Automated Breast Volume Scanner Research Articles

Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features.

Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic models even using a reduced number of features. Indeed, from tomography we achieved AUC-ROC using 19 features and using 7 of them; while from ABVS we attained an AUC-ROC of using 22 features and using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT - ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.

Automated Breast Volume Scanner Is More Valuable Than Hand-Held Ultrasound in Diagnosis of Small Breast cancer: An Analysis of 725 Patients With 912 Lesions Evaluations.

This study aimed to evaluate the clinical value of automated breast volume scanner (ABVS) compared with hand-held ultrasound (HHUS). From January 2015 to May 2019, a total of 912 breast lesions in 725 consecutive patients were included in this study. κ statistics were calculated to identify interobserver agreement of ABVS and HHUS. The diagnostic performance for ABVS and HHUS was expressed as the area under the receiver operating characteristic curve, as well as the corresponding 95% confidence interval, sensitivity, and specificity. The sensitivities of ABVS and HHUS were 95.95% and 93.69%, and the specificities were 85.47% and 81.20%, respectively. A difference that nearly reached statistical significance was observed in sensitivities between ABVS and HHUS (P = 0.0525). The specificity of ABVS was significantly higher than that of HHUS (P = 0.006). When lesions were classified according to their maximum diameter, the sensitivity and specificity of ABVS were significantly higher than HHUS for lesions ≤20 mm, while they made no statistical significance between ABVS and HHUS for lesions >20 mm. The interobserver agreement for ABVS was better than that of HHUS. Automated breast volume scanner was more valuable than HHUS in diagnosing breast cancer, especially for lesions ≤20 mm, and it could be a valuable diagnostic tool for breast cancer.

Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1–2 Positive Sentinel Nodes

BackgroundIn HR+/HER2- breast cancer patients with ≤3 positive axillary lymph nodes (ALNs), genomic tests can streamline chemotherapy decisions. Current studies, centered on tumor metrics, miss broader patient insights. Automated Breast Volume Scanning (ABVS) provides advanced 3D imaging, and its potential synergy with radiomics for ALN evaluation is untapped. ObjectiveThis study sought to combine ABVS radiomics and clinical characteristics in a nomogram to predict ≤3 positive ALNs in HR+/HER2- breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs), guiding clinicians in genetic test candidate selection. MethodsWe enrolled 511 early-stage breast cancer patients: 362 from A Hospital for training and 149 from B Hospital for validation. Using LASSO logistic regression, primary features were identified. A clinical-radiomics nomogram was developed to predict the likelihood of ≤3 positive ALNs in HR+/HER2- patients with 1-2 positive SLNs. We assessed the discriminative capability of the nomogram using the ROC curve. The model's calibration was confirmed through a calibration curve, while its fit was evaluated using the Hosmer-Lemeshow (HL) test. To determine the clinical net benefits, we employed the Decision Curve Analysis (DCA). ResultsIn the training group, 81.2% patients had ≤3 metastatic ALNs, and 83.2% in the validation group. We developed a clinical-radiomics nomogram by analyzing clinical characteristics and rad-scores. Factors like positive SLNs (OR=0.077), absence of negative SLNs (OR=11.138), lymphovascular invasion (OR=0.248), and rad-score (OR=0.003) significantly correlated with ≤3 positive ALNs. The clinical-radiomics nomogram, with an AUC of 0.910 in training and 0.882 in validation, outperformed the rad-score-free clinical nomogram (AUCs of 0.796 and 0.782). Calibration curves and the HL test (P-values 0.688 and 0.691) confirmed its robustness. DCA showed the clinical-radiomics nomogram provided superior net benefits in predicting ALN burden across specific threshold probabilities. ConclusionsWe developed a clinical-radiomics nomogram that integrated radiomics from ABVS images and clinical data to predict the presence of ≤3 positive ALNs in HR+/HER2- patients with 1-2 positive SLNs, aiding oncologists in identifying candidates for genomic tests, bypassing ALND. In the era of precision medicine, combining genomic tests with SLNB refines both surgical and systemic patient treatments.

Ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS) combined with molecular biomarkers in predicting axillary lymph node metastasis of clinical T1-T2 breast cancer.

