Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review

A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast

ABSTRACT

We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers With Partially Annotated Ultrasound Images

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may not always be optimal for the classification task due to individual experience of sonologists, resulting in the issue of coarse annotation to limit the diagnosis performance of a CAD model. To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to improve diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the initial ROI-level labels are considered as coarse annotations before model training. In the first training stage, a candidate selection mechanism is then designed to refine manual ROIs in the fully annotated images and generate accurate pseudo-ROIs for the partially annotated images under the guidance of class labels. The training set is updated with more accurate ROI labels for the second training stage. A fusion network is developed to integrate detection network and classification network into a unified end-to-end framework as the final CAD model in the second training stage. A self-distillation strategy is designed on this model for joint optimization to further improves its diagnosis performance. The proposed TSDDNet is evaluated on three B-mode ultrasound datasets, and the experimental results indicate that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

Value of Contrast-Enhanced Microflow Imaging in Diagnosis of Breast Masses in Comparison with Contrast-Enhanced Ultrasound

RATIONALE AND OBJECTIVES

To investigate the value of contrast-enhanced microflow imaging (CEUS-MFI) in distinguishing benign and malignant breast masses.

METHODS

A total of 116 breast masses classified as Breast Imaging Reporting and Data System (BI-RADS) category 3-5 by ultrasound (US) were included. Both contrast-enhanced ultrasound (CEUS) and CEUS-MFI were performed before excision or biopsy, with features and diagnostic efficiency analyzed. The US and CEUS BI-RADS 4A masses were also re-assessed by CEUS-MFI.

RESULTS

The features of CEUS-MFI including both interior and peripheral enlarged, twisted vessels (both P < 0.05), penetrating vessels (P = 0.007), and radial/spiculated vessels (P < 0.001) were more frequently detected in malignant masses, while peripheral annular vessels were mostly observed in benign masses (P < 0.001). Interestingly, a significant difference in the orientation of penetrating vessels between benign and malignant masses was found (P < 0.001), with parallel orientation mostly displayed in benign masses, while vertical or multiple-direction orientation mostly displayed in malignant masses. The microvascular architecture of breast masses was categorized into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. Benign masses mainly displayed tree-like pattern (77.1% vs 10.9%, P < 0.05); malignant masses mainly displayed root hair-like (34.8% vs 5.7%, P < 0.05) and crab claw-like patterns (50.0% vs 1.4%, P < 0.05). The diagnostic efficiency of CEUS-MFI was higher relative to CEUS and US. In addition, CEUS-MFI decreased the biopsy rates of US and CEUS BI-RADS 4A masses without missing malignancies.

CONCLUSION

CEUS-MFI could be a valuable and promising technique in diagnosis of breast masses, and could provide more diagnostic information for radiologists.

Development of the quantitative PET prostate phantom (Q3P) for improved quality assurance of 18F-PSMA PET imaging in metastatic prostate cancer

BACKGROUND

Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37 mm fillable spheres. The NEMA phantom neglects, however, to model focal (3-10-mm), high-uptake lesions that are increasingly observed in prostate-specific membrane antigen (PSMA) PET images. PSMA-targeting radiopharmaceuticals allow for enhanced detection of metastatic prostate cancers. As such, there is significant need to develop an updated phantom which considers both the quantitative and lesion detectability of this new paradigm in oncological PET imaging.

PURPOSE

In this work, we present the Quantitative PET Prostate Phantom (Q3P); a portable and modular phantom that can be used to improve and harmonize imaging protocols for 18F-PSMA PET scans.

METHODS

A one-piece cylindrical phantom was designed effectively in two halves, which we call modules. Module 1 was designed to mimic lesions in the presence of background, and Module 2 mimicked very high contrast conditions (i.e., very low background) that can be observed in 18F-PSMA PET scans. Shell-less radioactive spheres (3-16-mm) were cast using epoxy resin mixed with sodium-22 (22Na), a long half-life positron emitter with positron range similar to 18F. To establish realistic lesion contrast, the 22Na spheres were mounted in a cylindrical chamber that can be filled with an 18F background (module 1). Thirteen exchangeable spherical cavity inserts (3-37-mm) were machined in two parts and solvent welded together, and filled with 18F (50 kBq/mL) to model lesions with very high contrast (module 2). Five 2.5-min PET scans were acquired on a 5-ring GE Discovery MI PET/CT scanner (General Electric, USA). Lesions were segmented using 41% of SUVmax fixed thresholding (41% FT) and recovery coefficients (RCs) were computed from 5 noise realizations.

RESULTS

The manufactured phantom is portable (5.7 kg) and scan preparation takes less than 40 min. The total 22Na activity is 250 kBq, allowing it to be shipped as an exempt package under International Atomic Energy Agency (IAEA) regulations. Recovery coefficients, computed using PSF modeling and no post-reconstruction smoothing, were 130.3% (16 mm), 147.1% (10 mm), 87.2% (6 mm), and 7.0% (3 mm) for RCmax, which decreased to 91.1% (16 mm), 90.6% (10 mm), 53.2% (6 mm), and 3.6% (3 mm) for RCmean in the 22Na spheres. Comparatively, 18F sphere recovery was 110.7% (17 mm), 123.6% (10 mm), 106.5% (7 mm), and 23.3% (3 mm) for RCmax, which was reduced to 76.7% (17 mm), 77.7% (10 mm), 66.8% (7 mm), and 13.5% (3 mm), for RCmean.

CONCLUSIONS

A standardized imaging phantom was developed for lesion quantification assessment in 18F-PSMA PET images. The phantom is configurable, providing users with the opportunity to modify background activity levels or sphere sizes according to clinical demands. Distributed to the community, the Q3P phantom has the potential to enable better assessment of lesion quantification and harmonization of 18F-PSMA PET imaging, which may lead to more robust predictive metrics and better outcome prediction in metastatic prostate cancer.

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature's significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.

A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis

BACKGROUND

Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment.

METHODS

We proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date.

RESULTS

Here, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 - 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance.

CONCLUSIONS

Such a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.

