A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients.

Objective: BRCA1/2 status is a key to personalized therapy for invasive breast cancer patients. This study aimed to explore the association between ultrasound radiomics features and germline BRCA1/2 mutation in patients with invasive breast cancer. Materials and methods: In this retrospective study, 100 lesions in 92 BRCA1/2-mutated patients and 390 lesions in 357 non-BRCA1/2-mutated patients were included and randomly assigned as training and validation datasets in a ratio of 7:3. Gray-scale ultrasound images of the largest plane of the lesions were used for feature extraction. Maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression were used to select features. The multivariate logistic regression method was used to construct predictive models based on clinicopathological factors, radiomics features, or a combination of them. Results: In the clinical model, age at first diagnosis, family history of BRCA1/2-related malignancies, HER2 status, and Ki-67 level were found to be independent predictors for BRCA1/2 mutation. In the radiomics model, 10 significant features were selected from the 1032 radiomics features extracted from US images. The AUCs of the radiomics model were not inferior to those of the clinical model in both training dataset [0.712 (95% CI, 0.647-0.776) vs 0.768 (95% CI, 0.704-0.835); p = 0.429] and validation dataset [0.705 (95% CI, 0.597-0.808) vs 0.723 (95% CI, 0.625-0.828); p = 0.820]. The AUCs of the nomogram model combining clinical and radiomics features were 0.804 (95% CI, 0.748-0.861) in the training dataset and 0.811 (95% CI, 0.724-0.894) in the validation dataset, which were proved significantly higher than those of the clinical model alone by DeLong's test (p = 0.041; p = 0.007). To be noted, the negative predictive values (NPVs) of the nomogram model reached a favorable 0.93 in both datasets. Conclusion: This machine nomogram model combining ultrasound-based radiomics and clinical features exhibited a promising performance in identifying germline BRCA1/2 mutation in patients with invasive breast cancer and may help avoid unnecessary gene tests in clinical practice.

Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers.

BACKGROUND: For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. METHODS: This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. FINDINGS: The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865-0.968) in the internal validation cohort, and 0.900 (95% CI 0.819-0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719-0.889], p = 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704-0.878], p = 0.013) in the external test cohort. INTERPRETATION: The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. FUNDING: Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).

Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma.

Background and Aims: Microvascular invasion (MVI) is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC. Methods: A total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training (n = 301), validation (n = 102), and test (n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI. Results: Compared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1-7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7-21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23-8.91], p = 0.011) in the test group. Conclusions: The proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis.

Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer.

Background: This study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features. Methods: A retrospective analysis of 281 patients with pathologically confirmed breast cancer was performed between January 2015 and March 2018. All patients were randomly divided into a training cohort (n = 197) and a validation cohort (n = 84). Univariate and multivariable logistic regression analyses were performed to identify the clinically important predictors of ALNM when developin1 g the nomogram. The area under the curve (AUC), calibration plots, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the nomogram. Results: In univariate and multivariate analyses, lymphovascular invasion (LVI), axillary lymph node (ALN) cortex thickness, and an obliterated ALN fatty hilum were identified as independent predictors and integrated to develop a nomogram for predicting ALNM. The nomogram showed favorable sensitivity for ALNM with AUCs of 0.87 (95% confidence interval (CI), 0.81-0.92) and 0.84 (95% CI, 0.73-0.92) in the training and validation cohorts, respectively. The calibration plots of the nomogram showed good agreement between the nomogram prediction and actual ALNM diagnosis (P > 0.05). Decision curve analysis (DCA) revealed the net benefit of the nomogram. Conclusions: This study developed a nomogram based on three daily available clinical parameters, with good accuracy and clinical utility, which may help the radiologist in decision-making for ultrasound-guided fine needle aspiration cytology/biopsy (US-FNAC/B) according to the nomogram score.

The Brassicaceae genome resource (TBGR): A comprehensive genome platform for Brassicaceae plants.

