Benign Breast Disease Research Articles

Usefulness of intralesional contrast injection as a prebiopsy confirmation technique in a case of breast hemangioma

Hemangiomas are uncommon benign vascular lesions of breast. They have nonspecific imaging features and can be isoechoic or hypoechoic or of heterogeneous echotexture in ultrasound (USG). When isoechoic, it may be difficult to identify these lesions with confidence against a fatty background. Here, we report such a case of a high-density lesion detected in screening mammogram, which was not so obvious in USG. We used the technique of intralesional contrast injection into the probable USG correlate for confirmation, before proceeding with the biopsy and establishing the diagnosis of hemangioma. Thus, this simple prebiopsy confirmation technique helps in identifying the definitive USG correlate, thereby facilitating a USG-guided biopsy. This is very useful, especially in the peripheral healthcare centers where stereotactic facility may not be available. Another learning point from this case is that in case of superficially located lesions that are readily visible in mammogram but less conspicuous in USG, the possibility of hemangioma can be considered in the differentials. This observation has been mentioned in other case reports as well and reiterated in our case. However, due to the nonspecific imaging features and low incidence, it is difficult to make diagnosis of hemangioma without tissue sampling. The imaging features of breast hemangioma in mammogram and ultrasound are reviewed and described in this case report with an emphasis on the role of intralesional contrast injection in lesion localization.

Case report of a rare benign breast lesion: Unveiling nipple adenoma

Nipple adenoma is a rare and benign proliferative lesion originating from the lactiferous ducts of the nipple. It predominantly affects middle-aged women and can mimic the clinical and radiological features of more concerning breast malignancies. Due to its uncommon occurrence and overlapping characteristics with malignant lesions, a definitive diagnosis often requires histopathological examination. We present a case of nipple adenoma in a 30-year-old woman, detailing the clinical, radiological, and histopathological features, as well as the management and outcomes.

Diseases of the primary cilia: a clinical characteristics review.

Ciliopathies encompass a broad spectrum of diseases stemming from dysfunction of the primary (non-motile) cilia, present on almost all cells in the human body. These disorders include autosomal dominant and recessive polycystic kidney diseases, nephronophthisis, and multisystem ciliopathies such as Joubert, Meckel, Bardet-Biedl, Alström, oral-facial-digital syndromes, and skeletal ciliopathies. The majority of these ciliopathies are associated with fibrocystic kidney disease resulting in progressive kidney dysfunction. In addition, many ciliopathies are associated with extra-renal manifestations including congenital hepatic fibrosis, retinal dystrophy, obesity, and brain and skeletal anomalies. The diagnoses may be challenging due to their overlapping clinical features and molecular heterogeneity. To date, over 190 genes encoding proteins that localize to the primary cilia have been identified as disease-causing. This review will discuss the clinical features of the most frequently encountered disorders of primary cilia.

Serum exosomal miR-200c is a potential diagnostic biomarker for breast cancer

Background Breast cancer (BC) is one of the most common malignancies in women. Exosomes are widely found in body fluids and carry microRNAs (miRNAs) that reflect the biological properties of the parental cells. Our study aimed to investigate the differential expression of miR-200c in BC serum exosomes and its diagnostic value. Methodology miRNA profiles in culture supernatant exosomes of normal mammary epithelial cells MCF-10A and BC cells (MCF-7, MDA-MB-231, MCF-7 Taxol) were examined by miRNA deep sequencing to screen for significantly differentially expressed miRNAs; Transmission electron microscopy (TEM), Nanoparticle tracking analysis (NTA), and Western blot were used to identify exosomes; qPCR was used to detect the expression level of miR-200c in cellular exosomes and serum exosomes; The efficacy of individual and combined tests of each indicator to diagnose BC was evaluated using receiver operating characteristic (ROC) curves. Results We identified typical exosome features by TEM, NTA and Western blot, indicating successful exosome extraction. Then our miRNA sequencing results and qRT-PCR experiments showed that miR-200c was significantly down-regulated in BC cell exosomes. In addition, we divided the clinical serum samples into two cohorts according to region, and in independent cohort I, the serum exosomal miR-200c levels of BC patients were significantly lower than those of healthy controls. In cohort II, serum exosomal miR-200c expression was significantly lower in the BC group than in the control and benign breast disease (BBD) groups, whereas miR-200c expression in the BBD group was not statistically different from that in the control group. ROC analyses in both independent cohorts confirmed that serum exosomal miR-200c could differentiate between patients with and without BC disease and could be used as an early diagnostic marker for BC disease. Conclusion Serum exosome miR-200c can be used as a potential biomarker for the diagnosis of BC, and combined with conventional serum diagnostic markers AFP, CA125 and CA153 can help to improve diagnostic efficiency.

