4a Lesions Research Articles

Ultrasound strain elastography to improve diagnostic performance of breast lesions by reclassifying BI-RADS 3 and 4a lesions: a multicenter diagnostic study.

To investigate the added value of strain elastography (SE) by recategorizing ultrasound (US) Breast Imaging Reporting and Data System (BI-RADS) 3 and 4a lesions. A total of 4371 patients underwent US and SE with BI-RADS 2-5 categories solid breast lesions were included from thirty-two hospitals. We evaluated the elastographic images according to elasticity scores (ES) and strain ratios (SR). Three combined methods (BI-RADS+ES, BI-RADS+SR, BI-RADS+ES+SR) and two reclassified methods were used (method one: upgrading BI-RADS 3 and downgrading BI-RADS 4a, method two: downgrading BI-RADS 4a alone). The diagnostic performance and the potential reduction of unnecessary biopsies were evaluated. Combining BI-RADS with SE had a higher area under the curve (AUC) than BI-RADS alone (0.822-0.898 vs. 0.794, P<0.01). For reclassified method one, the sensitivity, specificity, and accuracy were 99.36%, 66.70%, 78.36% for BI-RADS+ES and 98.01%, 66.45%, 77.72% for BI-RADS+SR, and 99.42%, 66.70%, 78.38% for BI-RADS+ES+SR, respectively. For reclassified method two, the sensitivity, specificity, and accuracy were 99.17%, 70.72%, 80.87% for BI-RADS+ES and 97.76%, 81.75%, 87.46% for BI-RADS+SR, and 99.23%, 69.83%, 80.32% for BI-RADS+ES+SR, respectively. Downgrading BI-RADS 4a alone had higher AUC, specificity, and accuracy (P<0.01) and similar sensitivity (P>0.05) to upgrading BI-RADS 3 and downgrading BI-RADS 4a. Combining SE with BI-RADS could help reduce unnecessary biopsies by 17.64%-55.20%. Combining BI-RADS with SE improved the diagnostic performance in distinguishing benign from malignant lesions and could decrease false-positive breast biopsy rates. Downgrading BI-RADS 4a lesions alone might be sufficient for achieving good diagnostic performance. Downgrading BI-RADS category 4a lesions alone had higher AUC, specificity, and accuracy, and similar sensitivity to upgrading or downgrading BI-RADS category 3 and 4a lesions.

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.

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies. We included clinical and ultrasonographic attributes from 1526 breast lesions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6 that underwent US-guided breast biopsy in four institutions. We selected the most informative attributes to train nine machine learning models, ensemble models and models with tuned threshold to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset). We tested the performance of the final model with 403 new suspicious lesions. The most informative attributes were shape, margin, orientation and size of the lesions, the resistance index of the internal vessel, the age of the patient and the presence of a palpable lump. The highest mean negative predictive value (NPV) was achieved with the K-Nearest Neighbors algorithm (97.9%). Making ensembles did not improve the performance. Tuning the threshold did improve the performance of the models and we chose the algorithm XGBoost with the tuned threshold as the final one. The tested performance of the final model was: NPV 98.1%, false negative 1.9%, positive predictive value 77.1%, false positive 22.9%. Applying this final model, we would have missed 2 of the 231 malignant lesions of the test dataset (0.8%). Machine learning can help physicians predict malignancy in suspicious breast lesions identified by the US. Our final model would be able to avoid 60.4% of the biopsies in benign lesions missing less than 1% of the cancer cases.

Age‑integrated breast imaging reporting and data system assessment model to improve the accuracy of breast cancer diagnosis.

Early diagnosis is an effective strategy for decreasing breast cancer mortality. Ultrasonography is one of the most predominant imaging modalities for breast cancer owing to its convenience and non-invasiveness. The present study aimed to develop a model that integrates age with Breast Imaging Reporting and Data System (BI-RADS) lexicon to improve diagnostic accuracy of ultrasonography in breast cancer. This retrospective study comprised two cohorts: A training cohort with 975 female patients from Renmin Hospital of Wuhan University (Wuhan, China) and a validation cohort with 500 female patients from Maternal and Child Health Hospital of Hubei Province (Wuhan, China). Logistic regression was used to construct a model combining BI-RADS score with age and to determine the age-based prevalence of breast cancer to predict a cut-off age. The model that integrated age with BI-RADS scores demonstrated the best performance compared with models based solely on age or BI-RADS scores, with an area under the curve (AUC) of 0.872 (95% CI: 0.850-0.894, P<0.001). Furthermore, among participants aged <30 years, the prevalence of breast cancer was lower than the lower limit of the reference range (2%) for BI-RADS subcategory 4A lesions but within the reference range for BI-RADS category 3 lesions, as indicated by linear regression analysis. Therefore, it is recommended that management for this subset of participants are categorized as BI-RADS category 3, meaning that biopsies typically indicated could be replaced with short-term follow-up. In conclusion, the integrated assessment model based on age and BI-RADS may enhance accuracy of ultrasonography in diagnosing breast lesions and young patients with BI-RADS subcategory 4A lesions may be exempted from biopsy.

