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Background: HER2 is a key biomarker for breast cancer treatment and prognosis. Traditional assessment methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are effective but costly and time-consuming. Our model incorporates these methods alongside photoacoustic imaging to enhance diagnostic accuracy and provide more comprehensive clinical insights., Materials and Methods: A total of 301 breast tumors were included in this study, divided into HER2-positive (3+ or 2+ with gene amplification) and HER2-negative (below 3+ and 2+ without gene amplification) groups. Samples were split into training and validation sets in a 7:3 ratio. Statistical analyses involved t-tests, chi-square tests, and rank-sum tests. Predictive factors were identified using univariate and multivariate logistic regression, leading to the creation of three models: ModA (clinical factors only), ModB (clinical plus ultrasound factors), and ModC (clinical, ultrasound, and photoacoustic imaging-derived oxygen saturation (SO2))., Results: The area under the curve (AUC) for ModA was 0.756 (95 % CI: 0.69-0.82), ModB increased to 0.866 (95 % CI: 0.82-0.91), and ModC showed the highest performance with an AUC of 0.877 (95 % CI: 0.83-0.92). These results indicate that the comprehensive model combining clinical, ultrasound, and photoacoustic imaging data (ModC) performed best in predicting HER2 expression., Conclusion: The findings suggest that integrating clinical, ultrasound, and photoacoustic imaging data significantly enhances the accuracy of predicting HER2 expression. For personalised breast cancer treatment, the integrated model could provide a comprehensive and reproducible decision support tool., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objectives: To develop a nomogram based on photoacoustic imaging (PAI) radiomics and BI-RADs to identify breast cancer (BC) in BI-RADS 4 or 5 lesions detected by ultrasound (US)., Methods: In this retrospective study, 119 females with 119 breast lesions at US and PAI examination were included (January 2022 to December 2022). Patients were divided into the training set (n = 83) or testing set (n = 36) to develop a nomogram to identify BC in BI-RADS 4 or 5 lesions. Relevant factors at clinic, BI-RADS category, and PAI were reviewed. Univariate and multivariate regression was used to evaluate factors for associations with BC. To evaluate the diagnostic performance of nomogram, the area under the curve (AUC) of receiver operating characteristic curve, accuracy, specificity and sensitivity was employed., Results: The nomogram that included BI-RADS category and PAI radiomics score demonstrated a high AUC of 0.925 (95%CI: 0.8467-0.9712) in the training set and 0.926 (95%CI: 0.846-1.000) in the test set. The nomogram also showed significantly better discrimination than the radiomics score (P = .048) or BI-RADS category (P = .009) in the training set. These significant differences were demonstrated in the testing set, outperform the radiomics score (P = .038) and BI-RADS category (P = .013)., Conclusions: The nomogram developed with BI-RADS and PAI radiomics score can effectively identify BC in BI-RADS 4 or 5 lesions. This technique has the potential to further improve early diagnostic accuracy for BC., Competing Interests: Disclosure The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Hypoxia is a hallmark of breast cancer (BC). Photoacoustic (PA) imaging, based on the use of laser-generated ultrasound (US), can detect oxygen saturation (So 2 ) in the tissues of breast lesion patients., Purpose: To measure the oxygenation status of tissue in and on both sides of the lesion in breast lesion participants using a multimodal Photoacoustic/ultrasound (PA/US) imaging system and to determine the correlation between So 2 measured by PA imaging and benign or malignant disease., Materials and Methods: Multimodal PA/US imaging and gray-scale US (GSUS) of breast lesion was performed in consecutive breast lesion participants imaged in the US Outpatient Clinic between 2022 and 2023. Dual-wavelength PA imaging was used to measure the So 2 value inside the lesion and on both sides of the tissue, and to distinguish benign from malignant lesions based on the So 2 value. The ability of So 2 to distinguish benign from malignant breast lesions was evaluated by the receiver operating characteristic curve (ROC) and the De-Long test., Results: A total of 120 breast lesion participants (median age, 42.5 years) were included in the study. The malignant lesions exhibited lower So 2 levels compared to benign lesions (malignant: 71.30%; benign: 83.81%; P < .01). Moreover, PA/US imaging demonstrates superior diagnostic results compared to GSUS, with an area under the curve (AUC) of 0.89 versus 0.70, sensitivity of 89.58% versus 85.42%, and specificity of 86.11% versus 55.56% at the So 2 cut-off value of 78.85 (P < .001). The false positive rate in GSUS reduced by 30.75%, and the false negative rate diminished by 4.16% with PA /US diagnosis. Finally, the So 2 on both sides tissues of malignant lesions are lower than that of benign lesions (P < .01)., Conclusion: PA imaging allows for the assessment of So 2 within the lesions of breast lesion patients, thereby facilitating a superior distinction between benign and malignant lesions., Competing Interests: Disclosure We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled., (Copyright © 2024. Published by Elsevier Inc.)

