Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient's history: A review

Targeting breast cancer: the promise of phage-based nanomedicines

BACKGROUND

Breast cancer is a leading cause of cancer-related mortality among women worldwide, characterized by its aggressive nature, propensity for metastasis, and resistance to standard treatment modalities. Traditional therapies, including surgery, chemotherapy, and radiation, often encounter significant limitations such as systemic toxicity and lack of specificity.

OBJECTIVE

This review aims to evaluate the recent advancements in phage-based nanomedicines as a novel approach for targeted breast cancer therapy, focusing on their mechanisms of action, therapeutic benefits, and the challenges faced in clinical implementation.

METHODS

A comprehensive literature review was conducted, analyzing studies that investigate the application of bacteriophages in cancer therapy, particularly in breast cancer. The review highlights the integration of nanotechnology with phage therapy, examining the potential for enhanced targeting and reduced side effects.

RESULTS

Phage-based nanomedicines have shown promise in selectively targeting breast cancer cells while sparing healthy tissues, thereby improving therapeutic efficacy and safety profiles. The unique properties of bacteriophages, including their ability to be engineered for specific targeting and their natural ability to induce immune responses, present significant advantages over conventional treatments.

CONCLUSION

The integration of phage therapy with nanotechnology represents a promising frontier in the fight against breast cancer. This review underscores the need for continued research to address existing challenges and to explore the full potential of phage-based nanomedicines in improving patient outcomes in breast cancer treatment.

Exploring the Evolution of Breast Cancer Imaging: A Review of Conventional and Emerging Modalities

Breast cancer (BC) is one of the leading causes of malignancy among women, and its prevalence is exponentially rising globally. Early and accurate imaging is critical for early detection, diagnosis, and treatment planning. This comprehensive review explores the current status of BC imaging, from the conventional methods such as mammography, ultrasound (US) and magnetic resonance imaging (MRI) to more advanced techniques including contrast-enhanced imaging, tomosynthesis, and molecular breast imaging (MBI). Conventional imaging remains the foundation for screening, as mammography is the most widely preferred modality. US and MRI are usually employed in dense breasts in highly suspicious cases that are not detected on a mammogram. However, the limitations posed by these traditional techniques can be curtailed using advanced modalities to enhance diagnostic accuracy. These emerging techniques provide faster and earlier detection of malignancy, particularly in high-risk patients, and substantially reduce the burden of missed cases. Emerging technologies, including photoacoustic imaging (PAI) and contrast-enhanced ultrasound (CEUS), show promising potential in visualizing microvascular structures and enhancing diagnostic accuracy. Additionally, artificial intelligence (AI) is revolutionizing BC imaging across all modalities by optimizing interpretation, enhancing sensitivity, and enabling personalized risk assessment. Although technological innovation continues to improve imaging quality and diagnostic precision, challenges such as cost, accessibility, overdiagnosis, and disparities in care remain a concern. Moving forward, a collaborative multimodal strategy that incorporates personalized imaging protocols and equitable access will be crucial for improving BC screening and management. The future of breast imaging lies not in replacing existing modalities but in developing a system where each technology complements the other, leading to earlier detection, more effective treatment, and enhanced outcomes.

Design Principles of an Engineered Metastatic Niche for Monitoring of Cancer Progression

Across many types of cancer, metastatic disease is associated with a substantial decrease in 5-year survival rates relative to only a localized primary tumor. Many patients self-report metastatic disease due to disruption of normal organ or tissue function, and earlier detection could enable treatment with a lower burden of disease. We have previously reported a subcutaneous biomaterial implant for early detection by serving as an engineered metastatic niche, which has been reported to recruit tumor cells before colonization of solid organs. In this report, we investigated the design principles of the scaffold and defined the conditions for use in disease detection. Using the metastatic 4T1 triple-negative breast cancer model, we identified that a porous structure was essential to capture tumor and immune cells. Scaffolds of multiple diameters were investigated for their ability to serve as a metastatic niche, with a porous scaffold with a diameter as small as 2 mm identifying disease accurately. Additionally, scaffolds that had been in vivo for 1-5 weeks were able to identify disease accurately. Finally, the sensitivity of the scaffold relative to liquid biopsies was analyzed, with scaffolds accurately detecting disease at earlier time points than liquid biopsy. Collectively, these studies inform the design principles and use conditions for porous scaffolds to detect metastatic disease.