Characteristics of primary tuberculous mastitis in tuberculosis endemic country: A case series

Tuberculous mastitis (TM) is a rare form of tuberculosis, occurring as a primary disease when there is no evidence of tuberculosis in other locations. There are no clear clinical features of TM, especially in the absence of a previous tuberculosis infection. Due to its unclear clinical picture, diagnosis is difficult, and it is often confused with breast carcinoma or pyogenic abscesses. The aim of this study was to report our experience and discuss the characteristics and diagnostic modalities in cases of primary TM in a tuberculosis-endemic area. Method A case series study was conducted at the Arifin Achmad Regional Hospital, reporting four cases of primary tuberculous mastitis in January 2024. The patients were women aged 24-41 years. Case presentation All patients presented with complaints of breast pain for the last 2 weeks to 2 months and complained of symptoms in the form of a lump in the breast that was reddish in colour and mastalgia. One patient was diagnosed during pregnancy, and one had a history of prior breastfeeding. One patient presented with FNAB results for breast carcinoma. The other patient complained of an ulcer on her breast. Physical examination revealed axillary lymphadenopathy in all patients. Histopathological examination revealed tuberculous mastitis in all patients and 1 with tuberculous mastitis, fibrocystic changes, and Atypical Ductal Hyperplasia (ADH). Conclusion Patients with tuberculous mastitis who visited our institution had symptoms similar to those of abscesses and breast cancer. The FNAC test is the most reliable, but false-negative results can occur. Diagnosis requires teamwork between the patient’s doctor, anatomical pathologist, and radiologist. This research requires a larger scale to describe actual conditions.

Microcalcifications in benign breast biopsies: association with lesion type and risk.

To characterize associations of microcalcifications (calcs) with benign breast disease lesion subtypes and assess whether tissue calcs affect risks of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC). We analyzed detailed histopathologic data for 4,819 BBD biopsies from a single institution cohort (2002-2013) followed for DCIS or IBC for a median of 7.4years for cases (N = 338) and 11.2years for controls. Natural language processing was used to identify biopsies containing calcs based on pathology reports. Univariable and multivariable regression models were applied to assess associations with BBD lesion type and age-adjusted Cox proportional hazard regressions were performed to model risk of IBC or DCIS stratified by the presence or absence of calcs. Calcs were identified in 2063 (42.8%) biopsies. Calcs were associated with older age at BBD diagnosis (56.2 versus 49.0years; P < 0.001). Overall, the risk of developing IBC or DCIS did not differ significantly between patients with calcs (HR 1.13, 95% CI 0.90, 1.41) as compared to patients without calcs. Stratification by BBD severity or subtype, age at BBD biopsy, outcomes of IBC versus DCIS, and mammography technique (screen-film versus full-field digital mammography) did not significantly alter association between calcs and risk. Our analysis of calcs in BBD biopsies did not find a significant association between calcs and risk of breast cancer.

Mutation Spectrum Comparison between Benign Breast Lesion Cohort, Unselected Cancer Cohort and High-Risk Breast Cancer Cohort.