Objective: Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening., Methods: Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts., Results: This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening., Conclusions: The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts., Competing Interests: Declaration of competing interest We declare that we have no financial or personal relationships with other people or organisations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Background: The risk of ductal carcinoma in situ (DCIS) identified by biopsy often increases during surgery. Therefore, confirming the DCIS grade preoperatively is necessary for clinical decision-making., Purpose: To train a three-classification deep learning (DL) model based on ultrasound (US), combining clinical data, mammography (MG), US, and core needle biopsy (CNB) pathology to predict low-grade DCIS, intermediate-to-high-grade DCIS, and upstaged DCIS., Materials and Methods: Data of 733 patients with 754 DCIS cases confirmed by biopsy were retrospectively collected from May 2013 to June 2022 (N1), and other data (N2) were confirmed by biopsy as low-grade DCIS. The lesions were randomly divided into training (n=471), validation (n=142), and test (n = 141) sets to establish the DCIS-Net. Information on the DCIS-Net, clinical (age and sign), US (size, calcifications, type, breast imaging reporting and data system [BI-RADS]), MG (microcalcifications, BI-RADS), and CNB pathology (nuclear grade, architectural features, and immunohistochemistry) were collected. Logistic regression and random forest analyses were conducted to develop Multimodal DCIS-Net to calculate the specificity, sensitivity, accuracy, receiver operating characteristic curve, and area under the curve (AUC)., Results: In the test set of N1, the accuracy and AUC of the multimodal DCIS-Net were 0.752-0.766 and 0.859-0.907 in the three-classification task, respectively. The accuracy and AUC for discriminating DCIS from upstaged DCIS were 0.751-0.780 and 0.829-0.861, respectively. In the test set of N2, the accuracy and AUC of discriminating low-grade DCIS from upstaged low-grade DCIS were 0.769-0.987 and 0.818-0.939, respectively. DL was ranked from one to five in the importance of features in the multimodal-DCIS-Net., Conclusion: By developing the DCIS-Net and integrating it with multimodal information, diagnosing low-grade DCIS, intermediate-to high-grade DCIS, and upstaged DCIS is possible. It can also be used to distinguish DCIS from upstaged DCIS and low-grade DCIS from upstaged low-grade DCIS, which could pave the way for the DCIS clinical workflow., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The application of artificial intelligence (AI) in the ultrasound (US) diagnosis of breast cancer (BCa) is increasingly prevalent. However, the impact of US-probe frequencies on the diagnostic efficacy of AI models has not been clearly established., Objectives: To explore the impact of using US-video of variable frequencies on the diagnostic efficacy of AI in breast US screening., Methods: This study utilized different frequency US-probes (L14: frequency range: 3.0-14.0 MHz, central frequency 9 MHz, L9: frequency range: 2.5-9.0 MHz, central frequency 6.5 MHz and L13: frequency range: 3.6-13.5 MHz, central frequency 8 MHz, L7: frequency range: 3-7 MHz, central frequency 4.0 MHz, linear arrays) to collect breast-video and applied an entropy-based deep learning approach for evaluation. We analyzed the average two-dimensional image entropy (2-DIE) of these videos and the performance of AI models in processing videos from these different frequencies to assess how probe frequency affects AI diagnostic performance., Results: The study found that in testing set 1, L9 was higher than L14 in average 2-DIE; in testing set 2, L13 was higher in average 2-DIE than L7. The diagnostic efficacy of US-data, utilized in AI model analysis, varied across different frequencies (AUC: L9 > L14: 0.849 vs. 0.784; L13 > L7: 0.920 vs. 0.887)., Conclusion: This study indicate that US-data acquired using probes with varying frequencies exhibit diverse average 2-DIE values, and datasets characterized by higher average 2-DIE demonstrate enhanced diagnostic outcomes in AI-driven BCa diagnosis. Unlike other studies, our research emphasizes the importance of US-probe frequency selection on AI model diagnostic performance, rather than focusing solely on the AI algorithms themselves. These insights offer a new perspective for early BCa screening and diagnosis and are of significant for future choices of US equipment and optimization of AI algorithms., (© 2023. The Author(s).)

Purpose: To assess whether gray-scale ultrasound (US) based radiomic features can help distinguish HER2 expressions (ie, HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer., Materials and Methods: This retrospective study encompassed female breast cancer patients who underwent US examinations at two distinct centers from February 2021 to July 2023. Tumor segmentation and radiomic feature extraction were performed on grayscale US images. Decision Tree analysis was employed to simultaneously evaluate feature importance, and the Least Absolute Shrinkage and Selection Operator technique was utilized for feature selection to construct the radiomic signature. The Area Under the Curve (AUC) of the Receiver Operating Characteristic curve was employed to assess the performance of the radiomic features. Multivariate logistic regression was used to identify independent predictors for distinguishing HER2 expression in the dataset., Results: The training set comprised 292 patients from Center 1 (median, 51 years; interquartile range [IQR]: 45-61), while the external validation set included 131 patients from Center 2 (median, 51 years; IQR: 45-62). In the external validation dataset, the radiomic features achieved AUC of 0.76 for distinguishing between HER2-low and positive tumors versus HER2-zero tumors. The AUC for differentiating HER2-low (1+) from HER2-zero tumors was 0.74, and for distinguishing HER2-low (2+) from HER2-zero tumors, the AUC was 0.77. In the multivariate analysis assessing HER2-low and HER2-positive versus HER2-zero tumors, internal echoes (P = .029) and margins (P < .001) emerged as independent predictive factors., Conclusion: The radiomic signature and tumor descriptors from gray-scale US may predict distinct HER2 expressions of breast cancers with therapeutic implications., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Aim: Mitochondria are essential for energy metabolism in the tumor microenvironment and the survival of cancer cells., Background: ADP-ribosylation factor-like GTPase 5b (ARL5B) has been found to be associated with mitochondrial dysfunction and breast cancer (BC) progression, but the underlying mechanism needs to be further understood., Objective: We investigated the effects of ARL5B on the apoptosis and glycolysis of breast cancer cells and its underlying mechanisms., Methods: Quantitative reverse transcription-PCR (qRT-PCR) and western blot assays were used to detect the expression of ARL5B in breast cancer tissues and cells. An ARL5B loss-of-function assay was performed to verify its role in BC development., Results: ARL5B was upregulated in breast cancer tissues and cell lines. ARL5B knockdown induced apoptosis and activated the mitochondrial pathway in breast cancer cells. Interestingly, the inhibition of ARL5B repressed the aerobic glycolysis of breast cancer cells. The role of ARL5B in breast cancer cells was exerted by mediating the activation of viral RNA sensor MDA5-evoked signaling. Silencing ARL5B triggered MDA5 signaling by upregulating the key proteins involved in the MDA5 pathway. Importantly, MDA5 silencing reversed the effects of ARL5B knockdown on mitochondrial-mediated apoptosis and glycolysis, whereas poly (I:C), as a ligand for MDA5, further enhanced ARL5B knockdown- facilitated