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Background: Breast cancer is the most frequent cancer among women. Early detection of breast cancer is critical for improving prognosis. The current study aims to establish the added value of ultrasound (US) elastography to the BIRADS classification system in the categorization of ambiguous breast lesions following mammography as a first step. Methods: This cross-sectional study was performed at the Radiology Department of Zagazig University. Cases were referred from the outpatient clinic of the general surgery department with suspected breast lesions. All study population subjected to full history taking (clinical presentation, age, family and past history), clinical examination, histopathological and imaging examination (Conventional B-mode breast US and breast elastography). Results: Elasticity and velocity detected by shear wave elastography were significantly elevated among cancerous lesions compared to benign lesions (P =0.02, 0.01, respectively). There was a significant variance between the qualitative measurements between malignant and benign lesions (P =0.02). Combining conventional imaging with shear wave elastography (SWE) reported a sensitivity of 83.3% and specificity of 91.7% with an AUC of 0.94. Conclusion: SWE is a simple method with great diagnostic accuracy that can be easily combined with a B-mode US examination in the same session, increasing its specificity. It demonstrated usefulness in reducing the frequency of needless biopsies. [ABSTRACT FROM AUTHOR]

Background: This study aims to explore the accuracy of Convolutional Neural Network (CNN) models in predicting malignancy in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging (DCE-BMRI). Methods: A total of 273 benign lesions (benign group) and 274 malignant lesions (malignant group) were collected and randomly divided into a training set (246 benign and 245 malignant lesions) and a testing set (28 benign and 28 malignant lesions) in a 9:1 ratio. An additional 53 lesions from 53 patients were designated as the validation set. Five models—VGG16, VGG19, DenseNet201, ResNet50, and MobileNetV2—were evaluated. Model performance was assessed using accuracy (Ac) in the training and testing sets, and precision (Pr), recall (Rc), F1 score (F1), and area under the receiver operating characteristic curve (AUC) in the validation set. Results: The accuracy of VGG19 on the test set (0.96) is higher than that of VGG16 (0.91), DenseNet201 (0.91), ResNet50 (0.67), and MobileNetV2 (0.88). For the validation set, VGG19 achieved higher performance metrics (Pr 0.75, Rc 0.76, F1 0.73, AUC 0.76) compared to the other models, specifically VGG16 (Pr 0.73, Rc 0.75, F1 0.70, AUC 0.73), DenseNet201 (Pr 0.71, Rc 0.74, F1 0.69, AUC 0.71), ResNet50 (Pr 0.65, Rc 0.68, F1 0.60, AUC 0.65), and MobileNetV2 (Pr 0.73, Rc 0.75, F1 0.71, AUC 0.73). S4 model achieved higher performance metrics (Pr 0.89, Rc 0.88, F1 0.87, AUC 0.89) compared to the other four fine-tuned models, specifically S1 (Pr 0.75, Rc 0.76, F1 0.74, AUC 0.75), S2 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77), S3 (Pr 0.76, Rc 0.76, F1 0.73, AUC 0.75), and S5 (Pr 0.77, Rc 0.79, F1 0.75, AUC 0.77). Additionally, S4 model showed the lowest loss value in the testing set. Notably, the AUC of S4 for BI-RADS 3 was 0.90 and for BI-RADS 4 was 0.86, both significantly higher than the 0.65 AUC for BI-RADS 5. Conclusions: The S4 model we propose has demonstrated superior performance in predicting the likelihood of malignancy in DCE-BMRI, making it a promising candidate for clinical application in patients with breast diseases. However, further validation is essential, highlighting the need for additional data to confirm its efficacy. [ABSTRACT FROM AUTHOR]

Introduction: FNAC has a pivotal role in the evaluation of breast lesions. Cytological grading systems are being utilized for the categorization of breast lesions. Modified Masood’s scoring System (MMSS) is used to improve the grading system for accurate pre-operative diagnosis. This study aims to assess the effectiveness and accuracy of the Modified Masood Scoring System for evaluating breast lesions and their correlation with histopathological diagnosis. Materials and Methods: This is a prospective study conducted in the Department of Pathology, MGM Medical College, Navi Mumbai which included 40 patients who presented with clinically palpable breast lump and were subjected to FNAC along with histopathological examination. The cytological smears were grouped into four categories according to MMSS. Correlation and concordance analysis between cytological categories and histopathological diagnosis was carried out. Observations and Results: The age of the patients ranged from 17 to 70 years with a mean age of 38.12 years, right-sided breast lesions were more common as compared to the left side. Of the total 40 cases, 27 cases were benign, and 13 cases were malignant on histopathology. The most common benign breast lesion was Fibroadenoma [Figure 1] and the malignant lesion was infiltrating duct carcinoma [Figure 2]. The sensitivity, specificity, Positive predictive value, Negative predictive value, and diagnostic accuracy were 76.9%, 100%, 100%, 90%, and 92.5% respectively. Conclusion: MMSS is simple, cost-effective, easily reproducible, reliable, and can be applied to FNAC aspirates to increase the diagnostic accuracy of Breast Lesions. [ABSTRACT FROM AUTHOR]

Vascularity plays a pivotal role in the progression of breast lesions and may be associated with their aggressiveness and likelihood of being malignant. Contrast-enhanced imaging techniques are necessary to evaluate vascularity due to the limited sensitivity of conventional color Doppler techniques, in which motion artifacts are eliminated using wall filters. However, in this process, low-flow signals from small vessels also get removed unintentionally. Advancements in technology have revolutionized the way ultrasound images are generated, resulting in tremendous improvements in Doppler imaging techniques. The new, ultrasound-based noncontrast microvascular imaging techniques overcome the limitations of conventional Doppler, and are highly sensitive for detecting microvessels and low flow. The resultant high Doppler sensitivity leads to detection of vascularity in more breast lesions. It is important for radiologists to understand the imaging principles and the clinical implications of the new techniques, to optimally utilize them and aid correct diagnosis. Angio-PLUS is one such recent advancement, which uses unfocused or plane waves and three-dimensional wall filtering to analyze tissue motion in time, space, and amplitude domains that effectively distinguish between blood flow and tissue. The information is beneficial for assessing the lesion vascularity without using contrast. This article aims to explain the Doppler imaging techniques, their clinical applications, scanning methods, and review the common Doppler-based diagnostic criteria used in the evaluation of breast lesions. [ABSTRACT FROM AUTHOR]