The Brassicaceae is an important plant family. We built a user-friendly, web-based, comparative, and functional genomic database, The Brassicaceae Genome Resource (TBGR, http://www.tbgr.org.cn), based on 82 released genomes from 27 Brassicaceae species. The TBGR database contains a large number of important functional genes, including 4,096 glucosinolate genes, 6,625 auxin genes, 13,805 flowering genes, 36,632 resistance genes, 1,939 anthocyanin genes, and 1,231 m6A genes. A total of 1,174,049 specific guide sequences for clustered regularly interspaced short palindromic repeats and 5,856,479 transposable elements were detected in Brassicaceae. TBGR also provides information on synteny, duplication, and orthologs for 27 Brassicaceae species. The TBGR database contains 1,183,851 gene annotations obtained using the TrEMBL, Swiss-Prot, Nr, GO, and Pfam databases. The BLAST, Synteny, Primer Design, Seq_fetch, and JBrowse tools are provided to help users perform comparative genomic analyses. All the genome assemblies, gene models, annotations, and bioinformatics results can be easily downloaded from the TBGR database. We plan to improve and continuously update the database with newly assembled genomes and comparative genomic studies. We expect the TBGR database to become a key resource for the study of the Brassicaceae.

Quantitative analysis of shear wave elastic heterogeneity for prediction of lymphovascular invasion in breast cancer.

OBJECTIVE: To evaluate the correlation between elastic heterogeneity (EH) and lymphovascular invasion (LVI) in breast cancers and assess the clinical value of using EH to predict LVI pre-operatively. METHODS: This retrospective study consisted of 376 patients with breast cancers that had undergone shear wave elastography (SWE) with virtual touch tissue imaging quantification between June 2017 and June 2018. The EH was determined as the difference between the averaged three highest and three lowest shear wave value. Clinicalpathological parameters including histological type and grades, LVI, axillary lymph node status and molecular markers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 and Ki-67) were reviewed and recorded. Relationship EH and clinicalpathological parameters was investigated respectively. The diagnostic performance of EH in distinguishing LVI or not was analyzed. RESULTS: At multivariate regression analysis, only EH (p = 0.017) was positively correlated with LVI in all tumors. EH (p = 0.003) and Ki-67 (p = 0.025) were positively correlated with LVI in tumors ≤ 2 cm. None of clinicalpathological parameters were correlated with LVI in tumors > 2 cm (p > 0.05 for all). Using EH to predict LVI in tumors ≤ 2 cm, the sensitivity and negative predictive value were 93 and 89% respectively. CONCLUSION: EH has the potential to be served as an imaging biomarker to predict LVI in breast cancer especially for tumors ≤ 2 cm. ADVANCES IN KNOWLEDGE: There was no association between LVI and other most commonly used elastic features such as SWVmean and SWVmax. Elastic heterogeneity is an independent predictor of LVI, so it can provide additional prognostic information for routine preoperative breast cancer assessment.For tumors ≤ 2cm, using EH value higher than 1.36 m/s to predict LVI involvement, the sensitivity and negative predictive value can reach to 93% and 89%, respectively, suggesting that breast cancer with negative EH value was more likely to be absent of LVI.

Molecular Basis of Ubiquitination Catalyzed by the Bacterial Transglutaminase MavC.

The Legionella pneumophila effector MavC is a transglutaminase that carries out atypical ubiquitination of the host ubiquitin (Ub)-conjugation enzyme UBE2N by catalyzing the formation of an isopeptide bond between Gln40Ub and Lys92UBE2N, which leads to inhibition of signaling in the NF-κB pathway. In the absence of UBE2N, MavC deamidates Ub at Gln40 or catalyzes self-ubiquitination. However, the mechanisms underlying these enzymatic activities of MavC are poorly understood at the molecular level. This study reports the structure of the MavC-UBE2N-Ub ternary complex representing a snapshot of MavC-catalyzed crosslinking of UBE2N and Ub, which reveals the way by which UBE2N-Ub binds to the Insertion and Tail domains of MavC. Based on the structural and experimental data, the catalytic mechanism for the deamidase and transglutaminase activities of MavC is proposed. Finally, by comparing the structures of MavC and MvcA, the homologous protein that reverses MavC-induced UBE2N ubiquitination, several essential regions and two key residues (Trp255MavC and Phe268MvcA) responsible for their respective enzymatic activities are identified. The results provide insights into the mechanisms for substrate recognition and ubiquitination mediated by MavC as well as explanations for the opposite activity of MavC and MvcA in terms of regulation of UBE2N ubiquitination.

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

Cryo-EM structures of the human PA200 and PA200-20S complex reveal regulation of proteasome gate opening and two PA200 apertures.

