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

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.

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.

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.