Automated Breast Volume Scanner Images Research Articles

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.

1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System

The objective of our study was to assess, in a reader study, radiologists' performance in interpretation of automated breast volume scanner (ABVS) images with the aid of a computer-aided detection (CADe) system. Our study is a retrospective observer study with the purpose of investigating the effectiveness of using a CADe system as an aid for radiologists in interpretation of ABVS images. The multiple-reader, multiple-case study was designed to compare the diagnostic performance of radiologists with and without CADe. The study included 1000 cases selected from ABVS examinations in our institution in 2012. Among those cases were 206 malignant, 486 benign and 308 normal cases. The cancer cases were consecutive; the benign and normal cases were randomly selected. All malignant and benign cases were confirmed by biopsy or surgery, and normal cases were confirmed by 2-y follow-up. Reader performance was compared in terms of area under the receiver operating characteristic curve, sensitivity and specificity. Additionally, the reading time per case for each reader was recorded. Nine radiologists from our institution participated in the study. Three had more than 8 y of ultrasound experience and more than 4 y of ABVS experience (group A); 3 had more than 5 y of ultrasound experience (group B), and 3 had more than 1 y of ultrasound experience (group C). Both group B and group C had no ABVS experience. The CADe system used was the QVCAD System (QView Medical, Inc., Los Altos, CA, USA). It is designed to aid radiologists in searching for suspicious areas in ABVS images. CADe results are presented to the reader simultaneously with the ABVS images; that is, the radiologists read the ABVS images concurrently with the CADe results. The cases were randomly assigned for each reader into two equal-size groups, 1 and 2. Initially the readers read their group 1 cases with the aid of CADe and their group 2 cases without CADe. After a 1-mo washout period, they re-read their group 1 cases without CADe and their group 2 cases with CADe. The areas under the receiver operating characteristic curves of all readers were 0.784 for reading with CADe and 0.747 without CADe. Areas under the curves with and without CADe were 0.833 and 0.829 for group A, 0.757 and 0.696 for group B and 0.759 and 0.718 for group C. All differences in areas under the curve were statistically significant (p < 0.05), except that for group A. The average reading time was 9.3% (p < < 0.05) faster with CADe for all readers. In summary, CADe improves radiologist performance with respect to both accuracy and reading time for the detection of breast cancer using the ABVS, with the greater benefit for those inexperienced with ABVS.

Breast and Tumour Volume Measurements in Breast Cancer Patients Using 3-D Automated Breast Volume Scanner Images

BackgroundThe resection volume in relation to the breast volume is known to influence cosmetic outcome following breast-conserving therapy. It was hypothesised that three-dimensional ultrasonography (3-D US) could be used to preoperatively assess breast and tumour volume and show high association with histopathological measurements.MethodsBreast volume by the 3D-US was compared to the water displacement method (WDM), mastectomy specimen weight, 3-D MRI and three different calculations for breast volume on mammography. Tumour volume by the 3-D US was compared to the histopathological tumour volume and 3-D MRI. Relatedness was based on the intraclass correlation coefficient (ICC) with corresponding 95% confidence interval (95% CI). Bland–Altman plots were used to graphically display the agreement for the different assessment techniques. All measurements were performed by one observer.ResultsA total of 36 patients were included, 20 and 23 for the evaluation of breast and tumour volume (ductal invasive carcinomas), respectively. 3-D US breast volume showed ‘excellent’ association with WDM, ICC 0.92 [95% CI (0.80–0.97)]. 3-D US tumour volume showed a ‘excellent’ association with histopathological tumour volume, ICC 0.78 [95% CI (0.55–0.91)]. Bland–Altman plots showed an increased overestimation in lager tumour volumes measured by 3-D MRI compared to histopathological volume.Conclusions3-D US showed a high association with gold standard WDM for the preoperative assessment of breast volume and the histopathological measurement of tumour volume. 3-D US is an patient-friendly preoperative available technique to calculate both breast volume and tumour volume. Volume measurements are promising in outcome prediction of intended breast-conserving treatment.

A novel application of the Automated Breast Volume Scanner (ABVS) in the diagnosis of soft tissue tumors

AimTo explore the feasibility of the application of the Automated Breast Volume Scanner (ABVS) ACUSON S2000 in the diagnosis of soft tissue tumors. MethodsThe data of handheld ultrasound (HHUS) scans and ABVS volume three-dimensional (3D) reconstruction were collected from 66 patients with soft tissue tumors. The diagnosis rates for the two methods were compared. Additionally, the sonographic features of the lesions on ABVS imaging were evaluated. The data of the sonographic “hyperechoic rim sign” and “heterogeneous texture sign” were analyzed for the differential diagnosis. ResultsAutomatic 3D reconstruction and high-resolution images from the coronal and sagittal planes were obtained using the ABVS. Although the ABVS is similar to HHUS in terms of sensitivity (81.8% vs. 77.3%, respectively), specificity (93.2% vs. 88.6%, respectively), and accuracy (89.4% vs. 84.8%, respectively), the success rates for full view of the lesion and detection rates of multiple lesions are significantly improved by the ABVS. Furthermore, the “hyperechoic rim sign” can be regarded as a specific diagnostic factor for benign tumors, with a diagnostic specificity and a positive predictive value of 86.4% and 91.4%, respectively. Additionally, the “heterogeneous texture” appeared commonly in malignant tumors, with a diagnostic sensitivity and a negative predictive value of 81.8% and 87.5%, respectively. ConclusionCompared with conventional two-dimensional imaging, automatic 3D reconstruction and high-resolution images from three vertical planes can be displayed by the ABVS, and the global anatomy and surrounding tissue of the lesions can be clearly presented. Thus, ABVS imaging may help to differentiate between benign and malignant soft tissue tumors.

Diagnostic Value of an Automated Breast Volume Scanner for Abdominal Hernias

This study explored the diagnostic values of an automated breast volume scanner (ABVS) for abdominal external hernias. Conventional sonograms and ABVS images from 128 abdominal external hernias in 104 patients (98 male and 6 female; age range, 41-79 years; mean age ± SD, 68.0 ± 14.6 years) were analyzed. The results were identified by surgical outcomes. The hernia type, hernial ring position, hernial sac size, hernia content, and hernia structure were evaluated by both sonographic modalities. The sensitivity and accuracy differences between the ABVS and conventional sonography for diagnosis of abdominal hernias and hernia size measurements were compared. The hernia types, as confirmed by surgery, included 45 indirect inguinal hernias (30 reducible and 15 irreducible), 12 reducible direct inguinal hernias, 5 femoral hernias, 62 incisional hernias (42 isolated and 20 multiple), and 4 umbilical hernias. The sensitivity of the ABVS was higher than that of conventional sonography for incisional hernias (P < .01), whereas there were no statistical differences in sensitivity for other types of hernias. The ABVS hernial sac number detection rate for both isolated and multiple incisional hernias was significantly higher compared with that of conventional sonography (both P < .01). The ABVS measurements correlated well with surgical results (length, P = .47; width, P = .31). Automated breast volume scanner images have the outstanding advantage of displaying the entire scope of the internal structure and the relationship with adjacent tissues of abdominal hernias. Therefore, an ABVS has good application prospects for diagnosis of abdominal external hernias and merits further research.