Mutation study for high-risk breast and ovarian cancer (HBOC) has been extensively studied in patients of different ethnicities. Here we compared the germline mutation rate and mutation spectrum of patients (n = 4341) with benign breast diseases or breast cancers, with and without other risk factors. Three cohorts of Chinese patients were recruited. The first cohort, high-risk cohort (HR, n = 3935) included high-risk breast cancer patients fulfilling high-risk HBOC criteria and who are recruited at our genetics clinic. The second cohort, unselected cancer cohort (CC, n = 307) was from general recruitment of patients with breast cancer at breast surgery clinics. The third cohort, benign breast lesion cohort (NC, n = 99) comprised 99 patients with benign breast diseases such as fibroadenoma, fibroadenomatoid hyperplasia, and intraductal papilloma. Thirty HBOC related genes were sequenced on the above-mentioned patient cohorts. The germline mutation rates of HR, CC, and NC cohort were 11.9%, 6.5%, and 8.1%, respectively. In the CC cohort, 29.3% (90/307) of patients fulfilled the National Comprehensive Cancer Network (NCCN) high-risk genetic test criteria 2022 v.2. The mutation rate for this group of patients was 11.1%, similar to that of the HR cohort, while the mutation rate for those not fulfilling testing criteria was 4.6%, like that of the NC cohort. High penetrance genes (BRCA1/2, CDH1, PALB2, PTEN, and TP53) mutations were only found in the HR (10.6%) and CC (3.3%) cohorts but were not found in the NC cohort. ATM, BRIP1, RAD51C, and RAD51D mutations were identified in all cohorts. RAD51C and RAD51D mutations showed conflicting penetrance. An unexpectedly high mutation rate of total 2% was found in the NC cohort but it was only 0.3% and 0.5% in the HR cohort and CC cohort, respectively. Our results show a clinical need to enhance genetic testing of unselected breast cancer patients to identify the high-risk patients.

Lesion conspicuity and contrast kinetics as predictors to differentiate benign and malignant breast lesions in contrast-enhanced mammogram

BackgroundContrast-enhanced mammography (CEM) is a recently developed, cost-effective imaging technique that offers both anatomical and functional breast imaging. Lesion conspicuity, a newly introduced lexicon in the ACR BIRADS supplementary atlas on CEM (2022), lacks sufficient data to correlate with malignancy likelihood. The feasibility of assessing contrast kinetics with CEM remains uncertain, and there is a scarcity of available data. Our research aims to address these gaps.ResultsTwo radiologists, blinded to pathological reports, independently evaluated 504 CEM enhanced breast lesions with histopathology reports, out of which 176 were benign and 328 were malignant. Subjective qualitative assessment of lesion conspicuity and contrast kinetics was done for each enhancing lesion. The lesion conspicuity was classified as low, moderate, or high. The kinetic behavior of each lesion was categorized into either persistent, plateau, or washout. The distribution of lesion conspicuity among benign and malignant lesions, respectively, was as follows: for low conspicuity, 74.4% versus 25.6%; for moderate conspicuity, 30.6% versus 69.4%; and for high conspicuity, 8.4% versus 91.6%. Regarding contrast kinetics and their distribution between benign and malignant lesions, persistent kinetics was detected in 95.6% compared to 4.4%, plateau kinetics in 43.4% versus 56.6%, and washout kinetics in 3.5% versus 96.5%. Statistically significant differences in distribution between benign and malignant lesions were observed for both lexicons (P < 0.001). The inter-observer agreement for lesion conspicuity (kappa = 0.97) and contrast kinetics (kappa = 0.92) was deemed excellent.ConclusionThe addition of lesion conspicuity and contrast kinetics as lexicons in CEM could enhance its diagnostic accuracy.

Effects of Left Internal Mammary Artery (LIMA) Harvesting During Coronary Artery By-Pass Grafting Surgery on Benign Breast Diseases

Introduction: Coronary artery bypass surgery of the primary conduits employed in coronary artery bypass grafting surgery (CABG) is the left internal mammary artery (LIMA), which has garnered considerable attention due to its favorable characteristics and outcomes. There are not enough studies examining the relationship between benign breast diseases (BBD) and LIMA removal. This study is one of the pioneering studies investigating the effects of LIMA removal after bypass surgery in patients with benign breast diseases. This study aims to evaluate the effect of LIMA removal on benign breast diseases in female patients undergoing bypass surgery by analyzing pre- and post-operative breast imaging. Material and Methods: A total of 47 of 452 female patients who underwent bypass surgery between February 18, 2019, and December 31, 2021, were included in the study. All patients’ LIMA were harvested and evaluated, regarding any progression of benign breast diseases. Results: There was a significant difference between the Breast Imaging Reporting and Data System (BI-RADS) classes of the patients before and after CABG (p

