To develop an AI model using ultrasound features of carotid plaque for predicting the risk of acute ischemic stroke (AIS) and assess its efficacy in comparison with conventional regression prediction models. This study retrospectively included 923 patients who underwent US and CEUS examinations of carotid plaque at our institution. They were randomly divided into training + validation and test set in an 8:2 ratio. Additionally, 143 prospectively collected patients from three other centers were included as an external test set. Two expert radiologists described and documented the ultrasound images. Logistic regression analysis was used to analyze plaque ultrasound characteristics, leading to the establishment of statistical predictive models for AIS risk based on US alone and US combined with CEUS. AI models were developed using ResNet34 architecture trained on ultrasound images. ROC curves were generated, and AUC values were computed to compare the performance of the statistical models with the AI models. During a median follow-up of 5.3 years, 523 patients experienced AIS, while 543 had no history of stroke. The AUC was 0.719 for the model using US alone, 0.819 for the model combining US with CEUS, and 0.917 for the model incorporating AI, with all pairwise comparisons being statistically significant (p < 0.05). Furthermore, the AUC of the AI model in the external test set was 0.866, indicating good generalization ability and stability. US and CEUS characteristics of carotid plaque were strongly associated with AIS. Deep learning enhances AIS prediction in carotid plaque assessment using ultrasound. Question Can AI enhance the prediction of AIS risk by analyzing the characteristics of carotid artery plaques using US and CEUS compared to conventional statistical models? Findings The AI model integrating US and CEUS plaque features significantly outperformed traditional methods in AIS risk prediction, with CEUS providing substantial value beyond US alone. Clinical relevance This AI-driven approach offers an automated and standardized method for stratifying stroke risk directly from ultrasound images of carotid plaques. This advancement facilitates early identification of high-risk individuals, eliminating the need for additional testing or labor-intensive manual analysis.

Immunogenic cell death (ICD) represents a promising strategy to stimulate antitumor immunity. Doxorubicin (DOX) is one drug that can trigger ICD. However, DOX has limitations for treating triple-negative breast cancer (TNBC). Its ability to stimulate a strong immune response is weak. The drug also promotes an immunosuppressive tumor microenvironment. This suppression hinders the antitumor immune response. Since reactive oxygen species (ROS) generation is a pivotal ICD trigger, we therefore hypothesized that nintedanib, a triple tyrosine kinase inhibitor known to promote immune cell infiltration and activation within tumors, potentiates DOX-induced ICD by augmenting intratumoral ROS generation. To address this, we engineered a targeted nanoenhancer for TNBC by coencapsulating liquid nintedanib and perfluoropentane (PFP), within a peptide-functionalized (tLyp-1) liposomal shell, creating tLyp-1-Nintedanib-PFP Nanoparticles (tNP-NPs) for use in sonodynamic therapy. This tNP-NP platform is intended for combination therapy with DOX in a chemoimmunotherapeutic strategy. With the assistance of low-intensity focused ultrasound (LIFU), tNP-NPs exhibited potent targeting and penetrating capabilities toward 4T1 TNBC cells, enhanced drug cytotoxicity, ROS production, and ICD markers in vitro. Subsequently, for the in vivo study, to mitigate systemic toxicity, an injectable thermosensitive hydrogel coencapsulating tNP-NPs and DOX was applied for local peritumoral injection. In orthotopic 4T1 TNBC models, this combination elicited potent ICD, promoting dendritic cell (DC) cells maturation and cytotoxic T lymphocyte activation within tumors and draining lymph nodes. This approach achieved exceptional antitumor efficacy and systemic immunity while reversing the immunosuppressive tumor microenvironment, without observable organ toxicity, presenting a robust chemoimmunotherapeutic strategy for TNBC.

