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Background Contrast-enhanced US (CEUS) can be used preoperatively for evaluating muscle invasion in bladder cancer, which is important for determining appropriate treatment. However, diagnostic criteria for assessing this at CEUS have not been standardized. Purpose To develop and validate a CEUS Vesical Imaging Reporting and Data System (VI-RADS) for evaluating muscle invasion in bladder cancer. Materials and Methods This single-center prospective study consecutively enrolled patients with suspected bladder cancer. Participants underwent transabdominal or intracavity CEUS between July 2021 and May 2023. Participants were divided into a training set and a validation set at a 2:1 ratio based on the chronologic order of enrollment. The training set was used to identify major imaging features to include in CEUS VI-RADS, and the likelihood of muscle invasion per category was determined using a pathologic reference standard. The optimal VI-RADS category cutoff for muscle invasion was determined with use of the maximum Youden index. The validation set was assessed by novice and expert readers and used to validate the diagnostic performance and interreader agreement of the developed system. Results Overall, 126 participants (median age, 64 years [IQR, 57-71 years]; 107 male) and 67 participants (median age, 64 years [IQR, 56-69 years]; 49 male) were included in the training and validation set, respectively. In the training set, the optimal CEUS VI-RADS category cutoff for muscle invasion was VI-RADS 4 or higher (Youden index, 0.77). In the validation set, CEUS VI-RADS achieved good performance for both novice and expert readers (area under the receiver operating characteristic curve, 0.80 [95% CI: 0.70, 0.90] vs 0.88 [95% CI: 0.80, 0.97]; P = .09). The interreader agreement regarding the evaluation of CEUS VI-RADS category was 0.77 (95% CI: 0.65, 0.85) for novice readers, 0.87 (95% CI: 0.79, 0.92) for expert readers, and 0.78 (95% CI: 0.70, 0.84) for all readers. Conclusion The developed CEUS VI-RADS showed good performance and interreader agreement for the assessment of muscle invasion in bladder cancer. Chinese Clinical Trial Registry no. ChiCTR2100049435 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Morrell in this issue.

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BACKGROUND: Large extremity hematoma can rarely happen after contrast extravasation during a contrast-enhanced computed tomography scan. Some hematomas need prompt surgical managements.
METHODS: A 77-year-old man had acute ischemic stroke and received the thrombolytic and antiplatelet therapies. He had a contrast extravasation during the computed tomography scan and developed a large hematoma in the right forearm, despite without evidence of compartment syndrome.
METHODS: Right forearm hematoma, status post contrast extravasation.
METHODS: The patient responded poorly to the routine care with arm elevation, cold pack, and wet dressing, and was finally treated by the surgical debridement, vacuum sealing drainage, fascioplasty, and skin flap repair.
RESULTS: Right forearm wound healed with a scar.
CONCLUSIONS: Large extremity hematoma can happen after contrast extravasation during computed tomography scan, which may require surgical treatments. Careful preparation, close monitor, and prompt managements should be applied in high-risk patients.

Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologically-proven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid attenuated inversion recovery images, which were divided into enhancing and non-enhancing groups. Predictive radiomics models based on machine learning with 6 different classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation cohort (n = 99). Receiver-operator characteristic curve was used to assess the predictive performance of these radiomics models. This study demonstrated that the radiomics model comprising of 15 features using the Gaussian process as a classifier had the highest predictive performance in both the training cohort and the internal validation cohort, with the area under the curve being 0.88 and 0.80, respectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.82, 0.76 and 0.96, respectively, in the external validation cohort. This study suggests that the T2-FLAIR-based machine learning radiomics model can accurately predict enhancement pattern of glioma.

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture\'s testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance.

OBJECTIVE: To describe and categorize splenic cystic-appearing lesions (S-CAL) with B-mode and contrast-enhanced ultrasound (CEUS).
METHODS: This retrospective study investigated S-CALs in 111 patients between 2003 and 2023 in an interdisciplinary ultrasound center with B-mode ultrasound, color Doppler sonography (CDS), and CEUS. S-CAL was characterized by echogenicity, diameter, and shape, as well as additional features like septation, calcification, or wall thickening, and CDS/CEUS were used to identify perfusion. Histological examination or imaging follow-up was necessary to determine the nature of S-CAL. Moreover, \'S-CAL with risk\' was defined, necessitating further procedures. Four types (0-III) of S-CALs were defined based on ultrasound parameters. Fisher\'s exact test was used to compare non-parametric data.
RESULTS: S-CAL of 111 patients (58 female, 53 men-average age: 58.6 years) was examined. Final diagnoses were: splenic cyst (n = 64, 57.7%); splenic abscess (n = 10, 9.0%); intrasplenic pseudoaneurysm (n = 10, 9.0%); splenic metastasis (n = 10, 9.0%); splenic infarction (n = 6, 5.4%); splenic hematoma (n = 4, 3.6%); other (n = 7, 6.3%). S-CAL groupings were type 0 (n = 11, 9.9%), type I (n = 33, 29.7%), type II (n = 24, 21.6%), and type III (n = 43, 38.7%). \'S-CAL with risk\' was diagnosed in n = 41 (36.9%). Malignant S-CAL was only seen in type II (n = 2, 8.2%) and type III (n = 9, 20.9%) (p < 0.001). \'S-CALs with risk\' were found more frequently in type 0 (n = 11, 100%), type II (n = 16, 66.7%) and type III (n = 13, 30.2%) than in type I (n = 1, 3%) (p < 0.001).
CONCLUSIONS: B-mode ultrasound, CDS, and CEUS are useful to further characterize and follow-up S-CAL and identify \'S-CAL with risk\', requiring further procedures.
CONCLUSIONS: Ultrasound imaging is valuable for the detection, categorization, and monitoring of cystic-appearing lesions of the spleen, as well as for the identification of those with risk.
CONCLUSIONS: An S-CAL may introduce uncertainty in clinical practice as imaging-based risk stratification is missing. B-mode and CEUS, along with the clinical context and follow-up, assist in characterizing and identifying \'S-CAL with risk\'. S-CALs encompass various lesions, including simple cysts, metastases, abscesses, and intrasplenic pseudoaneurysms.