Activity-Based Fluorescence Diagnostics for Cancer

Fluorescence imaging is one of the most promising approaches to achieve intraoperative assessment of the tumor/normal tissue margins during cancer surgery. This is critical to improve the patients' prognosis, and therefore various molecular fluorescence imaging probes have been developed for the identification of cancer lesions during surgery. Among them, "activatable" fluorescence probes that react with cancer-specific biomarker enzymes to generate fluorescence signals have great potential for high-contrast cancer imaging due to their low background fluorescence and high signal amplification by enzymatic turnover. Over the past two decades, activatable fluorescence probes employing various fluorescence control mechanisms have been developed worldwide for this purpose. Furthermore, new biomarker enzymatic activities for specific types of cancers have been identified, enabling visualization of various types of cancers with high sensitivity and specificity. This Review focuses on recent advances in the design, function and characteristics of activatable fluorescence probes that target cancer-specific enzymatic activities for cancer imaging and also discusses future prospects in the field of activity-based diagnostics for cancer.

Biocompatible and bioactivable terpolymer-lipid-MnO2 Nanoparticle-based MRI contrast agent for improving tumor detection and delineation

Early and precise detection of solid tumor cancers is critical for improving therapeutic outcomes. In this regard, magnetic resonance imaging (MRI) has become a useful tool for tumor diagnosis and image-guided therapy. However, its effectiveness is limited by the shortcomings of clinically available gadolinium-based contrast agents (GBCAs), i.e. poor tumor penetration and retention, and safety concerns. Thus, we have developed a novel nanoparticulate contrast agent using a biocompatible terpolymer and lipids to encapsulate manganese dioxide nanoparticles (TPL-MDNP). The TPL-MDNP accumulated in tumor tissue and produced paramagnetic Mn2+ ions, enhancing T1-weight MRI contrast via the reaction with H2O2 rich in the acidic tumor microenvironment. Compared to the clinically used GBCA, Gadovist®1.0, TPL-MDNP generated stronger T1-weighted MR signals by over 2.0-fold at 30 % less of the recommended clinical dose with well-defined tumor delineation in preclinical orthotopic tumor models of brain, breast, prostate, and pancreas. Importantly, the MRI signals were retained for 60 min by TPL-MDNP, much longer than Gadovist®1.0. Biocompatibility of TPL-MDNP was evaluated and found to be safe up to 4-fold of the dose used for MRI. A robust large-scale manufacturing process was developed with batch-to-batch consistency. A lyophilization formulation was designed to maintain the nanostructure and storage stability of the new contrast agent.

MR-guided percutaneous microwave coagulation of small breast tumors

BACKGROUND

To evaluate the technical success and patient safety of magnetic resonance-guided percutaneous microwave coagulation (MR-guided PMC) for breast malignancies.

METHODS

From May 2018 to December 2019, 26 patients with breast tumors measuring 2 cm or less were recruited to participate in a prospective, single-institution clinical study. The primary endpoint of this study was the evaluation of treatment efficacy for each patient. Histochemical staining with α-nicotinamide adenine dinucleotide and reduced (NADH)-diaphorase was used to determine cell viability following and efficacy of PMC. The complications and self-reported sensations from all patients during and after ablation were also assessed. The technical success of the PMC procedure was defined when the area of the NADH-diaphorase negative region fully covered the hematoxylin-eosin (H&E) staining region in the tumor.

RESULTS

All patients had a complete response to ablation with no residual carcinoma on histopathological specimen. The mean energy, ablation duration, and procedure duration per tumor were 36.0 ± 4.2 kJ, 252.9 ± 30.9 S, and 104.2 ± 13.5 min, respectively. During the ablation, 14 patients underwent prolonged ablation time, and 1 patient required adjusting of the antenna position. Eleven patients had feelings of subtle heat or swelling, and 3 patients experienced slight pain. After ablation, one patient took two painkillers because of moderate pain, and no patients had postoperative oozing or other complications after PMC. Induration around the ablation area appeared in 16 patients.

CONCLUSION

MR-guided PMC of small breast tumors is feasible and could be applied in clinical practice in the future.

CRITICAL RELEVANCE STATEMENT

MR-guided PMC of small breast tumors is feasible and could be applied in clinical practice in the future.

KEY POINTS

• MR-guided PMC of small breast tumors is feasible. • PMC was successfully performed for all patients. • All patients were satisfied with the final cosmetic result.

Intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer

Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI-RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (≥ 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.

Diffusion-Weighted MRI-Based Virtual Elastography and Shear-Wave Elastography for the Assessment of Breast Lesions

BACKGROUND

Diffusion-weighted imaging (DWI)-based virtual MR elastography (DWI-vMRE) in the assessment of breast lesions is still in the research stage.

PURPOSE

To investigate the usefulness of elasticity values on DWI-vMRE in the evaluation of breast lesions, and the correlation with the values calculated from shear-wave elastography (SWE).

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

153 patients (mean age ± standard deviation: 55 ± 12 years) with 153 pathological confirmed breast lesions (24 benign and 129 malignant lesions).

FIELD STRENGTH/SEQUENCE

1.5-T MRI, multi-b readout segmented echo planar imaging (b-values of 0, 200, 800, and 1000 sec/mm2 ).

ASSESSMENT

For DWI-vMRE assessment, lesions were manually segmented using apparent diffusion coefficient (ADC0-1000 ) map, then the region of interests were copied to the map of shifted-ADC (sADC200-800 , sADC 200-1500 ). For SWE assessment, the shear modulus of the lesions was measured by US elastic modulus (μUSE ). Intraclass/interclass kappa coefficients were calculated to measure the consistency.

STATISTICAL TESTS

Pearson's correlation was used to assess the relationship between sADC and μUSE . A receiver operating characteristic analysis with the area under the curve (AUC) was performed to compare the diagnostic accuracy between benign and malignant breast lesions of sADC and μUSE . A P value <0.05 was considered statistically significant.

RESULTS

There were significant differences between benign and malignant breast lesions of μUSE (24.17 ± 10.64 vs. 37.20 ± 12.61), sADC200-800 (1.38 ± 0.31 vs. 0.97 ± 0.18 × 10-3  mm2 /sec), and sADC200-1500 (1.14 ± 0.30 vs. 0.78 ± 0.13 × 10-3  mm2 /sec). In all breast lesions, a moderate but significant correlation was observed between μUSE and sADC200-800 /sADC200-1500 (r = -0.49/-0.44). AUC values to differentiate benign from malignant lesions were as follows: μUSE , 0.78; sADC200-800 , 0.89; sADC200-1500 , 0.89.

