Multiview deep learning networks based on automated breast volume scanner images for identifying breast cancer in BI-RADS 4.

Objectives: To develop and validate a deep learning (DL) based automatic segmentation and classification system to classify benign and malignant BI-RADS 4 lesions imaged with ABVS. Methods: From May to December 2020, patients with BI-RADS 4 lesions from Centre 1 and Centre 2 were retrospectively enrolled and divided into a training set (Centre 1) and an independent test set (Centre 2). All included patients underwent an ABVS examination within one week before the biopsy. A two-stage DL framework consisting of an automatic segmentation module and an automatic classification module was developed. The preprocessed ABVS images were input into the segmentation module for BI-RADS 4 lesion segmentation. The classification model was constructed to extract features and output the probability of malignancy. The diagnostic performances among different ABVS views (axial, sagittal, coronal, and multi-view) and DL architectures (Inception-v3, ResNet 50, and MobileNet) were compared. Results: A total of 251 BI-RADS 4 lesions from 216 patients were included (178 in the training set and 73 in the independent test set). The average Dice coefficient, precision, and recall of the segmentation module in the test set were 0.817 ± 0.142, 0.903 ± 0.183, and 0.886 ± 0.187, respectively. The DL model based on multiview ABVS images and Inception-v3 achieved the best performance, with an AUC, sensitivity, specificity, PPV, and NPV of 0.949 (95% CI: 0.945-0.953), 82.14%, 95.56%, 92.00%, and 89.58%, respectively, in the test set. Conclusions: The developed multiview DL model enables automatic segmentation and classification of BI-RADS 4 lesions in ABVS images.

US-based radiomics analysis of different machine learning models for differentiating benign and malignant BI-RADS 4A breast lesions.

RATIONALE AND OBJECTIVES: To investigate and authenticate the effectiveness of various radiomics models in distinguishing between benign and malignant BI-RADS 4A lesions. METHODS: A total of 936 patients with pathologically confirmed 4A lesions were included in the study (training cohort: n = 655; test cohort: n = 281). Radiomic features were derived from greyscale US images. Following dimensionality reduction and feature selection, radiomics models were developed using logistic regression (LR), support vector machine (SVM), random forest (RF), eXtreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms. Univariate and multivariable logistic regression analyses were employed to investigate clinical-radiological characteristics and determine variables for creating a clinical model. Five combined models integrating radiomic and clinical parameters were constructed by using each algorithm, and comparison with radiologists' performance was performed. SHapley Additive exPlanations (SHAP) approach was used to elucidate the radiomic model by ranking the significance of features based on their contribution to the evaluation. RESULTS: A total of 1561 radiomic features were extracted. Thirty-six features were deemed significant by dimensionality reduction and selection. The radiomic models showed good performance with AUCs of 0.829-0.945 in training cohort; and 0.805-0.857 in test cohort. The combined model developed by using LR showed the best performance (AUC, training cohort: 0.909; test cohort: 0.905), which is superior to radiologists' performance. Decision curve analysis (DCA) of this combined model indicated better clinical efficacy than clinical and radiomic models. CONCLUSIONS: The combined model integrating radiomic and clinical features demonstrated excellent performance in differentiating between benign and malignant 4A lesions. It may offer a non-invasive and efficient approach to aid in clinical decision-making.

Spectral analysis enhanced net (SAE-Net) to classify breast lesions with BI-RADS category 4 or higher.

Early ultrasound screening for breast cancer reduces mortality significantly. The main evaluation criterion for breast ultrasound screening is the Breast Imaging-Reporting and Data System (BI-RADS), which categorizes breast lesions into categories 0-6 based on ultrasound grayscale images. Due to the limitations of ultrasound grayscale imaging, lesions with categories 4 and 5 necessitate additional biopsy for the confirmation of benign or malignant status. In this paper, the SAE-Net was proposed to combine the tissue microstructure information with the morphological information, thus improving the identification of high-grade breast lesions. The SAE-Net consists of a grayscale image branch and a spectral pattern branch. The grayscale image branch used the classical deep learning backbone model to learn the image morphological features from grayscale images, while the spectral pattern branch is designed to learn the microstructure features from ultrasound radio frequency (RF) signals. Our experimental results show that the best SAE-Net model has an area under the receiver operating characteristic curve (AUROC) of 12% higher and a Youden index of 19% higher than the single backbone model. These results demonstrate the effectiveness of our method, which potentially optimizes biopsy exemption and diagnostic efficiency.

