Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Case 329.

HISTORY: A 45-year-old female patient who was previously healthy presented after several weeks of fullness in the right upper quadrant of the abdomen. The patient did not experience pain, nausea, vomiting, or jaundice, and had no contributory past medical or surgical history, including no history of malignancy. Upon examination, vital signs were within normal limits and the patient was appeared well with soft palpable fullness in the right upper quadrant. The abdomen was nontender and nondistended. Laboratory investigation revealed no abnormalities, with a normal complete blood cell count and normal serum tumor markers that included α-fetoprotein (<2.0 ng/mL; reference, <8.3 ng/mL), cancer antigen 19-9 (21.6 U/mL; reference, <35 U/mL), and carcinoembryonic antigen (1.3 ng/mL; reference, <5 ng/mL). CT of the abdomen and pelvis was performed with intravenous contrast material in the emergency department (Fig 1). Subsequently, combined MRI and MR cholangiopancreatography of the abdomen was performed with and without intravenous contrast material for further evaluation (Fig 2). CT of the chest performed during the same encounter was unremarkable.

Contrast-enhanced US in Renal Transplant Complications: Overview and Imaging Features.

Renal transplant is the first-line treatment of end-stage renal disease. The increasing number of transplants performed every year has led to a larger population of transplant patients. Complications may arise during the perioperative and postoperative periods, and imaging plays a key role in this scenario. Contrast-enhanced US (CEUS) is a safe tool that adds additional value to US. Contrast agents are usually administered intravenously, but urinary tract anatomy and complications such as stenosis or leak can be studied using intracavitary administration of contrast agents. Assessment of the graft and iliac vessels with CEUS is particularly helpful in identifying vascular and parenchymal complications, such as arterial or venous thrombosis and stenosis, acute tubular injury, or cortical necrosis, which can lead to graft loss. Furthermore, infectious and malignant graft involvement can be accurately studied with CEUS, which can help in detection of renal abscesses and in the differentiation between benign and malignant disease. CEUS is also useful in interventional procedures, helping to guide percutaneous aspiration of collections with better delimitation of the graft boundaries and to guide renal graft biopsies by avoiding avascular areas. Potential postprocedural vascular complications, such as pseudoaneurysm, arteriovenous fistula, or active bleeding, are identified with CEUS. In addition, newer quantification tools such as CEUS perfusion are promising, but further studies are needed to approve its use for clinical purposes. ©RSNA, 2024 Supplemental material is available for this article.

Contrast-enhanced Mammography versus MR Imaging of the Breast.

Breast MR imaging and contrast-enhanced mammography (CEM) are both techniques that employ intravenously injected contrast agent to assess breast lesions. This approach is associated with a very high sensitivity for malignant lesions that typically exhibit rapid enhancement due to the leakiness of neovasculature. CEM may be readily available at the breast imaging department and can be performed on the spot. Breast MR imaging provides stronger enhancement than the x-ray-based techniques and offers higher sensitivity. From a patient perspective, both modalities have their benefits and downsides; thus, patient preference could also play a role in the selection of the imaging technique.

Background Parenchymal Enhancement: A Comprehensive Update.

Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors.

BACKGROUND: To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs). METHODS: This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve. RESULTS: TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone. CONCLUSIONS: Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Non-contrast Breast MR Imaging.

Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.

MRI of Soft-Tissue Tumors: What to Include in the Report.

MRI serves as a critical step in the workup, local staging, and treatment planning of extremity soft-tissue masses. For the radiologist to meaningfully contribute to the management of soft-tissue masses, they need to provide a detailed list of descriptors of the lesion outlined in an organized report. While it is occasionally possible to use MRI to provide a diagnosis for patients with a mass, it is more often used to help with determining the differential diagnosis and planning of biopsies, surgery, radiation treatment, and chemotherapy (when provided). Each descriptor on the list outlined in this article is specifically aimed to assist in one or more facets of the overall approach to soft-tissue masses. This applies to all masses, but in particular sarcomas. Those descriptors are useful to help narrow the differential diagnosis and ensure concordance with a pathologic diagnosis and its accompanying grade assignment of soft-tissue sarcomas. These include a lesion's borders and shape, signal characteristics, and contrast enhancement pattern; the presence of peritumoral edema and peritumoral enhancement; and the presence of lymph nodes. The items most helpful in assisting surgical planning include a lesion's anatomic location, site of origin, size, location relative to a landmark, relationship to adjacent structures, and vascularity including feeding and draining vessels. The authors provide some background information on soft-tissue sarcomas, including their diagnosis and treatment, for the general radiologist and as a refresher for radiologists who are more experienced in tumor imaging. ©RSNA, 2024 See the invited commentary by Murphey in this issue.

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.

BACKGROUND: Early prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients. METHODS: The DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways. RESULTS: A 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635-0.741 and AUC = 0.650, 95%CI: 0.595-0.705) and tested (AUC = 0.686, 95%CI: 0.594-0.778 and AUC = 0.626, 95%CI: 0.529-0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722-0.816 and test: 0.762, 95%CI: 0.679-0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665-0.767 and test AUC = 0.695, 95%CI: 0.656-0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system. CONCLUSION: Dynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

Diagnostic accuracy of contrast-enhanced computed tomography in assessing bone invasion in patients with oral squamous cell carcinoma.