DATA CONCLUSIONS

Both SWE and DWI-vMRE could be used for the differentiation of benign versus malignant breast lesions. Furthermore, DWI-vMRE with the use of sADC show relatively higher AUC values than SWE.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Targeting the Oxytocin Receptor for Breast Cancer Management: A Niche for Peptide Tracers

Breast cancer is a leading cause of death in women, and its management highly depends on early disease diagnosis and monitoring. This remains challenging due to breast cancer's heterogeneity and a scarcity of specific biomarkers that could predict responses to therapy and enable personalized treatment. This Perspective describes the diagnostic landscape for breast cancer management, molecular strategies targeting receptors overexpressed in tumors, the theranostic potential of the oxytocin receptor (OTR) as an emerging breast cancer target, and the development of OTR-specific optical and nuclear tracers to study, visualize, and treat tumors. A special focus is on the chemistry and pharmacology underpinning OTR tracer development, preclinical in vitro and in vivo studies, challenges, and future directions. The use of peptide-based tracers targeting upregulated receptors in cancer is a highly promising strategy complementing current diagnostics and therapies and providing new opportunities to improve cancer management and patient survival.

Current Trends and Beyond Conventional Approaches: Advancements in Breast Cancer Surgery through Three-Dimensional Imaging, Virtual Reality, Augmented Reality, and the Emerging Metaverse

Breast cancer stands as the most prevalent cancer globally, necessitating comprehensive care. A multidisciplinary approach proves crucial for precise diagnosis and treatment, ultimately leading to effective disease management. While surgical interventions continue to evolve and remain integral for curative treatment, imaging assumes a fundamental role in breast cancer detection. Advanced imaging techniques not only facilitate improved diagnosis but also contribute significantly to the overall enhancement of breast cancer management. This review article aims to provide an overview of innovative technologies such as virtual reality, augmented reality, and three-dimensional imaging, utilized in the medical field to elevate the diagnosis and treatment of breast cancer. Additionally, the article delves into an emerging technology known as the metaverse, still under development. Through the analysis of impactful research and comparison of their findings, this study offers valuable insights into the advantages of each innovative technique. The goal is to provide physicians, surgeons, and radiologists with information on how to enhance breast cancer management.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer

PURPOSE

We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions.

MATERIALS AND METHODS

A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson's coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses.

RESULTS

Among all the radiomics models, the "Mass + Margin3mm" model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882-0.930) and 0.922 (95% CI 0.894-0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816-0.889) in primary cohort, 0.870 (95% CI 0.818-0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781-0.884) in primary cohort, 0.911 (95% CI 0.858-0.964) in validation cohort).

CONCLUSIONS

The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

A neural network with a human learning paradigm for breast fibroadenoma segmentation in sonography

BACKGROUND

Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography.

METHODS

Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods.

RESULTS

Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD.

CONCLUSIONS

Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing.

A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer

Objective

To compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.

Methods

Published studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan's nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.

Results

A total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73-0.95)], specificity was 0.93 [95% CI (0.82-0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94-0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84-0.91)], specificity was 0.76 [95% CI (0.66-0.84)], AUC was 0.89 [95% CI (0.86-0.92)] and DOR was 24. The Deeks' funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.

Conclusion

Compared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.

Harnessing the power of Microscale AcoustoFluidics: A perspective based on BAW cancer diagnostics

Cancer directly affects one in every three people, and mortality rates strongly correlate with the stage at which diagnosis occurs. Each of the multitude of methods used in cancer diagnostics has its own set of advantages and disadvantages. Two common drawbacks are a limited information value of image based diagnostic methods and high invasiveness when opting for methods that provide greater insight. Microfluidics offers a promising avenue for isolating circulating tumor cells from blood samples, offering high informational value at predetermined time intervals while being minimally invasive. Microscale AcoustoFluidics, an active method capable of manipulating objects within a fluid, has shown its potential use for the isolation and measurement of circulating tumor cells, but its full potential has yet to be harnessed. Extensive research has focused on isolating single cells, although the significance of clusters should not be overlooked and requires attention within the field. Moreover, there is room for improvement by designing smaller and automated devices to enhance user-friendliness and efficiency as illustrated by the use of bulk acoustic wave devices in cancer diagnostics. This next generation of setups and devices could minimize streaming forces and thereby enable the manipulation of smaller objects, thus aiding in the implementation of personalized oncology for the next generation of cancer treatments.

The discriminatory diagnostic value of multimodal ultrasound combined with blood cell analysis for granulomatous lobular mastitis and invasive ductal carcinoma of the breast

OBJECTIVE

To explore the discriminatory diagnostic value of multimodal ultrasound(US) combined with blood cell analysis (BCA) for Granulomatous Lobular Mastitis (GLM) and Invasive Ductal Carcinoma (IDC) of the breast.

METHODS

A total of 157 breast disease patients were collected and divided into two groups based on postoperative pathological results: the GLM group (57 cases with 57 lesions) and the IDC group (100 cases with 100 lesions). Differences in multimodal ultrasound features and the presence of BCA were compared between the two groups. The receiver operating characteristic (ROC) curve was used to calculate the optimal cutoff values, sensitivity, specificity, 95% confidence interval (CI), and the area under the curve (AUC) for patient age, lesion size, lesion resistive index (RI), and white blood cell (WBC) count in BCA. Sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy, and AUC were calculated for different diagnostic methods.

RESULTS

There were statistically significant differences (P < 0.05) observed between GLM and IDC patients in terms of age, breast pain, the factors in Conventional US (lesion size, RI, nipple delineation, solitary/multiple lesions, margin, liquefaction area, growth direction, microcalcifications, posterior echogenicity and abnormal axillary lymph nodes), the factors in CEUS (contrast agent enhancement intensity, enhancement pattern, enhancement range, and crab-like enhancement) and the factors in BCA (white blood cells, neutrophils, lymphocytes and monocytes). ROC curve analysis results showed that the optimal cutoff values for distinguishing GLM from IDC were 40.5 years for age, 7.15 cm for lesion size, 0.655 for lesion RI, and 10.525*109/L for white blood cells. The diagnostic accuracy of conventional US combined with CEUS (US-CEUS) was the highest (97.45%). The diagnostic performance AUCs for US-CEUS, CEUS, and US were 0.965, 0.921 and 0.832, respectively.