Evaluating diagnostic significance: The utilization of elastography and contrast-enhanced ultrasound for differential diagnosis in breast lesions.

OBJECTIVE: The primary aim of this study is to assess the diagnostic efficacy of elastography and contrast-enhanced ultrasound (CEUS) in the identification of breast lesions subsequent to the optimization and correction of the BI-RADS category 4 classification obtained through conventional ultrasound. The objective is to augment both the specificity and accuracy of breast lesion diagnosis, thereby establishing a reliable framework for reducing unnecessary biopsies in clinical settings. METHODS: A cohort comprising 50 cases of breast lesions classified under BI-RADS category 4 was collected during the period from November 2022 and November 2023. These cases were examined utilizing strain elastography (SE), shear wave elastography (SWE), and CEUS. Novel scoring methodologies for ultrasonic elastography (UE) and CEUS were formulated for this investigation. Subsequently, the developed UE and CEUS scoring systems were used to refine and optimize the conventional BI-RADS classification, either in isolation or in conjunction. Based on the revised classification, the benign group was classified as category 3 and the suspected malignant group was classified as category 4a and above, with pathological results serving as the definitive reference standard. The diagnostic efficacy of the optimized UE and CEUS, both independently and in combination, was meticulously scrutinized and compared using receiver operating characteristic (ROC) curve analysis, with pathological findings as the reference standard. RESULTS: Within the study group, malignancy manifested in 11 cases. Prior to the implementation of the optimization criteria, 78% (39 out of 50) of patients underwent biopsies deemed unnecessary. Following the application of optimization criteria, specifically a threshold of≥8.5 points for the UE scoring method and≥6.5 points for the CEUS scoring method, the incidence of unnecessary biopsies diminished significantly. Reduction rates were observed at 53.8% (21 out of 39) with the UE protocol, 56.4% (22 out of 39) with the CEUS protocol, and 89.7% (35 out of 39) with the combined UE and CEUS optimization protocols. CONCLUSION: The diagnostic efficacy of conventional ultrasound BI-RADS category 4 classification for breast lesions is enhanced following optimized correction using UE and CEUS, either independently or in conjunction. The application of the combined protocol demonstrates a notable reduction in the incidence of unnecessary biopsies.

A meta-analysis of MRI radiomics-based diagnosis for BI-RADS 4 breast lesions.

OBJECTIVE: The aim of this study is to conduct a systematic evaluation of the diagnostic efficacy of Breast Imaging Reporting and Data System (BI-RADS) 4 benign and malignant breast lesions using magnetic resonance imaging (MRI) radiomics. METHODS: A systematic search identified relevant studies. Eligible studies were screened, assessed for quality, and analyzed for diagnostic accuracy. Subgroup and sensitivity analyses explored heterogeneity, while publication bias, clinical relevance and threshold effect were evaluated. RESULTS: This study analyzed a total of 11 studies involving 1,915 lesions in 1,893 patients with BI-RADS 4 classification. The results showed that the combined sensitivity and specificity of MRI radiomics for diagnosing BI-RADS 4 lesions were 0.88 (95% CI 0.83-0.92) and 0.79 (95% CI 0.72-0.84). The positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 4.2 (95% CI 3.1-5.7), 0.15 (95% CI: 0.10-0.22), and 29.0 (95% CI 15-55). The summary receiver operating characteristic (SROC) analysis yielded an area under the curve (AUC) of 0.90 (95% CI 0.87-0.92), indicating good diagnostic performance. The study found no significant threshold effect or publication bias, and heterogeneity among studies was attributed to various factors like feature selection algorithm, radiomics algorithms, etc. Overall, the results suggest that MRI radiomics has the potential to improve the diagnostic accuracy of BI-RADS 4 lesions and enhance patient outcomes. CONCLUSION: MRI-based radiomics is highly effective in diagnosing BI-RADS 4 benign and malignant breast lesions, enabling improving patients' medical outcomes and quality of life.

Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.

BACKGROUND: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. PURPOSE: This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. METHODS: First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results. RESULTS: With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p < 0.05). CONCLUSION: The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.

