Downgrading BI-RADS categories in ultrasound using strain elastography and computer-aided diagnosis system: a multicenter, prospective study.

OBJECTIVE: To determine whether adding elastography strain ratio (SR) and a deep learning based computer-aided diagnosis (CAD) system to breast ultrasound (US) can help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3 & 4a-c categories and avoid unnecessary biopsies. METHODS: This prospective, multicenter study included 1049 masses (691 benign, 358 malignant) with assigned BI-RADS 3 & 4a-c between 2020 and 2022. CAD results was dichotomized possibly malignant vs. benign. All patients underwent SR and CAD examinations and histopathological findings were the standard of reference. Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after reclassified (new BI-RADS 3) with SR and CAD were the outcome measures. RESULTS: Following the routine conventional breast US assessment, 48.6% (336 of 691 masses) underwent unnecessary biopsies. After reclassifying BI-RADS 4a masses (SR cut-off < 2.90, CAD dichotomized possibly benign), 25.62% (177 of 691 masses) underwent an unnecessary biopsies corresponding to a 50.14% (177 vs. 355) reduction of unnecessary biopsies. After reclassification, only 1.72% (9 of 523 masses) malignancies were missed in the new BI-RADS 3 group. CONCLUSION: Adding SR and CAD to clinical practice may show an optimal performance in reclassifying BI-RADS 4a to 3 categories, and 50.14% masses would be benefit by keeping the rate of undetected malignancies with an acceptable value of 1.72%. ADVANCES IN KNOWLEDGE: Leveraging the potential of SR in conjunction with CAD holds immense promise in substantially reducing the biopsy frequency associated with BI-RADS 3 and 4A lesions, thereby conferring substantial advantages upon patients encompassed within this cohort.

Effect of Pretreatments on the Chemical, Bioactive and Physicochemical Properties of Cinnamomum camphora Seed Kernel Extracts.

Cinnamomum camphora seed kernels (CCSKs) are rich in phytochemicals, especially plant extracts. Phytochemicals play a vital role in therapy due to their strong antioxidant and anti-inflammatory activities. Extracts from CCSK can be obtained through multiple steps, including pretreatment, extraction and purification, and the purpose of pretreatment is to separate the oil from other substances in CCSKs. However, C. camphora seed kernel extracts (CKEs) were usually considered as by-products and discarded, and their potential bioactive values were underestimated. Additionally, little has been known about the effect of pretreatment on CKE. This study aimed to investigate the effects of pretreatment methods (including the solvent extraction method, cold pressing method, aqueous extraction method and sub-critical fluid extraction method) on the extraction yields, phytochemical profiles, volatile compounds and antioxidant capacities of different CKE samples. The results showed that the CKE samples were rich in phenolic compounds (15.28-20.29%) and alkaloids (24.44-27.41%). The extraction yield, bioactive substances content and in vitro antioxidant capacity of CKE pretreated by the sub-critical fluid extraction method (CKE-SCFE) were better than CKEs obtained by other methods. CKE pretreated by the solvent extraction method (CKE-SE) showed the best lipid emulsion protective capacity. Moreover, the volatile substances composition of the CKE samples was greatly influenced by the pretreatment method. The results provided a fundamental basis for evaluating the quality and nutritional value of CKE and increasing the economic value of by-products derived from CCSK.

Development of an Ultrasound-based Nomogram for Predicting Pathologic Complete Response and Axillary Response in Node-Positive Patients with Triple- Negative Breast Cancer.