In the post-American College of Surgeons Oncology Group Z0011 trial era, clinicians are attempting to preoperatively evaluate axillary lymph node (ALN) status using ultrasound. However, the value of preoperative ultrasound examination remains uncertain. The study aimed to investigate the ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS), in combination with molecular biomarkers, to predict the risk of ALN metastasis (ALNM) in clinical T1-T2 breast cancer. A retrospective case-control analysis was conducted on 168 patients with clinical T1-T2 breast cancer at Peking University First Hospital between January 2013 and August 2021. Preoperative ABVS and HHUS examinations were performed. According to the pathology results of the ALN, patients were divided into metastatic and nonmetastatic groups. Logistic regression analyses were used to analyze the ultrasonic characteristics of ABVS and HHUS on clinical T1-T2 breast cancer, and molecular biomarkers were incorporated to predict the risk of ALNM. Of the 168 patients, 88 (52.4%) had ipsilateral ALNM while 80 (47.6%) had no ipsilateral ALNM. The univariate analysis showed that shorter tumor-skin distance (P=0.011), the Adler blood flow grade of II-III (P=0.014), and larger tumor size on ABVS (P<0.001) were associated with ALNM. The multivariate logistic analysis showed that these three risk factors, including the tumor-skin distance [odds ratio (OR) =0.279; P=0.024], the Adler blood flow grade (OR =2.164; P=0.046), and the tumor size on ABVS (OR =1.033; P=0.002), were independent predictive parameters. The tumor-skin distance, tumor size on ABVS, and Adler blood flow grade have diagnostic value for ALNM in clinical T1-T2 breast cancer.

Retracted: Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer.

[This retracts the article DOI: 10.1155/2022/3199884.].

Comparative analysis of automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in female breast cancer detection.

This study aimed to compare the diagnostic value of an automated breast volume scanner (ABVS) combined with conventional hand-held ultrasound and mammography in detecting female breast cancer. Early detection is vital in improving patient outcomes for this prevalent disease. Seventy-eight suspicious breast lesions from 60 patients were examined between August 2019 and July 2020. Each patient underwent ABVS, conventional hand-held ultrasound, and mammography. Diagnostic values, including coincidence rate, sensitivity, specificity, positive predictive value, and negative predictive value, were calculated using histopathology results as the "gold standard." Histopathology confirmed 55 malignant (70.51%) and 23 benign lesions (29.48%). ABVS combined with conventional hand-held ultrasound identified 56 malignant (52 confirmed, 4 benign) and 22 benign nodules (3 confirmed, 19 benign). Mammography detected 48 malignant (45 confirmed, 3 benign) and 30 benign nodules (10 confirmed, 20 benign). ABVS combined with conventional hand-held ultrasound had a sensitivity of 94.5%, specificity of 82.6%, positive predictive value of 92.9%, and negative predictive value of 86.4%. Mammography showed a sensitivity of 81.8%, specificity of 87.0%, positive predictive value of 93.8%, and negative predictive value of 66.7%. ABVS combined with conventional hand-held ultrasound showed high diagnostic value in detecting female breast cancer. The "convergence sign" in the coronal section played a significant role. It slightly outperformed mammography and offered advantages in terms of cost, convenience, comfort, and absence of radiation. Further promotion and implementation are supported.

Automated 3-D Ultrasound Elastography of the Breast: An In Vivo Validation Study

Studies have indicated that adding 2-D quasi-static elastography to B-mode ultrasound imaging improved the specificity for malignant lesion detection, as malignant lesions are often stiffer (increased strain ratio) compared with benign lesions. This method is limited by its user dependency and so unsuitable for breast screening. To overcome this limitation, we implemented quasi-static elastography in an automated breast volume scanner (ABVS), which is an operator-independent 3-D ultrasound system and is especially useful for screening women with dense breasts. The study aim was to investigate if 3-D quasi-static elastography implemented in a clinically used ABVS can discriminate between benign and malignant breast lesions. Volumetric breast ultrasound radiofrequency data sets of 82 patients were acquired before and after automated transducer lifting. Lesions were annotated and strain was calculated using an in-house-developed strain algorithm. Two strain ratio types were calculated per lesion: using axial and maximal principal strain (i.e., strain in dominant direction). Forty-four lesions were detected: 9 carcinomas, 23 cysts and 12 other benign lesions. A significant difference was found between malignant (median: 1.7, range: [1.0-3.2]) and benign (1.0, [0.6-1.9]) using maximal principal strain ratios. Axial strain ratio did not reveal a significant difference between benign (0.6, [-12.7 to 4.9]) and malignant lesions (0.8, [-3.5 to 5.1]). Three-dimensional strain imaging was successfully implemented on a clinically used ABVS to obtain, visualize and analyze in vivo strain images in three dimensions. Results revealed that maximal principal strain ratios are significantly increased in malignant compared with benign lesions.