Purpose: To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods: iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. Results: The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. Conclusion: iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

How Does Diagnostic Accuracy Evolve with Increased Breast MRI Experience?

Introduction: Our institution is part of a provincial program providing annual breast MRI screenings to high-risk women. We assessed how MRI experience, background parenchymal enhancement (BPE), and the amount of fibroglandular tissue (FGT) affect the biopsy-proven predictive value (PPV3) and accuracy for detecting suspicious MRI findings. Methods: From all high-risk screening breast MRIs conducted between 1 July 2011 and 30 June 2020, we reviewed all BI-RADS 4/5 observations with pathological tissue diagnoses. Overall and annual PPV3s were computed. Radiologists with fewer than ten observations were excluded from performance analyses. PPV3s were computed for each radiologist. We assessed how MRI experience, BPE, and FGT impacted diagnostic accuracy using logistic regression analyses, defining positive cases as malignancies alone (definition A) or malignant or high-risk lesions (definition B). Findings: There were 536 BI-RADS 4/5 observations with tissue diagnoses, including 77 malignant and 51 high-risk lesions. A total of 516 observations were included in the radiologist performance analyses. The average radiologist's PPV3 was 16 ± 6% (definition A) and 25 ± 8% (definition B). MRI experience in years correlated significantly with positive cases (definition B, OR = 1.05, p = 0.03), independent of BPE or FGT. Diagnostic accuracy improved exponentially with increased MRI experience (definition B, OR of 1.27 and 1.61 for 5 and 10 years, respectively, p = 0.03 for both). Lower levels of BPE significantly correlated with increased odds of findings being malignant, independent of FGT and MRI experience. Summary: More extensive MRI reading experience improves radiologists' diagnostic accuracy for high-risk or malignant lesions, even in MRI studies with increased BPE.

Diagnosis of Benign and Malignant BI-RADS 4 Breast Masses by Contrastenhanced Ultrasound Combined with Shear Wave Elastography.

BACKGROUND: Breast cancer, one of the most prevalent malignant tumors in females, usually occurs in the breast epithelial tissues. OBJECTIVE: The study aimed to explore the diagnostic value of contrast-enhanced ultrasound (CEUS) combined with shear wave elastography (SWE) in the diagnosis of benign and malignant breast masses in BI-RADS (Breast Imaging Reporting and Data System) 4. METHODS: Examination outcomes and clinical information of 83 patients with BI-RADS 4 breast masses were analyzed retrospectively. These included patients who received CEUS, SWE, and pathological examinations. The difference of CEUS in determining the classification of BI-RADS 4 breast masses was evaluated using histopathological outcomes of breast masses as a reference standard. The diagnostic value of CEUS, SWE, and CEUS combined with SWE in the diagnosis of benign and malignant breast masses in BI-RADS 4 was also explored. RESULTS: Pathological biopsy results revealed 63 malignant masses and 20 benign masses among 83 BI-RADS 4 breast masses, with a 75.9% incidence of malignant masses. After the diagnosis of BI-RADS 4 breast masses with CEUS, SWE, and CEUS+SWE, the incidence of malignancy was 56.6%, 78.3%, and 73.5%, respectively. CEUS+SWE showed higher sensitivity (93.7% vs. 81% and 68.3%), specificity (90% vs. 30% and 80%), positive predictive value (96.7% vs. 78.5% and 91.5%), negative predictive value (81.8% vs. 33.3% and 44.4%), and diagnostic coincidence rate (92.8% vs. 68.7% and 71.1%) than SWE and CEUS alone in diagnosing pathological type of breast masses. Moreover, CEUS combined with SWE exhibited a larger area under the receiver operating characteristic (ROC) curve (0.918) than SWE (0.741, p = 0.028) and CEUS (0.555, p < 0.001) alone in the diagnosis of BI-RADS 4 breast masses. CONCLUSION: Overall, the diagnostic value of CEUS+SWE for the pathological type of BI-RADS is preferred over CEUS and SWE alone. CEUS+SWE showed higher values than CEUS and SWE alone in diagnosing BI-RADS 4 breast masses. Specifically, CEUS+SWE can correctly identify benign and malignant masses, reduce unnecessary trauma, and avoid misdiagnosis. In summary, CEUS combined with SWE can serve as an effective diagnostic method and avoid delaying the best treatment opportunity for some malignant lesions.