BACKGROUND: The accurate prediction of pathological complete response (pCR) in the breast and axillary lymph nodes (ALN) before neoadjuvant chemotherapy (NAC) is of utmost importance for the development of treatment strategies. We aim to construct a nomogram on ultrasound (US) and clinical-pathologic factors to predict breast and ALN pCR in node-positive triple-negative breast cancers (TNBCs). METHODS: Patients identified with TNBCs from institution 1 (n = 328) were used for training cohort and those from institution 2 (n = 192) were for validation cohort. US was conducted before and after NAC, and characteristics were obtained from medical records. Univariate and multivariate regression analysis were performed to identify US and clinical-pathologic factors associated with breast and ALN pCR in the training cohort. The assessment of predictive performance was conducted using the receiving operating characteristic curve (ROC), discrimination, and calibration. RESULTS: Overall, 34.6% of patients achieved breast pCR and 48.1% of patients achieved ALN pCR. The nomogram 1 used for predicting pCR in the breast (AUC, 0.84; 95% CI: 0.79, 0.88) outperformed the clinical (AUC, 0.73; 95% CI: 0.68, 0.78) and US models (AUC, 0.79; 95% CI: 0.74, 0.83). The nomogram 2 used for predicting pCR in the axllia (AUC, 0.83; 95% CI: 0.78, 0.87) also outperformed the clinical (AUC, 0.64; 95% CI: 0.58, 0.69) and US models (AUC, 0.80; 95% CI: 0.75, 0.84). The calibration curve and discrimination curve indicate that the nomogram has good calibration performance and clinical applicability. CONCLUSION: The nomogram showed promising predictive performance for predicting breast and ALN pCR in patients with TNBCs.

Identification of hub genes within the CCL18 signaling pathway in hepatocellular carcinoma through bioinformatics analysis.

Introduction: Hepatocellular carcinoma (HCC) is an aggressive malignancy, and CCL18, a marker of M2 macrophage activation, is often associated with tumor immune suppression. However, the role of CCL18 and its signaling pathway in HCC is still limited. Our study focuses on investigating the prognostic impact of CCL18 and its signaling pathway in HCC patients and biological functions in vitro. Methods: HCC-related RNA-seq data were obtained from TCGA, ICGC, and GEO. The 6 hub genes with the highest correlation to prognosis were identified using univariate Cox and LASSO regression analysis. Multivariate Cox regression analysis was performed to assess their independent prognostic potential and a nomogram was constructed. In vitro experiments, including CCK8, EdU, RT-qPCR, western blot, and transwell assays, were conducted to investigate the biological effects of exogenous CCL18 and 6 hub genes. A core network of highly expressed proteins in the high-risk group of tumors was constructed. Immune cell infiltration was evaluated using the ESTIMATE and CIBERSORT packages. Finally, potential treatments were explored using the OncoPredict package and CAMP database. Results: We identified 6 survival-related genes (BMI1, CCR3, CDC25C, CFL1, LDHA, RAC1) within the CCL18 signaling pathway in HCC patients. A nomogram was constructed using the TCGA_LIHC cohort to predict patient survival probability. Exogenous CCL18, as well as overexpression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1, can promote proliferation, migration, invasion, stemness, and increased expression of PD-L1 protein in LM3 and MHCC-97H cell lines. In the high-risk group of patients from the TCGA_LIHC cohort, immune suppression was observed, with a strong correlation to 21 immune-related genes and suppressive immune cells. Conclusion: Exogenous CCL18 promotes LM3 and MHCC-97H cells proliferation, migration, invasion, stemness, and immune evasion. The high expression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1 can serve as a biomarkers for immune evasion in HCC.

The Emergence of the Potential Therapeutic Targets: Ultrasound-Based Radiomics in the Prediction of Human Epidermal Growth Factor Receptor 2-Low Breast Cancer.