Diagnostic Accuracy of Mammography and Ultrasonography Screening for Breast Cancer in Pregnant and Lactating Women

Breast cancer was of significant health concern affecting women worldwide. Objective: To assess diagnostic accuracy of mammography and ultrasonography in differentiating malignant and benign breast lesions in pregnant and lactating women. Methods: A cross sectional retrospective study was conducted at Radiology department of Shahida Islam Medical Complex, Lodhran from May 2023 to April 2024. A sample size of 242 females was calculated. Electronic medical records were reviewed for radiological examination including screening ultrasound and mammography. All those pregnant and lactating females on which ultrasound and mammograms were performed were included. SPSS version 23.0 was used for data analysis. Diagnostic accuracy of both ultrasound and mammography were calculated in terms of malignant or benign and sensitivity and specificity. Mann-Whitney U test was applied between mammography, ultrasonography and BI-RADS categories. Results: Of 242 females, 110 underwent mammography and 132 underwent ultrasound. Negative mammography was observed in 71 females in which at biopsy, 24 were benign. Negative ultrasound with only benign lesion was seen in 68 females and 10 of which were confirmed at biopsy. Specificity of ultrasonography was 100 %, sensitivity 85.7 %, positive predictive value of 100 % while negative predictive value of 25 %. Specificity of mammography was 100 %, sensitivity 92.3 %, positive predictive value of 100 % while negative predictive value of 42.8 % (p&lt;0.001). Conclusions: Although both ultrasound and mammography were found to be specific, use of mammography was considered better in terms of sensitivity and diagnostic accuracy.

Is there an association between mastitis and breast cancer? a retrospective cohort study from Germany.

The aim of the study was to explore the association between mastitis and subsequent breast cancer. This retrospective cohort study included women aged ≥ 18years with an initial mastitis diagnosis from 315 office-based gynecologists in Germany between January 2005 and December 2021. Women without mastitis were matched to women with mastitis using propensity score matching based on age, index year, average yearly consultation frequency during the follow-up period, and coexisting diseases such as obesity, benign mammary dysplasia, hypertrophy of the breast, unspecified lump of breast, and other disorders of the breast. The 10-year cumulative incidence of breast cancer for the mastitis-cohort and non-mastitis-cohort was studied with Kaplan-Meier curves using the log-rank test. The association between mastitis and breast cancer was studied separately for four age groups with univariable Cox regression analyses. In the follow-up period of 7months to 10years after the index date, 2.9% of mastitis patients and 2.4% of matched non-mastitis patients were diagnosed with breast cancer. A Cox regression analysis revealed a significant association between mastitis and subsequent breast cancer (HR: 1.37; 95% CI: 1.11-1.70). According to the age-stratified analyses, a strong and significant association was only observed in the age group > 50years (HR: 1.73; 95% 1.25-2.40). The findings of our retrospective cohort study support an association between mastitis and subsequent breast cancer diagnoses in women aged > 50years. The pathophysiological basis and possibility of confounders however requires further investigation.

Enhancing Breast Cancer Detection through Advanced AI-Driven Ultrasound Technology: A Comprehensive Evaluation of Vis-BUS.