Correlación entre BI-RADS 4 y resultados histopatológicos en lesiones atípicas de mama

Introducción: Las lesiones atípicas de la glándula mamaria afectan a un total de 20 000 mujeres en el mundo. La categorización BI-RADS 4 se considera indefinida y tiene variación considerable a malignidad hasta con cinco años de seguimiento. Objetivo: Establecer la correlación entre los informes BI-RADS 4 y hallazgos histopatológicos en mujeres con diagnóstico de patología atípica de mama que aceden a la consulta de Ginecología y Obstetricia del Hospital Provincial General Docente de Riobamba, Ecuador. Método: Se realizó un estudio de tipo analítico correlacional, retrospectivo, no experimental, de corte transversal en el periodo enero-diciembre de 2021, en 78 pacientes de 20 a 70 años. Los datos fueron tomados de las historias clínicas. Para la validez de pruebas se usó pruebas estadísticas tipo Ji cuadrado de correlación con intervalos de confianza del 95 % e índice de error del 5 %. Resultados: El principal factor de riesgo observado fue la edad ≥40 años (26,92 %), seguido de: deformidad mamaria (20,51 %), y recurrencia de nódulos (15,38 %). En relación a las subcategorías del informe BI-RADS 4, se observó que el tipo C fue la de mayor prevalencia con 39 casos (50 %). Predominó la hiperplasia ductal con el 44,87 % y la fue de un 95,83 % con una especificidad del 70 %. Hubo una significación asintónica de 0,001 entre BI-RADS 4 y resultados histopatológicos. Conclusiones: Las lesiones subcategorizadas como BI-RADS 4C tienen mayor probabilidad de malignizar debido a que se asocian principalmente a hiperplasia ductal, siendo esta el principal cáncer mamario en mujeres mayores de 40 años.

Introduction: Atypical lesions of the mammary gland affect a total of 20,000 women worldwide. The BI-RADS 4 categorization is considered indefinite and has considerable variation to malignancy with up to five years of follow-up. Objective: To establish the correlation between BI-RADS 4 reports and histopathological findings in women with a diagnosis of atypical breast pathology who attend the Gynecology and Obstetrics consultation of the Hospital Provincial General Docente de Riobamba, Ecuador. Method: A correlational, retrospective, non-experimental, cross-sectional analytical study was carried out in the period January-December 2021, in 78 patients aged 20 to 70 years. The data were taken from the medical records. For the validity of the tests, Chi-square correlation statistical tests were used with 95% confidence intervals and an error rate of 5%. Results: The main risk factor observed was age ≥40 years (26.92%), followed by: breast deformity (20.51%), and recurrence of nodules (15.38%). In relation to the subcategories of the BI-RADS 4 report, it was observed that type C was the most prevalent with 39 cases (50%). Ductal hyperplasia predominated with 44.87% and was 95.83% with a specificity of 70%. There was an asymptomatic significance of 0.001 between BI-RADS 4 and histopathological results. Conclusions: Lesions subcategorized as BI-RADS 4C are more likely to become malignant because they are mainly associated with ductal hyperplasia, this being the main breast cancer in women over 40 years of age.

Introdução: Lesões atípicas da glândula mamária afetam um total de 20.000 mulheres em todo o mundo. A categorização BI-RADS 4 é considerada indefinida e apresenta variação considerável para malignidade com até cinco anos de acompanhamento. Objetivo: Estabelecer a correlação entre os laudos BI-RADS 4 e os achados histopatológicos em mulheres com diagnóstico de patologia mamária atípica que atendem na consulta de Ginecologia e Obstetrícia do Hospital Provincial General Docente de Riobamba, Equador. Método: Estudo correlacional, retrospectivo, não experimental, transversal, analítico, foi realizado no período de janeiro a dezembro de 2021, em 78 pacientes com idade entre 20 e 70 anos. Os dados foram retirados dos prontuários médicos. Para a validade dos testes foram utilizados testes estatísticos de correlação qui-quadrado com intervalos de confiança de 95% e taxa de erro de 5%. Resultados: O principal fator de risco observado foi idade ≥40 anos (26,92%), seguido de: deformidade mamária (20,51%) e recorrência de nódulos (15,38%). Em relação às subcategorias do relatório BI-RADS 4, observou-se que o tipo C foi o mais prevalente com 39 casos (50%). A hiperplasia ductal predominou com 44,87% e foi de 95,83% com especificidade de 70%. Houve significância assintomática de 0,001 entre o BI-RADS 4 e os resultados histopatológicos. Conclusões: Lesões subcategorizadas como BI-RADS 4C têm maior probabilidade de se tornarem malignas porque estão associadas principalmente à hiperplasia ductal, sendo este o principal câncer de mama em mulheres com mais de 40 anos.