Advances in various imaging modalities for breast lesions have improved diagnostic capabilities not only for tumors but also for non-tumorous lesions. Contrast-enhanced ultrasound (CEUS) plays a crucial role not only in the differential diagnosis of breast lesions, identification of sentinel lymph nodes, and diagnosis of lymph node metastasis but also in assessing the therapeutic effects of neoadjuvant chemotherapy (NAC). In CEUS, two image interpretation approaches, i.e., qualitative analysis and quantitative analysis, are employed and applied in various clinical settings. In this paper, we review CEUS for breast lesions, including its various applications. [ABSTRACT FROM AUTHOR]

Vascularity plays a pivotal role in the progression of breast lesions and may be associated with their aggressiveness and likelihood of being malignant. Contrast-enhanced imaging techniques are necessary to evaluate vascularity due to the limited sensitivity of conventional color Doppler techniques, in which motion artifacts are eliminated using wall filters. However, in this process, low-flow signals from small vessels also get removed unintentionally. Advancements in technology have revolutionized the way ultrasound images are generated, resulting in tremendous improvements in Doppler imaging techniques. The new, ultrasound-based noncontrast microvascular imaging techniques overcome the limitations of conventional Doppler, and are highly sensitive for detecting microvessels and low flow. The resultant high Doppler sensitivity leads to detection of vascularity in more breast lesions. It is important for radiologists to understand the imaging principles and the clinical implications of the new techniques, to optimally utilize them and aid correct diagnosis. Angio-PLUS is one such recent advancement, which uses unfocused or plane waves and three-dimensional wall filtering to analyze tissue motion in time, space, and amplitude domains that effectively distinguish between blood flow and tissue. The information is beneficial for assessing the lesion vascularity without using contrast. This article aims to explain the Doppler imaging techniques, their clinical applications, scanning methods, and review the common Doppler-based diagnostic criteria used in the evaluation of breast lesions.

Abstract Background Patients with dense breasts have a higher risk factor for developing breast cancer. Digital mammography (DM) exhibits a reduced sensitivity in dense breast tissue as a result of overlapping fibro-glandular tissue. Digital breast tomosynthesis (DBT) solves this problem, so allows better characterization of masses, asymmetries, and parenchymal distortion. It is useful in screening studies for early detection of breast lesions by accurate detection and description of their margins, shape, and extent as well as surrounding structures. Objective We aimed to evaluate the added value of DBT in characterizing of different breast lesions in dense breasts compared to DM. Methods A Prospective Cohort study involved ninety female cases having dense breast parenchyma (ACR C & ACR D) having breast complaint lesions detected by DM or ultrasound or recalled from screening units, during the period from June 2021 to Jan 2023. All patient underwent DM and DBT with correlation with the pathological finding or follow up. Results As regards the characterization of breast mass DBT had higher sensitivity, higher specificity (98%, 87% respectively). However, DM showed less sensitivity, less specificity (58%, 80%,) with (p

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. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data. [ABSTRACT FROM AUTHOR]

Background: Patients with dense breasts have a higher risk factor for developing breast cancer. Digital mammography (DM) exhibits a reduced sensitivity in dense breast tissue as a result of overlapping fibro-glandular tissue. Digital breast tomosynthesis (DBT) solves this problem, so allows better characterization of masses, asymmetries, and parenchymal distortion. It is useful in screening studies for early detection of breast lesions by accurate detection and description of their margins, shape, and extent as well as surrounding structures. Objective: We aimed to evaluate the added value of DBT in characterizing of different breast lesions in dense breasts compared to DM. Methods: A Prospective Cohort study involved ninety female cases having dense breast parenchyma (ACR C & ACR D) having breast complaint lesions detected by DM or ultrasound or recalled from screening units, during the period from June 2021 to Jan 2023. All patient underwent DM and DBT with correlation with the pathological finding or follow up. Results: As regards the characterization of breast mass DBT had higher sensitivity, higher specificity (98%, 87% respectively). However, DM showed less sensitivity, less specificity (58%, 80%,) with (p < 0.0001, p = 0.0005 respectively). Conclusions: DBT effectively visualizes overlapping tissue in dense breasts. DBT is important due to its ability to detect breast cancer earlier, characterize lesions accurately, and inspect the margins of masses precisely, along with decreasing recall rates for false-positive results. [ABSTRACT FROM AUTHOR]

Aim: To explore the performance of single or combined ultrasound (US) in diagnosing malignant breast lesions, and then compare the results with a magnetic resonance imaging (MRI) aspect. Material and methods: Patients’ demographics, tumor markers, and imaging examination were collected. Diagnostic models based on B-mode, color Doppler ultrasounds (CDU), strain elastography (SE), MRI, B-mode US + CDU, B-mode US + SE, and B-mode US + CDU + SE were developed using logistic regression analysis. The performance was assessed using the area under the curve (AUC) with 95% confidence intervals (CIs). Performance of MRI and B-mode US + CDU was compared using DeLong analysis. Results: For single imaging modality, MRI showed the best performance, with AUC of 0.938 (95%CI: 0.888-0.988). For combined US modalities, combination of B-mode US and CDU had the best performance, with AUC of 0.948 (95%CI: 0.877-1.000). There was no significant difference in the diagnostic performance between the combination of B-mode US and CDU and MRI (p>0.05). Conclusions: Our study found the performance of B-mode + CDU was comparable to MRI. Our findings suggested that the combination of B-mode US and CDU was recommended to diagnose malignant breast lesions in order to save time and expense and provide guidance to make decisions for a biopsy. [ABSTRACT FROM AUTHOR]