Proteasomes are highly abundant and conserved protease complexes that eliminate unwanted proteins in the cells. As a single-chain ATP-independent nuclear proteasome activator, proteasome activator 200 (PA200) associates with 20S core particle to form proteasome complex that catalyzes polyubiquitin-independent degradation of acetylated histones, thus playing a pivotal role in DNA repair and spermatogenesis. Here, we present cryo-electron microscopy (cryo-EM) structures of the human PA200-20S complex and PA200 at 2.72 Å and 3.75 Å, respectively. PA200 exhibits a dome-like architecture that caps 20S and uses its C-terminal YYA (Tyr-Tyr-Ala) to induce the α-ring rearrangements and partial opening of the 20S gate. Our structural data also indicate that PA200 has two openings formed by numerous positively charged residues that respectively bind (5,6)-bisdiphosphoinositol tetrakisphosphate (5,6[PP]2-InsP4) and inositol hexakisphosphate (InsP6) and are likely to be the gates that lead unfolded proteins through PA200 and into the 20S. Besides, our structural analysis of PA200 found that the bromodomain (BRD)-like (BRDL) domain of PA200 shows considerable sequence variation in comparison to other human BRDs, as it contains only 82 residues because of a short ZA loop, and cannot be classified into any of the eight typical human BRD families. Taken together, the results obtained from this study provide important insights into human PA200-induced 20S gate opening for substrate degradation and the opportunities to explore the mechanism for its recognition of H4 histone in acetylation-mediated proteasomal degradation.

Shear-Wave Elastography of the Breast: Added Value of a Quality Map in Diagnosis and Prediction of the Biological Characteristics of Breast Cancer.

OBJECTIVE: To determine the added value of a shear-wave elastography (SWE) quality map (QM) in the diagnosis of breast lesions and in predicting the biological characteristics of invasive breast cancer. MATERIALS AND METHODS: Between January 2016 and February 2019, this study included 368 women with 368 pathologically proven breast lesions, which appeared as poor-quality regions in the QM of SWE. To measure shear-wave velocity (SWV), seven regions of interest were placed in each lesion with and without QM guidance. Under QM guidance, poor-quality areas were avoided. Diagnostic performance was calculated for mean SWV (SWVmean), max SWV (SWVmax), and standard deviation (SD) with QM guidance (SWVmean + QM, SWVmax + QM, and SD + QM, respectively) and without QM guidance (SWVmean - QM, SWVmax - QM, and SD - QM, respectively). For invasive cancers, the relationship between SWV findings and biological characteristics was investigated with and without QM guidance. RESULTS: Of the 368 women (mean age, 47 years; SD, 10.8 years) enrolled, 159 had benign breast lesions and 209 had malignant breast lesions. SWVmean + QM (3.6 ± 1.39 m/s) and SD + QM (1.02 ± 0.84) were significantly different from SWVmean - QM (3.29 ± 1.22 m/s) and SD - QM (1.46 ± 1.06), respectively (all p < 0.001). For differential diagnosis of breast lesions, the sensitivity and areas under the receiver operating characteristic curve (AUC) of SWVmean + QM (sensitivity: 89%; AUC: 0.932) were better than those of SWVmean - QM (sensitivity, 84.2%; AUC, 0.912) (all p < 0.05). There was no significant difference in sensitivity and specificity between SD + QM and SD - QM (all p = 1.000). Among the biological characteristics of invasive cancers, lymphovascular involvement, axillary lymph node metastasis, negative estrogen receptor status, negative progesterone receptor status, positive human epidermal growth factor receptor status, and aggressive molecular subtypes showed higher SWVmean + QM (all p < 0.05), while only lymphovascular involvement showed higher SWVmean - QM (p = 0.036). CONCLUSION: The use of QM in SWE might improve the diagnostic performance for breast lesions and facilitate prediction of the biological characteristics of invasive breast cancers.

Added Value of Different Types of Elastography in Evaluating Ultrasonography Detected Breast Lesions: A Compared Study With Mammography.