Sonographic Characteristics and Pathology Correlation of Breast Imaging Reporting and Data System (BI-RADS) Category 4 Lesions.

INTRODUCTION: The Breast Imaging-Reporting and Database System (BI-RADS) category 4 is designated for breast lumps that do not display the typical features of malignancy but still raise enough suspicion to warrant a recommendation for a biopsy, as malignancy cannot be ruled out through imaging alone. The main objective of this study was to investigate the sonographic characteristics and pathology correlation of BI-RADS 4 breast lesions and determine the positive predictive rate of BI-RADS 4 lesions in diagnosing breast cancer, using histopathology as the gold standard. METHODS: This was a cross-sectional study conducted at the Department of Radiology, Aga Khan University Hospital in Karachi, spanning from May 2021 to August 2022, with a duration of 15 months. The study focused on female patients over the age of 18 who presented with suspicious breast lesions on ultrasound. Both mammography and ultrasound-guided core needle biopsy were performed on these patients, followed by a detailed histopathological evaluation of the biopsy specimens. To calculate the positive predictive value (PPV), true positive cases were identified through both histopathology and ultrasonography. RESULTS: A total of 227 cases were categorized as BI-RADS 4 lesions, with the patients' mean age being 47.8 ± 14.3 years (range: 17 - 88). Among the biopsied lesions, 101 cases were confirmed to be true positive for breast malignancies, resulting in a PPV for malignancy of 44.9%. Conversely, there were 124 false positive cases out of the 227 BI-RADS 4 category lesions (54.63%). The primary indication for presentation was a breast lump, and out of the 101 confirmed malignant cases, 70 (69.3%) were associated with malignancy. CONCLUSION: BI-RADS 4 can be utilized to assess suspicious breast lumps; however, for more reliable results and to avoid false negatives, histopathological confirmation should complement the imaging findings.

Correlation Of Bi-Rads 4 Subcategories Breast Lesions On Tomosynthesis And Histopathological Examination With P63 Immunohistochemistry Expression.

Mammography is considered to be the gold standard for screening and detection of breast malignancies. Among different biochemical markers used to detect carcinoma of breasts, p63 has been widely popularized for its effectiveness in the detection of myoepithelial cells which are an important indicator of breast benignity. In this study, we plan to statistically analyze and correlate the Breast Imaging Reporting and Data System (BI-RADS) 4 subcategories grading on mammogram imaging with p63 immunostaining. A total of 80 patients were taken into the study within a period of two years (2016-2018) after ensuring the inclusion and exclusion criteria. They were further sorted into different BI-RADS 4 subcategories, i.e., taking into consideration X-ray mammogram and tomosynthesis findings, 57 samples were categorized as low suspicion (BI-RADS 4A), while 12 were classified as intermediate (BI-RADS 4B), and the remaining 11 samples were categorized as highly suspicious (BI-RADS 4C). Although considered to be leaning toward malignancy, a BI-RADS reading of 4 (namely 4A-low suspicion, 4B-moderate suspicion, and 4C-high suspicion for malignancy) needs further evaluation for accurate diagnosis. There have been cases within our own observation where a lesion that is highly suspicious of malignancy has turned out to be a benign finding. Further, evaluating the expression of a p63 marker can help prevent mutilating surgeries for indeterminate lesions. The present study has been conducted to study the correlation of tomosynthesis grading of lesions that has been categorized from low-to-high suspicion, with a p63 immunostaining pattern in these lesions.

Evaluation of Real-world Data for Breast-MRI BI-RADS IV Lesions.