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To compare the diagnostic performance of different mathematical models for DWI and explore whether parameters reflecting spatial and temporal heterogeneity can demonstrate better diagnostic accuracy than the diffusion coefficient parameter in distinguishing benign and malignant breast lesions, using whole-tumor histogram analysis. This retrospective study was approved by the institutional ethics committee and included 104 malignant and 42 benign cases. All patients underwent breast magnetic resonance imaging (MRI) with a 3.0 T MR scanner using the simultaneous multi-slice (SMS) readout-segment ed echo-planar imaging (rs-EPI). Histogram metrics of Mono- apparent diffusion coefficient (ADC), CTRW, and FROC-derived parameters were compared between benign and malignant breast lesions, and the diagnostic performance of each diffusion parameter was evaluated. Statistical analysis was performed using Mann-Whitney U test and receiver operating characteristic (ROC) curve. The D FROC -median exhibited the highest AUC for distinguishing benign and malignant breast lesions (AUC = 0.965). The temporal heterogeneity parameter α CTRW -median generated a statistically higher AUC compared to the spatial heterogeneity parameter β CTRW -median (AUC = 0.850 and 0.741, respectively; p = 0.047). Finally, the combination of median values of CTRW parameters displayed a slightly higher AUC than that of FROC parameters, with no significant difference however (AUC = 0.971 and 0.965, respectively; p = 0.172). The diffusion coefficient parameter exhibited superior diagnostic performance in distinguishing breast lesions when compared to the temporal and spatial heterogeneity parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. Purpose: To develop a deep learning‐based whole‐process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. Study Type: Retrospective. Population: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. Field Strength/Sequence: 3.0T/dynamic contrast‐enhanced (DCE)‐MRI sequence. Assessment: DLWPS was developed using segmentation and classification models. The DLWPS‐based segmentation model was developed by the U‐Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS‐based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS‐assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA‐sequencing data. Statistical Tests: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. Results: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS‐assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. Data Conclusion: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. Level of Evidence: 4 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the diagnostic efficiency of the Demetics for breast lesions and assessment of Ki‐67 status. Material: This retrospective study included 291 patients. Three combined methods (method 1: upgraded BI‐RADS when Demetics classified the breast lesion as malignant; method 2: downgraded BI‐RADS when Demetics classified the breast lesion as benign; method 3: BI‐RADS was upgraded or downgraded according to Demetrics' diagnosis) were used to compare the diagnostic efficiency of two radiologists with different seniority before and after using Demetics. The correlation between the visual heatmap by Demetics and the Ki‐67 expression level of breast cancer was explored. Results: The sensitivity, specificity, and area under curve (AUC) of diagnosis by Demetics, junior radiologist and senior radiologist were 89.5%, 83.1%, 0.863; 76.9%, 82.4%, 0.797 and 81.1%, 89.9%, 0.855, respectively. Method 1 was the best for senior radiologist, which increased AUC from 0.855 to 0.884. For junior radiologist, Method 3 was the best method, improving sensitivity (88.8% vs. 76.9%) and specificity (87.2% vs. 82.4%). Demetics paid more attention to the peripheral area of breast cancer with high expression of Ki‐67. Conclusion: Demetics has shown good diagnostic efficiency in the assisted diagnosis of breast lesions and is expected to further distinguish Ki‐67 status of breast cancer. [ABSTRACT FROM AUTHOR]

It is possible to diagnose benign & malignant breast lesions with the aid of fine needle aspiration cytology. Masood created a scoring method known as the Modified Masood's Scoring Index to help classify breast lesions. To estimate the cytological scoring of breast masses and correlation of findings with histological diagnosis. This study was done in the Cyto-Histology Department, Integral Hospital Lucknow for the duration of two years. The results were compared with gold standard histological diagnosis and classified using Modified Masood's Scoring Index. A total of 105 cases, females aged 15 to 70 with a mean age of 25.37 (SD=9.71) years were examined. On the left side, breast lumps were more common (547.1%) than on the right. The highest number of benign instances (84%) discovered in the younger age group, but malignant cases were more common in those over 35. Cytological analysis revealed that 73 (69.5%) of the cases had proliferative breast disease without atypia. Modified Masood's Scoring Index’s overall sensitivity, specificity, diagnostic accuracy, positive predictive value, and negative predictive value were 100%, 97.87%, 98.1%, 84.62%, and 100%, respectively. A valuable diagnostic technique for breast masses is fine needle aspiration cytology, which offers a quick and precise diagnosis. Masood's Modified Scoring Index demonstrated a strong histopathological correlation. [ABSTRACT FROM AUTHOR]

Previous studies have indicated that amide proton transfer-weighted imaging (APTWI) could be utilized for differentiating benign and malignant tumors. The APTWI technology has increasingly being applied to breast tumor research in recent years. However, according to the latest literature retrieval, no relevant previous studies compared the value of APTWI and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in distinguishing benign lesions from malignant lesions. In the present study, the application of APTWI and DCE for differentiating the benign and malignant breast lesions was investigated.