BACKGROUND: The purpose of this study was to compare the diagnostic performance of ultrasonography (US) and mammography in the differential diagnosis of breast lesions after adding different types of elastography to US. PATIENTS AND METHODS: This institutional review board-approved study included 316 breast lesions in 289 women between July 2016 and July 2018. All these lesions were evaluated with conventional US, elastography, and mammography before biopsy or surgery. Elastography, including elasticity imaging (EI), virtual touch tissue imaging (VTI), and virtual touch imaging quantification (VTIQ), were used to downgrade US Breast Imaging-Reporting and Data System category 4A lesions. Diagnostic performances were calculated for mammography, US elastography, and the combination of US and elastography. RESULTS: The sensitivity of US (100%) was significantly higher than that of mammography (84.6%; P < .001), but the specificity of US (14.5%) was significantly lower than that of mammography (59.1%; P < .001). After adding EI, VTI, and VTIQ to US, the specificity was significantly increased from 14.5% to 69.4%, 72.6%, and 78.0%, respectively (P < .001), and were significantly higher than that of mammography (P = .043, P = .006, and P < .001, respectively). The sensitivity of US + EI (96.2%) and US + VTI (96.2%) was lower than that of US alone, although not significantly (100%; P = .063 and P = .063, respectively). CONCLUSION: The addition of different types of elastography to US improved the diagnostic performance in the differential diagnosis of breast lesions when compared with mammography.

Distinguishing intrahepatic cholangiocarcinoma from hepatocellular carcinoma in patients with and without risks: the evaluation of the LR-M criteria of contrast-enhanced ultrasound liver imaging reporting and data system version 2017.

PURPOSE: To assess the diagnostic performance of the LR-M criteria of Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System version 2017 in differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) in patients with and without risk factors for HCC. METHODS: Fifty-four ICC in patients with risks and 55 ICC in patients without risks and matched control cases of HCC with and without risks (n = 59 and n = 55, respectively) were enrolled. The enhanced features of the lesions were retrospectively analyzed according to LR-M criteria. The diagnostic performances including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of LR-M criteria were assessed. RESULT: Peripheral rim-like hyperenhancement, early washout (< 45 or 60s), and marked washout did not differ between ICCs with and without risks, while all of these features were more common in ICCs than in HCCs (p < 0.05) no matter if patients were with and without risk factors. Using the LR-M criteria to differentiate ICC from HCC, the AUC, sensitivity, specificity, and accuracy were 0.92, 97.25%, 87.72%, and 92.38%, respectively. If early washout onset was adjusted to < 45 s, the specificity was significantly increased to 95.61% (p = 0.004) without losing sensitivity (96.33%, p = 0.945). The rate of HCCs misdiagnosed as ICCs would decrease from 12.3 to 4.4%. CONCLUSION: Although the LR-M criteria showed high sensitivity in distinguishing ICCs from HCCs in patients with and without risks, the specificity would be significantly increased after adjustments to current criteria. KEY POINTS: • The LR-M criteria of CEUS-LI-RADS v2017 could be used for distinguishing ICC from HCC not only in patients with risk factors for HCC but also in those without risk factors. • The diagnostic performance of differentiating ICC from HCC by using the LR-M criteria showed high AUC (0.92), high sensitivity (97.25%), intermediate specificity (87.72%), and high accuracy (92.38%). • If the onset of early washout was adjusted to < 45 s, the specificity was significantly increased from 87.72 to 95.61% (p = 0.004) without losing sensitivity (p = 0.945), and the rate of HCCs misdiagnosed as ICCs would decrease from 12.3 to 4.4%.

Legionella pneumophila regulates the activity of UBE2N by deamidase-mediated deubiquitination.

The Legionella pneumophila effector MavC induces ubiquitination of the E2 ubiquitin-conjugating enzyme UBE2N by transglutamination, thereby abolishing its function in the synthesis of K63 -type polyubiquitin chains. The inhibition of UBE2N activity creates a conundrum because this E2 enzyme is important in multiple signaling pathways, including some that are important for intracellular L. pneumophila replication. Here, we show that prolonged inhibition of UBE2N activity by MavC restricts intracellular bacterial replication and that the activity of UBE2N is restored by MvcA, an ortholog of MavC (50% identity) with ubiquitin deamidase activity. MvcA functions to deubiquitinate UBE2N-Ub using the same catalytic triad required for its deamidase activity. Structural analysis of the MvcA-UBE2N-Ub complex reveals a crucial role of the insertion domain in MvcA in substrate recognition. Our study establishes a deubiquitination mechanism catalyzed by a deamidase, which, together with MavC, imposes temporal regulation of the activity of UBE2N during L. pneumophila infection.

Early differentiating between the chemotherapy responders and nonresponders: preliminary results with ultrasonic spectrum analysis of the RF time series in preclinical breast cancer models.