BACKGROUND/AIM: Magnetic resonance imaging (MRI) is an important diagnostic tool in the detection of breast cancer. The Breast Center of the municipal Hospital Holweide, Cologne, annually cares for and treats patients with changes in the breast. A special problem is posed by Breast Imaging-Reporting and Data System (BI-RADS) 4 lesions. If a BI-RADS 4 finding is present, is a vacuum biopsy indicated in every case or, if there is already an indication for surgery due to other findings, can the corresponding finding be removed openly without histological clarification? We require real world data regarding the actual in-center likelihood of a BIRADS 4 lesion to be DCIS (Ductal carcinoma in situ) or invasive disease. PATIENTS AND METHODS: This is a retrospective study of 1,641 patients who received MRI examination in the radiological department of the municipal hospital Holweide in 2012 and 2013. Each BI-RADS 4 finding (or higher) classified by MRI was compared with the final histological result. RESULTS: 347 MRIs showed BI-RADS 4 findings or higher and 280 (80.7%) cases showed benign histology. In 67 (19.3%) cases, histology showed DCIS or invasive carcinoma. CONCLUSION: BI-RADS 4 lesions have a low probability of malignancy based on real-world data from this center. If there is already an indication for surgery due to other lesions, the patient can also be offered a simultaneous open biopsy in the context of the already initiated surgical treatment. Each center should know the sensitivity and specificity of the MRI imaging performed and counsel patients based on that.

A multidisciplinary approach to axillary lymph node staging with ultrasound in the setting of a highly suggestive or suspicious breast mass.

The radiologists' role in axillary imaging in the setting of a suspicious breast mass is evolving in light of the Z0011 trial leading to expected practice variation. The purpose of our project was to generate a standardized algorithm guiding the utilization of axillary ultrasound in the setting of a highly suggestive or highly suspicious breast mass (BI-RADS 4C or 5) without a known cancer diagnosis. The algorithm was created with Z0011 practices in mind while reflecting the clinical preferences of our radiology and surgical teams. The four breast surgeons at our academic institution were individually queried regarding their preferred axillary imaging and biopsy approach. The best practices for axillary imaging were then developed in a breast imaging intradepartmental meeting. There was agreement among the surgical group that the presence of suspicious axillary lymph node (s) on ultrasound could be used for treatment planning and patient discussion but would not be used for surgical planning in most cases. They also agreed that an ultrasound-guided core needle biopsy of a suspicious axillary lymph node should be deferred until after surgical consultation. Discussion among our breast radiologists resulted in the consensus that axillary ultrasound in the setting of a BIRADS 4 or 5 mass should be deferred at its initial presentation unless there is palpable lymphadenopathy, suspicious lymph node on mammography, or a tumor is at least stage T3, presumably excluding them from Z0011 criteria. The decision was also made to defer biopsies of suspicious axillary lymph nodes without prior surgical consultation/discussion.

A comparative efficacy study of diagnostic digital breast tomosynthesis and digital mammography in BI-RADS 4 breast cancer diagnosis.

PURPOSE: Probability of malignancy for BI-RADS 4-designated breast lesions ranges from 2% to 95%, contributing to high false-positive biopsy rates. We compare clinical performance of digital breast tomosynthesis (DBT) versus digital mammography (2D) among our BI-RADS 4 population without prior history of breast cancer. METHODS: We extracted retrospective data i.e., clinical, mammogram reports, and biopsy data, from electronic medical records across Houston Methodist's nine hospitals for patients who underwent diagnostic examinations using both modalities (02/01/2015 - 09/30/2020). 2D and DBT cohorts were not intra-individual matched, and there was no direct mammogram evaluation. Using Student's t test, Fisher's exact test, and Chi-squared test, we evaluated the data to determine statistical significance of differences between modalities in BI-RADS 4 cases. We calculated adjusted odds-ratio between modalities for cancer detection rate (CDR) and biopsy-derived positive predictive value (PPV3). RESULTS: There were 6,356 encounters (6,020 patients) in 2D and 5,896 encounters (5,637 patients) in DBT assessed as BI-RADS 4. Using Fisher's exact test, DBT mammography cases were significantly assessed as BI-RADS 4 5.66% more often than those undergoing 2D mammography, P = 0.0046 (1.0566 95% CI: 1.0169-1.0977). The CDRs were 112.65 (2D) and 120.76 (DBT), adjusted odds-ratio: 1.04 (0.93, 1.16)), P = 0.5029, while PPV3 were 14.41% (2D) and 15.99% (DBT), adjusted odds-ratio: 1.09 (0.97, 1.22), P = 0.1483; both logistic regression-adjusted for all other factors. CONCLUSION: DBT did not achieve better performance and sensitivity in assigning BI-RADS 4 cases compared with 2D, showed no significant advantage in CDR and PPV3, and does not reduce false-positive biopsies among BI-RADS 4-assessed patients.