Breast lesions are mainly classified as benign and malignant. When breast cancer is diagnosed, the patient is evaluated and a treatment plan is prepared. For benign lesions, follow-up or surgery may be performed. Imaging and biopsy results are essential when deciding on surgery. In particular, when there is a discrepancy between radiologic imaging and pathology results, excision may be useful for definitive diagnosis. We planned to present an approach to suspicious breast lesions with radiology pathology discordance through the presentation of three patients diagnosed with malignancy. Between February 2018 and March 2023, patients who underwent wire marking and excision for suspicious breast lesions were analyzed. Three patients diagnosed with malignancy after total excision were analyzed in detail. Of 33 patients, 2 (6.06%) patients had ductal carcinoma in situ and 1 (3.03%) patient had invasive tubular carcinoma. Preoperative and postoperative pathological findings were consistent with benign changes in all three cases. After total excision, a definitive diagnosis was made, and follow-up and treatment were planned. In cases of radiology-pathology discordance, total excision may be effective in detecting atypia, carcinoma in situ, or invasive carcinoma not detected on pathologic examination. Therefore, even if the biopsy result is not malignant, wire-guided total excision of radiologically suspicious lesions should be considered as a diagnosis and treatment method. [ABSTRACT FROM AUTHOR]

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Background: Diagnosis of breast cancer has been widely improved since the development of high-resolution ultrasound. Ultrasonography is an indispensable tool in breast imaging. FNAC is relevant and important for pre-operative pathological evaluation of in management of breast cancer. Present study was aimed to study ultrasound guided evaluation of breast lesions followed by correlation of its findings with FNAC (cytopathological correlation). Material and Methods: Present study was single-center, prospective, observational study, conducted in females, age > 18 years, complaining of palpable breast lesions, OR had suspicious lesions on mammography. BIRAD score interpreted by USG was used for prediction of malignancy. Results: In present study, 233 women were included. Majority were from 31-60 years age group, with mean age of 47.22 ± 11.43 years. Common indication for USG were lump (71.2%) followed by pain (16.3%), screening (7.7%) & nipple discharge (4.7%). FNAC report was Malignant (56.7%) in majority cases as compared to benign (43.3%). Sensitivity and Specificity of BIRAD ≥ IVb was highest (90.9% & 89.1% respectively) followed by BIRAD ≥ IVa (88.6% & 75.3% respectively) & BIRAD ≥ IVc (71.2% & 95.1% respectively). No statistically significant difference was noted between oval and round shape (p- 0.859). Benign lesions on FNAC were commonly seen with Circumscribed margins (64.4%) while malignant lesions on FNAC were commonly seen with microlobulated margins (57.6%), association was statistically significant (p<0.001). Conclusion: Using integrated approach employing imaging and FNAC (cytological method) procedure is the best way to in addressing the diagnostic needs of patients with breast diseases. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: The diagnostic efficiencies of the VGG19, ResNet50, and DenseNet201 models were tested under the same dataset. The model with the highest performance was selected and modified utilizing three fine-tuning strategies (S1-3). Fifty additional lesions were selected to form the validation set to verify the generalization abilities of these models. The accuracy (Ac) of the different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), F1 score (), and area under the receiver operating characteristic curve (AUC), were primary performance indicators. Finally, the kappa test was used to compare the degree of agreement between the DTL models and pathological diagnosis in differentiating malignant from benign breast lesions. Results: The Pr, Rc, f1, and AUC of VGG19 (86.0%, 0.81, 0.81, and 0.81, respectively) were higher than those of DenseNet201 (70.0%, 0.61, 0.63, and 0.61, respectively) and ResNet50 (61.0%, 0.59, 0.59, and 0.59). After fine-tuning, the Pr, Rc, f1, and AUC of S1 (87.0%, 0.86, 0.86, and 0.86, respectively) were higher than those of VGG19. Notably, the degree of agreement between S1 and pathological diagnosis in differentiating malignant from benign breast lesions was 0.720 (κ = 0.720), which was higher than that of DenseNet201 (κ = 0.440), VGG19 (κ = 0.640), and ResNet50 (κ = 0.280). Conclusion: The VGG19 model is an effective method for identifying benign and malignant breast lesions on DCE-MRI, and its performance can be further improved via fine-tuning. Overall, our findings insinuate that this technique holds potential clinical application value. [ABSTRACT FROM AUTHOR]

Introduction: Values obtained by shear wave elastography (SWE) in breast lesions were compared with Apparent Diffusion Coefficient (ADC) values. This study aimed to demonstrate the usability of SWE, which is an easy-to-apply, cost-effective, quantitative data-providing and user-independent method, to distinguish benign and malignant lesions. Methods: In the retrospective study, SWE measurements were made by placing the smallest region of interest (ROI) obtained in 24 lesions, in the areas where the red color was most intense on the color map. The obtained values were compared with the ADC values determined using the ROI drawn freely from the entire lesion in ADC mapping and the round ROI drawn from the darkest area. Results: A high level of correlation (p=0.001, r=0.790) was detected between the kPa values measured in SWE and the minimum ADC values measured in diffusion, and a moderate correlation (p=0.001, r=0.670) was found between the ADC values measured from the entire lesion. A significant difference was detected between SWE, minimum ADC and mean ADC values of benign and malignant lesions (p=0.001). Discussion and Conclusion: There is a high correlation between ADC values obtained in diffusion-weighted imaging (DWI) and SWE values in breast lesions. SWE, which is easy to apply, cost-effective and provides quantitative data, can be used instead of ADC mapping to distinguish benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

Abstract Background In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. Methods A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. Results The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). Conclusion The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses.

Abstract Background Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. Patients and methods This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. Results In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. Conclusions PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.

Abstract Background To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes. Methods In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification. Results Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852–0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values. Conclusions All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors.