Application of dynamic enhanced scanning with GD-EOB-DTPA MRI based on deep learning algorithm for lesion diagnosis in liver cancer patients

Background

Improvements in the clinical diagnostic use of magnetic resonance imaging (MRI) for the identification of liver disorders have been made possible by gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA). Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) technology is in high demand.

Objectives

The purpose of the study is to segment the liver using an enhanced multi-gradient deep convolution neural network (EMGDCNN) and to identify and categorize a localized liver lesion using a Gd-EOB-DTPA-enhanced MRI.

Methods

We provided the classifier images of the liver in five states (unenhanced, arterial, portal venous, equilibrium, and hepatobiliary) and labeled them with localized liver diseases (hepatocellular carcinoma, metastasis, hemangiomas, cysts, and scarring). The Shanghai Public Health Clinical Center ethics committee recruited 132 participants between August 2021 and February 2022. Fisher's exact test analyses liver lesion Gd-EOB-DTPA-enhanced MRI data.

Results

Our method could identify and classify liver lesions at the same time. On average, 25 false positives and 0.6 real positives were found in the test instances. The percentage of correct answers was 0.790. AUC, sensitivity, and specificity evaluate the procedure. Our technique outperforms others in extensive testing.

Conclusion

EMGDCNN may identify and categorize a localized hepatic lesion in Gd-EOB-DTPA-enhanced MRI. We found that one network can detect and classify. Radiologists need higher detection capability.

Radiologic findings in women after Autologous Fat Transfer (AFT) based breast reconstruction: A Systematic Review

Purpose

Autologous fat transfer (AFT) is increasingly used in breast reconstructive surgery. Due to post-surgical changes, in breast imaging after AFT, it can be challenging to differentiate between benign and suspicious findings. This systematic review aimed to present an overview of the literature on breast imaging after AFT-based breast reconstruction. The descriptive radiologic findings focus on different breast imaging modalities (i.e., mammography (MG), ultrasound (US), and breast magnetic resonance imaging (MRI)) to provide an overview of the most commonly reported benign and suspicious findings.

Results

The literature search yielded 20 studies from 2006-2022 that reported AFT-based breast reconstructions and included the radiologic evaluation of the included breast imaging modalities. Only six of the 20 included studies provided qualitative descriptions of radiologic findings. Fat necrosis was most frequently reported. On MG, fat necrosis was described in a variety of stages such as oil cyst or cytosteatonecrosis with or without calcifications. On US, it was described as a nonvascular hypo- or anechoic mass, and on breast MRI, it was most frequently reported as hypointense homogenous architectural distortion. Additional biopsies to differentiate between benign and malignant findings after AFT-based breast reconstruction were reported in 13 of the 20 studies. Among all included studies in the current review, a total of 34 of 137 biopsies were considered malignant (24.8%).

Conclusion

Qualitative descriptions of the reported radiologic findings after AFT for breast reconstruction were limited. Additional biopsies can be considered to differentiate between benign and suspicious findings. More experience and research are necessary to improve the interpretation of breast imaging after AFT-based breast reconstructions.

PROMRIINE (PRe-operatory Magnetic Resonance Imaging is INEffective) Study: A Systematic Review and Meta-analysis of the Impact of Magnetic Resonance Imaging on Surgical Decisions and Clinical Outcomes in Women with Breast Cancer

BACKGROUND

The purpose of this study was to review and summarize the association between preoperative magnetic resonance imaging (MRI) and surgical outcomes in women with newly diagnosed invasive breast cancer from published randomized controlled trials (RCT).

MATERIALS AND METHODS

Two independent researchers conducted a systematic review through a comprehensive search of electronic databases, including PubMed, Medline, Embase, Ovid, Cochrane Library, and Web of Science. If there was disagreement between the two reviewers, a third reviewer assessed the manuscript to determine whether it should be included for data extraction. The quality of the papers was assessed using the risk of bias tool, and the evidence was analyzed using GRADE. Meta-analyses using a fixed-effects model were used to estimate the pooled risk ratio (RR) and 95% confidence interval (CI).

RESULTS

Initially, 21 studies were identified, 15 of which were observational comparative studies. A total of five RCTs were included, and they suggested that preoperative MRI significantly reduced the rate of immediate breast-conserving surgery and increased the risk for mastectomy.

CONCLUSIONS

From the RCT perspective, preoperative MRI for newly diagnosed invasive breast cancer did not improve surgical outcomes and may increase the risk of mastectomy.