CONCLUSION

Multifactorial analysis of multimodal ultrasound features and BCA had high clinical application value in the differential diagnosis of GLM and IDC.

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

BACKGROUND AND OBJECTIVES

This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images.

MATERIALS AND METHODS

We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient.

RESULTS

The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar.

CONCLUSION

The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

Exploring the potential of contrast agents in breast cancer echography: current state and future directions

Breast cancer stands as the most frequent malignancy and leading cause of death among women. Early and accurate detection of this pathology represents a crucial factor in enhancing both incidence and mortality rates. Ultrasound (US) examination has been extensively adopted in clinical practice due to its non-invasiveness, affordability, ease of implementation, and wide accessibility, thus representing a valuable first-line diagnostic tool for the study of the mammary gland. In this scenario, recent developments in nanomedicine are paving the way for new interpretations and applications of US diagnostics, which are becoming increasingly personalized based on the molecular phenotype of each tumor, allowing for more precise and accurate evaluations. This review highlights the current state-of-the-art of US diagnosis of breast cancer, as well as the recent advancements related to the application of US contrast agents to the field of molecular diagnostics, still under preclinical study.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Sono-processes: Emerging systems and their applicability within the (bio-)medical field

Sonochemistry, although established in various fields, is still an emerging field finding new effects of ultrasound on chemical systems and are of particular interest for the biomedical field. This interdisciplinary area of research explores the use of acoustic waves with frequencies ranging from 20 kHz to 1 MHz to induce physical and chemical changes. By subjecting liquids to ultrasonic waves, sonochemistry has demonstrated the ability to accelerate reaction rates, alter chemical reaction pathways, and change physical properties of the system while operating under mild reaction conditions. It has found its way into diverse industries including food processing, pharmaceuticals, material science, and environmental remediation. This review provides an overview of the principles, advancements, and applications of sonochemistry with a particular focus on the domain of (bio-)medicine. Despite the numerous benefits sonochemistry has to offer, most of the research in the (bio-)medical field remains in the laboratory stage. Translation of these systems into clinical practice is complex as parameters used for medical ultrasound are limited and toxic side effects must be minimized in order to meet regulatory approval. However, directing attention towards the applicability of the system in clinical practice from the early stages of research holds significant potential to further amplify the role of sonochemistry in clinical applications.

Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images

Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer-aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segmenting and identifying features of lesions. Here, we present deep learning (DL)-based methods to segment the lesions and then classify benign from malignant, utilizing both B-mode and strain elastography (SE-mode) images. We propose a weighted multimodal U-Net (W-MM-U-Net) model for segmenting lesions where optimum weight is assigned on different imaging modalities using a weighted-skip connection method to emphasize its importance. We design a multimodal fusion framework (MFF) on cropped B-mode and SE-mode ultrasound (US) lesion images to classify benign and malignant lesions. The MFF consists of an integrated feature network (IFN) and a decision network (DN). Unlike other recent fusion methods, the proposed MFF method can simultaneously learn complementary information from convolutional neural networks (CNNs) trained using B-mode and SE-mode US images. The features from the CNNs are ensembled using the multimodal EmbraceNet model and DN classifies the images using those features. The experimental results (sensitivity of 100 ± 0.00% and specificity of 94.28 ± 7.00%) on the real-world clinical data showed that the proposed method outperforms the existing single- and multimodal methods. The proposed method predicts seven benign patients as benign three times out of five trials and six malignant patients as malignant five out of five trials. The proposed method would potentially enhance the classification accuracy of radiologists for breast cancer detection in US images.

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

Background

In routine clinical examinations, solid hypoechoic breast lesions are frequently encountered, but accurately distinguishing them poses a challenge. This study proposed a clinical-radiomics nomogram based on multimodal ultrasound that enhances the diagnostic accuracy for solid hypoechoic breast lesions.

Method

This retrospective study analyzed ultrasound strain elastography (SE) and automated breast volume scanner images (ABVS) of 423 solid hypoechoic breast lesions from 423 female patients in our hospital between August 2019 and May 2022. They were assigned to the training (n=296) and validation (n=127) groups in a 7:3 ratio by generating random numbers. Radiomics features were extracted and screened from ABVS and SE images, followed by the calculation of the radiomics score (Radscore) based on these features. Subsequently, a nomogram was constructed through multivariate logistic regression to assess the malignancy risk in breast lesions by combining Radscore with Breast Imaging Reporting and Data System (BI-RADS) scores and clinical risk factors associated with breast malignant lesions. The diagnostic performance, calibration performance, and clinical usefulness of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, the calibration curve, and the decision analysis curve, respectively.

Results

The diagnostic performance of the nomogram is significantly superior to that of both the clinical diagnostic model (BI-RADS model) and the multimodal radiomics model (SE+ABVS radiomics model) in training (AUC: 0.972 vs 0.930 vs 0.941) and validation group (AUC:0.964 vs 0.916 vs 0.933). In addition, the nomogram also exhibited a favorable goodness-of-fit and could lead to greater net benefits for patients.

Conclusion

The nomogram enables a more effective assessment of the malignancy risk of solid hypoechoic breast lesions; therefore, it can serve as a new and efficient diagnostic tool for clinical diagnosis.