Rationale and objectives: Clinical research is crucial for evaluating new medical procedures and devices. It is important for healthcare units and hospitals to minimize the disruptions caused by conducting clinical studies; however, complex clinical pathways require dedicated recruitment and study designs.This work presents the effective introduction of novel microwave breast imaging (MBI), via MammoWave apparatus, into the clinical routine of an operative screening and diagnostic breast imaging department for conducting a multicentric clinical study. Materials and methods: Microwave breast imaging, using MammoWave apparatus, was performed on volunteers coming from different clinical pathways. Clinical data, comprising demographics and conventional radiologic reports (used as reference standard), was collected; a satisfaction questionnaire was filled by every volunteer. Microwave images were analyzed by an automatic clinical decision support system, which quantified their corresponding features to discriminate between breasts with no relevant radiological findings (NF) and breasts with described findings (WF). Results: Conventional breast imaging (DBT, US, MRI) and MBI were performed and adapted to assure best clinical practices and optimum pathways. 180 volunteers, both symptomatic and asymptomatic, were enrolled in the study. After microwave images' quality assessment, 48 NF (15 dense) and 169 WF (88 dense) breasts were used for the prospective study; 48 (18 dense) breasts suffered from a histology-confirmed carcinoma. An overall sensitivity of 85.8 % in breasts lesions’ detection was achieved by the microwave imaging apparatus. Conclusion: An optimum recruitment strategy was implemented to assess MBI. Future trials may show the clinical usefulness of microwave imaging, which may play an important role in breast screening.

This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision. [ABSTRACT FROM AUTHOR]

Background: Breast lesions are a significant global health concern, often requiring invasive procedures for definite diagnosis. Cytological and molecular markers present alternative, potentially less intrusive diagnostic methodologies. This study aims to compare the effectiveness of cytological and molecular markers for the diagnosis of breast lesions. Methods: A crosssectional study was performed on a cohort of 500 patients presenting with breast lesions, undergoing cytological evaluation (Fine-Needle Aspiration Cytology) and molecular marker testing (HER2/neu, ER, PR, and Ki-67). Concordance between the two methods was evaluated. Results: Both cytological and molecular marker evaluations showed significant diagnostic potential. There was substantial concordance between the two modalities. However, molecular markers exhibited a slightly higher diagnostic sensitivity and specificity, especially in the detection of pre-malignant and malignant lesions. Conclusion: Cytological and molecular markers each offer valuable, complementary diagnostic approaches. While cytology provides rapid, costeffective results, molecular markers offer increased sensitivity and specificity. Their combined usage can help minimize invasive procedures and improve patient outcomes. Further research is recommended to refine the usage of these diagnostic tools. [ABSTRACT FROM AUTHOR]

Despite mammography (MG) being among the most widespread techniques in breast cancer screening, tumour detection and classification remain challenging tasks due to the high morphological variability of the lesions. The extraction of radiomics features has proved to be a promising approach in MG. However, radiomics features can suffer from dependency on factors such as acquisition protocol, segmentation accuracy, feature extraction and engineering methods, which prevent the implementation of robust and clinically reliable radiomics workflow in MG. In this study, the variability and robustness of radiomics features is investigated as a function of lesion segmentation in MG images from a public database. A statistical analysis is carried out to assess feature variability and a radiomics robustness score is introduced based on the significance of the statistical tests performed. The obtained results indicate that variability is observable not only as a function of the abnormality type (calcification and masses), but also among feature categories (first-order and second-order), image view (craniocaudal and medial lateral oblique), and the type of lesions (benign and malignant). Furthermore, through the proposed approach, it is possible to identify those radiomics characteristics with a higher discriminative power between benign and malignant lesions and a lower dependency on segmentation, thus suggesting the most appropriate choice of robust features to be used as inputs to automated classification algorithms. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions. Methods: A systematic search of PubMed, EMBASE, Cochrane, OVID, SCI, and SCOPUS was performed to find relevant studies which applied SMI to differentiate benign and malignant breast lesions. All the studies were published before October 10, 2022. Only studies published in English were collected. Quality Assessment of Diagnostic Accuracy Studies‐2 (QUADAS‐2) tool was applied to assess the quality of the included studies. Summary receiver operating characteristic (SROC) modeling was also performed to the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions. Subgroup analyses and meta‐regression were performed to find out the heterogeneity. Results: Twenty studies which include a total of 2873 lesions (1748 benign and 1125 malignant) in 2740 patients were evaluated in this meta‐analysis. The summary sensitivity and specificity were 0.82 (95% confidence interval [CI]: 0.76–0.86), 0.70 (95% CI: 0.64–0.76) for SMI vascular degree, 0.77 (95% CI: 0.67–0.84), 0.79 (95% CI: 0.75–0.83) for SMI vascular distribution, 0.78 (95% CI: 0.70–0.84), 0.75 (95% CI: 0.69–0.80) for SMI vascular morphology, 0.81 (95% CI: 0.72–0.87), 0.80 (95% CI: 0.75–0.85) SMI penetration vessel. For SMI overall vascular features, the summary sensitivity and summary specificity were 0.74 (95% CI: 0.61–0.84) and 0.80 (95% CI: 0.76–0.84). The result of subgroup analysis and meta‐analysis showed malignant rate and country might be the cause of heterogeneity of diagnostic accuracy of vascular grade and morphology. Conclusion: SMI vascular features have high sensitivity and specificity in the differentiation of benign and malignant lesions. Future international multicenter studies in various regions with large sample size are required to confirm these findings. [ABSTRACT FROM AUTHOR]

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue but also increases false positives, resulting in unnecessary biopsies. Our study investigated the use of Google AutoML Vision (Mountain View, California), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Approach: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model, were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patients biopsy was used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision-recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% [95% confidence interval (CI) of 73.5% to 100%], specificity of 83.3% (CI=51.6% to 97.9%), positive predictive value (PPV) of 85.7% (CI=62.9% to 95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development (AUC=0.67) and during prediction in the independent dataset [sensitivity=75% (CI=42.8 to 94.5), specificity=80% (CI=51.9 to 95.7), PPV=75% (CI=50.8 to 90.0), and NPV=80% (CI=59.3% to 91.7%)], but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions.