UNASSIGNED: The prevalence of abdominal aortic aneurysms (AAA) increases with age. Elective intervention for AAA is critical to prevent rupture associated with very high mortality among older males.
UNASSIGNED: The aim of this study was to address the impact of post-contrast acute kidney-PC-AKI injury among patients treated with endovascular repair of ruptured AAA-EVAR on outcomes such as new onset chronic kidney disease-CKD and mortality among patients within a two-year trial.
UNASSIGNED: The same study group (of n = 192 patients) underwent reassessment, two years after EVAR treatment. The overall mortality rate was 16.67%, and it was higher in the AKI group - 38.89%. CKD patients had a mortality rate of 23.88% (n = 16). Among patients with an aneurysm diameter >67 mm mortality rate reached 20% (n = 6), while in the previously reported diabetes mellitus group 37.93% (n = 11). New onset of CKD was diagnosed in 23% of cases. Preexisting CKD patients with PC- AKI contributed to a 33.33% mortality rate (n = 8).
UNASSIGNED: This study concludes that PC-AKI impacts outcomes and survival in endovascularly treated AAAs. Type 2 diabetes and preexisting chronic kidney disease are associated with higher mortality within a 2-year follow-up, however gender factor was not significant. A larger aneurysm diameter is related with a higher prevalence of PC-AKI. These factors should be taken into account during screening, qualifying patients for the treatment and treating patients with AAA. It may help to identify high-risk individuals and tailor preventive measurements and treatment options accordingly, improving treatment results and reducing mortality.

UNASSIGNED: The arterial input function (AIF) plays a crucial role in correcting the time-dependent concentration of the contrast agent within the arterial system, accounting for variations in agent injection parameters (speed, timing, etc.) across patients. Understanding the significance of the AIF can enhance the accuracy of tissue vascular perfusion assessment through indocyanine green-based dynamic contrast-enhanced fluorescence imaging (DCE-FI).
UNASSIGNED: We evaluate the impact of the AIF on perfusion assessment through DCE-FI.
UNASSIGNED: A total of 144 AIFs were acquired from 110 patients using a pulse dye densitometer. Simulation and patient intraoperative imaging were conducted to validate the significance of AIF for perfusion assessment based on kinetic parameters extracted from fluorescence images before and after AIF correction. The kinetic model accuracy was evaluated by assessing the variability of kinetic parameters using individual AIF versus population-based AIF.
UNASSIGNED: Individual AIF can reduce the variability in kinetic parameters, and population-based AIF can potentially replace individual AIF for estimating wash-out rate ( k ep ), maximum intensity ( I max ), ingress slope with lower differences compared with those in estimating blood flow, volume transfer constant ( K trans ), and time to peak.
UNASSIGNED: Individual AIF can provide the most accurate perfusion assessment compared with assessment without AIF or based on population-based AIF correction.

Diffusible iodine-based contrast-enhanced computed tomography (diceCT) is an increasingly used digital complement, supplement, or alternative to traditional dissection-based anatomical research. The diceCT protocol, which has evolved and expanded over the past decade, employs passive diffusion of Lugol\'s iodine (KI3) to increase soft tissue radiodensity and improve structure contrast in the CT or microCT imaging of specimens. The development and application of diceCT has focused largely on specimens under 1 kg, and the varying reporting of methods on studies of both small and large specimens has initiated, but not yet established, an effective diceCT protocol for larger specimens based on monitored experiments of several fundamental variables (e.g., Lugol\'s iodine concentration, duration, and impacts of Lugol\'s iodine on tissues). In this study, we have experimentally assessed the efficacy of diceCT protocols for imaging whole-body specimens of the 1-4.5 kg Australian brushtail possum (Trichosurus vulpecula) using sequential CT imaging assessment across experimental conditions. We assessed the impact of varying Lugol\'s iodine concentration, the presence/absence of skin, solution volume and agitation on tissue radiodensity changes through weekly CT-based monitoring of tissue radiodensities over an 8-week experimental period. We have also quantified tissue volumetric changes across our experiment to assess the impact of diceCT applications on subsequent analyses of imaging datasets. Our results indicate that substantial changes in both soft-tissue radiodensity and soft-tissue volume occur within the first 28 days of Lugol\'s iodine treatment, followed by a slower rate of progressive soft-tissue radiodensity and volume changes across the experiment duration. Our results demonstrate the negligible benefit of skinning larger specimens to improve solution diffusion, and document significant soft-tissue volumetric changes with high concentration solutions (e.g., 10%) and long-duration exposure (e.g., beyond 5 weeks) that should guide individual diceCT protocol design and/or quantification and analysis for mammal specimens above 1 kg.