BACKGROUND: This study was aimed to assess whether ultrasonic spectrum analysis of radiofrequency (RF) time series using a clinical ultrasound system allows for early differentiating between the chemotherapy responders and nonresponders in human breast cancer xenografts that imitate clinical responding and nonresponding tumors. METHODS: Clinically responding (n = 20; MCF-7) and nonresponding (n = 20; MBA-MD-231) breast cancer xenografts were established in 40 nude mice. Ten mice from each group received either chemotherapy (adriamycin, 4 mg/kg) or saline as controls. Each tumor was imaged longitudinally with a clinical ultrasound scanner at baseline (day 0) and subsequently on days 2, 4, 6, 8 and 12 following treatment, and the corresponding RF time-series data were collected. Changes in six RF time-series parameters (slope, intercept, S1, S2, S3 and S4) were compared with the measurement of the tumor cell density, and their differential performances of the treatment response were analyzed. RESULTS: Adriamycin significantly inhibited tumor growth and decreased the cancer cell density in responders (P < 0.001) but not in nonresponders (P > 0.05). Fold changes of slope were significantly increased in responders two days after adriamycin treatment (P = 0.002), but not in nonresponders (P > 0.05). Early changes in slope on day 2 could differentiate the treatment response in 100% of both responders (95% CI, 62.9-100.0%) and nonresponders (95% CI, 88.4-100%). CONCLUSIONS: Ultrasonic RF time series allowed for the monitoring of the tumor response to chemotherapy and could further serve as biomarkers for early differentiating between the treatment responders and nonresponders.

Reducing Unnecessary Biopsy of Breast Lesions: Preliminary Results with Combination of Strain and Shear-Wave Elastography.

The aim of our study was to compare strain elastography (SE), acoustic radiation force impulse-inducing Virtual Touch Imaging ([VTI] Siemens Medical Solutions, Mountain View, CA, USA), Virtual Touch Imaging Quantification ([VTIQ] Siemens Medical Solutions) and combined methods in the evaluation of ultrasound (US) Breast Imaging-Reporting and Data System (BI-RADS) category 4 lesions to explore an applicable way to reduce unnecessary biopsy by reducing false positives of conventional US without yielding false-negative cases. A total of 267 patients with 278 BI-RADS category 4 lesions (151 benign and 127 malignant) were evaluated with conventional B-mode US, SE, VTI and VTIQ implemented on a Siemens Acuson S2000 US system. Diagnostic performance, including area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were evaluated. Overall, VTI alone exhibited the highest NPV (91.74%), although combined elastic methods exhibited higher NPV than single methods, with the highest NPV at 100% when the VTI, SE and VTIQ methods were combined. Compared with conventional US, PPV increased from 45.7% (127 of 278) to 63.18% (127 of 201) when adding combined elastography (VTI + SE +VTIQ). In addition, 52.5% (63/120) and 50.8% (61/120) of BI-RADS 4 A lesions were downgraded when using combined methods (VTI + SE and VTI + SE + VTIQ, respectively) without missing any cancer. However, 2 intraductal papillomas and 1 phyllodes tumor were not identified. In conclusion, the combination of different elastic methods have the potential to downgrade BI-RADS 4A lesions to reduce false-positive biopsies without increasing the risk of missing cancers.

Shear Wave Elastography of Breast Lesions: Quantitative Analysis of Elastic Heterogeneity Improves Diagnostic Performance.

The aim of this study was to evaluate whether quantitative analysis of elastic heterogeneity (EH) could improve the diagnostic performance of shear wave elastography (SWE) in breast lesions. From August 2016 to August 2017, 280 patients were enrolled in this prospective study. All lesions were evaluated with the ultrasound Breast Imaging Reporting and Data System (BI-RADS) and SWE with Virtual Touch tissue imaging quantification. The shear wave velocity (SWV) of the three areas of highest stiffness and lowest stiffness within the lesions were measured to calculate the maximum SWV (SWVmax), mean SWV (SWVmean) and EH. The EH was determined as the difference between the averaged highest SWV and lowest SWV. The diagnostic performance-including the area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity of BI-RADS, EH, SWVmax and SWVmean-were analyzed. The AUC of EH, SWVmax and SWVmean were 0.963, 0.949 and 0.937, respectively. The sensitivity of EH was 93.75%, which was significantly higher than that of SWVmax (84.37%) and SWVmean (84.37%) (p < 0.001); there was no significant difference in the specificity among EH, SWVmax and SWVmean (p > 0.05). For category 4A lesions, EH predicted all the malignant lesions, while two cancers were misdiagnosed by SWVmax and SWVmean, respectively. Quantitative analysis of EH can improve the sensitivity of SWE for the differential diagnosis of breast lesions without loss of specificity.