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions.

In routine clinical examinations, solid hypoechoic breast lesions are frequently encountered, but accurately distinguishing them poses a challenge. This study proposed a clinical-radiomics nomogram based on multimodal ultrasound that enhances the diagnostic accuracy for solid hypoechoic breast lesions. This retrospective study analyzed ultrasound strain elastography (SE) and automated breast volume scanner images (ABVS) of 423 solid hypoechoic breast lesions from 423 female patients in our hospital between August 2019 and May 2022. They were assigned to the training (n=296) and validation (n=127) groups in a 7:3 ratio by generating random numbers. Radiomics features were extracted and screened from ABVS and SE images, followed by the calculation of the radiomics score (Radscore) based on these features. Subsequently, a nomogram was constructed through multivariate logistic regression to assess the malignancy risk in breast lesions by combining Radscore with Breast Imaging Reporting and Data System (BI-RADS) scores and clinical risk factors associated with breast malignant lesions. The diagnostic performance, calibration performance, and clinical usefulness of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, the calibration curve, and the decision analysis curve, respectively. The diagnostic performance of the nomogram is significantly superior to that of both the clinical diagnostic model (BI-RADS model) and the multimodal radiomics model (SE+ABVS radiomics model) in training (AUC: 0.972 vs 0.930 vs 0.941) and validation group (AUC:0.964 vs 0.916 vs 0.933). In addition, the nomogram also exhibited a favorable goodness-of-fit and could lead to greater net benefits for patients. The nomogram enables a more effective assessment of the malignancy risk of solid hypoechoic breast lesions; therefore, it can serve as a new and efficient diagnostic tool for clinical diagnosis.

Automated breast volume scanner based Radiomics for non-invasively prediction of lymphovascular invasion status in breast cancer

PurposeLymphovascular invasion (LVI) indicates resistance to preoperative adjuvant chemotherapy and a poor prognosis and can only be diagnosed by postoperative pathological examinations in breast cancer. Thus, a technique for preoperative diagnosis of LVI is urgently needed. We aim to explore the ability of an automated breast volume scanner (ABVS)-based radiomics model to noninvasively predict the LVI status in breast cancer.MethodsWe conducted a retrospective analysis of data from 335 patients diagnosed with T1-3 breast cancer between October 2019 and September 2022. The patients were divided into training cohort and validation cohort with a ratio of 7:3. For each patient, 5901 radiomics features were extracted from ABVS images. Feature selection was performed using LASSO method. We created machine learning models for different feature sets with support vector machine algorithm to predict LVI. And significant clinicopathologic factors were identified by univariate and multivariate logistic regression to combine with three radiomics signatures as to develop a fusion model.ResultsThe three SVM-based prediction models, demonstrated relatively high efficacy in identifying LVI of breast cancer, with AUCs of 79.00%, 80.00% and 79.40% and an accuracy of 71.00%, 80.00% and 75.00% in the validation cohort for AP, SP and CP plane image. The fusion model achieved the highest AUC of 87.90% and an accuracy of 85.00% in the validation cohort.ConclusionsThe combination of radiomics features from ABVS images and an SVM prediction model showed promising performance for preoperative noninvasive prediction of LVI in breast cancer.

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers.