Application value of MRI-guided wire localization to the non-palpable breast lesions only shown in Breast MRI

IntroductionMagnetic resonance imaging (MRI)-guided wire localization can be applied to assist to remove suspected breast lesions accurately. This study aimed to evaluate the clinical application value of this technique in Chinese women.MethodsA total of 126 patients (131 lesions) who had underwent such technique in our hospital from April 2017 to June 2023 were enrolled. 1.5T MRI system and a wire localization device were used. Image characteristics, clinical features and postoperative pathology were collected and analyzed.ResultsAll of 126 patients (131 lesions) were successfully localized by MRI and excised for biopsy. There were 39 malignant lesions (29.77%) and 92 benign lesions (70.23%). There was no significant correlation between the morphology of DCE-MRI and the ratio of malignant lesions (P=0.763), while there was a statistical correlation between the BPE, TIC curve and the malignancy rate (P&amp;lt;0.05). All the lesions were assessed according to BI-RADS category of MRI (C4A=77, C4B=40, C4C=12, C5=2). The malignancy rates were as follows: 16.88% for 4A lesions (13/77), 37.50% for 4B lesions (15/40), 75.00% for 4C lesions (9/12) and 100% for 5 lesions (2/2). There was a significant correlation between the BI-RADS category and the incidence of benign-to-malignant lesions (P&amp;lt;0.001).ConclusionMRI-guided wire localization can assist to remove suspected breast lesions early, safely and accurately. This technique makes up for the deficiency of X-ray and ultrasound, improves the accuracy of diagnosis and resection therapy in intraductal carcinoma and early invasive carcinoma, and helps to improve the the prognosis of breast cancer.

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.

Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network

ObjectivesTo develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions. MethodsPatients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed. Results1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%. ConclusionDL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.

Contrast-enhanced ultrasound to predict malignant upgrading of atypical ductal hyperplasia

BackgroundA malignancy might be found at surgery in cases of atypical ductal hyperplasia (ADH) diagnosed via US-guided core needle biopsy (CNB). The objective of this study was to investigate the diagnostic performance of contrast-enhanced ultrasound (CEUS) in predicting ADH diagnosed by US-guided CNB that was upgraded to malignancy after surgery.MethodsIn this retrospective study, 110 CNB-diagnosed ADH lesions in 109 consecutive women who underwent US, CEUS, and surgery between June 2018 and June 2023 were included. CEUS was incorporated into US BI-RADS and yielded a CEUS-adjusted BI-RADS. The diagnostic performance of US BI-RADS and CEUS-adjusted BI-RADS for ADH were analyzed and compared.ResultsThe mean age of the 109 women was 49.7 years ± 11.6 (SD). The upgrade rate of ADH at CNB was 48.2% (53 of 110). The sensitivity, specificity, positive predictive value, and negative predictive value of CEUS for identification of malignant upgrading were 96.2%, 66.7%,72.9%, and 95.0%, respectively, based on BI-RADS category 4B threshold. The two false-negative cases were low-grade ductal carcinoma in situ. Compared with the US, CEUS-adjusted BI-RADS had better specificity for lesions smaller than 2 cm (76.7% vs. 96.7%, P = 0.031). After CEUS, 16 (10 malignant and 6 nonmalignant) of the 45 original US BI-RADS category 4A lesions were up-classified to BI-RADS 4B, and 3 (1 malignant and 2 nonmalignant) of the 41 original US BI-RADS category 4B lesions were down-classified to BI-RADS 4A.ConclusionsCEUS is helpful in predicting malignant upgrading of ADH, especially for lesions smaller than 2 cm and those classified as BI-RADS 4A and 4B on ultrasound.

