Search

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. [ABSTRACT FROM AUTHOR]

Background The application of photoacoustic imaging (PAI), utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. However, its effectiveness in distinguishing between malignant and benign nodules remains insufficiently explored. Purpose This study aims to develop nomogram models for predicting the benign or malignant nature of breast nodules using PAI. Method A prospective cohort study enrolled 369 breast nodules, subjecting them to PAI and ultrasound examination. The training and testing cohorts were randomly divided into two cohorts in a ratio of 3:1. Based on the source of the variables, three models were developed, Model 1: photoacoustic-BIRADS+BMI + blood oxygenation, Model 2: BIRADS+Shape+Intranodal blood (Doppler) + BMI, Model 3: photoacoustic-BIRADS+BIRADS+ Shape+Intranodal blood (Doppler) + BMI + blood oxygenation. Risk factors were identified through logistic regression, resulting in the creation of three predictive models. These models were evaluated using calibration curves, subject receiver operating characteristic (ROC), and decision curve analysis. Results The area under the ROC curve for the training cohort was 0.91 (95% confidence interval, 95% CI: 0.88–0.95), 0.92 (95% CI: 0.89–0.95), and 0.97 (95% CI: 0.96–0.99) for Models 1–3, and the ROC curve for the testing cohort was 0.95 (95% CI: 0.91–0.98), 0.89 (95% CI: 0.83–0.96), and 0.97 (95% CI: 0.95–0.99) for Models 1–3. Conclusions The calibration curves demonstrate that the model's predictions agree with the actual values. Decision curve analysis suggests a good clinical application. Key message What is already known on this topic—   The application of PAI, utilizing laser-induced ultrasound, shows potential in assessing blood oxygenation in breast nodules. What this study adds—   This study marks a significant advancement in breast cancer diagnostics by developing and validating nomograms that combine PAI with traditional ultrasound, enhancing the accuracy of breast nodule classification. How this study might affect research, practice or policy—   This breakthrough addresses a critical research gap and is poised to revolutionize early and precise detection of breast cancer, potentially influencing future research, clinical practices, and screening policies. [ABSTRACT FROM AUTHOR]

Purpose We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. Methods A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. Results In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P  < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). Conclusion The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy. Key messages What is already known on this topic   The application of artificial intelligence in transrectal ultrasound can assist in the diagnosis of prostate cancer (PCa). What this study adds   Differing from previous studies, our study combined magnetic resonance imaging fusion targeted biopsy and systematic biopsy, in analyzing segmented needle tract ultrasound images of prostate biopsies. Excluding the interference of inaccurate region of interest outlining, a deep learning PCa diagnostic model with better diagnostic efficacy was constructed. How this study might affect research, practice, or policy   Through our model screening, a greater number of suspected PCa patients who need further biopsy can receive the proper diagnosis and treatment, avoiding unnecessary biopsy in patients without PCa. [ABSTRACT FROM AUTHOR]

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: Gmax > Emax > Cmax > Csd > Esd > Gsd > Emean > Gmean > Cmean. There were no significant differences in Emin, Gmin, or Cmin. 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. [ABSTRACT FROM AUTHOR]

Aims: In this systematic review and meta-analysis, we discuss the value of grey-scale ultrasonography (US) in diagnosing adhesive capsulitis of the shoulder (ACS).Material and Methods: We retrieved relevant studies from PubMed, Cochrane Library, and Embase before 8 April 2019. We selected 7 studies concerning 446 patients (490 shoulders) that used grey-scale US to diagnose ACS and magnetic resonance imaging (MRI) or arthroscopy as the reference standard. We assessed the diagnostic accuracy of US on the basis of combined sensitivity, specificity, likelihood ratio (LR), and the area under the summary receiver operating characteristic (SROC) curve (AUC).Results: The combined sensitivity, specificity, positive LR and negative LR were found to be 88% (95%CI: 74-95), 96% (95%CI: 88-99), 23.89 (95%CI: 6.31-90.51) and 0.12 (95%CI: 0.05-0.29), respectively. The AUC was 0.97 (95%CI: 0.96-0.98). ACS was diagnosed on the basis of four US features: coracohumeral ligament thickening, inferior capsule/axillary recess capsule thickening, rotator interval abnormality, and restriction of the range of motion. The corresponding sensitivities were 64.4 (95%CI: 48.8-78.1), 82.1 (95%CI: 73.8-88.7), 82.6 (95%CI: 74.1-89.2) and 94.3 (95%CI: 84.3-98.8), respectively, and specificities were 88.9 (95%CI: 76.0-96.3), 95.7 (95%CI: 90.3-98.6), 93.9 (95%CI: 89.8-96.7), and 90.9 (95%CI: 75.7-98.1), respectively.Conclusions: Our meta-analysis showed that grey-scale US plays a significant role in the diagnosis of ACS. Because of its high sensitivity and specificity, US can be added to the existing clinical diagnosis program. [ABSTRACT FROM AUTHOR]