Breast contrast-enhanced ultrasound (CEUS)has been used in clinical practice for nearly two decades. CEUS provides real-time assessment of microvascular perfusion, offering valuable functional information complementary to conventional ultrasound. Nonetheless, the lack of standardization in relevant techniques has hindered the widespread adoption and further development of this technology. As a specialized committee, Superficial Organs and Peripheral Vessels Committee of Chinese Association of Ultrasound in Medicine and Engineering has always been committed to standardizing the clinical application of ultrasound. This expert consensus aims to standardize examination procedures, image interpretation, and reporting for diagnostic settings. It outlines key indications and provides recommendations on acquisition techniques, qualitative and quantitative analysis of enhancement patterns, and standardized lexicon for reporting.

Background: Current predictive models for disease-free survival (DFS) in invasive breast cancer predominantly utilize clinical and pathological factors, with minimal incorporation of ultrasound (US) and contrast-enhanced ultrasound (CEUS) characteristics. This study aimed to establish a multimodal map integrating US, clinical features, and US data to enhance the prediction of DFS in invasive breast cancer. Methods: The study utilized three retrospective datasets obtained from three academic medical centers, covering the period from March 2014 to December 2022. Clinical data, gray scale US, and CEUS were assessed in 942 adult patients undergoing breast cancer resection. The training and internal test sets were supplied by The First Medical Center of the PLA General Hospital, while the external test sets were sourced from The Fourth Medical Center of the PLA General Hospital and the Specialist Medical Center of the Strategic Support Forces. The patients were followed up by phone or clinic visits. DFS was evaluated as a prognostic outcome. Cox regression analysis identified prognostic factors, leading to the construction of three nomograms. The model performance was evaluated using the C-index, time-dependent receiver improvement (IDI), and net reclassification index (NRI). Results: A total of 942 patients were enrolled, with a mean age of 51.91 years [interquartile range (IQR), 44.25-58.69 years]. The patients were included with the median DFS of 36 months. Cox regression analysis identified menopausal status, body mass index (BMI), color Doppler flow imaging (CDFI), tumor size on (TNM) staging as significant risk factors for invasive breast cancer. The nomogram combining US, CEUS, and clinical data demonstrated excellent predictive performance, achieving a C-index of 0.811 in the training set, 0.816 in the internal validation set, and 0.819 in the external validation set. Calibration curves confirmed that the predicted survival probabilities aligned closely with observed outcomes. Comparative analysis of ROC curves, IDI, NRI, and DCA confirmed that the integrated nomogram outperformed models based solely on US and clinical data or clinical data alone in predicting 24- and 36-month DFS. Conclusions: The integration of CEUS and clinical factors for non-invasive DFS prediction improves personalized risk stratification, minimizing unnecessary interventions for low-risk patients and ensuring clinical applicability and incorporation into standard oncology practice.

Parathyroid ultrasound is widely used in clinical practice and plays a crucial role in the diagnosis and treatment of parathyroid diseases. Nevertheless, ultrasound physicians frequently encounter a number of challenges and doubts in their professional practice. For this reason, Superficial Organs and Peripheral Vessels Committee of Chinese Association of Ultrasound in Medicine and Engineering has formulated the expert consensus on certain common clinical problems of parathyroid ultrasound based on the current research progress and clinical experience, in order to guide the clinical practice. This consensus describes in detail the diagnostic and interventional common problems of parathyroid ultrasound and provides in-depth discussion on related contents.