Assessment of the contribution of the ADC value to the Kaiser score in the differential diagnosis of breast lesions with non-mass enhancement morphology on MRI

PURPOSE

To investigate the effectiveness of diffusion-weighted imaging (DWI) as a supplementary tool to the Kaiser score (KS) in diagnosing breast cancer in non-mass enhancement (NME) lesions using breast magnetic resonance imaging (MRI).

METHODS

This single-center, retrospective study analyzed 360 cases with NME on MRI images. Two breast radiologists independently evaluated each lesion using the Kaiser score (KS) and apparent diffusion coefficient (ADC) values, without knowledge of the pathological outcomes. NME lesions with a KS above 4 and an ADC value below 1.3 × 10-3mm2/s were classified as malignant. Inter-rater reliability was determined using Cohen's Kappa (κ) statistics. The diagnostic performance of KS, DWI, and their combination was assessed by calculating sensitivity, specificity, and the area under the curve (AUC), and the results were compared across the benign and malignant groups.

RESULTS

The diagnostic performance of KS surpassed that of DWI in predicting the malignancy of NMEs (p = 0.003). The sensitivity of KS alone was 93 %; however, when ADC data was incorporated, the sensitivity decreased to 86 %, with no significant difference observed (p = 0.060). The specificity of the combined KS and ADC (94 %) was significantly higher than that of KS alone (89 %) and DWI alone (73 %) (p < 0.001).

CONCLUSION

Our findings indicated that although the combination of KS and ADC increased specificity and reduced unnecessary biopsies, the resulting decrease in sensitivity was unacceptable. Therefore, KS alone is superior to the KS-ADC combination in detecting malignancy in NME lesions.

Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Comparison of Performance in Diagnosis and Characterization of Breast Lesions: Contrast-Enhanced Mammography Versus Breast Magnetic Resonance Imaging

INTRODUCTION

In contemporary medical practice, magnetic resonance imaging (MRI) is the most sensitive modality for detecting breast cancer. Contrast-enhanced mammography (CEM), a relatively recent technology, represents another contrast-enhanced imaging technique that has the potential to serve as an alternative to breast MRI. Our main goal is to compare the diagnostic accuracy including assessment of sensitivity and specificity of these 2 contrast-enhanced breast imaging methods, CEM and MRI, in the diagnosis and characterization of breast lesions.

MATERIAL AND METHODS

Our prospective study included patients who were clinically suspected of malignancy and/or had suspicious findings detected by mammography or ultrasound. A total of 116 patients were included, and both CEM and MRI examinations were performed on all patients. All CEM examinations were conducted at our institution, while 56.89% of all MRI examinations were carried out at external centers. While histopathological results were accessible for all malignant lesions, the final diagnosis for 80.5% of benign lesions was established through typical imaging findings and adequate follow-up.

RESULTS

This study encompassed a total of 219 lesions, with 125 out of 219 (57.07%) malignant lesions and 94 out of 219 (42.92%) benign lesions. The sensitivity and specificity values were 98.40% and 81.91%, respectively, for CEM, and 100% and 75.33%, respectively, for MRI. Moreover, CEM showcased comparable performance to MRI in evaluating women with dense breasts.

CONCLUSION

CEM and MRI were compared for breast lesion diagnosis, with MRI showing higher sensitivity and CEM higher specificity; however, the differences were not statistically significant.

Identification and Quantification of Extracellular Vesicles: Comparison of SDS-PAGE Analysis and Biosensor Analysis with QCM and IDT Chips

This study presents and compares two methods for identifying the types of extracellular vesicles (EVs) from different cell lines. Through SDS-PAGE analysis, we discovered that the ratio of CD63 to CD81 in different EVs is consistent and distinct, making it a reliable characteristic for recognizing EVs secreted by cancer cells. However, the electrophoresis and imaging processes may introduce errors in the concentration values, especially at lower concentrations, rendering this method potentially less effective. An alternative approach involves the use of quartz crystal microbalance (QCM) and electroanalytical interdigitated electrode (IDT) biosensors for EV type identification and quantification. The QCM frequency shift caused by EVs is directly proportional to their concentration, while electroanalysis relies on measuring the curvature of the I-V curve as a distinguishing feature, which is also proportional to EV concentration. Linear regression lines for the QCM frequency shift and the electroanalysis curvature of various EV types are plotted separately, enabling the estimation of the corresponding concentration for an unknown EV type on the graphs. By intersecting the results from both biosensors, the unknown EV type can be identified. The biosensor analysis method proves to be an effective means of analyzing both the type and concentration of EVs from different cell lines.

