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Objectives To predict kidney fibrosis in patients with chronic kidney disease using radiomics of two-dimensional ultrasound (B-mode) and Sound Touch Elastography (STE) images in combination with clinical features. Methods The Mindray Resona 7 ultrasonic diagnostic apparatus with SC5-1U convex array probe (bandwidth frequency of 1–5 MHz) was used to perform two-dimensional ultrasound and STE software. The severity of cortical tubulointerstitial fibrosis was divided into three grades: mild interstitial fibrosis and tubular atrophy (IFTA), fibrotic area < 25%; moderate IFTA, fibrotic area 26–50%; and severe IFTA, fibrotic area > 50%. After extracting radiomics from B-mode and STE images in these patients, we analyzed two classification schemes: mild versus moderate-to-severe IFTA, and mild-to-moderate versus severe IFTA. A nomogram was constructed based on multiple logistic regression analyses, combining clinical and radiomics. The performance of the nomogram for differentiation was evaluated using receiver operating characteristic (ROC), calibration, and decision curves. Results A total of 150 patients undergoing kidney biopsy were enrolled (mild IFTA: n = 74; moderate IFTA: n = 33; severe IFTA: n = 43) and randomized into training (n = 105) and validation cohorts (n = 45). To differentiate between mild and moderate-to-severe IFTA, a nomogram incorporating STE radiomics, albumin, and estimated glomerular filtration (eGFR) rate achieved an area under the ROC curve (AUC) of 0.91 (95% confidence interval [CI]: 0.85–0.97) and 0.85 (95% CI: 0.77–0.98) in the training and validation cohorts, respectively. Between mild-to-moderate and severe IFTA, the nomogram incorporating B-mode and STE radiomics features, age, and eGFR achieved an AUC of 0.93 (95% CI: 0.89–0.98) and 0.83 (95% CI: 0.70–0.95) in the training and validation cohorts, respectively. Finally, we performed a decision curve analysis and found that the nomogram using both radiomics and clinical features exhibited better predictability than any other model (DeLong test, p < 0.05 for the training and validation cohorts). Conclusion A nomogram based on two-dimensional ultrasound and STE radiomics and clinical features served as a non-invasive tool capable of differentiating kidney fibrosis of different severities. Key Points • Radiomics calculated based on the ultrasound imaging may be used to predict the severities of kidney fibrosis. • Radiomics may be used to identify clinical features associated with the progression of tubulointerstitial fibrosis in patients with CKD. • Non-invasive ultrasound imaging-based radiomics method with accuracy aids in detecting renal fibrosis with different IFTA severities.

Aim: The study retrospectively analysed the accuracy of preoperative contrast-enhanced ultrasound (CEUS) in differenti - ating stage Ta-T1 or low-grade bladder cancer (BC) from stage T2 or high-grade bladder cancer. Material and methods: We systematically searched the literature indexed in PubMed, Embase, and the Cochrane Library for original diagnostic articles of bladder cancer. The diagnostic accuracy of CEUS was compared with cystoscopy and/or transurethral resection of bladder tumors (TURBT). The bivariate logistic regression model was used for data pooling, couple forest plot, diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC). Results: Five studies met the selection criteria; the overall number of reported bladder cancers patients were 436. The pooled-sensitivity (P-SEN), pooled-specificity (P-SPE), pooled- positive likelihood ratio (PLR+), pooled-negative likelihood ratio (PLR−), DOR, and area under the SROC curve were 94.0% (95%CI: 85%–98%), 90% (95%CI: 83%–95%), 9.5 (95%CI: 5.1–17.6), 0.06 (95%CI: 0.02–0.17), 147 (95%CI: 35–612) and 97% (95% CI: 95%–98%) respectively. Conclusion: CEUS reaches a high efficiency in discriminating Ta-T1 or low-grade bladder cancer from stage T2 or high-grade bladder cancer. It can be a promising method in patients to distinguish T staging and grading of bladder cancer because of its high sensitivity, specificity and diagnostic accuracy.