Neoadjuvant chemotherapy (NAC) plays a significant role in breast cancer (BC) management; however, its efficacy varies among patients. Current evaluation methods may lead to delayed treatment alterations, and traditional imaging modalities often yield inaccurate results. Radiomics, an emerging field in medical imaging, offers potential for improved tumor characterization and personalized medicine. Nevertheless, its application in early and accurately predicting NAC response remains underinvestigated. This study aims to develop an automated breast volume scanner (ABVS)-based radiomics model to facilitate early detection of suboptimal NAC response, ultimately promoting personalized therapeutic approaches for BC patients. This retrospective study involved 248 BC patients receiving NAC. Standard guidelines were followed, and patients were classified as responders or non-responders based on treatment outcomes. ABVS images were obtained before and during NAC, and radiomics features were extracted using the PyRadiomics toolkit. Inter-observer consistency and hierarchical feature selection were assessed. Three machine learning classifiers, logistic regression, support vector machine, and random forest, were trained and validated using a five-fold cross-validation with three repetitions. Model performance was comprehensively evaluated based on discrimination, calibration, and clinical utility. Of the 248 BC patients, 157 (63.3%) were responders, and 91 (36.7%) were non-responders. Radiomics feature selection revealed 7 pre-NAC and 6 post-NAC ABVS features, with higher weights for post-NAC features (min >0.05) than pre-NAC (max <0.03). The three post-NAC classifiers demonstrated AUCs of approximately 0.9, indicating excellent discrimination. DCA curves revealed a substantial net benefit when the threshold probability exceeded 40%. Conversely, the three pre-NAC classifiers had AUCs between 0.7 and 0.8, suggesting moderate discrimination and limited clinical utility based on their DCA curves. The ABVS-based radiomics model effectively predicted suboptimal NAC responses in BC patients, with early post-NAC classifiers outperforming pre-NAC classifiers in discrimination and clinical utility. It could enhance personalized treatment and improve patient outcomes in BC management.

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.

Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases. 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA). The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively). Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Abstract LB115: Simultaneous whole-body ultrasound contrast imaging: a novel high throughput preclinical platform for targeted molecular imaging studies

Abstract Ultrasound contrast agent (UCA) based molecular imaging is a rapidly growing field in biomedical research. Ultrasound (US) is a non-radiative imaging modality that is frequently used to investigate various human diseases in the clinic. US molecular imaging is a novel imaging modality that utilizes targeted contrast agents to add molecular information about the imaging target, including vascular biomarkers of cancer. Pre-clinical animal models are commonly used to evaluate and validate targeted contrast agents before extending them for FDA-approval. Key impediments in the preclinical evaluations are the short in vivo half-life of microbubbles (MBs) (less than 30 minutes), limited transducer scanning area, and focusing one target at a time, which all make this strategy low throughput. Moreover, whole-body imaging strategy is not available for US imaging compared to other imaging modalities, such as PET, CT, and MRI. To overcome these imitations, we have developed a multi-mouse US imaging platform using an Automated Breast Volume Scanner (ABVS) system. In addition, we have synthesized a novel breast cancer vascular-specific B7-H3 targeted microbubbles (TMBs) using a microfluidic device. We tested these TMBs in a genetically engineered transgenic mice model [FVB/N-Tg (MMTV-PyMT)634Mul/J] (n=32) that develops breast cancer in the mammary fat pad. The US imaging results of conventional Vevo 2100 system indicated that B7-H3-TMBs could provide target specific contrast signals in the tumor vasculature compared to control MBs. We used this MBs for whole-body imaging using an ABVS system with two mice simultaneously in a stage. We imaged multiple tumors (10 tumors/mouse, 2 mice per scan, n=20 tumors) with a single bolus injection of TMBs per mouse. The results successfully showed tumor specific contrast signals from tumors independent of tumor size. Introduction of this whole-body scanner, and its ability to scan multiple mice simultaneously allows rapid imaging of multiple tumors with 3D quantification of tumor size by administering a single TMB injection enabling without significant contrast loss. Our approach makes this US-MI as high throughput and can be used to test multiple contrast agents for investigating different aspects of biological problems including screening of vascular biomarkers of human diseases. Citation Format: Arutselvan Natarajan, Farbod Tabesh, Dongwoon Hyun, Ramasamy Paulmurugan, Jeremy Dahl. Simultaneous whole-body ultrasound contrast imaging: a novel high throughput preclinical platform for targeted molecular imaging studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB115.