This study aims to enhance breast cancer detection accuracy through an AI-driven ultrasound tool, Vis-BUS, developed by Barreleye Inc., Seoul, South Korea. Vis-BUS incorporates Lesion Detection AI (LD-AI) and Lesion Analysis AI (LA-AI), along with a Cancer Probability Score (CPS), to differentiate between benign and malignant breast lesions. A retrospective analysis was conducted on 258 breast ultrasound examinations to evaluate Vis-BUS's performance. The primary methods included the application of LD-AI and LA-AI to b-mode ultrasound images and the generation of CPS for each lesion. Diagnostic accuracy was assessed using metrics such as the Area Under the Receiver Operating Characteristic curve (AUROC) and the Area Under the Precision-Recall curve (AUPRC). The study found that Vis-BUS achieved high diagnostic accuracy, with an AUROC of 0.964 and an AUPRC of 0.967, indicating its effectiveness in distinguishing between benign and malignant lesions. Logistic regression analysis identified that 'Fatty' lesion density had an extremely high odds ratio (OR) of 27.7781, suggesting potential convergence issues. The 'Unknown' density category had an OR of 0.3185, indicating a lower likelihood of correct classification. Medium and large lesion sizes were associated with lower likelihoods of correct classification, with ORs of 0.7891 and 0.8014, respectively. The presence of microcalcifications showed an OR of 1.360. Among Breast Imaging-Reporting and Data System categories, category C5 had a significantly higher OR of 10.173, reflecting a higher likelihood of correct classification. Vis-BUS significantly improves diagnostic precision and supports clinical decision-making in breast cancer screening. However, further refinement is needed in areas like lesion density characterization and calcification detection to optimize its performance.

EVALUATION OF FOCAL BREAST LESIONS USING ULTRASOUND ELASTOGRAPHY

Breast lesions pose a significant diagnostic challenge, necessitating advanced imaging techniques for accurate characterization. Ultrasound elastography, including strain and shear wave methods, offers promising diagnostic capabilities for distinguishing between benign and malignant breast lesions. Objective: This study aimed to evaluate the effectiveness of ultrasound elastography in characterizing focal breast lesions and to compare the diagnostic performance of strain and shear wave elastography techniques. Methods: A prospective observational study was conducted, enrolling 200 women aged 18 and older with focal breast lesions detected on conventional ultrasound. Participants underwent both strain and shear wave elastography. Lesion characteristics, including stiffness scores and echogenicity, were documented. Histopathological analysis was performed on biopsied lesions to confirm diagnoses. Diagnostic accuracy metrics were calculated for both elastography techniques, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The average age of participants was 52 ± 10 years. Most lesions were solid (70%), with an average size of 22 mm. Shear wave elastography exhibited higher stiffness scores (average 4.5) than strain elastography (average 3.2). Diagnostic accuracy for benign lesions showed a sensitivity of 85% and specificity of 90% with elastography. For malignant lesions, sensitivity was 90% and specificity was 85%. The ROC curve analysis indicated the superior performance of shear wave elastography with an AUC of 0.85 compared to 0.80 for strain elastography. Conclusion: Ultrasound elastography, particularly shear wave elastography, demonstrates high diagnostic accuracy in characterizing breast lesions, providing valuable support in clinical decision-making.

Cytokine levels in breast cancer are highly dependent on cytomegalovirus (CMV) status

PurposeBreast cancer accounts for 30% of all female cancers in the US. Cytomegalovirus (CMV), a herpesvirus that establishes lifelong infection, may play a role in breast cancer. CMV is not oncogenic, yet viral DNA and proteins have been detected in breast tumors, indicating possible contribution to tumor development. CMV encodes cmvIL-10, a homolog of human cellular IL-10 (cIL-10) with potent immunosuppressive activities. We investigated the relationship between CMV infection, cytokines, and breast cancer.MethodsWe evaluated CMV serostatus and cytokine levels in plasma of women with benign breast disease (n = 38), in situ carcinoma (n = 41), invasive carcinoma, no lymph node involvement (Inv/LN−; n = 41), and invasive with lymph node involvement (Inv/LN+; n = 37).ResultsFifty percent of the patient samples (n = 79) were CMV seropositive. There was no correlation between CMV status and diagnosis (p = 0.75). For CMV+ patients, there was a trend toward higher CMV IgG levels in invasive disease (p = 0.172). CmvIL-10 levels were higher in CMV+ in situ patients compared to the Inv/LN− and Inv/LN+ groups (p = 0.020). Similarly, cIL-10 levels were higher in CMV+ in situ patients compared to the Inv/LN− and Inv/LN+ groups (p = 0.043). The results were quite different in CMV− patients where cIL-10 levels were highest in Inv/LN− compared to benign, in situ, or Inv/LN+ (p = 0.019). African American patients were significantly associated with CMV+ status (p = 0.001) and had lower cmvIL-10 levels than Caucasian patients (p = 0.046).ConclusionNo association was observed between CMV IgG and diagnosis, but CMV infection influences cytokine production and contributes to altered cytokine profiles in breast cancer.

