Digital Breast Tomosynthesis: Potential Benefits in Routine Clinical Practice

Kulkarni, Supriya; Freitas, Vivianne; Muradali, Derek

doi:10.1177/08465371211025229

Cited by 10 publications

(6 citation statements)

References 59 publications

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“…Images generated by DBT allow for the evaluation of abnormal findings, such as masses or calcifications, by providing a more detailed view of their shape, size, and location within the breast tissue. However, it has increased radiation exposure compared to 2D mammography and potentially higher costs [ 6 ].…”

Section: Results and Discussion—innovations In Breast Cancer Carementioning

confidence: 99%

Innovations and Challenges in Breast Cancer Care: A Review

2023

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Breast cancer care has seen tremendous advancements in recent years through various innovations to improve early detection, diagnosis, treatment, and survival. These innovations include advancements in imaging techniques, minimally invasive surgical techniques, targeted therapies and personalized medicine, radiation therapy, and multidisciplinary care. It is essential to recognize that challenges and limitations exist while significant advancements in breast cancer care exist. Continued research, advocacy, and efforts to address these challenges are necessary to make these innovations accessible to all patients while carefully considering and managing the ethical, social, and practical implications.

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Section: Results and Discussion—innovations In Breast Cancer Carementioning

confidence: 99%

Innovations and Challenges in Breast Cancer Care: A Review

2023

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“…After acquiring several thin, highresolution images, the DBT system produces a quasi-3D format of reconstructed breast images with the aim of reducing the effect of tissue distortion. In addition, unlike conventional imaging techniques the radiation dose required is not high, and the produced images appear to have greater resolution and contrast [7,8]. DBT represents a more accurate diagnostic index than twodimensional imaging for the evaluation of morphological features, for example, the shape and margin of various breast cancer immunophenotypes, so it can play an important role in molecular imaging and prognosis [9] in addition, DBT is accepted for breast cancer screening in women in the United States Due to reduced tissue overlap, offers better cancer detection sensitivity than mammography, especially for dense breasts, and better visualization of masses, potentially reducing Recall Rates [10,11].…”

Section: Introductionmentioning

confidence: 99%

Novel feature extraction based on DCT-DOST features for classification of Digital Breast Tomosynthesis images into benign and malignant tumors

Moghadam,

Rashidi

2024

Preprint

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Background: Breast cancer is caused by the uncontrolled growth of abnormal cells, resulting in a mass in the breast tissue. Early detection of the disease can significantly improve its prognosis and treatment outcome. Digital Breast Tomosynthesis, a three-dimensional imaging technology of the breast tissue, is emerging as a standard for breast imaging with improved screening and diagnostic results. The additional information obtained from tomosynthesis reduces the misleading effects of tissue overlap and improves the detection, identification, and localization of abnormalities. Benign-malignant breast tumors classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to classify benign-malignancy of masses in Digital Breast Tomosynthesis images based on new DCT-DOST and Radiomic features on an open database of TCIA that consist of 224 lesion bounding boxes. Methods: In order to effectively obtain both DCT-DOST and Radiomics characteristics, a 2D central slice of the DBT image, which encompasses a substantial anatomical size of the breast tumor, was employed. In the pre-processing stage, rescale intensity method was used to improve contrast and image quality. After that, by using a binary mask, the mass of the breast tissue was subjected to segmentation. Then, using radiomic and DCT-DOST features, new features were extracted from the images. In addition, we also investigated the importance of feature selection and class balancing. By pooling the radiomics and DCT-DOST features into a hybrid feature set, we investigated the compatibility of these two sets with respect to benign malignancy prediction. Results: Finally, for classification using Random Forest algorithm, K-Nearest Neighbor and Support Vector Machine, it was shown that the best result of the evaluation metrics are respectively equal to 87.80%, 78.51%, 82.78% and 75.19% in terms of mean AUC, Accuracy, Sensitivity and Specificity which was obtained by Random Forest classifier. Conclusion: The findings from the empirical analysis reveal that integrating DCT-DOST and Radiomic features into the same learning algorithm improves the discrimination power.

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“…Moreover, the required radiation dose is not high, contrary to the conventional imaging techniques, and the generated images appear to have greater resolution and contrast [4]. The DBT represents a more accurate diagnostic indicator than 2D imaging for evaluating the morphological features, e.g., shape and margin of the different immunophenotypes of the breast cancer, thus being able to play a crucial role in the molecular imaging and prognosis [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

et al. 2022

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Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

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Digital Breast Tomosynthesis: Potential Benefits in Routine Clinical Practice

Cited by 10 publications

References 59 publications

Innovations and Challenges in Breast Cancer Care: A Review

Innovations and Challenges in Breast Cancer Care: A Review

Novel feature extraction based on DCT-DOST features for classification of Digital Breast Tomosynthesis images into benign and malignant tumors

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

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