A Five-Year Review of the Outcomes of Breast Imaging Reporting and Data System 4 Lesions in Hospital Universiti Sains Malaysia.

PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon used in reporting breast imaging has several categories with specific positive predictive values for breast cancer. Among those, BI-RADS 4 is associated with a wider range of risk for breast cancer, which makes the decision for biopsy difficult. The study aim was to determine the malignancy rate and clinical outcomes of BI-RADS 4 lesions in Hospital Universiti Sains Malaysia (HUSM) for a period of five years. METHODS: This was a retrospective study of patients diagnosed by mammographic or ultrasonographic findings with BI-RADS 4 breast lesions in HUSM, Kelantan from July 2015 to June 2020. Data were collected from the medical records and an electronic database. Patients with BI-RADS 4 lesions who underwent biopsy and had a known tissue diagnosis were included in this study. The data was used to calculate the malignancy rate and associated positive predictive factors for breast cancer associated with BI-RADS 4 lesions. RESULTS: From the mammography and ultrasonography performed at HUSM from July 2015 to June 2020, a total of 256 lesions were categorized as BI-RADS 4. However, only 198 BI-RADS 4 lesions underwent biopsy and were included in the study. Of these 198 lesions, 26.8% were malignant on histopathological examination of the biopsy samples. Simple logistic regression analysis showed that age, diabetes mellitus, hypertension, number of parity, and certain mammogram findings were significantly associated with breast cancer. Invasive breast cancer was the most common type. Fibrocystic disease was the most common benign pathology, followed by fibroadenoma. CONCLUSION: The malignancy rate of BI-RADS 4 lesions in HUSM was similar to previously reported rates. A thorough evaluation of positive predictive factors and careful selection of patients for biopsy in BI-RADS 4 lesions will minimize unwanted biopsies and associated patient anxiety, in addition to reducing the health care burden.

The uncertainty of boundary can improve the classification accuracy of BI-RADS 4A ultrasound image.

PURPOSE: The Breast Imaging-Reporting and Data System (BI-RADS) for ultrasound imaging provides a widely used reporting schema for breast imaging. Previous studies have shown that in ultrasound imaging, 90% of BI-RADS 4A tumors are benign lesions after biopsies. Unnecessary biopsy procedures can be avoided by accurate classification of BI-RADS 4A tumors. However, the classification task is challenging and has not been fully investigated by existing studies. For benign and malignant tumors of BI-RADS 4A, the appearances of intra-class tumors are highly variable, the characteristics of inter-class tumors is overall-similar. Discriminative features need to be found to improve classification accuracy of BI-RADS 4A tumors. METHODS: In this study, we designed the network using the clinical features of BI-RADS 4A tumors to improve the discrimination ability of network. The boundary information is embedded into the input of the network using the uncertainty. A fine-grained data augmentation method is used to find discriminative features in tumor information embedded with boundary information. Two mathematical methods, voting-based and variance-based, are used to define the uncertainty of boundary, and the differences of these two definitions are compared in a classification network. RESULTS: The dataset we used to evaluate our method had 1155 2D grayscale images. Each image represented a unique BI-RADS 4A tumor. Among them, 248 tumors were proven to be malignant by biopsy, and the remaining 907 were benign. A weakly supervised data augmentation network (WS-DAN) was used as the backbone classification network, which showed competitive performance in finding discriminative features. Using the auxiliary input of the uncertain boundaries defined by the voting method, the area under the curve (AUC) value of our method was 0.8347 (sensitivity = 0.7774, specificity = 0.7459). The AUC value of the variance-based uncertainty was 0.7789. The voting-based uncertainty was higher than the baseline (AUC = 0.803), which only inputs the original image. Compared with the classic classification network, our method had a significant effect improvement (p < 0.01). CONCLUSIONS: Using the uncertain boundaries defined by the voting methods as auxiliary information, we obtained a better performance in the classification of BI-RADS 4A ultrasound images, while variance-based uncertain boundaries had no effect on improving classification performance. Additionally, fine-grained network helped find discriminative features comparing with the commonly used classification networks.