Trends and Reasons of Referral of Benign Breast Disease Patients in a Tertiary Hospital in India

Trends and Reasons of Referral of Benign Breast Disease Patients in a Tertiary Hospital in India

Computer-Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach.

One of the most promising adjuncts for screening breast cancer is ultrasound (US) radio-frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This research aimed to propound a machine learning (ML) approach for automatically classifying benign, probably benign, suspicious, and malignant breast lesions based on the features extracted from the accumulated US RF time series. In this article, 220 data of the aforementioned categories, recorded from 118 patients, were analyzed. The dataset, named RFTSBU, was registered by a SuperSonic Imagine Aixplorer medical/research system equipped with a linear transducer. The regions of interest (ROIs) of the B-mode images were manually selected by an expert radiologist before computing the suggested features. Regarding time, frequency, and time-frequency domains, 291 various features were extracted from each ROI. Finally, the features were classified by a pioneering technique named the reference classification method (RCM). Furthermore, the Lee filter was applied to evaluate the effectiveness of reducing speckle noise on the outcomes. The accuracy of two-class, three-class, and four-class classifications were respectively calculated 98.59 ± 0.71%, 98.13 ± 0.69%, and 96.10 ± 0.66% (considering 10 repetitions) while support vector machine (SVM) and K-nearest neighbor (KNN) classifiers with 5-fold cross-validation were utilized. This article represented the proposed approach, named CCRFML, to distinguish between breast lesions based on registered in vivo RF time series employing an ML framework. The proposed method's impressive level of classification accuracy attests to its capability of effectively assisting medical professionals in the noninvasive differentiation of breast lesions.

Multimodal analysis of methylation and fragmentomic profiles in plasma cell free DNA for differentiation of benign and malignant breast tumors.

7 Background: Breast cancer ranks as the second leading cause of cancer-related mortality among women globally. Early detection of breast cancer is crucial for improving patient outcomes and reducing mortality rates. Liquid biopsy, which relies on circulating tumor DNA (ctDNA) shed by breast tumors into the bloodstream, presents a promising non-invasive approach for early breast cancer detection. However, accurately distinguishing between benign breast abnormalities and malignant tumors remains a significant clinical challenge, as misdiagnosis can lead to unnecessary invasive procedures. Methods: Herein, we employed a multimodal analysis approach, namely SPOT-MAS (Screen for the Presence of Tumor by DNA Methylation and Size), to profile alterations in methylation and fragment length patterns of cell-free DNA (cfDNA) from 169 breast cancer-confirmed patients and 99 patients diagnosed with benign breast lumps including cysts, fibroadenomas, and fibrocystic changes. A robust machine learning model was constructed using these signatures to differentiate between breast cancer patients and individuals with benign lesions. Results: Our genome-wide analyses identified distinct profiles of methylation changes, copy number alterations, and end motifs in cfDNA, enabling discrimination between breast cancer patients and individuals with benign conditions. Notably, we observed a significant enrichment of Thymine and Adenine at cfDNA cleavage sites in breast cancer patients. Moreover, our target sequencing analyses uncovered distinct methylation patterns in the regulatory regions of multiple genes, including hypermethylation in GPR126 or hypomethylation in TOP1 or MAFB in breast cancer cfDNA. Our multi-featured model achieved an AUC of 0.92 (95% CI: 0.88–0.97), a specificity of 97.44% and sensitivities of 57.14% and 61.70% for stage I and stage II–III patients, respectively. Furthermore, our multimodal assay effectively differentiated multiple molecular subtypes of breast tumors from benign lesions, achieving the highest sensitivity of 71.43% with Luminal A, followed by Luminal B, Triple Negative Breast Cancer (TNBC), Luminal B-HER2, and HER2 with sensitivities of 66.67%, 60%, 58.33% and 55.56%, respectively. Conclusions: Our findings demonstrate the potential of cancer-specific methylation and fragmentomic patterns in plasma cfDNA as novel biomarkers for accurately discriminating between breast cancer and benign lesions. This capability reduces the false-positive rate and helps avoid unnecessary biopsies in current clinical practice.

