Background: Intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) and dynamic contrast‐enhanced MRI (DCE‐MRI) are sensitive imaging modalities for detecting liver lesions, but their value in evaluating cirrhosis‐related nodules remains unclear.Purpose: To investigate whether IVIM‐DWI and DCE‐MRI can differentiate different types of cirrhosis‐related nodules, and whether these modalities can monitor changes in cell density and angiogenesis during the malignant transformation of cirrhosis‐related nodules in a rat modelStudy Type: Prospective.Animal Model: Thirty‐five male Sprague–Dawley rats with 106 cirrhosis‐related nodules (19 regenerative nodules [RNs], 47 dysplastic nodules [DNs], and 40 hepatocellular carcinomas [HCCs]).Field Strength/Sequence: IVIM‐DWI and DCE sequence at 3.0T MRI.Assessment: IVIM‐DWI parameters (D, D*, f, and apparent diffusion coefficient [ADC]) and DCE‐MRI parameters (K^trans, K_ep, and V_e) were calculated by two radiologists using postprocessing software. The “cell density” and “unpaired arterial ratio” were analyzed with a microscope by two pathologists.Statistical Tests: MRI parameters were compared among the different types of nodules by one‐way analysis of variance or the Kruskal–Wallis test. The Pearson correlation test was used to analyze the correlation of MRI parameters with the pathological types of nodules, cell density, and unpaired arterial ratio.Results: The K^trans, K_ep, and V_e values of HCCs were significantly higher than those of DNs and RNs. D and ADC values were significantly lower in HCCs than in DNs and RNs. There were moderate positive correlations of K^trans with the pathological types of nodules and the unpaired arterial ratio. Moderate negative correlations were observed among D, ADC, and the pathological types of nodules, between D and cell density, and between ADC and cell density.Data Conclusion: IVIM‐DWI and DCE‐MRI are valuable in differentiating different types of cirrhotic‐related nodules. D and ADC are correlated with changes in cell density during the malignant transformation of cirrhosis‐related nodules, while K^trans is correlated with increased angiogenesis.Level of Evidence: 1Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2020;51:812–823.

Background: We developed a deep learning radiomics nomogram (DLRN) using CT scans to improve clinical decision-making and risk stratification for early recurrence of hepatocellular carcinoma (HCC) after transplantation, which typically has a poor prognosis. Materials and methods: In this two-center study, 245 HCC patients who had contrast-enhanced CT before liver transplantation were split into a training set (n = 184) and a validation set (n = 61). We extracted radiomics and deep learning features from tumor and peritumor areas on preoperative CT images. The DLRN was created by combining these features with significant clinical variables using multivariate logistic regression. Its performance was validated against four traditional risk criteria to assess its additional value. Results: The DLRN model showed strong predictive accuracy for early HCC recurrence post-transplant, with AUCs of 0.884 and 0.829 in training and validation groups. High DLRN scores significantly increased relapse risk by 16.370 times (95% CI: 7.100–31.690; p < 0.001). Combining DLRN with Metro-Ticket 2.0 criteria yielded the best prediction (AUC: training/validation: 0.936/0.863). Conclusion: The CT-based DLRN offers a non-invasive method for predicting early recurrence following liver transplantation in patients with HCC. Furthermore, it provides substantial additional predictive value with traditional prognostic scoring systems. Critical relevance statement: AI-driven predictive models utilizing preoperative CT imaging enable accurate identification of early HCC recurrence risk following liver transplantation, facilitating risk-stratified surveillance protocols and optimized post-transplant management. Key Points: A CT-based DLRN for predicting early HCC recurrence post-transplant was developed. The DLRN predicted recurrence with high accuracy (AUC: 0.829) and 16.370-fold increased recurrence risk. Combining DLRN with Metro-Ticket 2.0 criteria achieved optimal prediction (AUC: 0.863). Graphical

A novel nanocomposite, [Eu(BTD)₃(DPBT)]–BSA@MnO₂, is reported to serve as an effective nanoprobe for bimodal time-gated luminescence (TGL) and magnetic resonance (MR) imaging of H₂O₂in vitro and in vivo.