Visualizing tactile feedback: an overview of current technologies with a focus on ultrasound elastography

Tissue elasticity remains an essential biomarker of health and is indicative of irregularities such as tumors or infection. The timely detection of such abnormalities is crucial for the prevention of disease progression and complications that arise from late-stage illnesses. However, at both the bedside and the operating table, there is a distinct lack of tactile feedback for deep-seated tissue. As surgical techniques advance toward remote or minimally invasive options to reduce infection risk and hasten healing time, surgeons lose the ability to manually palpate tissue. Furthermore, palpation of deep structures results in decreased accuracy, with the additional barrier of needing years of experience for adequate confidence of diagnoses. This review delves into the current modalities used to fulfill the clinical need of quantifying physical touch. It covers research efforts involving tactile sensing for remote or minimally invasive surgeries, as well as the potential of ultrasound elastography to further this field with non-invasive real-time imaging of the organ's biomechanical properties. Elastography monitors tissue response to acoustic or mechanical energy and reconstructs an image representative of the elastic profile in the region of interest. This intuitive visualization of tissue elasticity surpasses the tactile information provided by sensors currently used to augment or supplement manual palpation. Focusing on common ultrasound elastography modalities, we evaluate various sensing mechanisms used for measuring tactile information and describe their emerging use in clinical settings where palpation is insufficient or restricted. With the ongoing advancements in ultrasound technology, particularly the emergence of micromachined ultrasound transducers, these devices hold great potential in facilitating early detection of tissue abnormalities and providing an objective measure of patient health.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

From bedside to portable and wearable: development of a conformable ultrasound patch for deep breast tissue imaging

A breakthrough study from Du et al. has developed a wearable, ultrasound imaging patch for standardized and reproducible breast tissue imaging. The technology utilizes a honeycomb patch design to facilitate guided movement of the ultrasound array, enabling comprehensive, multiangle breast imaging. The system was validated in vitro and in vivo with a single human subject and has the potential for early-stage breast cancer detection. This study addressed the current limitations of wearable ultrasound technologies, including imaging over large, curvilinear organs and integration of superior piezoelectric materials for high-performance ultrasound arrays. The transition of ultrasound from the bedside to portable and wearable devices will pave the way for integration with big data collection, such as artificial intelligence (AI)-based diagnosis and personalized ultrasonographic profile generation, for rapid and objective measurements. This advancement is especially important in the context of breast cancer, where early diagnosis and assessment of medical therapy responses are paramount to patient care.

Superb microvascular imaging for evaluation of microvascularity in breast nodules compared with conventional Doppler imaging

Background

Neovascularity visualization in breast nodules is challenging due to the limitations of conventional Doppler imaging methods. This study aims to assess the performance of superb microvascular imaging (SMI) in evaluating the microvascularity of breast nodules (diameter ≤2 cm). The comparison of performances of SMI with color Doppler flow imaging (CDFI) and power Doppler imaging (PDI) was made by using a three-factor scoring system of vascularity. This study also investigated the common features of microvascularity in small malignant nodules on SMI for early differentiating from benign nodules.

Methods

Ninety-one female patients (with 125 breast nodules) were enrolled in this retrospective study. All the breast nodules were examined by grayscale ultrasonography (US), CDFI, PDI, and SMI. The number, morphologic features, and distribution of blood vessels were scored to evaluate the nodular vascularity in light of the three-factor scoring system. The diagnostic value of SMI for microvascularity in breast nodules was analyzed and compared with CDFI and PDI.

Results

Histological analysis showed 53 malignant and 72 benign nodules. The vascularity grades detected by SMI were significantly different from those of CDFI and PDI (P<0.05). SMI detected 47 grade-IV nodules of the total 125 nodules (37.6%), which was more than those detected by CDFI (10.4%, 13/125) and PDI (12.8%, 16/125), while more grade-I nodules were detected by CDFI (42.4%, 53/125) and PDI (36.8%, 46/125) compared with SMI (21.6%, 27/125). Differences in the vessel number, morphologic features, and distribution between benign and malignant breast nodules were significant on SMI (P<0.05). The vessel number ≥6, penetrating vessels, and a mixed distribution of vessels in peripheral and central nodular tissues were the common features of microvascularity in the grade-IV malignant nodules on SMI, whereas the blood vessels in the benign nodules were straight and branching and peripherally distributed.

Conclusions

In comparison with CDFI and PDI, SMI enhances microvascularity detection, depicts the microvascular architecture in breast nodules and has potential in the differential diagnosis of malignant nodules from benign nodules.

Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study

BACKGROUND

This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images.

METHODS

A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed.

RESULTS

A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively.

CONCLUSIONS

This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.

A novel tunable metal-clad planar waveguide with 0.62PMN-0.38PT material for detection of cancer cells

A dynamically tunable metal clad planar waveguide having 0.62PMN-0.38PT material is simulated and optimized for detection of cancer cells. Angular interrogation of the TE0 mode of waveguide shows that critical angle increases greater than the resonance angle with increasing of cover refractive index, which limits the detection range of waveguide. To overcome this limitation, proposed waveguide applies a potential on the PMN-PT adlayer. Although a sensitivity of 105.42 degree/RIU was achieved at 70 Volts in testing the proposed waveguide, it was found that the optimal performance parameters were obtained at 60 Volts. At this voltage, the waveguide demonstrated detection range 1.3330-1.5030, a detection accuracy 2393.33, and a figure of merit 2243.59 RIU-1 , which enabled the detection of the entire range of the targeted cancer cells. Therefore, it is recommended to apply a potential of 60 Volts to achieve the best performance from the proposed waveguide.

Vacuum-assisted biopsy system for breast lesions: a potential therapeutic approach

Purpose

The primary objective is to optimize the population eligible for Mammotome Minimally Invasive Surgery (MIS) by refining selection criteria. This involves maximizing procedure benefits, minimizing malignancy risk, and reducing the rate of malignant outcomes.

Patients and methods

A total of 1158 female patients who came to our hospital from November 2016 to August 2021 for the Mammotome MIS were analyzed retrospectively. Following χ2 tests to screen for risk variables, binary logistic regression analysis was used to determine the independent predictors of malignant lesions. In addition, the correlation between age and lesion diameter was investigated for BI-RADS ultrasound (US) category 4a lesions in order to better understand the relationship between these variables.

Results

The malignancy rates of BI-RADS US category 3, category 4a and category 4b patients who underwent the Mammotome MIS were 0.6% (9/1562), 6.4% (37/578) and 8.3% (2/24) respectively. Malignant lesions were more common in patients over the age of 40, have visible blood supply, and BI-RADS category 4 of mammography. In BI-RADS US category 4a lesions, the diameter of malignant tumor was highly correlated with age, and this correlation was strengthened in patients over the age of 40 and with BI-RADS category 4 of mammography.

