Comparison of the sensitivity of mammography, ultrasound, magnetic resonance imaging and combinations of these imaging modalities for the detection of small (≤2 cm) breast cancer

Reliability of Kaiser Score in Assessing Additional Breast Lesions Identified on Staging MRI in Patients with Breast Cancer

(1) Background: The Kaiser score is a user-friendly tool that evaluates lesions on breast MRI and has been studied in the general population and a few specific clinical scenarios. We aim to evaluate the performance of the Kaiser score in the characterization of additional lesions identified on staging breast MRI. (2) Methods: The Kaiser score of the biopsied additional lesions identified on staging MRI in recently diagnosed breast cancer patients was retrospectively determined. Statistical analysis was performed to evaluate the diagnostic capability of the Kaiser score and whether it is affected by different imaging and pathological parameters of the additional and the index lesion. (3) Results: Seventy-six patients with ninety-two MRI-detected lesions constitute the studied population. There was a statistically significant difference in the Kaiser score between benign and malignant lesions, irrespective of the pathology of the index cancer (p = 0.221) or the size and the imaging features of the additional lesion. Using a cutoff of 5 and above for suspicious lesions, biopsy could have been avoided in 34/92 lesions. (4) Conclusions: The Kaiser score can assist radiologists in the evaluation of additional MRI lesions identified in recently diagnosed breast cancer patients, thus decreasing the number of unneeded biopsies and delays in definitive surgical management.

Correlations of Imaging and Therapy in Breast Cancer Based on Molecular Patterns: An Important Issue in the Diagnosis of Breast Cancer

Breast cancer is a global health issue affecting countries worldwide, imposing a significant economic burden due to expensive treatments and medical procedures, given the increasing incidence. In this review, our focus is on exploring the distinct imaging features of known molecular subtypes of breast cancer, underlining correlations observed in clinical practice and reported in recent studies. The imaging investigations used for assessment include screening modalities such as mammography and ultrasonography, as well as more complex investigations like MRI, which offers high sensitivity for loco-regional evaluation, and PET, which determines tumor metabolic activity using radioactive tracers. The purpose of this review is to provide a better understanding as well as a revision of the imaging differences exhibited by the molecular subtypes and histopathological types of breast cancer.

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.

Evaluation of HER2/neu Expression in Metastatic Axillary Lymph Node Tissue of Breast Cancer Patients Using [99mTc]Tc-(HE)3-G3

Anatomic visualization and molecular typing of metastatic regional lymph nodes in breast cancer patients are a serious clinical challenge in modern oncology. According to the results of previous studies, [99mTc]Tc-(HE)3-G3 has proven to be a promising diagnostic agent in differentiating the HER2/neu receptor status in primary breast tumors (p < 0.05, Mann-Whitney test). In this regard, the purpose of this study is to explore the possibilities of using [99mTc]Tc-(HE)3-G3 to determine the HER2/neu receptor status in the metastatic axillary lymph nodes (mALNs) of breast cancer patients. The study was conducted using clinical material from 20 breast cancer patients (T2-4N1-3M0-1) before systemic therapy (10 patients with positive and 10 patients with negative HER2/neu expression in mALNs) who underwent SPECT/CT scan 4 h after the administration of [99mTc]Tc-(HE)3-G3. Morphological and immunohistochemical studies of mALNs with assessment of the HER2/neu status were performed on all patients. We found that mALN-to-background and mALN-to-latissimus dorsi muscle ratios for [99mTc]Tc-(HE)3-G3 uptake 4 h after its administration may be used for typing of the HER2/neu status in mALNs of breast cancer patients (p < 0.05, Mann-Whitney test). In that case, sensitivity and specificity for the mALN-to-background ratio were identical at 80%, with the threshold value being > 12.25.

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review

Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

Mammography with deep learning for breast cancer detection

X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

PET/MRI and Novel Targets for Breast Cancer

Breast cancer, with its global prevalence and impact on women's health, necessitates effective early detection and accurate staging for optimal patient outcomes. Traditional imaging modalities such as mammography, ultrasound, and dynamic contrast-enhanced magnetic resonance imaging (MRI) play crucial roles in local-regional assessment, while bone scintigraphy and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) aid in evaluating distant metastasis. Despite the proven utility of 18F-FDG PET/CT in various cancers, its limitations in breast cancer, such as high false-negative rates for small and low-grade tumors, have driven exploration into novel targets for PET radiotracers, including estrogen receptor, human epidermal growth factor receptor-2, fibroblast activation protein, and hypoxia. The advent of PET/MRI, which combines metabolic PET information with high anatomical detail from MRI, has emerged as a promising tool for breast cancer diagnosis, staging, treatment response assessment, and restaging. Technical advancements including the integration of PET and MRI, considerations in patient preparation, and optimized imaging protocols contribute to the success of dedicated breast and whole-body PET/MRI. This comprehensive review offers the current technical aspects and clinical applications of PET/MRI for breast cancer. Additionally, novel targets in breast cancer for PET radiotracers beyond glucose metabolism are explored.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

Objective

Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography (CEM) images.