The classification of the breast imaging-reporting and data system (BI-RADS) based on magnetic resonance imaging (MRI) refers to the classification of the degree of lesions according to the image signs of lesions, which is usually subjective. Moreover, the benign and malignant lesions of BI-RADS 3-5 are overlapping, which is prone to unnecessary invasive treatment due to high diagnostic categories in clinical diagnosis. To address these problems, this research applied radiomics for feature extraction and fusion of T1-weighted (T1W) and dynamic contrast-enhanced (DEC) MRI. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen out the optimal feature collection of each type of MR image. Support vector machine (SVM), random forest (RF), K-nearest neighbour (KNN) and logistic regression (LR) algorithms were applied for BI-RADS 3-5 classification, based on which the benign and malignant lesions were further classified. The results showed that the classification accuracy of breast BI-RADS 3-5 by four radiomics models based on feature fusion was 81.25%, 87.50%, 78.38%, and 81.25%, respectively. Their accuracy in distinguishing the benign and malignant breast lesions was 90.91%, 93.55%, 92.73%, and 94.55%, respectively. This indicates that the combination of radiomics and machine learning correlation algorithm has a good effect on breast MRI BI-RADS classification and benign and malignant differentiation, and feature fusion can further improve the accuracy of classification prediction.

Background: Breast lesions are areas of abnormal breast tissue. It is estimated that 20% of women develop breast lesions. Breast lesions are either benign or malignant. Breast carcinomas are the most common cause of deaths in women accounting for 10.6% deaths in India, 6.9% deaths per year globally. Aims and Objectives: To study various histopathological patterns of breast lesions in women admitted in Government General Hospital. Materials and Methods: It is a prospective study in the department of pathology, Government General Hospital, Nizamabad, from December 2020 to July 2022. Grossing was done and details noted, tissue bits were processed and sections were stained with conventional H and E Staining. Results: Out of 70 cases of breast lesions, 55 (78.5%) are benign breast lesions and 15 (21.5%) are malignant breast lesions. Fibroadenoma is the most common breast lesion accounting for 33 cases, that is, 60 %, followed by fibrocystic diseases (21.8%). Infiltrating ductal carcinoma of No Special Type is the most common malignant breast lesion accounting for 10 cases, that is, 66.6%. Among invasive breast carcinomas, the majority of cases are Grade II (69%). Conclusion: Study of histopathological patterns of breast lesions plays an important role in diagnosis, treatment, and prognosis of breast lesions. This study highlighted incidence and pathological characteristics of a wide range of breast lesions.

Abstract Background Diffusion tensor imaging (DTI) is a novel approach which uses extra gradients to quantify diffusion in several directions (at least six). The purpose of this research was to determine the role of diffusion tensor magnetic resonance imaging in breast lesion differentiation. Results Apparent diffusion coefficient (ADC) values were significantly lower in malignant than benign lesions, with a cut-off value of 1.21 × 10−3 mm2/s, this gives a sensitivity of 88.46%, specificity 87.50% and accuracy 86.7%. Values of fractional anisotropy (FA) were higher significantly in malignant compared to benign lesions with a 0.15 cut-off value, has a 95.83% sensitivity, 96.15% specificity, and 95.6%, accuracy. Values of RA were significantly higher in malignant (0.180 ± 0.068) compared to benign lesions, with 0.13 cut-off value. Sensitivity, specificity, and accuracy were, respectively, 91.69%, 92.31%, and 90.2%. Values of λ1 were significantly lower in malignant (1.4 ± 0.453 × 10−3 mm2/s) than in benign (2.19 ± 0.659 × 10−3 mm2/s) lesions with a cut-off value of 1.71 × 10−3 mm2/s. Sensitivity and specificity were, respectively, 95.83 and 96.15%. The combined evaluation by (dynamic contrast enhancement) Sensitivity improved to 100% with DCE and DTI readings, while specificity remained at 95.6%. Conclusions DTI breast imaging is a noninvasive procedure which demonstrated a high potential utility for cancer detection and serving as a standalone technique or in conjunction with DCE-MRI, the discriminating values of FA, λ1 and λ1–λ3 were high. Their measurements were strongly associated with identification breast malignancy and combined evaluation by DTI parameters and DCE-MRI DTI enhanced the sensitivity, lowered the rate of false-negatives, and completely improved the accuracy of breast lesions differential diagnosis.

To improve the accuracy of computer aided diagnosis (CAD) based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in the differentiation of benign and malignant breast lesions, this study proposed a convolutional neural network model (AC_Ulsam_CNN) that is based on multi-modal feature fusion and the combination of asymmetric convolution (AC) and ultra-lightweight subspace attention module (Ulsam). Firstly, the transfer learning method was used to pre-train the model to screen out the most effective DCE-MRI time phase scan for differentiating benign and malignant breast lesions. Then, a network model based on AC_Ulsam_CNN was constructed based on the optimal time phase scan images to enhance the feature expression ability and robustness of the classification model. Finally, multimodal information such as breast imaging reporting and data system (BI-RADS) classification, apparent diffusion coefficient (ADC) and time-signal intensity curve (TIC) type were incorporated for feature fusion, to further improve the distinguishing performance of benign and malignant breast lesions. The performance of the model was verified by 5-fold cross-validation method, and the accuracy (ACC) of the proposed method was 0.826 and the area under the curve (AUC) was 0.877. The experimental results show that the proposed algorithm performs well in the classification of benign and malignant breast lesions with small sample size, and the fusion model based on multimodal data further enriches the feature information, thus this study improves the detection accuracy of lesions, and provides a new method for automatic differential diagnosis of benign and malignant breast lesions.