Deep learning-based computer-aided detection of ultrasound in breast cancer diagnosis: A systematic review and meta-analysis

The aim of this meta-analysis was to assess the diagnostic performance of deep learning (DL) and ultrasound in breast cancer diagnosis. Additionally, we categorized the included studies into two subgroups: B-mode ultrasound diagnostic subgroup and multimodal ultrasound diagnostic subgroup, and compared the performance differences of DL algorithms in breast cancer diagnosis using only B-mode ultrasound or multimodal ultrasound. We conducted a comprehensive search for relevant studies published from January 01, 2017 to July 31, 2023 in the MEDLINE and EMBASE databases. The quality of the included studies was evaluated using the QUADAS-2 tool and radiomics quality scores (RQS). Meta-analysis was performed using R software. Inter-study heterogeneity was assessed by I^2 values and Q-test P-values, with sources of heterogeneity analyzed through a random effects model based on test results. Summary receiver operating characteristics (SROC) curves were used for meta-analysis across multiple trials, while combined sensitivity, specificity, and AUC were calculated to quantify prediction accuracy. Subgroup analysis and sensitivity analyses were also conducted to identify potential sources of study heterogeneity. Publication bias was assessed using the funnel plot method. (PROSPERO identifier: CRD42024545758). The 20 studies included a total of 14,955 cases, with 4197 cases used for model testing. Among these cases were 1582 breast cancer patients and 2615 benign or other breast lesions. The combined sensitivity, specificity, and AUC values across all studies were found to be 0.93, 0.90, and 0.732, respectively. In subgroup analysis, the multimodal subgroup demonstrated superior performance with combined sensitivity, specificity, and AUC values of 0.93, 0.88, and 0.787, respectively; whereas the combined sensitivity, specificity, and AUC value for the model B subgroup was at a level of 0.92, 0.91, and 0.642, respectively. The integration of DL with ultrasound demonstrates high accuracy in the adjunctive diagnosis of breast cancer, while the fusion of DL and multimodal breast ultrasound exhibits superior diagnostic efficacy compared to B-mode ultrasound alone.

The Role of Diffusion-Weighted Imaging in Characterizing Benign and Malignant Breast Lesions: A Retrospective Study.

Introduction Diffusion-weighted imaging (DWI) is a promising magnetic resonance imaging (MRI) technique for differentiating between benign and malignant breast lesions. This study set out to assess the diagnostic utility of DWI and apparent diffusion coefficient (ADC) values in the characterization of breast lesions. Materials and methods A retrospective analysis comprised 30 patients with breast lesions who had breast MRI with DWI. The histopathological findings, ADC readings, and conventional MRI features were all analyzed. The receiver operating characteristic (ROC) curve analysis method was utilized to assess the diagnostic accuracy of DWI. Results Out of the 30 lesions, 22 (73.3%) were benign and eight (26.7%) were malignant. Malignant lesions exhibited significantly lower ADC values (p < 0.001) compared to benign lesions. An ADC cutoff value of 1.1 × 10-3mm2/s was optimal for differentiating benign from malignant lesions, yielding 90.81% sensitivity, 91.51% specificity, and 91.5% accuracy. Conclusion Combining DWI with quantitative ADC analysis is a helpful, non-invasive method for the characterization of breast lesions. It shows excellent diagnostic accuracy in identifying benign and malignant lesions, which may cut down on pointless biopsies and help with patient management.

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

To investigate and authenticate the effectiveness of various radiomics models in distinguishing between benign and malignant BI-RADS 4A lesions. 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. 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. 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.