Intraradiological pathology-calibrated electrical impedance spectroscopy in the evaluation of excision-required breast lesions.

PURPOSE: Evaluating a real-time complementary bioelectrical diagnostic device based on electrical impedance spectroscopy (EIS) for improving breast imaging-reporting and data system (BI-RADS) scoring accuracy, especially in high-risk or borderline breast diseases. The primary purpose is to characterize breast tumors based on their dielectric properties. Early detection of high-risk lesions and increasing the accuracy of tumor sampling and pathological diagnosis are secondary objectives of the study. METHODS: The tumor detection probe (TDP) was first applied to the mouse model for electrical safety evaluations by electrical current measurement. Then it was utilized for characterization of 138 human palpable breast lesions that were to undergo core needle biopsy (CNB), vacuum-assisted biopsy (VAB), or fine needle aspiration (FNA) on the surgeon's requests. Impedance phase slope (IPS) in frequency ranges of 100- 500 kHz and impedance magnitude in f = 1 kHz were extracted as the classification parameters. Consistency of radiological and pathological declarations for the excisional recommendation was then compared with the IPS values. RESULTS: Considering pathological results as the gold standard, meaningful correlations between IPS and pathophysiological status of lesions recommended for excision (such as atypical ductal hyperplasia, papillary lesions, complex sclerosing adenosis, and fibroadenoma) were observed (p < 0.0001). These pathophysiological properties may include cell size, membrane permeability, packing density, adenosis, cytoplasm structure, etc. Benign breast lesions showed IPS values greater than 0, while high-risk proliferative, precancerous, or cancerous lesions had negative IPS values. Statistical analysis showed 95% sensitivity with area under the curve (AUC) equal to 0.92. CONCLUSION: Borderline breast diseases and high-risk lesions that should be excised according to standard guidelines can be diagnosed with TDP before any sampling process. It is an important outcome for high-risk lesions that are radiologically underestimated to BI-RADS3, specifically in younger patients with dense breast masses that present challenges in mammographic and sonographic evaluations. Also, the lowest IPS value detects the most pathologic portions of the tumor for increasing sampling accuracy in large tumors. SIGNIFICANCE: Precise detection of high-risk breast masses, which may be declared BI-RADS3 instead of BI-RADS4a.

An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions.

BACKGROUND: The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed. METHODS: We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits. RESULTS: Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients. CONCLUSION: Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.

Mammography breast density: an effective supplemental modality for the precise grading of ultrasound BI-RADS 4 categories.

BACKGROUND: High breast density is significantly associated with an increased risk of breast diseases. Presently, suspected breast masses assessed as Breast Imaging-Reporting and Data System (BI-RADS) grade 4 provide a wide range of positive predictive values. Moreover, subcategories (4a, 4b, and 4c) are still under consideration as the diagnostic criteria are neither comprehensive nor objective. However, whether mammography breast density (MBD) has any impact on the accurate grading of BI-RADS 4 assessed by ultrasound (US) remains unknown. METHODS: A total of 1,086 women with 1,293 breast masses were included and assessed as BI-RADS 3-5 by US. The subcategories of MBD (from the ACR-a to the ACR-d group) were assessed by mammography according to the criteria of the American College of Radiology (ACR). The clinicopathological characteristics of these patients were reviewed retrospectively. The malignancy rates of breast masses among different subgroups assessed by BI-RADS were re-estimated with MBD. RESULTS: Almost all BI-RADS 3 masses were classified as benign and nearly all BI-RADS 5 masses were identified as malignant. Significant inverse associations between MBD and malignancy rates were detected between the BI-RADS 4a and BI-RADS 4b groups. Moreover, malignancy rates decreased significantly from ACR-a to ACR-d for BI-RADS 4a and 4b breast lesions (P<0.001). However, this trend was not observed in BI-RADS 4c breast lesions. CONCLUSIONS: MBD could serve as a crucial factor for the accurate grading of BI-RADS 4 lesions assessed by US. We strongly recommend the adoption of the MBD as a possible supplemental screening modality for US. Furthermore, it is equally beneficial for accurate risk assessment and screening recommendations based on MBD.