RATIONALE AND OBJECTIVES: To evaluate whether ultrasound-based radiomics features can effectively predict HER2-low expression in patients with breast cancer (BC). MATERIAL AND METHODS: Between January 2021 and June 2023, patients who received US scans with pathologically confirmed BC in this multicenter study were included. In total, 383 patients from institution 1 were comprised of training set, 233 patients from institution 2 were comprised of validation set and 149 patients from institution 3 were comprised of external validation set. Radiomics features were derived from conventional ultrasound (US) images. The minimum redundancy and maximum relevancy and the least absolute shrinkage and selector operation algorithm were used to generate an US-based radiomics score (RS). Multivariable logistic regression analysis was used to select variables associated with HER2 expressions. The diagnostic performance of the RS was evaluated through the area under the receiver operating characteristic curve (AUC). RESULTS: In the training set, the RS yield an AUC of 0.81 (95%CI: 0.76-0.84) for differentiation HER2-zero from HER2-low and -positive cases, and performed well in validation set (AUC 0.84, 95%CI: 0.78-0.88) and external validation set (AUC 0.82, 95%CI: 0.73-0.90). In the subgroups analysis, the RS showed good performance in distinguishing HER2-zero from HER2 1 + , HER2 2 + and HER2-low tumors (AUC range, 0.79-0.87). CONCLUSION: The RS based on conventional US is proven effective for predicting HER2-low expression in BC.

Combining Potential Strain Elastography and Radiomics for Diagnosing Breast Lesions in BI-RADS 4: Construction and Validation a Predictive Nomogram.

RATIONALE AND OBJECTIVES: To develop a nomogram by integrating B-mode ultrasound (US), strain ratio (SR), and radiomics signature (RS) effectively differentiating between benign and malignant lesions in the Breast Imaging Reporting and Data System (BI-RADS) 4. MATERIALS AND METHODS: We retrospectively recruited 709 consecutive patients who were assigned a BI-RADS 4 and underwent curative resection or biopsy between 2017 and 2022. US images were collected before surgery. A RS was developed through a multistep feature selection and construction process. Histology findings served as the gold standard. Univariate and multivariate regression analysis were employed to analyze the clinical and US characteristics and identify variables for developing a nomogram. The calibration and discrimination of the nomogram were conducted to evaluate its performance. RESULTS: The study included a total of 709 patients, with 497 in the training set and 212 in the validation set. In the training set, the B-mode US had an AUC of 0.84 (95% confidence interval [CI], 0.80, 0.87). The SR demonstrated an AUC of 0.78 (95% CI, 0.74, 0.82), while the RS showed an AUC of 0.85 (95% CI, 0.81, 0.88). Notably, the nomogram exhibited superior performance compared to the conventional US, SR, and RS (AUC=0.93, both p < 0.05, as per the Delong test). The clinical usefulness of the nomogram was favorable. CONCLUSION: The calibrated nomogram can be specifically designed to predict the malignancy of breast lesions in the BI-RADS 4 category.

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI.

OBJECTIVE: To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US). METHODS: A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set. RESULTS: Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884. CONCLUSION: A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR. ADVANCES IN KNOWLEDGE: A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.

NRP1 interacts with endoglin and VEGFR2 to modulate VEGF signaling and endothelial cell sprouting.

Endothelial cells express neuropilin 1 (NRP1), endoglin (ENG) and vascular endothelial growth factor receptor 2 (VEGFR2), which regulate VEGF-A-mediated vascular development and angiogenesis. However, the link between complex formation among these receptors with VEGF-A-induced signaling and biology is yet unclear. Here, we quantify surface receptor interactions by IgG-mediated immobilization of one receptor, and fluorescence recovery after photobleaching (FRAP) measurements of the mobility of another coexpressed receptor. We observe stable ENG/NRP1, ENG/VEGFR2, and NRP1/VEGFR2 complexes, which are enhanced by VEGF-A. ENG augments NRP1/VEGFR2 interactions, suggesting formation of tripartite complexes bridged by ENG. Effects on signaling are measured in murine embryonic endothelial cells expressing (MEEC+/+) or lacking (MEEC-/-) ENG, along with NRP1 and/or ENG overexpression or knockdown. We find that optimal VEGF-A-mediated phosphorylation of VEGFR2 and Erk1/2 requires ENG and NRP1. ENG or NRP1 increase VEGF-A-induced sprouting, becoming optimal in cells expressing all three receptors, and both processes are inhibited by a MEK1/2 inhibitor. We propose a model where the maximal potency of VEGF-A involves a tripartite complex where ENG bridges VEGFR2 and NRP1, providing an attractive therapeutic target for modulation of VEGF-A signaling and biological responses.