Comparison of automated breast volume scanning with conventional ultrasonography, mammography, and MRI to assess residual breast cancer after neoadjuvant therapy by molecular type

To compare the accuracy of hand-held ultrasonography (US), mammography (MG), magnetic resonance imaging (MRI), and automated breast volume scanning (ABVS) in defining residual breast cancer tumour size after neoadjuvant therapy (NAT). Patients diagnosed breast cancer and who received NAT at the Breast Center, Peking University People's Hospital, were enrolled prospectively. Imaging was performed after the last cycle of NAT. The residual tumour size, intraclass correlation coefficients (ICCs), and receiver operating characteristic (ROC) to predict pathological complete response (pCR) were analysed. A total of 156 patients with 159 tumours were analysed. ABVS had a moderate correlation with histopathology residual tumour size (ICC=0.666), and showed high agreement among triple-positive tumours (ICC=0.797). With 5 mm as the threshold, the coincidence rate reached 64.7% between ABVS and pathological size, which was significantly higher than that between US, MG, MRI, and pathological size (50%, 45.1%, 41.4%; p=0.009, p=0.001, p<0.001, respectively). For ROC analysis, ABVS demonstrated a higher area under the ROC curve, but with no statistical difference, except for MG (0.855, 0.816, 0.819, and 0.788, respectively; p=0.183 for US, p=0.044 for MG, and p=0.397 for MRI, with ABVS as the reference). The longest tumour diameter on ABVS had a moderate correlation with pathological residual invasive tumour size. ABVS was shown to have good ability to predict pCR and would appear to be a potential useful tool for the assessment after NAT for breast cancer.

The Diagnostic Significance of the BI-RADS Classification Combined With Automated Breast Volume Scanner and Shear Wave Elastography for Breast Lesions.

We herein compared the diagnostic accuracy of the BI-RADS, ABVS, SWE, and combined techniques for the classification of breast lesions. Breast lesions were appraised using the BI-RADS classification system as well as the combinations of BI-RADS plus ABVS (BI-RADS + ABVS) and BI-RADS plus SWE (BI-RADS + SWE), and both methods (BI-RADS + ABVS + SWE) by two specialties Medical Ultrasound physician. The Fisher's exact and χ2 tests were performed to compare the degree of malignancy for the various methods with a pathology ground truth. Receiver operating characteristic curves (ROC) were generated and the corresponding area under the curve (AUC) values were determined to test the diagnostic efficacy of the various methods and identify the optimal SWE cut-off indicative of malignancy. The incidence of the retraction phenomenon on ABVS images of the malignant group was significantly higher (P < .001) than that of the benign group. The specificity, sensitivity, and positive and negative predictive values of the BI-RADS classification were 88.72, 79.38, 83.70, and 85.50%, respectively. BI-RADS plus SWE-Max exhibited enhanced specificity, sensitivity, and positive and negative predictive values of 88.72, 92.78, 85.70, and 94.40%, respectively. Similarly, when BI-RADS + ABVS was utilized, the sensitivity and negative predictive value increased to 95.88 and 96.40%, respectively. BI-RADS + ABVS + SWE possessed the highest overall sensitivity (96.91%), specificity (94.74%), and positive (93.10%) and negative (97.70%) predictive values from all four indices. ABVS and SWE can reduce the subjectivity of BI-RADS. As a result, BI-RADS + ABVS + SWE resulted in the best diagnostic accuracy.

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner.

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.ObjectiveThe objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.MethodsUntargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.ResultsMetabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).ConclusionsOur study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.

Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model.

Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper’s ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper’s ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper’s ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer.

The incidence of cancer is increasing today, particularly lung and chest cancer. Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed. The goal of the proposed study is to investigate the value of automated breast volume scanning (ABUS) in conjunction with contrast-enhanced ultrasonography (CEUS) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (NAC) in treating the disease. For the research study, information on 98 patients who had NAC and surgery in the breast surgery department of the Shaanxi Provincial Cancer Hospital has been gathered. All patients have received four cycles of NAC and underwent conventional ultrasound (HUSS), CEUS, ABUS, and pathological examination. At the same time, receiver operating characteristic (ROC) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and NAC efficacy evaluation results. The study of this paper is totally based on the data collected from Shaanxi Provincial Cancer Hospital. The statistical and computational analyses are performed on the data collected for drawing inferences. When the findings are compared to the results of the pathological examination, HUSS has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of P < 0.05.ABUS combined with CEUS has shown no considerable differences in correlation study. Except for negative likelihood ratio, the diagnostic performance indexes of CEUS+ ABUS are substantially higher than HHUS with P < 0.05. ROC curve analysis is also performed which shows that CEUS and ABUS combination has higher precision in the analysis of breast cancer. ABUS pooled with CEUS shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients.