Aim: Rotator cuff (RC) tears are considered to be the main reason for shoulder pain. Although ultrasound is a useful method to detect it, its effectiveness when diagnosing RC tears has been a heated discussion. Therefore, we aimed at evaluating RC tears' ultrasound accuracy by conducting a systemically review and pooled comprehensive analysis.Materials and Methods: Relevant articles up to May 2018 were searched from the Cochrane Library, Embase, and Pubmed databases. Either arthroscopy or magnetic resonance imaging (MRI) was considered as a reference standard. The results were estimated by pooled-sensitivity (P-SEN), pooled-specificity (P-SPE), pooled-diagnostic odds ratio (P-DOR), pooled-likelihood ratio+ (PLR+), pooled-likelihood ratio- (P-LR-), and the area under the summary receiver operating characteristic (SROC).Result: We selected seven prospective studies in accordance with the inclusion criteria that covered 554 rotator cuff tears in 868 patients. The P-SEN, P-SPE, P-LR+, P-LR-, P-DOR, area under the SROC curve of diagnostic performance of ultrasound for RC and post-test probability were 95% (95% CI: 88 - 98), 72% (95% CI: 61 - 81), 3.41 (95% CI: 2.40 - 4.84), 0.08 (95% CI: 0.03 - 0.16), 45.31 (95% CI: 21.04 - 97.59), 89% (95% CI: 0.86 - 0.91), and 46% and 2%, respectively.Conclusion: Our metaanalysis demonstrates that ultrasound has a high efficiency for RC tears' diagnosis. It can be a promising method in patients with suspected RC tears because of its high sensitivity, specificity and diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: This study investigated the preoperative predictive efficiency of radiomics derived from photoacoustic (PA) imaging, integrated with the clinical features of Ki-67 expression in malignant breast cancer (BC), with a focus on both intratumoral and peritumoral regions., Methods: This study involved 359 patients, divided into a training set (n = 251) and a testing set (n = 108). Radiomic features were extracted from intratumoral and peritumoral regions using PA imaging. Multivariate logistic regression was employed to identify significant clinical factors. LASSO regression was used to select the features extracted from the training set. The selected radiomics features were combined with clinical features to develop a radiomics nomogram. The predictive efficiency of the model was assessed using the area under the receiver operating characteristic curve (AUC), and its clinical utility and accuracy were evaluated through decision curve analysis and calibration curves, respectively., Results: The developed nomogram combined 6 mm peritumoral data with intratumoral and clinical features and showed excellent diagnostic performance, achieving an AUC of 0.899 in the testing set. They both showed good calibrations. The outperformed models based solely on clinical features or other radiomics methods, with the 6 mm surrounding tumor area proving most effective in identifying Ki-67 status in BC patients., Conclusion: Integrating PA radiomics with clinical features offers a robust preoperative tool for predicting Ki-67 status in BC, optimizing the delineation of peritumoral regions for enhanced diagnostic precision. The model's strong performance supports its potential as a non-invasive adjunct to traditional biopsy methods, aiding in the personalized management of BC treatment., 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. Conflict of Interest 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 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: This study aims to assess the predictive ability of photoacoustic (PA) imaging-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions., Methods: This study involved 369 patients from Shenzhen People's Hospital, divided into a training set of 295 and a testing set of 74. PA imaging data were collected from all participants, and radiomics analysis was performed on intratumoral and various peritumoral regions. Features extracted from the training set were analyzed using LASSO regression to construct a model integrating radiomics features with clinical characteristics. Clinical factors were determined through multivariate logistic regression analysis. A radiomics nomogram was developed using logistic regression classifiers, combining radiomics features and clinical factors. The predictive efficacy of the model was evaluated using the areas under curves (AUC), and its clinical utility and accuracy were assessed through decision curve analysis and calibration curves, respectively., Results: The developed nomogram combines 5 mm peritumoral data with intratumoral and clinical features and shows excellent diagnostic performance, achieving an AUC of 0.972 in the training set and in the testing achieved 0.905. They both showed good calibrations. The model outperformed models based solely on clinical features or other radiomics methods, with the 5 mm surrounding tumor area proving most effective in identifying positive versus negative ALN in breast cancer patients., Conclusion: The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment., Summary: This study highlights the effectiveness of combining photoacoustic radiomics with clinical parameters to predict axillary lymph node status in breast cancer, identifying a 5 mm peritumoral model as particularly potent. Future research should aim to enhance this model's robustness by expanding the sample size and advancing imaging technologies for broader clinical application., Competing Interests: Declaration of Competing Interest 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 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Accurate prediction of breast cancer (BC) is essential for effective treatment planning and improving patient outcomes. This study proposes a novel deep learning (DL) approach using photoacoustic (PA) imaging to enhance BC prediction accuracy. We enrolled 334 patients with breast lesions from Shenzhen People's Hospital between January 2022 and January 2024. Our method employs a ResNet50-based model combined with attention mechanisms to analyze photoacoustic ultrasound (PA-US) images. Experiments demonstrated that the PAUS-ResAM50 model achieved superior performance, with an AUC of 0.917 (95% CI: 0.884 -0.951), sensitivity of 0.750, accuracy of 0.854, and specificity of 0.920 in the training set. In the testing set, the model maintained high performance with an AUC of 0.870 (95% CI: 0.778-0.962), sensitivity of 0.786, specificity of 0.872, and accuracy of 0.836. Our model significantly outperformed other models, including PAUS-ResNet50, BMUS-ResAM50, and BMUS-ResNet50, as validated by the DeLong test (p < 0.05 for all comparisons). Additionally, the PAUS-ResAM50 model improved radiologists' diagnostic specificity without reducing sensitivity, highlighting its potential for clinical application. In conclusion, the PAUS-ResAM50 model demonstrates substantial promise for optimizing BC diagnosis and aiding radiologists in early detection of BC., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2024 Optica Publishing Group.)