The Role of Elastography in Reducing Unnecessary Biopsies in BI-RADS 4a and 4b Breast Lesions

The Role of Elastography in Reducing Unnecessary Biopsies in BI-RADS 4a and 4b Breast Lesions

Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4)

BackgroundThis study aims to providing a reliable method that has good compliance and is easy to master to improve the accuracy of NMLE diagnosis.MethodsThis study retrospectively analyzed 122 cases of breast non-mass-like enhancement (NMLE) lesions confirmed by postoperative histology. MRI features and clinical features of benign and malignant non-mass enhancement breast lesions were compared by using independent sample t test, χ2test and Fisher exact test. P < 0.05 was considered statistically significant. Statistically significant parameters were then included in logistic regression analysis to build a multiparameter differential diagnosis modelto subdivide the BI-RADS Category 4.ResultsThe distribution (odds ratio (OR) = 8.70), internal enhancement pattern (OR = 6.29), ADC value (OR = 5.56), and vascular sign (OR = 2.84) of the lesions were closely related to the benignity and malignancy of the lesions. These signs were used to build the MRI multiparameter model for differentiating benign and malignant non-mass enhancement breast lesions. ROC analysis revealed that its optimal diagnostic cut-off value was 5. The diagnostic specificity and sensitivity were 87.01% and 82.22%, respectively. Lesions with 1–6 points were considered BI-RADS category 4 lesions, and the positive predictive values of subtypes 4a, 4b, and 4c lesions were15.79%, 31.25%, and 77.78%, respectively.ConclusionsComprehensively analyzing the features of MRI of non-mass enhancement breast lesions and building the multiparameter differential diagnosis model could improve the differential diagnostic performance of benign and malignant lesions.

Value of micro-flow imaging and high-definition micro-flow imaging in differentiating malignant and benign breast lesions

To evaluate the value of non-contrast micro-flow imaging (MFI) and high-definition micro-flow imaging (HD-MFI) in differentiating malignant and benign breast lesions. One hundred and thirty-three patients with 138 breast lesions (80 benign and 58 malignant lesions) were examined using colour Doppler flow imaging (CDFI), MFI, and HD-MFI before biopsy, with blood flow signals graded into four types (grade 0, 1, 2, and 3) and penetrating vessels evaluated. The micro-vascular patterns of MFI and HD-MFI were evaluated and classified into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. The diagnostic efficiency of micro-vascular patterns was analysed. Moreover, ultrasound Breast Imaging Reporting and Data System (BI-RADS) 4A lesions were also re-assessed according to the micro-vascular patterns of MFI or HD-MFI. The capability of detecting blood flow and penetrating vessels from high to low was HD-MFI, MFI, and CDFI, respectively (p<0.05). Rich blood flow signals, penetrating vessels, and root hair-like or crab claw-like pattern were more likely in malignant breast lesions, while few blood flow signals, tree-like pattern were mostly in benign lesions (p<0.05). The diagnostic efficiency of HD-MFI and MFI were higher than CDFI (p>0.05). MFI could reduce unnecessary biopsy of 52 US BI-RADS 4A lesions but with two malignancies missed, while 56 ultrasound BI-RADS 4A lesions could be downgraded by HD-MFI with none malignancies missed. MFI and HD-MFI can detect more blood flow in breast lesions than CDFI, and could help distinguish benign and malignant breast lesions. HD-MFI could reduce the unnecessary biopsy of US BI-RADS 4A lesions without missed malignancy.

Vacuum-assisted biopsy system for breast lesions: a potential therapeutic approach.

The primary objective is to optimize the population eligible for Mammotome Minimally Invasive Surgery (MIS) by refining selection criteria. This involves maximizing procedure benefits, minimizing malignancy risk, and reducing the rate of malignant outcomes. A total of 1158 female patients who came to our hospital from November 2016 to August 2021 for the Mammotome MIS were analyzed retrospectively. Following χ2 tests to screen for risk variables, binary logistic regression analysis was used to determine the independent predictors of malignant lesions. In addition, the correlation between age and lesion diameter was investigated for BI-RADS ultrasound (US) category 4a lesions in order to better understand the relationship between these variables. The malignancy rates of BI-RADS US category 3, category 4a and category 4b patients who underwent the Mammotome MIS were 0.6% (9/1562), 6.4% (37/578) and 8.3% (2/24) respectively. Malignant lesions were more common in patients over the age of 40, have visible blood supply, and BI-RADS category 4 of mammography. In BI-RADS US category 4a lesions, the diameter of malignant tumor was highly correlated with age, and this correlation was strengthened in patients over the age of 40 and with BI-RADS category 4 of mammography. The results of this study demonstrate that the clinical data and imaging results, particularly age, blood supply, and mammography classification, offer valuable insights to optimize patients' surgical options and decrease the incidence of malignant outcomes.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods.

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period. Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth. For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions. Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Can Short-term Follow-up with Ultrasound be Offered as an Acceptable Alternative to Immediate Biopsy or Surgery for Patients with First Ultrasound Diagnosis of BI-RADS 4A Lesions?

