Role of high resolution ultrasound complementary to digital mammography

Gupta, Kamini; Sandhu, P.; Arora, S; Bedi, Gaganpreet

doi:10.4103/aam.aam_36_17

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…4 USG, MRI and molecular breast imaging are usually not considered as primary screening tools and are employed as adjuncts to assess the abnormalities detected in MMG. 5 American college of radiography developed the breast-imaging reporting and data system (BI-RADS) lexicon, a standard terminology used to summarize the findings of various breast-imaging techniques such as MMG, USG and MRI. 4 We too employed the BI-RADS lexicon in our study.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of mammography and sonomammography and its correlation with histopathology in the detection of breast cancer

Yamakanamardi

Hiremath

2021

Int Surg J

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Background: Mammography (MMG) plays a pivotal role in the early diagnosis of breast cancer (BC). However, it is sometimes difficult to use it to diagnose palpable breast lesions among young patients. Ultrasound can be used as an adjunct in differentiating cystic and solid masses. Studies evaluating the combination of MMG and sonomammography (SMG) as an adjunct to detect Breast cancer, are quite scarce in the literature. This study aimed to assess the accuracy of MMG and to comprehend the role of ultrasound as an adjunct to MMG for finding breast lesions.Methods: Women attending the outpatient department (OPD) with complaints of breast lump, or those undergoing MMG screening were included. All patients then subsequently underwent MMG, SMG and biopsy. MMG and SMG findings were then correlated with the histopathology results.Results: Irregular shape and calcifications (MMG) and hypoechoic pattern (SMG) were found to be significant features differentiating malignant from benign lesions. Calcifications in benign tumors were observed 5.05 times less frequently than in malignant tumors. MMG combined with SMG had a sensitivity of 90.4%, specificity of 82.4%, positive and negative predictive value of 95% and 67% respectively, along with an accuracy of 88.9% in differentiating benign from malignant masses.Conclusions: SMG used as an adjunct to MMG is a reliable modality, especially in detecting lesions that are not picked up on MMG, including intraductal papilloma and duct ectasia.

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Section: Introductionmentioning

confidence: 99%

Accuracy of mammography and sonomammography and its correlation with histopathology in the detection of breast cancer

Yamakanamardi

Hiremath

2021

Int Surg J

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“…Thus, the spatial resolution of liver ultrasound images can affect one's ability to discriminate hemangiomas, and the proposed algorithm is expected to provide significant advantages in this regard. Improving the spatial resolution of breast ultrasound imaging is an effective method for detecting fine calcific formations, and it has been reported that a sensitivity of up to 95% is achievable [45]. In addition, breast ultrasound images with improved spatial resolution can be used better distinguish between solid and cystic lesions and characterize their complexity [46].…”

Section: Resultsmentioning

confidence: 99%

A Multiscale Deep Encoder–Decoder with Phase Congruency Algorithm Based on Deep Learning for Improving Diagnostic Ultrasound Image Quality

Kim,

Lee

2023

Applied Sciences

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Ultrasound imaging is widely used as a noninvasive lesion detection method in diagnostic medicine. Improving the quality of these ultrasound images is very important for accurate diagnosis, and deep learning-based algorithms have gained significant attention. This study proposes a multiscale deep encoder–decoder with phase congruency (MSDEPC) algorithm based on deep learning to improve the quality of diagnostic ultrasound images. The MSDEPC algorithm included low-resolution (LR) images and edges as inputs and constructed a multiscale convolution and deconvolution network. Simulations were conducted using the Field 2 program, and data from real experimental research were obtained using five clinical datasets containing images of the carotid artery, liver hemangiomas, breast malignancy, thyroid carcinomas, and obstetric nuchal translucency. LR images, bicubic interpolation, and super-resolution convolutional neural networks (SRCNNs) were modeled as comparison groups. Through visual assessment, the image processed using the MSDEPC was the clearest, and the lesions were clearly distinguished. The structural similarity index metric (SSIM) value of the simulated ultrasound image using the MSDEPC algorithm improved by approximately 38.84% compared to LR. In addition, the peak signal-to-noise ratio (PSNR) and SSIM values of clinical ultrasound images using the MSDEPC algorithm improved by approximately 2.33 times and 88.58%, respectively, compared to LR. In conclusion, the MSDEPC algorithm is expected to significantly improve the spatial resolution of ultrasound images.

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“…Breast ultrasound is a non-invasive, relatively available imaging modality that is used as complementary to mammography in screening especially in dense breasts, differentiation of solid and cystic lesions, characterization of lesions, and diagnosis of intraductal mass as well as problem-solving for mammographically detected asymmetries [12,13].…”

Section: Discussionmentioning

confidence: 99%

Comparative study between conventional ultrasound strain elastography and an AI-enabled elastography software in differentiating breast masses

Kamal

Mahmoud

et al. 2022

Egypt J Radiol Nucl Med

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Background Conventional ultrasound elastography is a relatively novel noninvasive imaging study that assesses tissue stiffness and helps in the characterization of breast lesions. However, strain elastography is not available in some ultrasound machines especially those before 2003 and is susceptible by motion artifacts. Our aim was to compare the results of conventional ultrasound elastography and the results of an advanced intelligence-enabled elastography software. Also, we aimed to assess the feasibility of the AI-enabled elastography software to overcome the unavailability of the conventional elastography software in some new ultrasound machines. Results The study included 53 patients, who had breast lesions either clinically felt or detected during screening. All patients were subjected to both grayscale US imaging and conventional ultrasound elastography; quasi-static compression was applied during acquiring one of the cine-loops of the grayscale US imaging. Also, the cine-loops of the grayscale US imaging while quasi-static compression were processed by an AI-enabled elastography software. Then, the results of the strain ratio (SR) calculated by conventional elastography software and those by AI-enabled elastography software were compared. The strain ratio calculated using the AI-enabled elastography software showed better results than conventional ultrasound elastography strain ratio. The AI-enabled software shows better specificity, sensitivity, positive predictive values, and negative predictive values than the conventional ultrasound elastography. Conclusion The AI-enabled elastography software shows promising results compared to the conventional US elastography. Elastography does not have the potential to replace conventional B-mode US for the detection of breast cancer but may complement the conventional US to improve diagnostic performance.

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Role of high resolution ultrasound complementary to digital mammography

Cited by 8 publications

References 12 publications

Accuracy of mammography and sonomammography and its correlation with histopathology in the detection of breast cancer

Accuracy of mammography and sonomammography and its correlation with histopathology in the detection of breast cancer

A Multiscale Deep Encoder–Decoder with Phase Congruency Algorithm Based on Deep Learning for Improving Diagnostic Ultrasound Image Quality

Comparative study between conventional ultrasound strain elastography and an AI-enabled elastography software in differentiating breast masses

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