The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames., Competing Interests: 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., (© 2022 The Author(s).)

Background: Magnetic resonance imaging (MRI) has advantages in the diagnosis of prostate diseases, but there is also overdiagnosis. We compensate for this with fusion imaging and elastography. In this study, we want to evaluate Elastographic Q-analysis score (EQS) combined with Prostate Imaging Reporting and Data System (PI-RADS), based on transrectal ultrasound (TRUS)/multi-parameter magnetic resonance imaging (MP-MRI) fusion biopsy in differentiating benign and malignant prostate lesions., Methods: A total of 296 patients with 318 prostate lesions who underwent TRUS/MP-MRI fusion biopsy between October 2017 and October 2019 were retrospectively analysed. The performance of the EQS was evaluated on the sites of the suspicious areas of MP-MRI. The cut-off value of EQS was obtained according to receiver operating characteristic (ROC) curve, which was used to upgrade and downgrade the PI-RADS scores. The area under the curve (AUC), integrated discrimination improvement, and decision curve analysis were used to assess the new PI-RADS performance., Results: In total, 318 MP-MRI suspicious prostate lesions (94 malignant vs. 224 benign lesions). The EQS optimal threshold was 1.85, and the AUC was 0.816. All cases were constructed three models by using 1.85 as the cut-off value: upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS. The AUC of PI-RADS, upgrade-PI-RADS, downgrade-PI-RADS and complex-PI-RADS were 0.869, 0.867, 0.872 and 0.873 respectively. The diagnostic coincidence rate of PI-RADS was increased from 0.667 to 0.874 by using strain elastography, among which the diagnostic rate of prostate cancer was increased from 0.557 to 0.806, and the diagnostic rate of non-prostate cancer was increased from 0.775 to 0.967. The integrated discrimination improvement indicated that downgrade-PI-RADS had a better diagnostic capability (P<0.05). The net benefit of all models, which downgrade-PI-RADS can maximize the net benefit value of patients by decision curve analysis., Conclusions: The combination of PI-RADS and EQS with TRUS/MP-MRI fusion, particularly downgrade-PI-RADS, can reduce unnecessary biopsy procedures and prevent overdiagnosis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-932/coif). The authors have no conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

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.)