To evaluate the relevant factors associated with malignancy in Breast Imaging Reporting and Data System (BI-RADS) 4A and to determine whether it was possible to establish a safe follow-up guideline for lower-risk 4A lesions. In this retrospective study, patients categorized as BI-RADS 4A on ultrasound who underwent ultrasound-guided biopsy or/and surgery between June 2014 and April 2020 was analyzed. Classification-tree method and cox regression analysis were used to explore the possible correlation factors of malignancy. Among 9965 patients enrolled, 1211 (mean age, 44.3 ± 13.5years; range, 18-91years) patients categorized as BI-RADS 4A were eligible. The result of cox regression analysis revealed the malignant rate was only associated with patient age (hazard ratio (HR) = 1.038, p < 0.001, 95% confidence interval (CI): 1.029-1.048) and the mediolateral diameter of the lesion (HR = 1.261, p < 0.001, 95% CI: 1.159-1.372). The malignant rate for patients (≤ 36 y) with BI-RADS 4A lesions (the mediolateral diameter ≤ 0.9cm) was 0.0% (0/72). This subgroup included fibrocystic disease and adenosis in 39 patients (54.2%), fibroadenoma in 16 (22.2%), intraductal papilloma in 8 (11.1%), inflammatory lesions in 6 (8.3%), cyst in 2 (2.8%), and hamartoma in 1 (1.4%). Patient age and lesion size are associated with the rate of malignancy in BI-RADS 4A. For patients with lower-risk BI-RADS 4A lesions (≤ 2% likelihood of malignancy), short-term follow-up with ultrasound may be offered as an acceptable alternative to immediate biopsy or surgery.

The value of contrast-enhanced ultrasound in the diagnosis of BI-RADS-US 4a lesions less than 2 cm in diameter.

Breast cancer is the most common malignant tumor in women. Early diagnosis of benign and malignant breast tumors is of great significance. To retrospectively analyze the value of contrast-enhanced ultrasonography (CEUS) in the diagnosis of Breast Imaging-Reporting and Data System (BI-RADS) 4a breast lesions less than 2 cm in diameter. CEUS was performed for 143 breast masses less than 2 cm in diameter that were diagnosed as BI-RADS 4a by ultrasound and reclassified. Considering pathological diagnosis as the gold standard, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of reclassified lesions after CEUS for the diagnosis of benign and malignant masses were analyzed. BI-RADS 4a breast masses with a diameter less than 2 cm (n = 143) were confirmed by pathology; 103 and 40 were classified as benign and malignant, respectively. The sensitivity, specificity, PPV, and NPV of CEUS for the diagnosis were 90%, 86%, 72%, and 95%, respectively. The area under the receiver operating characteristic (ROC) curve of CEUS for the diagnosis of benign and malignant tumors after CEUS was 0.904. CEUS can help to improve the diagnostic accuracy of BI-RADS 4a masses with a diameter less than 2 cm.

Can magnetic resonance imaging texture analysis change the breast imaging reporting and data system category of breast lesions?

PurposeThis study aimed to reveal magnetic resonance imaging (MRI) texture analysis (TA)'s contribution to categorizing breast lesions according to the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. MethodTwo hundred and seventeen women with BI-RADS category 3, 4, and 5 lesions on breast MRI were included in the study. For TA, the region of interest was drawn manually to encompass the entire lesion on the fat-suppressed T2W and the first post-contrast T1W images. To identify the independent predictors of breast cancer, multivariate logistic regression analyses were performed using texture parameters. Estimated benign and malignant groups were formed according to the TA regression model. ResultsTexture parameters extracted from T2WI, including median, gray-level co-occurrence matrix (GLCM) contrast, GLCM correlation, GLCM joint entropy, GLCM sum entropy, and GLCM sum of squares, and parameters extracted from T1WI, including maximum, GLCM contrast, GLCM joint entropy, GLCM sum entropy, were independent predictors of breast cancer. In the estimated new groups according to the TA regression model, 19 (91%) of the benign 4a lesions were downgraded to BI-RADS category 3. ConclusionsThe addition of quantitative parameters obtained by MRI TA to BI-RADS criteria significantly increased the accuracy rate in differentiating benign and malignant breast lesions. When categorizing BI-RADS 4a lesions, the use of MRI TA in addition to conventional imaging findings may reduce unnecessary biopsy rates.