Radiomics analysis combining gray-scale ultrasound and mammography for differentiating breast adenosis from invasive ductal carcinoma

Objectives

To explore the utility of gray-scale ultrasound (GSUS) and mammography (MG) for radiomic analysis in distinguishing between breast adenosis and invasive ductal carcinoma (IDC).

Methods

Data from 147 female patients with pathologically confirmed breast lesions (breast adenosis: 61 patients; IDC: 86 patients) between January 2018 and December 2022 were retrospectively collected. A training cohort of 113 patients (breast adenosis: 50 patients; IDC: 63 patients) diagnosed from January 2018 to December 2021 and a time-independent test cohort of 34 patients (breast adenosis: 11 patients; IDC: 23 patients) diagnosed from January 2022 to December 2022 were included. Radiomic features of lesions were extracted from MG and GSUS images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the most discriminant features, followed by logistic regression (LR) to construct clinical and radiomic models, as well as a combined model merging radiomic and clinical features. Model performance was assessed using receiver operating characteristic (ROC) analysis.

Results

In the training cohort, the area under the curve (AUC) for radiomic models based on MG features, GSUS features, and their combination were 0.974, 0.936, and 0.991, respectively. In the test cohort, the AUCs were 0.885, 0.876, and 0.949, respectively. The combined model, incorporating clinical and all radiomic features, and the MG plus GSUS radiomics model were found to exhibit significantly higher AUCs than the clinical model in both the training cohort and test cohort (p<0.05). No significant differences were observed between the combined model and the MG plus GSUS radiomics model in the training cohort and test cohort (p>0.05).

Conclusion

The effectiveness of radiomic features derived from GSUS and MG in distinguishing between breast adenosis and IDC is demonstrated. Superior discriminatory efficacy is shown by the combined model, integrating both modalities.

DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation

Automated and precise segmentation of breast lesions can facilitate early diagnosis of breast cancer. Recent research studies employ deep learning for automatic segmentation of breast lesions using ultrasound imaging. Numerous studies introduce somewhat complex modifications to the well adapted segmentation network, U-Net for improved segmentation, however, at the expense of increased computational time. Towards this aspect, this study presents a low complex deep learning network, i.e., dense multiplicative attention enhanced encoder decoder network, for effective breast lesion segmentation in the ultrasound images. For the first time in this context, two dense multiplicative attention components are utilized in the encoding layer and the output layer of an encoder-decoder network with depthwise separable convolutions, to selectively enhance the relevant features. A rigorous performance evaluation using two public datasets demonstrates that the proposed network achieves dice coefficients of 0.83 and 0.86 respectively with an average segmentation latency of 19ms. Further, a noise robustness study using an in-clinic recorded dataset without pre-processing indicates that the proposed network achieves dice coefficient of 0.72. Exhaustive comparison with some commonly used networks indicate its adeptness with low time and computational complexity demonstrating feasibility in real time.

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.

The Accuracy of Breast Cancer Diagnostic Tools

Background Breast cancer (BC) remains a significant health concern, leading to illness and death among women globally. It is essential to detect BC early using imaging techniques that accurately reflect the final pathology, guiding suitable intervention strategies. Objectives This study aimed to evaluate the agreement between radiological findings and histopathological results in BC cases. Methods We conducted a retrospective review of breast core needle biopsies (CNBs) in women over a six-year period (2017-2022) at Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The pathological diagnoses were compared with the findings from preceding radiological investigations. We also compared the tumour sizes in the resection specimens with their radiological counterparts. Results A total of 641 cases were included in the study. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) yielded diagnostic accuracies of 85%, 77.9%, and 86.9%, respectively. MRI had the highest sensitivity at 72.2%, while US had the lowest at 61%. MRI provided the best agreement with the final resected tumor size. By contrast, mammography tended to overestimate the size (41.9%), and US most frequently underestimated it (67.7%). The connection between basal-like molecular subtypes and the Breast Imaging Reporting and Data System (BIRADS)-5 classifications was only statistically significant for MRI (p = 0.04). The luminal subtype was more likely to show speculation in mammography. Meanwhile, BIRADS-4 revealed a considerable number of benign pathologies across all the three modalities. Conclusions MRI demonstrated the highest accuracy, sensitivity, specificity, and positive predictive value (PPV) for diagnosing and estimating the tumor size. Mammography outperformed US in terms of sensitivity and yielded the highest negative predictive value (NPV). US, meanwhile, offered superior specificity, PPV, and accuracy. Therefore, combining these diagnostic methods could yield significant benefits.

