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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]