Association Between Shear Wave Elastography of Virtual Touch Tissue Imaging Quantification Parameters and the Ki-67 Proliferation Status in Luminal-Type Breast Cancer.

OBJECTIVES: To evaluate the association between shear wave elastography parameters using virtual touch tissue imaging quantification (VTIQ) and the Ki-67 index in luminal-type breast cancer. METHODS: Eighty-one patients with 82 lesions of pathologic confirmed luminal-type breast cancer underwent virtual touch tissue imaging quantification examination before surgery between December 2015 and June 2016. Patients were divided into 2 groups according to the Ki-67 index (≥14% versus < 14%), which is used to define luminal type B and luminal type A, respectively. The mean shear wave velocity (SWVmean ) and lesion-to-adjacent tissues ratio (SWV ratio) were calculated for each lesion. RESULTS: The SWVmean , SWV ratio, histologic grade, axillary lymph node involvement, and lymphovascular invasion showed a significant positive association with a high Ki-67 index (all P < .05). Receiver operating characteristic curve analysis for the differential diagnosis between high (≥14%) and low (<14%) Ki-67 groups displayed that the optimal cutoff value for SWVmean and SWV ratio were 3.99 meters per second and 2.861, with sensitivity 94% and 72%, specificity 40.6% and 56.2%, and area under the receiver operating characteristic curve of 0.689 and 0.651, respectively. Univariate analysis showed that SWVmean (P = .005), SWV ratio (P = .029), histologic grade (P = .011), presence of axillary node involvement (P = .004), and lymphovascular invasion (P = .008) were significantly associated with high Ki-67 status. Multivariable analysis displayed that SWVmean (hazard ratio [HR], 1.459, 95% confidence interval [CI], 1.028-2.072; P = .035), histologic grade (HR, 4.105; 95% CI, 1.142-14.763; P = .031), and presence of axillary node involvement (HR, 3.75; 95% CI, 1.228-11.453; P = .020) maintained significance for predicting high Ki-67 status. CONCLUSIONS: The SWVmean using the virtual touch tissue imaging quantification method showed significant correlation with the Ki-67 index, suggesting the potential to assess tumor proliferation status in luminal-type breast cancer with a noninvasive manner.

Combination of different types of elastography in downgrading ultrasound Breast Imaging-Reporting and Data System category 4a breast lesions.

OBJECTIVES: To determine whether a combination of different types of elastography could improve the accuracy of elastography-aided downgrading ultrasound (US) Breast Imaging-Reporting and Data System (BI-RADS) category 4a lesions. MATERIALS AND METHODS: From January 2016 to May 2018, 458 consecutive women with 494 US BI-RADS category 4a breast lesions were enrolled in the prospective study. These lesions were subject to conventional US supplemented with strain elastography of elasticity imaging (EI), virtual touch tissue imaging (VTI), and shear wave elastography of virtual touch imaging quantification (VTIQ). Diagnostic performances were calculated for BI-RADS, EI, VTI, and VTIQ as well as the combination of EI, VTI, and VTIQ (combination of EI and VTI [EI + VTI], combination of EI and VTIQ [EI + VTIQ], and combination of VTI and VTIQ [VTI + VTIQ]). RESULTS: Pathologically, 445 lesions (90.1%) were benign, and 49 (9.9%) were malignant. The specificities of EI, VTI, and VTIQ were significantly higher than those of BI-RADS (69.9%, 83.8%, 75.5% vs. 0, respectively, P < 0.001), while their sensitivities were significantly lower than those of BI-RADS (83.7%, 73.5%, 65.3% vs. 100%, respectively, P < 0.05). Among the combinations, EI + VTI and EI + VTIQ showed similar sensitivity to BI-RADS (98% vs 100%, P = 1.000; 93.9% vs 100%, P = 0.25), while the specificity of EI + VTI was significantly higher than that of EI + VTIQ and BI-RADS (P < 0.001). When using EI + VTI to downgrade lesions, 58.7% of these lesions were downgraded, among those 99.7% were benign. CONCLUSIONS: Combinations of EI and VTI to downgrade BI-RADS category 4a lesions may reduce the misdiagnosis of breast cancers and the number of unnecessary biopsies.