The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies

ObjectivesUltrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system and ultrasound elastography with conventional ultrasound (US) in increasing specificity and reducing unnecessary breast lesions biopsies. Materials and MethodsConventional US, CAD system, and strain elastography (SE) were retrospectively performed on 216 breast lesions before biopsy or surgery. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and biopsy rate were compared between conventional US and the combination of conventional US, SE, and CAD system. ResultsOf 216 lesions, 54 were malignant and 162 were benign. The addition of CAD system and SE to conventional US increased the AUC from 0.716 to 0.910 and specificity from 46.9% to 85.8% without a loss in sensitivity while 89.2% (66 of 74) of benign lesions in Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions would avoid unnecessary biopsies. ConclusionThe addition of CAD system and SE to conventional US improved specificity and AUC without loss of sensitivity, and reduced unnecessary biopsies.

Automatic Breast Volume Scanner and B-Ultrasound-Based Radiomics Nomogram for Clinician Management of BI-RADS 4A Lesions

To develop and validate a nomogram for predicting the risk of malignancy of breast imaging reporting and data system (BI-RADS) 4A lesions to reduce unnecessary invasive examinations. From January 2017 to July 2021, 190 cases of 4A lesions included in this study were divided into training and validation sets in a ratio of 8:2. Radiomics features were extracted from sonograms by Automatic Breast Volume Scanner (ABVS) and B-ultrasound. We constructed the radiomics model and calculated the rad-scores. Univariate and multivariate logistic regressions were used to assess demographics and lesion elastography values (virtual touch tissue image, shear wave velocity) and to develop clinical model. A clinical radiomics model was developed using rad-score and independent clinical factors, and a nomogram was plotted. Nomogram performance was evaluated using discrimination, calibration, and clinical utility. The nomogram included rad-score, age, and elastography, and showed good calibration. In the training set, the area under the receiver operating characteristic curve (AUC) of the clinical radiomics model (0.900, 95% confidence interval (CI): 0.843-0.958) was superior to that of the radiomics model (0.860, 95% CI: 0.799-0.921) and clinical model (0.816, 95% CI: 0.735-0.958) (p=0.024 and 0.008, respectively). The decision curve analysis showed that the clinical radiomics model had the highest net benefit in most threshold probability ranges. ABVS and B-ultrasound-based radiomics nomograms have satisfactory performance in differentiating benign and malignant 4A lesions. This can help clinicians make an accurate diagnosis of 4A lesions and reduce unnecessary biopsy.

Clinical use and adjustment of ultrasound elastography for breast lesions followed WFUMB guidelines and recommendations in the real world.

This study aimed to explore the value of strain elastography (SE) and shear wave elastography (SWE) following the World Federation of Ultrasound in Medicine and Biology (WFUMB) guidelines and recommendations in the real world in distinguishing benign and malignant breast lesions and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions. This prospective study included 274 breast lesions. The elastography score (ES) by the Tsukuba score, the strain ratio (SR) for SE, and Emax for SWE of the lesion(A) and the regions(A') included the lesion and the margin (0.5-5mm) surrounding the lesion were measured. The sensitivity, specificity, and AUC were calculated and compared by the cutoff values recommended by WFUMB guidelines. When scores of 1 to 3 were classified as probably benign by WFUMB recommendation, the ES was significantly higher in malignant lesions compared to benign lesions (p < 0.05) in all lesions. For the cohort by size >20mm, the sensitivity was 100%, and the specificity was 45.5%. ES had the highest AUC: 0.79(95% CI 0.72-0.86) with a sensitivity of 96.2%, and a specificity of 61.8% for the cohort by size ≤20 mm. For the Emax-A'-S2.5mm, when the high stiffness would be considered with Emax above 80 kPa in SWE, the malignant lesions were diagnosed with a sensitivity of 95.8%, a specificity of 43.3% for all lesions, a sensitivity of 88.5% for lesions with size ≤20 mm, and sensitivity of 100.0% for lesions with size >20mm. In 84 lesions of BI-RADS category 4a, if category 4a lesions with ES of 1-3 points or Emax-A'-S2.5 less than 80 kPa could be downgraded to category 3, 52 (61.9%) lesions could be no biopsy, including two malignancies. If category 4a lesions with ES of 1-3 points and Emax-A'-S2.5 less than 80kPa could be downgraded to category 3, 23 (27.4%) lesions could be no biopsy, with no malignancy. The elastography score for SE and Emax-A' for SWE after our modification were beneficial in the diagnosis of breast cancer. The combination of SWE and SE could effectively reduce the biopsy rate of BI-RADS category 4a lesions.

Positive prediction of malignancy in BI-RADS 4a, 4b and 4c lesions combined mammography and ultrasound: A developing country experience

Positive prediction of malignancy in BI-RADS 4a, 4b and 4c lesions combined mammography and ultrasound: A developing country experience