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a Mammogram shows an ovoid opacity at right breast 10'clock position. b, c Lesions proved by ABUS to be a solid ovoid lesion typical for fibroadenoma
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Background
Mammography is the most accepted, accurate, and effective modality in screening of breast cancer, yet its sensitivity is affected by the density of the breast tissue. Alternative methods for screening are the sonography and MRI but both had their limitations. A new option named ABUS (automated breast ultrasound system) is now proposed to...
Contexts in source publication
Context 1
... patient lies in supine position. A machine arm that is computer-guided acquired the images through the attached transducer. The images are acquired in rows in a longitudinal manner as well as transverse images. Antero-posterior (AP), lateral, and medial standard (2019) 50:51 views were taken for each breast (Fig. 2). There is ability to add some additional views in some cases as views for axillary tail and special upper quadrants localized ...
Context 2
... and malignant lesions categorized as BIRADS 4 and 5 (4 patients with BIRADS 4 (16%) and 3 with BIRADS 5 (12%)). Eight cases reported normal by mammography but only 3 by ABUS. The difference was in the BIRADS 2 and 3 lesions where ABUS detected 3 more lesions than the mammogram; one of them was of BIRADS 2 category and 2 of them were BIRADS 3 (Figs. 2, 3, and 4) NS non-significant, S significant, HS highly significant P value > 0.05 non-significant P value < 0.05 significant P value < 0.01 highly significant NS non-significant, S significant, HS highly significant P value > 0.05 non-significant P value < 0.05 significant P value < 0.01 highly significant NS non-significant, S ...
Citations
... Several studies demonstrate that 3D ABUS significantly elevates the detection of clinically relevant breast cancers, especially small, invasive, and node-negative ones, which might be missed by mammography alone [13]. Considering the articles reviewed, which focus on breast cancer screening and compare the detection outcomes of mammography alone to its combination with 3D ABUS, the additional value of 3D ABUS becomes evident [14,15,27]. A comprehensive analysis of these studies consistently reveals that integrating 3D ABUS with mammography leads to heightened sensitivity and improved cancer detection rates compared to mammography alone [16]. ...
Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40–56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‰ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‰ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.
... Ultrasound has better sensitivity for dense breasts and can be better for differentiating solid tumors from cysts than mammography [33]. Ultrasound technology is less expensive than mammography and may detect changes that are not visible in mammograms [34][35][36]. Radiologists can distinguish between various tissues. Segmenting breast ultrasound images may be valuable for tumor localization and breast cancer diagnosis [37]. ...
Introduction
Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.
Purpose
The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study.
Method
Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images.
Result
The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset.
Conclusion
The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.
... ABUS has a traditional plan for acquiring images that demand brief teaching by the medical staff carrying out it without the attendance of the qualified radiologists contrary to the HHUS. 3D ABUS eradicates the operator-dependent issue and allows duplicability (13). This study included 80 female patients who came for breast screening, their mean age is about 46 years ranging from 32 to 67 years. ...
... This study included 80 female patients who came for breast screening, their mean age is about 46 years ranging from 32 to 67 years. In the current research, conferring to mammography assessment there were 20 (25%) with BIRADS I, 25 (31.25%) with BIRADS II, 10 (12.5%) with BIRADS III, 16 (20%) with BIRADS IV, and 9 (11.25%) with V, conferring to lesion sorting there were 20 (25%) with negative findings (BIRADS I) and 60 with positive findings (BIRADS II, III, IV, and V) (75%), this is going nearly with Abdelkhalek et al [13] who stated that negative cases with mammography were 8 cases(32%)while positive cases were 17(68%).In the recent study according to ABUS examination, there were 17 (21.25%) with BIRADS I, 26 (32. ...
... Another recent study by Abd Elkhalek et al. [11] concluded that the sensitivity of the ABUS is about 100%, and that means, in all the results of the mammogram study, ABUS can detect it without significant change, while the specificity of the ABUS was about 62% and this was more evident in benign lesions. On the other hand, a study by Lee et al. in 2019 [7] found that adding ABUS to DBT and/or FFDM increased patient recall-rate in case of screening. ...