OBJECTIVE:This study aimed to develop a deep learning radiomics nomogram (DLRN) that integrated B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) images for preoperative lymphovascular invasion (LVI) prediction in invasive breast cancer (IBC). METHODS:Total 981 patients with IBC from three hospitals were retrospectively enrolled. Of 834 patients recruited from Hospital I, 688 were designated as the training cohort and 146 as the internal test cohort, whereas 147 patients from Hospitals II and III were assigned to constitute the external test cohort. Deep learning and handcrafted radiomics features of BMUS and CEUS images were extracted from breast cancer to construct a deep learning radiomics (DLR) signature. The DLRN was developed by integrating the DLR signature and independent clinicopathological parameters. The performance of the DLRN is evaluated with respect to discrimination, calibration, and clinical benefit. RESULTS:The DLRN exhibited good performance in predicting LVI, with areas under the receiver operating characteristic curves (AUCs) of 0.885 (95% confidence interval [CI,0.858-0.912), 0.914 (95% CI, 0.868-0.960) and 0.914 (95% CI, 0.867-0.960) in the training, internal test, and external test cohorts, respectively. The DLRN exhibited good stability and clinical practicability, as demonstrated by the calibration curve and decision curve analysis. In addition, the DLRN outperformed the traditional clinical model and the DLR signature for LVI prediction in the internal and external test cohorts (all p < 0.05). CONCLUSION:The DLRN exhibited good performance in predicting LVI, representing a non-invasive approach to preoperatively determining LVI status in IBC.

Background:This study investigated the relationship between Shear Wave Elastography (SWE), TGF-β1/MAPK signaling molecules, and epithelial-to-mesenchymal transition (EMT) in breast lesions, exploring the feasibility of SWE in early EMT identification for breast cancer. Methods:117 breast lesions in 107 patients from July to November 2023 were consecutively enrolled. SWE was performed preoperatively, and elastic parameters were documented. Immunohistochemistry (IHC) assessed the expression levels of TGF-β1, p38 MAPK, p-p38 MAPK, ERK1/2, p-ERK1/2, ERK5, p-ERK5, JNK, p-JNK, E-cadherin, β-catenin, N-cadherin, and Vimentin. Correlations between SWE parameters and biomarkers were analysed, and their diagnostic efficacy for axillary lymph node metastasis (LNM) was evaluated. Results:Among 117 breast lesions, 53 were classified as benign and 64 as malignant (25 exhibiting axillary LNM). Optimal SWE thresholds for distinguishing benign from malignant lesions were Emax = 106.7 kPa, Emean = 62.9 kPa, Emin = 22.5 kPa, Eratio = 3.4, and Esd = 21.2 kPa. For LNM prediction, cut-offs were Emax = 170.1 kPa, Emean = 118.5 kPa, and Eratio = 10.5. TGF-β1 and E-cadherin showed significant predictive value for LNM (AUCs: 0.774 and 0.704, respectively). E-cadherin negatively correlated with SWE parameters, while TGF-β1 and MAPK molecules (p38 MAPK, p-p38 MAPK) showed positive correlations. Lesions with "stiff rim sign" had significantly lower E-cadherin expression but elevated levels of TGF-β1 (P<0.001). Additionally, Vimentin, p38 MAPK and p-p38 MAPK levels were higher in the occurrence of the "stiff rim sign" (P all <0.05). Conclusion:TGF-β1, p38 MAPK, and E-cadherin demonstrated strong diagnostic capabilities and correlated with SWE parameters. SWE offers a promising non-invasive approach for assessing prognosis by identifying EMT characteristics at an earlier stage in breast cancer.

