Added Value of Different Types of Elastography in Evaluating Ultrasonography Detected Breast Lesions: A Compared Study With Mammography

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography

Background

Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases.

Patients and Methods

620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results

The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively).

Conclusion

Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Exploring the Role of 2D-Graphdiyne as a Charge Carrier Layer in Field-Effect Transistors for Non-Covalent Biological Immobilization against Human Diseases

Graphdiyne's (GDY's) outstanding features have made it a novel 2D nanomaterial and a great candidate for electronic gadgets and optoelectronic devices, and it has opened new opportunities for the development of highly sensitive electronic and optical detection methods as well. Here, we testified a non-covalent grafting strategy in which GDY serves as a charge carrier layer and a bioaffinity substrate to immobilize biological receptors on GDY-based field-effect transistor (FET) devices. Firm non-covalent anchoring of biological molecules via pyrene groups and electrostatic interactions in addition to preserved electrical properties of GDY endows it with features of an ultrasensitive and stable detection mechanism. With emerging new forms and extending the subtypes of the already existing fatal diseases, genetic and biological knowledge demands more details. In this regard, we constructed simple yet efficient platforms using GDY-based FET devices in order to detect different kinds of biological molecules that threaten human health. The resulted data showed that the proposed non-covalent bioaffinity assays in GDY-based FET devices could be considered reliable strategies for novel label-free biosensing platforms, which still reach a high on/off ratio of over 10⁴. The limits of detection of the FET devices to detect DNA strands, the CA19-9 antigen, microRNA-155, the CA15-3 antigen, and the COVID-19 antigen were 0.2 aM, 0.04 pU mL^-1, 0.11 aM, 0.043 pU mL^-1, and 0.003 fg mL^-1, respectively, in the linear ranges of 1 aM to 1 pM, 1 pU mL^-1 to 0.1 μU mL^-1, 1 aM to 1 pM, 1 pU mL^-1 to 10 μU mL^-1, and 1 fg mL^-1 to 10 ng mL^-1, respectively. Finally, the extraordinary performance of these label-free FET biosensors with low detection limits, high sensitivity and selectivity, capable of being miniaturized, and implantability for in vivo analysis makes them a great candidate in disease diagnostics and point-of-care testing.

Can Ultrasound Elastography Help Better Manage Mammographic BI-RADS Category 4 Breast Lesions?

BACKGROUND

To assess the performance of conventional ultrasound (US) combined with strain elastography (SE) in the Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions on mammography.

MATERIALS AND METHODS

Women with breast lesions identified as having mammography BI-RADS 4 lesions and underwent US examination were included in China. US features and US BI-RADS assessment were recorded in real-time and prospectively reported. The pathological result was referred to as the gold standard. The performance of US in the mammographic BI-RADS category 4 lesions was evaluated. Diagnostic performances of US BI-RADS, SE and combined both were compared.

RESULTS

A total of 751 women with 751 breast lesions classified as mammographic BI-RADS category 4 were included. For mammographic findings, 530 (70.6%) were true positive and 221 (29.4%) were false positive. Conventional US achieved higher positive predictive value (PPV) than mammography (78.5% vs. 70.6%, P=.001). The specificity increased from 34.4% to 47.1% (P< .001) without any loss in sensitivity and the PPV increased to 81.9% (P = .122) when conventional US was used in combination with SE. For conventional US combined with SE, it led to a correct diagnosis of no breast cancer in 104 of the 221 false-positive findings (47.1%) and achieved higher PPV than mammography regardless of patient age and lesion size.

CONCLUSION

Conventional US combined with SE is a helpful tool for the noninvasive examination of breast lesions classified as BI-RADS category 4 on mammography. It helped increase the PPV and had the potential to avoid unnecessary biopsies of BI-RADS category 4 lesions detected on mammography.

A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses

Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.

MR Elastography of the Breast: Evolution of Technique, Case Examples, and Future Directions

Recognizing that breast cancers present as firm, stiff lesions, the foundation of breast magnetic resonance elastography (MRE) is to combine tissue stiffness parameters with sensitive breast MR contrast-enhanced imaging. Breast MRE is a non-ionizing, cross-sectional MR imaging technique that provides for quantitative viscoelastic properties, including tissue stiffness, elasticity, and viscosity, of breast tissues. Currently, the technique continues to evolve as research surrounding the use of MRE in breast tissue is still developing. In the setting of a newly diagnosed cancer, associated desmoplasia, stiffening of the surrounding stroma, and necrosis are known to be prognostic factors that can add diagnostic information to patient treatment algorithms. In fact, mechanical properties of the tissue might also influence breast cancer risk. For these reasons, exploration of breast MRE has great clinical value. In this review, we will: (1) address the evolution of the various MRE techniques; (2) provide a brief overview of the current clinical studies in breast MRE with interspersed case examples; and (3) suggest directions for future research.