Background
As mammography has its known limitations in dense breast, additional imaging is usually needed. We aimed to evaluate the role of automated breast ultrasound in addition to tomosynthesis in detection and diagnosis of breast lesions in dense breasts. Seventy patients with dense breasts subjected to full-field digital mammography (FFDM) including digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS). Both studies were evaluated by two experienced radiologists to assess breast composition, mass characterization, asymmetry, calcification, axillary lymphadenopathy, extent of disease (EOD), skin thickening, retraction, architectural distortion, and BIRADS classification. All breast masses were interpreted as above described and then correlated with final pathological diagnosis.
Results
Study included 70 females presenting with different types of breast lesions. Eighty-two masses were detected: 53 benign ( n = 53/82), 29 malignant ( n = 29/82). Histopathology of the masses was reached by core biopsy ( n = 30), FNAC ( n = 14), and excisional biopsy ( n = 11). The rest of the masses ( n = 27/82) were confirmed by their characteristic sonographic appearances; 20 cases of multiple bilateral anechoic simple cysts, 7 typical fibroadenomas showed stationary course on follow-up. As regards the final BIRADS score given for both modalities, tomosynthesis showed accuracy of 93.1% in characterization of malignant masses with accuracy of 94.3% in benign masses, on the other hand automated ultrasound showed 100% accuracy in characterization of malignant masses with 98.1% accuracy in benign masses.
Conclusion
Adding ABUS to tomosynthesis has proven a valuable imaging tool for characterization of breast lesions in dense breasts both as screening and diagnostic tool. They proved to be more sensitive and specific than digital mammography alone in showing tissue overlap, tumor characterization, lesion margins, extent, and multiplicity of malignant lesions.
... Automated Breast Ultrasound (ABUS) can describe the entire breast in the form of a three-dimensional anatomical structure. So it can provide more vivid inner and edge information about the breast lesion and further improve the accuracy of the diagnosis of breast lesion (Abd Elkhalek et al., 2019;Rella et al., 2018). It provides more valuable information for the detection and diagnosis of breast cancer, especially early and atypical breast cancer. ...
People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in breast tumor classification using artificial intelligence technology. This paper proposes a method for classifying benign and malignant breast tumors using ABUS sequence based on deep learning. First, Images of Interest (IOI) will be extracted and Region of Interest (ROI) will be cropped in ABUS sequence by two preprocessing deep learning models, Extracting-IOI model and Cropping-ROI model. Then, we propose a Shallowly Dilated Convolutional Branch Network (SDCB-Net). We combine this network with the VGG16 transfer learning network to construct a brand-new Shared Extracting Feature Network (SEF-Net) to extract ROI sequence features. Finally, the correlation features of ABUS images are extracted and integrated by using GRU Classified Network (GRUC-Net) to achieve the accurate breast tumors classification. The final results show that the accuracy of the test set for classifying benign and malignant ABUS sequence is 92.86%. This method not only has high accuracy but also greatly improves the speed and efficiency of breast tumor classification. It has high clinical application significance that more women can discover breast tumors timely.
... Unlike our study, Abd Elkhalek et al., [9] according their results of a study of twenty-five female patients, age ranging 29-69 years complaining from breast pain or a palpable mass were submitted to ABUS and mammography. In their study, ABUS system was applied on 25 patients of mean age 43.4 with standard deviation of ±9.08. ...
Purpose
The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs).
Methods and materials
In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%). A deep convolutional neural network was trained after data augmentation with images of lesions and normal breast tissue, and a sliding-window approach for lesion detection was implemented. The algorithm was applied to a test dataset containing 128 images and performance was compared with readings of 2 experienced radiologists.
Results
Results of calculations performed on single images showed accuracy of 79.7% and AUC of 0.91 [95% CI: 0.85–0.96] in categorization according to BI-RADS. Moderate agreement between dCNN and ground truth has been achieved ( κ : 0.57 [95% CI: 0.50–0.64]) what is comparable with human readers. Analysis of whole dataset improved categorization accuracy to 90.9% and AUC of 0.91 [95% CI: 0.77–1.00], while achieving almost perfect agreement with ground truth ( κ : 0.82 [95% CI: 0.69–0.95]), performing on par with human readers. Furthermore, the object localization technique allowed the detection of lesion position slice-wise.
Conclusions
Our results show that a dCNN can be trained to detect and distinguish lesions in ABUS according to the BI-RADS classification with similar accuracy as experienced radiologists.
Key Points
• A deep convolutional neural network (dCNN) was trained for classification of ABUS lesions according to the BI-RADS atlas .
• A sliding-window approach allows accurate automatic detection and classification of lesions in ABUS examinations .