Background: In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. Methods: A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. Results: The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). Conclusion: The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses. [ABSTRACT FROM AUTHOR]

Stereotactic biopsy and open biopsy represent useful diagnostic tools of breast lesions. However, both affect the Quality of Life (QoL) in various degrees. We conducted this prospective longitudinal comparative study in order, first to access the impact of these techniques on short-term QoL and second to compare and critically discuss our results with those of literature review. Group A (58 patients) underwent vacuum-assisted stereotactic biopsy and Group B (46 patients) underwent open biopsy. The Health-Related Quality of Life (HRQol) was estimated using the European Quality of Life scale (EuroQol) and the SF-36 (The 36-Item Short-Form Health Status Survey) questionnaires. The stereotactic breast biopsy seems to be more accepted from the patients as it affects quality of life to a lesser extent than open breast biopsy. This difference is mainly attributable to a reduction of physical discomfort and pain. [ABSTRACT FROM AUTHOR]

Objective: This study aims to examine all the interventional procedures performed with ultrasonography in our department, to determine the success rates, complications, advantages, and disadvantages of these procedures, and to review our findings with the literature. Material and Method: From July 1999 to June 2004 in our mamography unit, out of 135 lesions (70 aspiration biopsies, 62 wire-guided excision biopsies, 3 tru-cut biopsies) that were localized preoperatively with ultrasonography-guided aspiration or needle (wire) localization biopsy in 115 patients, those that had results for histopathologic evaluation, were re-evaluated retrospectively. The success rates of ultrasonography guided interventional procedures were determined, and their complications were evaluated. Results: Out of 70 aspirated lesions, 7 were considered unsuccessful because they resulted in being "non-diagnostic material", and 3 were considered unsuccessful because no material could be obtained from the lesion. It was observed that out of 60 lesions with successful aspiration, 57 (95%) lesions were diagnosed as benign and 3 (5%) lesions were diagnosed as malignant. According to the histopathological results of 62 wire-guided excision biopsies, 43 (69.4%) lesions were benign and 19 (30.7%) lesions were malignant. All 3 lesions that were biopsied with tru-cut needle were diagnosed as benign. During the procedures, no complications occurred other than mild vasovagal reaction in 2 patients (%1.5) and bleeding in 5 patients (%3.7) which was manageable with short-term compression. Conclusion: The success rate of ultrasonography-guided interventional methods is high in our study. The accuracy of radiological methods is also high. Our results are consistent with the literature. Statistical studies and retrospective evaluation of lesions with histopathologic results increase the experience of diagnostic centers. [ABSTRACT FROM AUTHOR]

Background: Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. Patients and methods: This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. Results: In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. Conclusions: PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center. [ABSTRACT FROM AUTHOR]

Background: To evaluate multiple parameters in multiple b-value diffusion-weighted imaging (DWI) in characterizing breast lesions and predicting prognostic factors and molecular subtypes. Methods: In total, 504 patients who underwent 3-T magnetic resonance imaging (MRI) with T1-weighted dynamic contrast-enhanced (DCE) sequences, T2-weighted sequences and multiple b-value (7 values, from 0 to 3000 s/mm2) DWI were recruited. The average values of 13 parameters in 6 models were calculated and recorded. The pathological diagnosis of breast lesions was based on the latest World Health Organization (WHO) classification. Results: Twelve parameters exhibited statistical significance in differentiating benign and malignant lesions. alpha demonstrated the highest sensitivity (89.5%), while sigma demonstrated the highest specificity (77.7%). The stretched-exponential model (SEM) demonstrated the highest sensitivity (90.8%), while the biexponential model demonstrated the highest specificity (80.8%). The highest AUC (0.882, 95% CI, 0.852–0.912) was achieved when all 13 parameters were combined. Prognostic factors were correlated with different parameters, but the correlation was relatively weak. Among the 6 parameters with significant differences among molecular subtypes of breast cancer, the Luminal A group and Luminal B (HER2 negative) group had relatively low values, and the HER2-enriched group and TNBC group had relatively high values. Conclusions: All 13 parameters, independent or combined, provide valuable information in distinguishing malignant from benign breast lesions. These new parameters have limited meaning for predicting prognostic factors and molecular subtypes of malignant breast tumors. [ABSTRACT FROM AUTHOR]

Introduction: Breast carcinoma is found to be the leading cancer in women, both in developed and developing countries across the world. Though breast cancer is thought to be a disease of developed countries, 50% of the cases and 58% of the deaths occur in developing countries. It is found to have increased in developing countries due to lack of awareness and its later stages detection. Detection of cancer in early stage can improve the survival chances of the patient, and cancer control can be achieved. Aims: The objective of my study is to find accuracy of FNAC in diagnosis of breast malignant masses and to correlate the benign and malignant FNAC feathers with ultrasound diagnosis for early detection of Breast Carcinoma. Screening of the breast lesion using ultrasonography followed by FNAC of the lesion will help early detection and treatment of the disease. Materials and Methods: The study was prospective, cross sectional and hospital-based on 100 female patients of age ranging between 15-85 years with history of palpable breast masses referred to Radiological Department of DMCH for USG scan independently and in combination with FNAC correlation in the Department of Pathology of DMCH. Result: Out of the total 62 breast lump examined, 42 (67.74%) breast lumps were reported as benign and 19 (30.65%) were reported as malignant. In one case the result was indeterminate as the cytopathologist reported as "papillary lesion". The final histopathological result, however, turned out to be "ductal carcinoma in situ with solid, cribriform and micropapillary patterns". Conclusion: A breast tumour must be evaluated to rule out cancer. FNAC is a tissue diagnostic method, while ultrasonography is an imaging approach. In our research, the specificity of both diagnostic instruments in identifying malignant breast lump was found to be 100%, implying that a positive (malignant) result of either test can exclusively determine the treatment choice without further diagnostic inquiry. [ABSTRACT FROM AUTHOR]