The Impact of Preoperative Breast MRI on Timing of Surgical Management in Newly Diagnosed Breast Cancer

Purpose: Preoperative breast MRI has been recommended at our center since 2016 for invasive lobular carcinoma and cancers in dense breasts. This study examined how preoperative breast MRI impacted surgical timing and outcomes for patients with newly diagnosed breast cancer. Methods: Retrospective single-center study of consecutive women diagnosed with new breast cancer between June 1, 2019, and March 1, 2021, in whom preoperative breast MRI was recommended. MRI, tumor histology, breast density, post-MRI biopsy, positive predictive value of biopsy (PPV3), surgery, and margin status were recorded. Time from diagnosis to surgery was compared using t-tests. Results: There were 1054 patients reviewed, and 356 were included (mean age 60.9). Of these, 44.4% (158/356) underwent preoperative breast MRI, and 55.6% (198/356) did not. MRI referral was more likely for invasive lobular carcinoma, multifocal disease, and younger patients. Following preoperative MRI, 29.1% (46/158) patients required additional breast biopsies before surgery, for a PPV3 of 37% (17/46). The time between biopsy and surgery was 55.8 ± 21.4 days for patients with the MRI, compared to 42.8 ± 20.3 days for those without (P < .00001). MRI was not associated with the type of surgery (mastectomy vs breastconserving surgery) (P = .44) or rate of positive surgical margins (P = .52). Conclusion: Among patients who underwent preoperative breast MRI, we observed significant delays to surgery by almost 2 weeks. When preoperative MRI is requested, efforts should be made to mitigate associated delays.

Incorporating Value-Based Decisions in Breast Cancer Treatment Algorithms

Given the excellent prognosis and availability of evidence-based treatment, patients with early-stage breast cancer are at risk of overtreatment. In this review, we summarize key opportunities to incorporate value-based decisions to optimize the delivery of high-value treatment across the breast cancer care continuum.

An Innovative Faster R-CNN-Based Framework for Breast Cancer Detection in MRI

Replacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the "breast MRI preprocessing phase" to select the patient's slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient's images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.

The Importance of Ultrasonography in the Evaluation of Mammary Tumors in Bitches

The high incidence of mammary tumors in small animals is concerning. Patient history, clinical examination, physical evaluation, and imaging studies are important for clinical staging. Ultrasonography is commonly applied to investigate the presence of abdominal metastasis. However, it has been shown to provide important information regarding mammary tumors' architecture and advanced sonographic techniques can provide information regarding neovascularization, stiffness, and perfusion. Different techniques have been investigated to determine accuracy to predict the lesions' histological classification. This paper reviews the information regarding each sonographic technique in the evaluation of mammary tumors, describing the most common findings and their potential to accurately assess and predict malignancy. Even though the gold standard for the diagnosis of mammary lesions is the histopathological examination, some ultrasonographic features described can predict the potential of a lesion being malignant. Among the different sonographic techniques, elastography can be considered the most reliable modality to accurately differentiate benign from malignant tumors when malignant lesions present increased stiffness. However, the combination of all sonographic techniques can provide important information that can lead to a better therapeutic approach and clinical staging. Furthermore, the potential of the sonographic study, especially CEUS to monitor therapeutic progression, demonstrate the need of further studies.

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis

PURPOSE OF REVIEW

This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting.

RECENT FINDINGS

Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.

Modern Imaging Techniques in the Study and Disease Diagnosis of the Mammary Glands of Animals

The study of the structure and function of the animals' mammary glands is of key importance, as it reveals pathological processes at their onset, thus contributing to their immediate treatment. The most frequently studied mammary diseases are mastitis in cows and ewes and mammary tumours in dogs and cats. Various imaging techniques such as computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasonographic techniques (Doppler, contrast-enchanced, three-dimensional and elastography) are available and can be applied in research or clinical practice in order to evaluate possible abnormalities in mammary glands, as well as to assist in the differential diagnosis. In this review, the above imaging technologies are described, and the perspectives of each method are highlighted. It is inferred that ultrasonographic modalities are the most frequently used imaging techniques for the diagnosis of clinical or subclinical mastitis and treatment guidance on a farm. In companion animals, a combination of imaging techniques should be applied for a more accurate diagnosis of mammary tumours. In any case, the confirmation of the diagnosis is provided by laboratory techniques.