Quantitative Indicators of Retraction Phenomenon on an Automated Breast Volume Scanner: Initial Study in the Diagnosis and Prognostic Prediction of Breast Tumors

Retraction phenomenon is a unique sign on an automated breast volume scanner coronal plane image and has high specificity in differentiating benign lesions from malignant breast cancer. The purpose of this study was to quantify the retraction phenomenon by setting different rules to describe connected regions from different dimensions. In total, six quantitative indicators (FΩ1,FΓ,FS,FΩ2,FΩ3and FL) were obtained. FΩ1, FΩ2 and FΩ3 represent the relative areas of the connected region under different rules. FΓandFS represent the number ratio and absolute area of the connected region, respectively. FL represents the ratio of edge numbers. Two hundred fourteen patients with 214 lesions (90 benign and 124 malignant) were enrolled in this study. All quantitative indicators in the malignant group were significantly higher than those in the benign group (all p values <0.001). The indicator FΓ achieved the highest area under the receiver operating characteristic curve (AUC) (0.701, 95% confidence interval: 0.631-0.771). Both FΓ and FS had significant associations with axillary lymph node metastasis (p=0.023 and 0.049). Compared with the classic texture feature gray-level co-occurrence matrix, retraction phenomenon quantization improved the AUC by 8.3%. The results indicate that retraction phenomenon quantitative indicators have certain value in distinguishing benign and malignant breast lesions and seem to be associated with axillary lymph node metastasis.

The ultrasound diagnosis of breast atypical ductal hyperplasia type with pseudo-invasive growth (clinical observation)

Atypical ductal hyperplasia is a proliferation of monomorphic, evenly placed epithelial cells involving terminalduct lobular units. The term atypical ductal hyperplasia identifies a group of lesions with a combination of specifically defined architectural and cytological features that predict an increased risk of subsequent breast cancer. Our case described the visualization of breast atypical ductal hyperplasia with architectural distortion seen in a form of “radiance” phenomenon in a patient with burdened obstetric and gynecological anamnesis by a tridimensional breast ultrasound examination on the system Siemens Acuson S2000 with mammalogical device ABVS (Automated Breast Volume Scanner).

Machine learning based on automated breast volume scanner (ABVS) radiomics for differential diagnosis of benign and malignant BI‐RADS 4 lesions

AbstractBI‐RADS category 4 represents possibly malignant lesions and biopsy is recommended to distinguish benign and malignant. However, studies revealed that up to 67%–78% of BI‐RADS 4 lesions proved to be benign, but received unnecessary biopsies, which may cause unnecessary anxiety and discomfort to patients and increase the burden on the healthcare system. In this prospective study, machine learning (ML) based on the emerging breast ultrasound technology‐automated breast volume scanner (ABVS) was constructed to distinguish benign and malignant BI‐RADS 4 lesions and compared with different experienced radiologists. A total of 223 pathologically confirmed BI‐RADS 4 lesions were recruited and divided into training and testing cohorts. Radiomics features were extracted from axial, sagittal, and coronal ABVS images for each lesion. Seven feature selection methods and 13 ML algorithms were used to construct different ML pipelines, of which the DNN‐RFE (combination of recursive feature elimination and deep neural networks) had the best performance in both training and testing cohorts. The AUC value of the DNN‐RFE was significantly higher than less experienced radiologist at Delong's test (0.954 vs. 0.776, p = 0.004). Additionally, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the DNN‐RFE were 88.9%, 83.3%, 95.2%, 83.3%, and 95.2%, which also significantly better than less experienced radiologist at McNemar's test (p = 0.043). Therefore, ML based on ABVS radiomics may be a potential method to non‐invasively distinguish benign and malignant BI‐RADS 4 lesions.