Triple‑negative breast cancer (TNBC) is an aggressive malignancy with limited treatment options, leading to poor clinical outcomes and the need for novel therapeutic approaches. Nintedanib, a United States Food and Drug Administration‑approved multi‑kinase inhibitor with anti‑fibrotic and anti‑angiogenic properties, has shown promise in cancer treatment. However, its precise molecular effects on TNBC have not yet been fully elucidated. Therefore, the present study aimed to investigate the therapeutic potential of nintedanib in TNBC using in vitro and in vivo models, specifically focusing on its regulatory effects on key oncogenic pathways. The present study utilized TNBC cell lines (MDA‑MB‑231 and 4T1) and BALB/c mice to evaluate the antitumor efficacy of nintedanib. Cell viability and clonogenic capacity were assessed using Cell Counting Kit‑8 and colony formation assays. Subsequently, apoptosis induction and cell cycle progression were determined by flow cytometry, and cell migration and invasion were analyzed through scratch and Transwell assays. To identify underlying mechanisms, potential molecular targets were identified via bioinformatics and network pharmacology, and were validated through western blotting, immunofluorescence and immunohistochemistry. Finally, an orthotopic TNBC mouse model was established and monitored in real time by multimodal ultrasound imaging. The results revealed that nintedanib significantly inhibited TNBC cell proliferation and suppressed stem cell‑like properties. Furthermore, it induced cell cycle arrest at the G2/M phase and promoted apoptosis. Mechanistic analysis revealed that nintedanib activated tumor protein p73 (TP73), leading to the disruption of the p53‑peroxisome proliferator‑activated receptor α (PPARα)/PI3K‑Akt signaling axis. Additionally, it downregulated epithelial‑mesenchymal transition (EMT) markers, including Snail and zinc finger E‑box‑binding homeobox protein 1, thereby mitigating tumor invasiveness. In vivo, nintedanib treatment effectively reduced tumor growth, angiogenesis and stiffness, indicating its potential as a viable therapeutic agent for TNBC. In conclusion, nintedanib exerts potent anti‑TNBC effects by modulating TP73, disrupting oncogenic signaling via the p53‑PPARα/PI3K‑Akt axis, and attenuating EMT‑associated transcription factors. These findings highlight its potential as a promising targeted therapy for TNBC, warranting further clinical exploration.

To develop a predictive model to identify atypical ductal hyperplasia (ADH) that was underestimated by US-guided core needle biopsy (CNB) and to evaluate the risk factors for underestimation for ADH with intraductal papilloma diagnosed by CNB. In this retrospective study, 300 CNB-diagnosed ADH lesions in 291 consecutive women between January 2014 and July 2023 were included and divided into training set (n = 181), internal validation set (n = 54), and external validation set (n = 65). The review included clinical, pathological, and US features, as well as final outcomes. Multivariate logistic regression was employed to establish predictive model and to evaluate risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis, and utility (patient stratification into low and high-risk groups). Model was validated both internally and externally by calculating its performance on validation sets. The upgrade rate to malignancy was 51.0

Background A 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. Methods In 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. Results The 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. Conclusions CEUS 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.

Objective: The aim of the work described here was to evaluate the diagnostic performance of a new integrated strategy using breast ultrasound (US) combined with magnetic resonance imaging (MRI) to differentiate benign and malignant breast non-mass-like lesions (NMLs) detected on US.Methods: From October 2017 to January 2021, 183 NMLs detected on US that had undergone MRI examinations were included in this respective study. Pathological results were used as the reference standard. The integrated diagnostic strategy of breast US combined with MRI based on a combination of MRI Breast Imaging Reporting and Data System (BI-RADS) with discriminant sonographic indicators highly associated with malignancy was established and validated in a cohort of 61 women. The diagnostic performances of US, MRI and the combined method were calculated and compared.Results: In the training set, the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of US, MRI and the integrated diagnostic strategy using US combined with MRI for NMLs were 0.730, 93.7% and 52.3%; 0.849, 94.7% and 75.0%; and 0.901, 92.6% and 87.5%, respectively. Compared with US or MRI alone, the integrated diagnostic strategy significantly increased the AUC (p < 0.001, p = 0.007) and specific-ity (p < 0.001, p = 0.034) while maintaining high sensitivity (p = 0.774, p = 0.551). In the validation set, the integrated strategy of US combined with MRI (AUC = 0.899) also had good performance compared with US (AUC = 0.728) or MRI (AUC = 0.838).Conclusion: The integrated diagnostic strategy of US combined with MRI exhibited good performance for breast NMLs compared with either modality used alone, which can improve the diagnostic specificity while maintaining high sensitivity.