Conclusion

The results of this study demonstrate that the clinical data and imaging results, particularly age, blood supply, and mammography classification, offer valuable insights to optimize patients' surgical options and decrease the incidence of malignant outcomes.

Translating new breast ultrasound techniques into clinical practice: evaluating their intended uses and describing other unexpected uses for them

Several new ultrasound tools have been developed to further evaluate breast lesions detected on B-mode ultrasound. Strain elastography (SRE) was developed to assess the likelihood of malignancy of lesions based on their stiffness. This has been incorporated into the latest edition of the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) lexicon and atlas. However, no agreed cut-off stiffness values have been established to distinguish benign from malignant lesions making the translation into routine clinical practice difficult. Superb microvascular imaging (SMI) was developed to better evaluate the vascularity within sonographic lesions and assess their likelihood of malignancy. However, there is also no agreed cut-off value for vascular index (VI) to distinguish between benign and malignant lesions. MicroPure was developed to better visualize and evaluate calcifications seen on ultrasound. Its effective use in breast screening and evaluating the calcifications detected for likelihood of malignancy have not been established. This article describes the original intended uses of these applications and reviews the studies evaluating them, showing the varying success of the translation of these tools into routine clinical practice. Also described are some other uses of these tools for which they were not originally intended. This illustrates the importance of being perceptive to alternative uses of imaging tools in their translation from bench to bedside.

Conformable ultrasound breast patch for deep tissue scanning and imaging

Ultrasound is widely used for tissue imaging such as breast cancer diagnosis; however, fundamental challenges limit its integration with wearable technologies, namely, imaging over large-area curvilinear organs. We introduced a wearable, conformable ultrasound breast patch (cUSBr-Patch) that enables standardized and reproducible image acquisition over the entire breast with less reliance on operator training and applied transducer compression. A nature-inspired honeycomb-shaped patch combined with a phased array is guided by an easy-to-operate tracker that provides for large-area, deep scanning, and multiangle breast imaging capability. The in vitro studies and clinical trials reveal that the array using a piezoelectric crystal [Yb/Bi-Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3] (Yb/Bi-PIN-PMN-PT) exhibits a sufficient contrast resolution (~3 dB) and axial/lateral resolutions of 0.25/1.0 mm at 30 mm depth, allowing the observation of small cysts (~0.3 cm) in the breast. This research develops a first-of-its-kind ultrasound technology for breast tissue scanning and imaging that offers a noninvasive method for tracking real-time dynamic changes of soft tissue.

Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer

Purpose

The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures.

Patients and Methods

Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features.

Results

Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC.

Conclusion

The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.

Breast Tissue Reconstruction Using Polycaprolactone Ball Scaffolds in a Partial Mastectomy Pig Model

BACKGROUND

Breast cancer patients suffer from lowered quality of life (QoL) after surgery. Breast conservancy surgery (BCS) such as partial mastectomy is being practiced and studied as an alternative to solve this problem. This study confirmed breast tissue reconstruction in a pig model by fabricating a 3-dimensional (3D) printed Polycaprolactone spherical scaffold (PCL ball) to fit the tissue resected after partial mastectomy.

METHODS

A 3D printed Polycaprolactone spherical scaffold with a structure that can help adipose tissue regeneration was produced using computer-aided design (CAD). A physical property test was conducted for optimization. In order to enhance biocompatibility, collagen coating was applied and a comparative study was conducted for 3 months in a partial mastectomy pig model.

RESULTS

In order to identify adipose tissue and fibroglandular tissue, which mainly constitute breast tissue, the degree of adipose tissue and collagen regeneration was confirmed in a pig model after 3 months. As a result, it was confirmed that a lot of adipose tissue was regenerated in the PCL ball, whereas more collagen was regenerated in the collagen-coated Polycaprolactone spherical scaffold (PCL-COL ball). In addition, as a result of confirming the expression levels of TNF-a and IL-6, it was confirmed that PCL ball showed higher levels than PCL-COL ball.

CONCLUSION

Through this study, we were able to confirm the regeneration of adipose tissue through a 3-dimensional structure in a pig model. Studies were conducted on medium and large-sized animal models for the final purpose of clinical use and reconstruction of human breast tissue, and the possibility was confirmed.

Breast mass as the first sign of metastasis from rectal carcinoma: a case report and review of the literature

We present a case report of a 41-year-old woman who developed a left breast mass 18 months after undergoing Dixon rectal cancer surgery. The purpose of this case report is to highlight the possibility of breast metastases in patients with colorectal cancer and emphasize the importance of careful evaluation and follow-up as well as timely and accurate diagnosis and management of the metastatic disease. During the physical examination in 2021, we noted that the lower border of the mass was 9 cm from the anal verge and that it occupied approximately one-third of the intestinal lumen. A pathological biopsy revealed the mass in the patient's intestinal lumen was a rectal adenocarcinoma. The patient underwent Dixon surgery for rectal cancer and received subsequent chemotherapy. The patient had no prior history of breast-related medical conditions or a family history of breast cancer. During the current physical examination, we discovered multiple lymphadenopathies in the patient's left neck, bilateral axillae, and left inguinal region, but none elsewhere. We observed a large erythema of about 15x10 cm on the patient's left breast, with scattered hard nodes of varying sizes. Palpation of the area beyond the upper left breast revealed a mass measuring 3x3 cm. We conducted further examinations of the patient, which revealed the breast mass and lymphadenopathy on imaging. However, we did not find any other imaging that had significant diagnostic value. Based on the patient's conventional pathology and immunohistochemical findings, combined with the patient's past medical history, we strongly suspected that the patient's breast mass was of rectal origin. This was confirmed by the abdominal CT performed afterward. The patient was treated with a chemotherapy regimen consisting of irinotecan 260 mg, fluorouracil 2.25 g, and cetuximab 700 mg IV drip, which resulted in a favorable clinical response. This case illustrates that colorectal cancer can metastasize to unusual sites and underscores the importance of thorough evaluation and follow-up, particularly when symptoms are atypical. It also highlights the importance of timely and accurate diagnosis and management of metastatic disease to improve the patient's prognosis.

Factors associated with the proportion of abnormal results in screening mammograms: ecological study

OBJECTIVE

To analyze the factors associated with the proportion of abnormal results in screening mammograms.