Objectives: Preoperative differentiation of muscle-invasive bladder cancer (MIBC) is challenging. This study explores the application of the Dual-Energy CT (DECT) model and the vesical imaging reporting and data system (VI-RADS) in assessing MIBC and compares their diagnostic performance. Materials and methods: This single-center prospective study included 105 patients (33 MIBC and 72 non-MIBC cases). Two radiologists independently performed DECT morphological assessment, and evaluated quantitative DECT parameters and VI-RADS scores, blinded to the pathological findings. A DECT-based model was constructed by integrating independent quantitative predictors with tumor diameter ( D ). Diagnostic performance was compared using the area under the receiver operating characteristic curve (AUC). Subgroup analyses were performed for equivocal VI-RADS 3–4 cases and small lesions ( D < 3 cm). Results: VI-RADS achieved an AUC of 0.926 (sensitivity 0.970) but low specificity (0.542). The DECT model showed moderate performance (AUC = 0.761). However, the DECT + D model achieved an AUC of 0.925, comparable to VI-RADS ( p > 0.05), with improved specificity (0.833). Crucially, in equivocal VI-RADS 3–4 cases, DECT + D (AUC = 0.904) significantly outperformed VI-RADS (AUC = 0.652, p < 0.05), while standalone DECT achieved an AUC of 0.824. For D < 3 cm, DECT + D (AUC = 0.895) and DECT (AUC = 0.857) were comparable to VI-RADS (AUC = 0.866), but DECT maintained high specificity (0.934). A nomogram and web-based risk calculator were developed. Conclusion: The DECT-based model achieves diagnostic accuracy comparable to VI-RADS and demonstrates superior stability in clinically challenging subgroups. It serves as a robust, objective alternative for preoperative bladder cancer staging, particularly beneficial for patients with MRI contraindications. Critical relevance statement: This study demonstrates that a quantitative DECT-based model provides an accurate, objective alternative to MRI for preoperative bladder cancer staging, mitigating diagnostic ambiguity in clinically equivocal cases and ensuring accurate risk stratification in small lesions. Key Points: Preoperative differentiation of muscle invasion is critical for bladder cancer management. The DECT-based model yields diagnostic accuracy statistically comparable to VI-RADS. The DECT-based model improves diagnostic confidence in VI-RADS 3–4 lesions. Graphical

Background: Positive surgical margin (PSM) or apical positive surgical margin (APSM) is an established predictive factor of biochemical recurrence or disease progression in prostate cancer (PCa) patients after radical prostatectomy. Since there are limited usable magnetic resonance imaging (MRI)-based models, we sought to explore the role of three-dimensional (3D) visualization for preoperative MRI in the prediction of PSM or APSM.Methods: From December 2016 to April 2022, 149 consecutive PCa patients who underwent radical prostatectomy were retrospectively selected from the Second Affiliated Hospital of Dalian Medical University. According to the presence of PSM or APSM, patients were divided into a PSM group (n=41) and a without PSM group (n=108) and into an APSM group (n=33) and a without APSM group (n=116). Twenty-one parameters, including prostate apical shape, PCa distance to the membranous urethra, and pubic angle, were measured on 3D visualization of MRI. The development of the nomogram models was built by the findings of multivariate logistic regression analysis for significant factors.Results: To predict the probability of PSM, a longer PCa distance to the membranous urethra (OR=0.136, p=0.019) and the distance from the anterior peritoneum to the anterior border of the coccyx (work space AP, OR=0.240, p=0.030) were independent protective factors, while a type 3 prostate apical shape (OR=8.262, p=0.025) and larger pubic angle 2 (OR=5.303, p=0.029) were identified as independent risk factors. The nomogram model presented an area under the curve (AUC) of the receiver operating characteristic curve (ROC) of PSM of 0.777. In evaluating the incidence of APSM, we found that the distance to the membranous urethra (OR=0.135, p=0.014) was associated with a low risk of APSM, while larger pubic angle 1 (OR=4.666, p=0.043) was connected to a higher risk of APSM. The nomogram model showed that the AUC of APSM was 0.755.Conclusion: As 3D visualization for preoperative MRI showed good performance in predicting PSM or APSM, the tool might be potentially valuable, which also needs to be validated by multicenter, large-scale, prospective studies.