Background: Ductal carcinoma in situ with microinvasion (DCISM) represents 1% of all breast cancer cases and is arguably a more aggressive subtype of ductal carcinoma in situ (DCIS). Preoperative evaluation of DCISM usually relies on core needle biopsy, and non-invasive evaluation methods are relatively limited. This study aims to explore the features of conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) in DCISM and to analyze the US and clinicopathological predictors of infiltrating components. Methods: A retrospective collection of US, CEUS, and clinicopathologic data for DCIS and DCISM lesions was conducted from January 1, 2019 to June 30, 2022. The Breast Imaging Reporting and Data System (BI-RADS) criteria were used to evaluate breast lesions. On CEUS, the imaging features were scored using a 5-point scoring system to re-rate the BI-RADS category indicated by conventional US features. The pathological diagnosis served as the gold standard. Histopathologic features included comedo-type necrosis and pathological grade, while biomarkers included estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and the Ki-67 index. A logistic regression analysis was performed to identify the independent risk factors for DCISM. The diagnostic performance of the model was evaluated using the receiver operating characteristic (ROC) curve and calculating the area under the Results: A total of 89 women were included in the study. Of these, 66 had a pathologic diagnosis of DCIS (66 lesions, ranging in size from 0.6 to 4.9 cm), and 23 had a pathologic diagnosis of DCISM (23 lesions, ranging in size from 0.7 to 4.2 cm). Three features on conventional US (tumor size, margin, and calcification) and three enhancement features on CEUS (enhancement margin, enhancement mode, and enhancement scope) were found to be significantly different between the DCIS and DCISM lesions (P=0.03, P=0.04, P=0.02, P=0.03, P=0.03, P=0.007, respectively). Patients with DCISM were more likely to have a higher pathological grade, ER negativity, PR negativity, HER2 positivity, and a higher Ki-67 index than patients with DCIS (P<0.001, P=0.042, P=0.03, P=0.009, P=0.05, respectively). A multivariate logistic regression analysis further showed that only an enlarged enhancement scope and pathological grade were associated with DCISM. The sensitivity and specificity of this predictive model were 87.0% and 81.8%, respectively (AUC =0.89). The absence of calcifications, non-mass lesions, lack of vascularity, and the non-enlarged scope can lead to misdiagnosis of DCIS and DCISM. Conclusions: Understanding the CEUS and clinicopathologic features of DCISM lesions may alert clinicians to the possibility of microinvasion and guide appropriate management.

Background: Accurate diagnosis of breast cancer is of great importance to improve the prognosis of patients. Artificial intelligence (AI)-assisted diagnostic system for breast ultrasound is gradually being applied in the identification of benign and malignant breast lesions. This study aimed to evaluate the diagnostic performance and optimal application of AI-assisted ultrasonography for breast lesions in clinical setting. Methods: A total of 501 consecutive patients with 679 breast lesions were prospectively included in the study. Junior and senior radiologists were asked to interpret images of lesions with and without AI assistance, respectively. Three application modes of AI were employed: AI alone, adjusted Breast Imaging Reporting and Data System (BI-RADS; incorporating BI-RADS obtained by AI into BI-RADS obtained by radiologists), and second reading mode (combining characteristic information extracted by AI to conduct a second reading so as to obtain a new BI-RADS). The diagnostic performances of these application modes were analyzed and compared. Results: The area under the curve (AUC) of junior radiologists increased from 0.879 to 0.921 in BI-RADS(second reading), which was higher than that in BI-RADS(adjusted) (0.901), similar to that in AI alone (0.924), and lower than that obtained by senior radiologists (0.950). Using BI-RADS category 4A as the threshold, the sensitivity of junior radiologists was found to increase from 0.83 to 0.92 (P<0.001). Furthermore, the specificity increased from 0.79 to 0.85, which was higher than those of AI alone and BI-RADS(adjusted) (P<0.001). The unnecessary biopsy rate decreased by 14.70% (P=0.01). For senior radiologists, the sensitivity increased from 0.91 to 0.96 (P=0.01). Similar results were observed in the subgroup analysis of lesions <= 2 cm. For lesions >2 cm, only the specificity of junior radiologists increased from 0.39 to 0.52 (P=0.03). Conclusions: AI-assisted ultrasound is useful for the diagnosis of breast lesions, particularly for junior radiologists and lesions <= 2 cm. The use of the second reading mode can achieve excellent diagnostic performance.