Methods

In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system (MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion (AFF) algorithm that could intelligently incorporates multiple types of information from CEM images. The average free-response receiver operating characteristic score (AFROC-Score) was presented to validate system's detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases, comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists' performance.

Results

On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909 [95% confidence interval (95% CI): 0.822-0.996] and 0.912 (95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists' average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.

Conclusions

MDCS demonstrated excellent performance in the detection and classification of breast lesions, and greatly enhanced the overall performance of radiologists.

Utility of Ultrasound Imaging Features in Diagnosis of Breast Cancer

Background Currently, breast cancer (BC) is considered one of the most prevalent cancer worldwide in women and represents a global health challenge. Early diagnosis is the keystone in the management of BC patients. This study aims to assess the utility of ultrasonography (US) features of malignancy in the diagnosis of BC. Methods This retrospective cross-sectional study involved the electronic records of 326 female patients who were diagnosed with BC. A cross-tabulation test was performed to identify the association between the presence of each US feature (yes/no), and the final US diagnosis (benign/malignant). The strength of association of each feature was measured using the odds ratio (OR) which was assumed to be significant when > 1, with a 95% confidence interval (CI). Results The mean age of the female patients involved in this study was 45.36 ±12.16 years old (range, 17-90 years). Cross-tabulation test showed a significant association between the malignancy tumor and the irregular shape of the lesion (p < 0.001, OR=7.162, CI 2.726-18.814), non-circumscribed margins (p < 0.001, OR = 9.031, CI 3.200-25.489), tissue distortion (p < 0.001, OR = 18.095, CI 5.944-55.091), and the lymph node enlargement (p < 0.001, OR = 5.705, CI 2.332-13.960). Conclusion US imaging features of malignancy have a high sensitivity and positive predictive value for detection of the BC. However, the specificity of breast US imaging features is much lower because of the overlapping features in benign and malignant breast lesions. Breast lesions with an irregular shape, not circumscribed irregular or spiculated margins, hypo-echogenicity, tissue distortion, and those with lymphadenopathy have the highest likelihood of malignancy despite the low specificity. US is a highly valuable, safe, and affordable imaging modality with high diagnostic accuracy for BC.

Breast PET/MRI Hybrid Imaging and Targeted Tracers

The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledge regarding the possible clinical applications of hybrid PET/MRI is constantly evolving, thanks to the development and clinical availability of hybrid scanners, the development of new PET tracers and the rise of artificial intelligence (AI) techniques. In this state-of-the-art review on the use of hybrid breast PET/MRI, the most promising advanced MRI techniques (diffusion-weighted imaging, dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, and chemical exchange saturation transfer) are discussed. Current and experimental PET tracers (¹⁸ F-FDG, ¹⁸ F-NaF, choline, ¹⁸ F-FES, ¹⁸ F-FES, ⁸⁹ Zr-trastuzumab, choline derivatives, ¹⁸ F-FLT, and ⁶⁸ Ga-FAPI-46) are described in order to provide an overview on their molecular mechanisms of action and corresponding clinical applications. New perspectives represented by the use of radiomics and AI techniques are discussed. Furthermore, the current strengths and limitations of hybrid PET/MRI in the real world are highlighted. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis

Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.

Strengths and challenges of the artificial intelligence in the assessment of dense breasts

Objectives

High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts.

Methods

This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG).

Results

Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms.

Conclusion

Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer.

Advances in knowledge

Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

Purpose

In clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI).

Materials and methods

A total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity.

Results

In the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10–15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3.

Conclusions

Our model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.

Review on Multispectral Photoacoustic Analysis of Cancer: Thyroid and Breast

In recent decades, photoacoustic imaging has been used widely in biomedical research, providing molecular and functional information from biological tissues in vivo. In addition to being used for research in small animals, photoacoustic imaging has also been utilized for in vivo human studies, achieving a multispectral photoacoustic response in deep tissue. There have been several clinical trials for screening cancer patients by analyzing multispectral responses, which in turn provide metabolomic information about the underlying biological tissues. This review summarizes the methods and results of clinical photoacoustic trials available in the literature to date to classify cancerous tissues, specifically of the thyroid and breast. From the review, we can conclude that a great potential exists for photoacoustic imaging to be used as a complementary modality to improve diagnostic accuracy for suspicious tumors, thus significantly benefitting patients’ healthcare.

Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study

This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R² metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R² of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, G_mean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.

Fat necrosis: A consultant's conundrum

Fat necrosis of the breast is a benign non-suppurative inflammation of the adipose tissue and often mimics breast cancers, posing a diagnostic challenge for the clinician and radiologist. It has a myriad of appearances on different imaging techniques, ranging from the pathognomic oil cyst and benign dystrophic calcifications to indeterminate focal asymmetries, architectural distortions, and masses. A combination of different modalities can assist a radiologist in reaching a logical conclusion to avoid unnecessary interventions. The aim of this review article was to provide a comprehensive literature on the various imaging appearances of fat necrosis in the breast. Although a purely benign entity, the imaging appearances on mammography, contrast-enhanced mammography, ultrasound, and magnetic resonance imaging can be quite misleading, especially in post-therapy breasts. The purpose is to provide a comprehensive and all-inclusive review on fat necrosis with a proposed algorithm allowing a systematic approach to diagnosis.

Study on the Effect of MRI in the Diagnosis of Benign and Malignant Thoracic Tumors

In order to investigate the effectiveness and accuracy of magnetic resonance imaging (MRI) in the diagnosis of benign and malignant thoracic tumors, the research retrospectively selected 80 patients with thoracic tumors admitted from May 2019 to May 2020 as the study subject and all patients were underwent MRI detection. Using pathological diagnostic results as the gold standard, the research analyzed the detection of benign and malignant thoracic tumors by MRI, as well as the diagnostic sensitivity and specificity. After pathological diagnosis, there were 35 malignant tumors and 45 benign tumors. 41 cases of malignant tumors and 39 cases of benign tumors were diagnosed by MRI, with a diagnostic sensitivity of 80.00%, a diagnostic specificity of 71.11%, and a diagnostic compliance rate of 75.00%. In the MRI diagnosis of tumors in different parts of the chest, the diagnostic sensitivity for lung tumors, mediastinal tumors, chest wall tumors, and esophageal tumors was 83.33%, 71.43%, 83.33%, 75.00%, and 87.50%, respectively, and the specificity was 66.67%, 77.78%, 57.14%, 50.00%, and 91.67% according to and breast tumors, respectively. And the accuracy was 73.33%, 75.00%, 69.23, 62.50%, and 90.00%, respectively, with the highest diagnostic sensitivity, specificity, and accuracy for breast tumors. MRI has a good effect on the diagnosis of benign and malignant thoracic tumors and has a high diagnostic value, which is helpful to identify the location, nature, source, and lesion scope of the tumor. It is safe and worthy of promotion.

The association between conventional ultrasound and contrast-enhanced ultrasound appearances and pathological features in small breast cancer

OBJECTIVE

To investigate the association between ultrasound appearances and pathological features in small breast cancer.

MATERIALS AND METHODS

A total of 186 small breast cancers in 186 patients were analyzed in this retrospective study from January 2015 to December 2019 according to pathological results. Forty-seven cases of axillary lymph node metastasis were found. All patients underwent radical axillary surgery following conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) examinations. The association between ultrasound appearances and pathological features was analyzed using univariate distributions and multivariate analysis. Then, a logistic regression model was established using the pathological diagnosis of lymph node metastasis and biochemical indicators as the dependent variable and the ultrasound appearances as independent variables.

RESULTS

In small breast cancer, risk factors of axillary lymph node metastasis were crab claw-like enhancement on CEUS and abnormal axillary lymph nodes on US. The logistic regression model was established as follows: (axillary lymph node metastasis) = 1.100×(crab claw-like enhancement of CEUS) + 2.749×(abnormal axillary lymph nodes of US) -5.790. In addition, irregular shape on CEUS and posterior echo attenuation on US were risk factors for both positive estrogen receptor and progesterone receptor expression, whereas calcification on US was a risk factor for positive Her-2 expression. A specific relationship could be found using the following logistic models: (positive ER expression) = 1.367×(irregular shape of CEUS) + 1.441×(posterior echo attenuation of US) -5.668; (positive PR expression) = 1.265×(irregular shape of CEUS) + 1.136×(posterior echo attenuation of US) -4.320; (positive Her-2 expression) = 1.658×(calcification of US) -0.896.

CONCLUSION

Logistic models were established to provide significant value for the prediction of pre-operative lymph node metastasis and positive biochemical indicators, which may guide clinical treatment.