Background: Fine Needle Aspiration Cytology (FNAC) is a simple, minimally invasive, cost effective, outpatient based and a rapid diagnostic method for breast lesions. The aim is to determine the accuracy with the benign and malignant lesions can be differentiated by fine needle aspiration cytology (FNAC) and to correlate the findings with histopathological diagnosis Material and Methods: It is a prospective study comprising of 105 patients of breast lumps who underwent FNAC followed by histopathological examination. All the breast lump aspirates were stained with Haematoxylin and Eosin (H and E) stain. Results: Among total majority of the patients (29.5%) belong to 31-40 years age group. Benign breast lesions were found in 65.8%, among which fibroadenoma (40.9%) was the commonest lesion which was observed. Malignancy was observed in 34.2%; among them, ductal carcinoma was the predominant lesion (18.1%) which was seen. Breast lump lesion was more in right side (54.3%) than left breast. Conclusion: The most common benign tumor in the present study was fibroadenoma and the most common malignant tumor was invasive ductal carcinoma. [ABSTRACT FROM AUTHOR]

Introduction: One of the leading causes of cancer death in the less developed countries of the world is breast cancer. This is partly because of clinical advances to combat the disease which are not reaching the women living in those regions and also because of a shift in lifestyle, which is causing an increase in its incidence. All detected breast lesions are not malignant and all the benign masses do not progress to cancer; never-theless the precision of the final diagnosis can be greatly increased by radiological imaging (mammography, ultrasonography) and pathological diagnosis. Aims: To study the mammographic and ultrasonographic characteristics of breast lesions in patients, categorize the detected breast lesions according to BI-RADS, to correlate the categorized breast lesions (BI-RADS) with FNAC and compare the sensitivity of mammography with ultrasonography in diagnosing benign and malignant breast lesions. Materials and methos: This is a cross-sectional study completed during a period of one year in the DEPARTMENT OF RADIOLOGY, KPC MEDICAL COLLEGE AND HOSPITAL, JADAVPUR. KOLKATA. Total 50 patients were included in this study. Result: All fat containing and low density lesions on mammography were benign on FNAC and the lesions which were of high density turned out to be either benign or malignant. Out of the total 16 FNAC proven malignant cases, the most common site was the central (retroareolar) region (5 cases). Commonest site was upper outer quadrant for benign lesions. All the oval lesions on mammography were benign except for one. Out of the 18 round lesions, 15 were found to be malignant. All lesions with spiculated margins and with fine pleomorphic type of calcification in mammography were found to be positive for malignancy in both mammography and on FNAC. All the oval shaped lesions on USG were proved to be benign on FNAC. All spiculated margin lesions in ultrasonography were found to be positive for malignancy on FNAC, while all the well circumscribed lesions were found to be benign. Conclusion: Combined USG and mammography yielded the best result and can be used as a screening modality to detect malignancy earlier and to treat the patient earlier. [ABSTRACT FROM AUTHOR]

Abstract Background This study aimed to explore whether collagen fiber features and collagen type I alpha 1 (COL1A1) are related to the stiffness of breast lesions and whether COL1A1 can predict axillary lymph node metastasis (LNM). Methods Ninety-four patients with breast lesions were consecutively enrolled in the study. Amongst the 94 lesions, 30 were benign, and 64 were malignant (25 were accompanied by axillary lymph node metastasis). Ultrasound (US) and shear wave elastography (SWE) were performed for each breast lesion before surgery. Sirius red and immunohistochemical staining were used to examine the shape and arrangement of collagen fibers and COL1A1 expression in the included tissue samples. We analyzed the correlation between the staining results and SWE parameters and investigated the effectiveness of COL1A1 expression levels in predicting axillary LNM. Results The optimal cut-off values for Emax, Emean, and Eratio for diagnosing the benign and malignant groups, were 58.70 kPa, 52.50 kPa, and 3.05, respectively. The optimal cutoff for predicting axillary LNM were 107.5 kPa, 85.15 kPa, and 3.90, respectively. Herein, the collagen fiber shape and arrangement features in breast lesions were classified into three categories. One-way analysis of variance (ANOVA) showed that Emax, Emean, and Eratio differed between categories 0, 1, and 2 (P 0.145 was considered the cut-off value, and its efficacy in benign and malignant breast lesions was 0.808, with a sensitivity of 66% and a specificity of 90%. Furthermore, when the COL1A1 expression level > 0.150 was considered the cut-off, its efficacy in predicting axillary LNM was 0.796, with sensitivity and specificity of 96% and 59%, respectively. Conclusions The collagen fiber features and expression levels of COL1A1 positively correlated with the elastic parameters of breast lesions. The expression of COL1A1 may help diagnose benign and malignant breast lesions and predict axillary LNM.

Background: MRI has an essential role in breast imaging, along with mammography and ultrasonography. DCE- MRI is not 100% accurate in discriminating benign from malignant lesions. In attempt to improve the specificity of DCE-MRI, multiparametric MRI (mp-MRI) with additional functional parameters had been introduced. Objectives: To assess the added diagnostic value of MR spectroscopy in evaluation of suspicious breast lesions. Patients and methods: This prospective study included 60 females, their mean age was 42.1 ± 12 SD with 75 breast lesions categorized as BI-RADS 3 or BI-RADS 4 based on sonomamography. They were subjected to multiparametric breast MRI (T2WI, dynamic contrast-enhanced MR imaging, diffusion-weighted images, and MR spectroscopy). ROC-analysis was employed for comparison between the diagnostic accuracy of mp-MRI and mp-MRI combined with MRS to predict the malignant lesions using the histopathological results as a standard of reference. Results: 33 (55 %) patients had histopathologically diagnosed 43 (58%) benign breast lesions and 27 (45%) patients had 32 (42%) malignant lesions. Using mp-MRI (dynamic contrast-enhanced MR imaging, T2-weighted images and diffusion-weighted images) had 98% accuracy of with 97.8%, sensitivity and 98.6%, specificity in differentiating malignant from benign lesions. A higher diagnostic 99.2% accuracy was obtained from combined mp-MRI and MRS with 99.5% sensitivity, 98.6% specificity. Conclusion: Despite of its limitations, MRS is a promising functional MRI technique thus improves the diagnostic accuracy of MRI for characterization of suspicious breast lesions when combined to mp-MRI to avoid unnecessary biopsy.