Background:The proteomic differences between mass-like (ML) breast lesions and non-mass-like (NML) breast lesions were compared to explore the formation mechanism of NML ultrasonic morphological characteristics.Methods:From January to August 2021, tissue samples were collected from 10 patients with malignant ML (MML), 10 patients with malignant NML (MNML), 10 patients with benign ML (BML), and seven patients with benign NML (BNML). The proteomic differences between the BML and BNML groups and the MML and MNML groups were compared by data-independent acquisition (DIA) quantitative mass spectrometry. The differentially expressed proteins were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) networks analyses.Results:We identified a total of 623 significantly differentially expressed proteins in the MML/MNML group, and 167 significantly differentially expressed proteins in the BML/BNML group, with relative ratios >1.2 or <-0.83. The up-regulated differential proteins were more abundant in the tumor necrosis factor (TNF) signaling pathway in both the MML/MNML and BML/BNML groups, suggesting that the TNF signaling pathway may be related to the ultrasonic morphological characteristics of breast lesions. Dual specificity mitogen-activated protein kinase 3 (MP2K3), a protein factor in the TNF signaling pathway, exhibited significant upregulation in both the malignant and BML groups.Conclusions:The TNF signaling pathway may be associated with the ultrasonic morphological characteristics of breast lesions. MP2K3 is the up-regulated differential expression protein in the MML/MNML and BML/BNML groups, which may be related to the ultrasonic morphological characteristics of breast lesions.

To develop a predictive model using conventional ultrasound combined with CEUS to identify thoracic wall recurrence after mastectomy. A total of 162 women with pathologically confirmed thoracic wall lesions (benign 79, malignant 83; median size 1.9 cm, ranging 0.3–8.0 cm) underwent a mastectomy and were checked by both conventional ultrasound and CEUS and were retrospectively included. Logistic regression models of B-mode ultrasound (US) and color Doppler flow imaging (CDFI) with or without CEUS were established to assess the thoracic wall recurrence after mastectomy. The established models were validated by bootstrap resampling. The models were evaluated using calibration curve. The clinical benefit of models were assessed using decision curve analysis. The area under the receiver characteristic was 0.823 (95

Purpose. To evaluate the prognosis of patients with benign phyllodes tumors (PTs) treated by different surgical methods and to explore the influencing factors of local recurrence. Methods. We retrospectively analyzed 215 benign PTs from 193 patients who underwent surgery at Chinese PLA General Hospital between October 2008 and December 2020. We stratified our analysis according to surgical factors and explored the clinicopathological factors to influence local recurrence. Results. Among 193 patients, a total of 17 (8.8%, 17/193) recurred during follow-up. There were 89 patients in the US-VAE group, of whom 6 (6.7%) recurred; 8 of 57 patients (14%) in the local lumpectomy group recurred, while 3 of 47 patients (6.4%) in the extended lumpectomy group recurred ( P = 0.252 ). Multivariate logistic regression analysis showed that tumor diameter, mitosis, and history of breast myoma were independent risk factors for tumor recurrence ( P = 0.005 , P = 0.006 , and P = 0.004 , respectively). The intraoperative blood loss, operation time, and scar length of the US-VAE group were shorter than those of the other two groups ( P < 0.05 ). Conclusion. Negative surgical margins of benign PTs can obtain similar prognosis as negative surgical margins >10 mm. Therefore, we recommend that a follow-up observation policy be adopted for patients with unexpected benign PTs, rather than unnecessary open surgical resection. Patients’ maximum tumor diameter, mitosis, and fibroadenoma history were independent predictors for recurrence of benign PTs.