mitochondrial apoptosis and the inhibition of glycolysis., Conclusion: The knockdown of ARL5B activated MDA5 signaling and thus led to the enhanced mitochondrial- mediated apoptosis and glycolysis inhibition in breast cancer cells. Our study suggested that ARL5B might be a potential therapy target for BC., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objectives: Evaluating the value of screening breast masses by separate or combined use of multimodal Sound Touch Elastography., Methods: Women with 159 masses (mean size, 14.86 ± 6.57 mm; range, 5.30-30.00 mm) were enrolled in the study. The pathology results were adopted as diagnostic standards. The abilities of Young's modulus (E), shear modulus (G), and shear wave (C) to differentiate malignant and benign breast masses based on receiver operating characteristic curves were evaluated, and the optimal cutoff values were obtained. Sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated. Then, the values were combined to perform an overall analysis of Sound Touch Elastography using evidence-based medicine, construct forest plots, and calculate areas under the summary receiver operating characteristic curves, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic scores., Results: A total of 159 masses with a mean size of 14.86 ± 6.57 mm (range, 5.30-30.00 mm) were included. For the various parameters, the diagnostic values were as follows: G max  > E max  > C max  > C sd  > E sd  > G sd  > E mean  > G mean  > C mean . There were no significant differences in E min , G min , or C min . When the 9 parameters were combined, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic scores and areas under the summary receiver operating characteristic curves were 84% (95% confidence interval [CI], 79%-88%), 82% (95% CI, 80%-84%), 4.75 (95% CI, 4.15-5.43), 0.20 (95% CI, 0.15-0.25), 3.19 (95% CI, 2.84-3.54) and 90.2% (95% CI, 87%-92%), respectively., Conclusions: Sound Touch Elastography can be recognized as a new ultrasound-based diagnostic method for differentiation between benign and malignant breast masses., (© 2018 by the American Institute of Ultrasound in Medicine.)

The aim of the study described here was to screen breast lesions using either or both shear modulus (G) and its 1-mm shell (S) in sound touch elastography through a retrospective study of 209 consecutive women with breast lesions. The ability of G and S data to differentiate between malignant and benign lesions was evaluated using the receiver operating characteristic (ROC) curve. The optimal cutoff point, sensitivity, specificity, positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were calculated. Then, the parameters were pooled to determine the area under the summary receiver operating curve (AUSROC). The pooled sensitivity (PSen), pooled specificity (PSpe), pooled LR+ (PLR+), pooled LR- (PLR-) and diagnostic score (DS) were calculated. Pathologic examination results were used as the reference. In total, 209 patients with 155 benign and 54 malignant lesions were enrolled. For G max , G mean and G sd , the cutoff values were 35.15 kPa (p = 0.0001), 10.18 kPa (p = 0.0001) and 5.18 kPa (p = 0.0001), respectively. For S max , S mean and S sd , the cutoff values were 40.94 kPa (p = 0.001), 13.12 kPa (p = 0.0001) and 7.97 kPa (p = 0.0001), respectively. There were no significant differences in G min and S min between benign and malignant lesions. For the pooled six parameters, the PSen, PSpe, PLR+, PLR-, DS and AUSROC were 86% (95% confidence interval: 82%-89%), 82% (80%-85%), 4.90 (4.24-5.68), 0.17 (0.13-0.22), 3.36 (3.00-3.72) and 91% (88-93%), respectively. The G and S parameters of sound touch elastography could provide valuable data for the evaluation of breast lesions. Additionally, use of multiple parameters or combined use of the six parameters may be more effective in the evaluation of breast lesions., (Copyright © 2018 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)