METHODS

Ecological study, with data from DATASUS/SISCAN, Atlas Brasil do Desenvolvimento Humano, Fundação SEADE, and Sistema e-Gestor, from 2016 to 2019, of women aged 50 to 69 years in the 645 municipalities of São Paulo (Brazil). Independent variables were associated with the outcome: proportion of unsatisfactory coverage of abnormal test results (Breast Imaging Reporting and Data System - BI-RADS® 0, 4 and 5 proportion >10% of tests performed). Multiple Poisson regression was used.

RESULTS

Higher proportion of screening mammography (PR=1.20; 95%CI: 1.00;1.45), higher percentage of poor (PR=1.20; 95%CI: 1.07;1.36), low (PR=1.57; 95%CI: 1.38;1.78) and medium coverage of the Family Health Strategy (ESF) (PR=1.30; 95%CI: 1.09;1.52) were associated to the outcome.

CONCLUSION

Socioeconomic and FHS coverage factors mediate the proportion of mammograms with abnormal results in public health services. Therefore, they are important aspects in the fight against breast cancer.

CNT and Graphene-Based Transistor Biosensors for Cancer Detection: A Review

An essential aspect of successful cancer diagnosis is the identification of malignant tumors during the early stages of development, as this can significantly diminish patient mortality rates and increase their chances of survival. This task is facilitated by cancer biomarkers, which play a crucial role in determining the stage of cancer cells, monitoring their growth, and evaluating the success of treatment. However, conventional cancer detection methods involve several intricate steps, such as time-consuming nucleic acid amplification, target detection, and a complex treatment process that may not be appropriate for rapid screening. Biosensors are emerging as promising diagnostic tools for detecting cancer, and carbon nanotube (CNT)- and graphene-based transistor biosensors have shown great potential due to their unique electrical and mechanical properties. These biosensors have high sensitivity and selectivity, allowing for the rapid detection of cancer biomarkers at low concentrations. This review article discusses recent advances in the development of CNT- and graphene-based transistor biosensors for cancer detection.

Ultrasound-Based Grading System for Radiation-Induced Acute Breast Toxicity

OBJECTIVES

To introduce an ultrasound-based scoring system for radiation-induced breast toxicity and test its reliability.

METHODS

Breast ultrasound (BUS) was performed on 32 patients receiving breast radiotherapy (RT) to assess the radiation-induced acute toxicity. For each patient, both the untreated and irradiated breasts were scanned at five locations: 12:00, 3:00, 6:00, 9:00, and tumor bed to evaluate for heterogenous responses to radiation within the entire breast. In total, 314 images were analyzed. Based on ultrasound findings such as skin thickening, dermis boundary irregularity, and subcutaneous edema, a 4-level, Likert-like grading scheme is proposed: none (G0), mild (G1), moderate (G2), and severe (G3) toxicity. Two ultrasound experts graded the severity of breast toxicity independently and reported the inter- and intra-observer reliability of the grading system. Imaging findings were compared with standard clinical toxicity assessments using Common Terminology Criteria for Adverse Events (CTCAE).

RESULTS

The inter-observer Pearson correlation coefficient (PCC) was 0.87 (95% CI: 0.83-0.90, P < .001). For intra-observer repeatability, the PCC of the repeated scores was 0.83 (95% CI: 0.78-0.87, P < .001). Imaging findings were compared with standard clinical toxicity assessments using CTCAE scales. The PCC between BUS scores and CTCAE results was 0.62 (95% CI: 0.35-0.80, P < .001). Among all locations, 6:00 and tumor bed showed significantly greater toxicity compared with 12:00 (P = .04).

CONCLUSIONS

BUS can investigate the cutaneous and subcutaneous tissue changes after RT. This BUS-based grading system can complement subjective clinical assessments of radiation-induced breast toxicity with cutaneous and subcutaneous sonographic information.

Diagnostic value of multimodal ultrasound strategies in the differentiation of non-mass-like breast lesions

PURPOSE

The study aims to assess the diagnostic performance of convention ultrasound (US), Angio PLUS microvascular US imaging (AP), and shear-wave elastography (SWE) in differentiating malignant and benign non-mass-like (NML) breast lesions.

METHODS

Sixty patients aged 21-70 years with 60 NML lesions were recruited. All patients were examined by conventional US, AP, and SWE. According to the pathological results, the performances of the multimodal US strategies were analyzed, while the diagnostic efficiency of AP and SWE in serial and parallel was also explored.

RESULTS

Age, together with posterior features, microcalcification, and architectural distortion were considered significant in evaluating NML lesions. The sensitivity, specificity, PPV, NPV, and accuracy of AP combined SWE in serial were 72.7, 96.3, 96.0, 74.3, and 83.3%, while those in parallel were 90.9, 63.0, 75.0, 85.0, and 78.3%, respectively. The two in serial indicated the highest specificity, PPV, accuracy, and AUC value, which could increase the true positive rate and reduce the chance of misdiagnosis, while the two in parallel exhibited the best sensitivity and NPV, which might serve as an effective tool to avoid excessive or nonessential biopsy.

CONCLUSION

The multimodal US strategies could provide precise and reliable diagnostic results for NML breast lesions.

Diagnostic performance of mammography and ultrasound in breast cancer: a systematic review and meta-analysis

PURPOSE

The purpose of this study was to assess the diagnostic performance of mammography (MMG) and ultrasound (US) imaging for detecting breast cancer.

METHODS

Comprehensive searches of PubMed, Scopus and EMBASE from 2008 to 2021 were performed. A summary receiver operating characteristic curve (SROC) was constructed to summarize the overall test performance of MMG and US. Histopathologic analysis and/or close clinical and imaging follow-up for at least 6 months were used as golden reference.

RESULTS

Analysis of the studies revealed that the overall validity estimates of MMG and US in detecting breast cancer were as follows: pooled sensitivity per-patient were 0.82 (95% CI 0.76-0.87) and 0.83 (95% CI 0.71-0.91) respectively, The pooled specificities for detection of breast cancer using MMG, and US were 0.84 (95% CI 0.73-0.92) and 0.84 (95% CI 0.74-0.91) respectively. AUC of MMG, and US were 0.8933 and 0.8310 respectively. Pooled sensitivity and specificity per-lesion was 76% (95% CI 0.62-0.86) and 82% (95% CI 0.66-0.91) for MMG and 94% (95% CI 0.87-0.97) and 84% (95% CI 0.74-0.91) for US.