Background: Previous studies show the size of lesions could affect the diagnostic accuracy of contrast enhanced ultrasound (CEUS). It is unclear whether CEUS has good diagnostic performance for lesions 552.0 and 551.0 cm. It is beneficial for the early diagnosis to explore the application of CEUS in breast lesions of different sizes. This study aims to analyze the diagnostic performance of CEUS and explore diagnostic models better suited to breast lesions of different sizes. Methods: A total of 1,059 lesions (656 benign and 403 malignant) examined by ultrasound and CEUS with definite pathological results were included in this retrospective study and divided into training (n=847) and validation (n=212) sets. All lesions were divided into three groups according to size. Diagnostic models (M0: all lesions; M1: 551.0 cm, M2: >1.0-2.0 cm, and M3: >2.0 cm) were developed through logistic regression analyses of CEUS features from the training set. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) and validated in the validation set. Results: The median age of patients was 45 +/- 11 years (range, 18-80 years). The AUC values of M0 combined with the Breast Imaging Reporting and Data System (BI-RADS) in the training and validation sets were 0.921 and 0.922, respectively (P=0.893). The AUC values of M0 combined with BI-RADS in the three groups were 0.844, 0.936 and 0.928 respectively. M0 was less effective in diagnosing lesions 551.0 cm (0.844 vs. 0.921, P=0.029). The AUC of M1 combined with BI-RADS for lesions 551.0 cm was higher than that of M0 (0.893 vs. 0.844, P=0.047), and M2 and M3 had no statistical difference in diagnostic performance when compared with M0 (P=0.243; P=0.246). Conclusions: The diagnostic performance of CEUS was closely related to lesion size. Establishing a new diagnostic model for lesions 551.0 cm can improve the CEUS diagnostic performance for breast lesions 551.0 cm.

Background: Mass-like (ML) and non-mass-like (NML) are two manifestations of breast lesions on ultrasound. Contrast-enhanced ultrasound (CEUS) can make up for the limitation of B-ultrasound (US) in the observation of focal blood flow, and shear wave elastography (SWE) can supplement the hardness information of the lesion. The present study aimed to analyze the characteristic manifestations of US, CEUS, and SWE in NML and ML breast and evaluate whether the diagnostic performance of these three ultrasound techniques differs in terms of differentiating between benign and malignant breast lesions.Methods: From January to August 2021, 382 patients (417 breast lesions) underwent US, CEUS, and SWE examinations. Of these, 204 women (218 breast lesions) were included in our study due to subsequent biopsy or surgery with pathological findings. The patients were divided into ML and NML groups according to the ultrasound characteristics, and the differences in multimodal ultrasound performance between benign and malignant NML and benign and malignant ML breast lesions were compared. The diagnostic performance of US, US + CEUS, US + SWE, US + CEUS + SWE for ML, NML and all breast lesions was evaluated by analyzing sensitivity, specificity and area under receiver operating characteristic (ROC) curve (AUC).Results: Pathologically, the 218 lesions included 96 malignant and 122 benign breast lesions. The sensitivity and specificity of US + CEUS + SWE in all lesion groups, ML group and NML group were 92.7% and 90.2%, 95.9% and 90.3%, 91.3% and 79.3%, respectively. In all breast group, AUCs of US + CEUS, US + SWE, US + CEUS + SWE were statistically different from AUC of US (P=0.0010, 0.0001, 0.0001). In the ML group, the AUC of US + CEUS, US + SWE, US + CEUS + SWE were statistically different from that of US (P=0.0120, 0.0008, 0.0002). In the NML group, there was a statistical difference between US + SWE and US AUC (P=0.0149).Conclusions: US, CEUS, and SWE have an important diagnostic value for benign and malignant ML and NML breast lesions. Multimodal ultrasound combined with US, CEUS, and SWE can improve the diagnostic efficacy in distinguishing between benign and malignant ML and NML lesions.

Background The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS. Methods This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology. Results There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant. Conclusions Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.