12.2625782

G. Pacheco-Guevara, J. Castillo-Lopez, Y. Villaseñor-Navarro, M. Brandan

{"title":"Iodine quantification in dual-energy mammography: linearity, bias and variability","authors":"G. Pacheco-Guevara, J. Castillo-Lopez, Y. Villaseñor-Navarro, M. Brandan","doi":"10.1117/12.2625782","DOIUrl":"https://doi.org/10.1117/12.2625782","url":null,"abstract":"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.","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":"7 1","pages":"1228609 - 1228609-8"},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82013068","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