CONCLUSIONS

The meta-analysis found that, US and MMG has similar diagnostic performance in detecting breast cancer on per-patient basis after corrected threshold effect. However, on a per-lesion basis US was found to have a better diagnostic accuracy than MMG.

Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study

Background

Early diagnosis of breast cancer has always been a difficult clinical challenge. We developed a deep-learning model EDL-BC to discriminate early breast cancer with ultrasound (US) benign findings. This study aimed to investigate how the EDL-BC model could help radiologists improve the detection rate of early breast cancer while reducing misdiagnosis.

Methods

In this retrospective, multicentre cohort study, we developed an ensemble deep learning model called EDL-BC based on deep convolutional neural networks. The EDL-BC model was trained and internally validated on B-mode and color Doppler US image of 7955 lesions from 6795 patients between January 1, 2015 and December 31, 2021 in the First Affiliated Hospital of Army Medical University (SW), Chongqing, China. The model was assessed by internal and external validations, and outperformed radiologists. The model performance was validated in two independent external validation cohorts included 448 lesions from 391 patients between January 1 to December 31, 2021 in the Tangshan People's Hospital (TS), Chongqing, China, and 245 lesions from 235 patients between January 1 to December 31, 2021 in the Dazu People's Hospital (DZ), Chongqing, China. All lesions in the training and total validation cohort were US benign findings during screening and biopsy-confirmed malignant, benign, and benign with 3-year follow-up records. Six radiologists performed the clinical diagnostic performance of EDL-BC, and six radiologists independently reviewed the retrospective datasets on a web-based rating platform.

Findings

The area under the receiver operating characteristic curve (AUC) of the internal validation cohort and two independent external validation cohorts for EDL-BC was 0.950 (95% confidence interval [CI]: 0.909-0.969), 0.956 (95% [CI]: 0.939-0.971), and 0.907 (95% [CI]: 0.877-0.938), respectively. The sensitivity values were 94.4% (95% [CI]: 72.7%-99.9%), 100% (95% [CI]: 69.2%-100%), and 80% (95% [CI]: 28.4%-99.5%), respectively, at 0.76. The AUC for accurate diagnosis of EDL-BC (0.945 [95% [CI]: 0.933-0.965]) and radiologists with artificial intelligence (AI) assistance (0.899 [95% [CI]: 0.883-0.913]) was significantly higher than that of the radiologists without AI assistance (0.716 [95% [CI]: 0.693-0.738]; p < 0.0001). Furthermore, there were no significant differences between the EDL-BC model and radiologists with AI assistance (p = 0.099).

Interpretation

EDL-BC can identify subtle but informative elements on US images of breast lesions and can significantly improve radiologists' diagnostic performance for identifying patients with early breast cancer and benefiting the clinical practice.

Funding

The National Key R&D Program of China.

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.

Conventional ultrasound and contrast-enhanced ultrasound radiomics in breast cancer and molecular subtype diagnosis

Objectives

This study aimed to explore the value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) radiomics to diagnose breast cancer and predict its molecular subtype.

Method

A total of 170 lesions (121 malignant, 49 benign) were selected from March 2019 to January 2022. Malignant lesions were further divided into six categories of molecular subtype: (non-)Luminal A, (non-)Luminal B, (non-)human epidermal growth factor receptor 2 (HER2) overexpression, (non-)triple-negative breast cancer (TNBC), hormone receptor (HR) positivity/negativity, and HER2 positivity/negativity. Participants were examined using CUS and CEUS before surgery. Regions of interest images were manually segmented. The pyradiomics toolkit and the maximum relevance minimum redundancy algorithm were utilized to extract and select features, multivariate logistic regression models of CUS, CEUS, and CUS combined with CEUS radiomics were then constructed and evaluated by fivefold cross-validation.

Results

The accuracy of the CUS combined with CEUS model was superior to CUS model (85.4% vs. 81.3%, p<0.01). The accuracy of the CUS radiomics model in predicting the six categories of breast cancer is 68.2% (82/120), 69.3% (83/120), 83.7% (100/120), 86.7% (104/120), 73.5% (88/120), and 70.8% (85/120), respectively. In predicting breast cancer of Luminal A, HER2 overexpression, HR-positivity, and HER2 positivity, CEUS video improved the predictive performance of CUS radiomics model [accuracy=70.2% (84/120), 84.0% (101/120), 74.5% (89/120), and 72.5% (87/120), p<0.01].

Conclusion

CUS radiomics has the potential to diagnose breast cancer and predict its molecular subtype. Moreover, CEUS video has auxiliary predictive value for CUS radiomics.

Detection and Classification of Histopathological Breast Images Using a Fusion of CNN Frameworks

Breast cancer is responsible for the deaths of thousands of women each year. The diagnosis of breast cancer (BC) frequently makes the use of several imaging techniques. On the other hand, incorrect identification might occasionally result in unnecessary therapy and diagnosis. Therefore, the accurate identification of breast cancer can save a significant number of patients from undergoing unnecessary surgery and biopsy procedures. As a result of recent developments in the field, the performance of deep learning systems used for medical image processing has showed significant benefits. Deep learning (DL) models have found widespread use for the aim of extracting important features from histopathologic BC images. This has helped to improve the classification performance and has assisted in the automation of the process. In recent times, both convolutional neural networks (CNNs) and hybrid models of deep learning-based approaches have demonstrated impressive performance. In this research, three different types of CNN models are proposed: a straightforward CNN model (1-CNN), a fusion CNN model (2-CNN), and a three CNN model (3-CNN). The findings of the experiment demonstrate that the techniques based on the 3-CNN algorithm performed the best in terms of accuracy (90.10%), recall (89.90%), precision (89.80%), and f1-Score (89.90%). In conclusion, the CNN-based approaches that have been developed are contrasted with more modern machine learning and deep learning models. The application of CNN-based methods has resulted in a significant increase in the accuracy of the BC classification.