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Traditional markers, such as serum creatinine and blood urea nitrogen, frequently show delayed elevations following acute kidney injury (AKI), limiting their utility for prompt detection and timely intervention in AKI management. Shear wave elastography (SWE) exhibits potential for AKI diagnosis by measuring tissue stiffness. Our study aimed to evaluate the diagnostic performance of SWE in detecting AKI by measuring the stiffness of kidney tissue. Between July 2022 and December 2022, a total of 103 consecutive participants who met the eligibility criteria were prospectively enrolled, underwent SWE measurements, and were classified into AKI or non-AKI groups based on the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) criteria. A receiver operating characteristic (ROC) curve was drawn to examine the feasibility of differentiating between AKI and non-AKI patients and assessing diagnostic performance. The effects of tissue anisotropy on SWE measurements were also examined. Our results revealed that patients in the AKI group exhibited significantly increased stiffness values in specific kidney regions compared with those in the non-AKI group. For the diagnosis of AKI, the optimal cut-off values were identified as 9.9 kPa, 2.9 kPa, and 4.4 kPa for the upper pole medulla, middle cortex, and middle medulla, respectively, in the longitudinal plane. Correspondingly, the areas under the ROC curves for these regions were 0.737 (95% confidence interval [CI]: 0.637, 0.822), 0.736 (95% CI: 0.637, 0.821), and 0.784 (95% CI: 0.688, 0.861). Additionally, we observed a significant variability in stiffness values due to tissue anisotropy, specifically in the segments of the upper pole cortex, and medulla across both longitudinal and transverse planes. SWE serves as a noninvasive approach for the quantification of tissue stiffness and shows promise as an adjunctive tool for the assessment of AKI. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases. Patients and Methods: 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA). Results: The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959– 0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 − 0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively). Conclusion: Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses. [ABSTRACT FROM AUTHOR]