OBJECTIVES: This study aimed to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting bone invasion in oral squamous cell carcinoma (OSCC) patients and to explore clinicopathological factors associated with its reliability. MATERIALS AND METHODS: 417 patients underwent preoperative contrast-enhanced CT followed by radical surgery. The presence or absence of bone invasion served as the outcome variable, with histopathologic examination of the resection specimen considered the gold standard. Statistical analyses, comprising correlation analyses and the determination of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were conducted. RESULTS: CT exhibited 76.85% sensitivity, 82.20% specificity, 47.14% PPV, and 89.67% NPV. False-positive and false-negative rates were 11.27% and 5.99%, respectively. Artifacts affected assessment in 44 patients, but not in those with bone invasion. Tumor size, depth of invasion (DOI), tumor localization at the upper jaw, lymphatic invasion, and perineural invasion correlated with incorrect identification of bone invasion (Chi-square, p < 0.05). CONCLUSIONS: Despite utilizing thin-section CT, notable false-positive and false-negative results persisted. Patients with T3 tumors, DOI ≥ 10 mm, or upper jaw tumors are at higher risk for misidentification of bone invasion. Combining multiple methods may enhance diagnostic accuracy, and the integration of artificial intelligence or tracking electrolyte disturbances by tumor depth profiling shows promise for further assessment of bone invasion before histopathology. CLINICAL RELEVANCE: Surgeons should consider these insights when planning tumor resection. Supplementary imaging may be warranted in cases with high risk factors for misidentification. Further methodological advancements are crucial for enhancing diagnostic precision.

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

PURPOSE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism. MATERIALS AND METHODS: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction. RESULTS: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization. CONCLUSIONS: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.

Surface ligand-regulated renal clearance of MRI/SPECT dual-modality nanoprobes for tumor imaging.

BACKGROUND: The general sluggish clearance kinetics of functional inorganic nanoparticles tend to raise potential biosafety concerns for in vivo applications. Renal clearance is a possible elimination pathway for functional inorganic nanoparticles delivered through intravenous injection, but largely depending on the surface physical chemical properties of a given particle apart from its size and shape. RESULTS: In this study, three small-molecule ligands that bear a diphosphonate (DP) group, but different terminal groups on the other side, i.e., anionic, cationic, and zwitterionic groups, were synthesized and used to modify ultrasmall Fe3O4 nanoparticles for evaluating the surface structure-dependent renal clearance behaviors. Systematic studies suggested that the variation of the surface ligands did not significantly increase the hydrodynamic diameter of ultrasmall Fe3O4 nanoparticles, nor influence their magnetic resonance imaging (MRI) contrast enhancement effects. Among the three particle samples, Fe3O4 nanoparticle coated with zwitterionic ligands, i.e., Fe3O4@DMSA, exhibited optimal renal clearance efficiency and reduced reticuloendothelial uptake. Therefore, this sample was further labeled with 99mTc through the DP moieties to achieve a renal-clearable MRI/single-photon emission computed tomography (SPECT) dual-modality imaging nanoprobe. The resulting nanoprobe showed satisfactory imaging capacities in a 4T1 xenograft tumor mouse model. Furthermore, the biocompatibility of Fe3O4@DMSA was evaluated both in vitro and in vivo through safety assessment experiments. CONCLUSIONS: We believe that the current investigations offer a simple and effective strategy for constructing renal-clearable nanoparticles for precise disease diagnosis.

Semiconducting Polymers Based on Asymmetric Thiadiazoloquinoxaline for Augmented In Vivo NIR-II Photoacoustic Imaging.

A photoacoustic (PA) imaging technique using the second near-infrared (NIR-II) window has attracted more and more attention because of its merits of deeper penetration depth and higher signal-to-noise (S/N) ratio than that using the first near-infrared (NIR-I) one. However, the design and development of high-performance PA imaging contrast agents in the NIR-II window is still a challenge. A semiconducting polymer, constructed by asymmetric units, exhibits regiorandom characteristics that effectively increase the distortion of the backbone. This increase in the degree of twist can regulate the twisted intramolecular charge transfer (TICT) effect, resulting in an enhancement of the PA signal. In this paper, an asymmetric structural acceptor strategy is developed to improve the PA signals of the resulting semiconducting polymer (PATQ-MP) in the NIR-II window with improved brightness, higher S/N ratio, and better photothermal conversion efficiency compared to polymers with the same main-chain structure containing a symmetric acceptor. DFT analysis showed that PATQ-MP containing an asymmetric acceptor monomer had a larger dihedral angle, which effectively improved the PA signal intensity by enhancing the TICT effect. The PEG-encapsulated PATQ-MP nanoparticles exhibit promising performance in the PA imaging of mouse tumors in vivo, demonstrating the clear identification of microvessels as small as 100 µm along with rapid metabolism within a span of 5 h. Therefore, this work provides a unique molecular design strategy for improving the signal intensity of PA imaging in the NIR-II window.

Contrast-enhanced Imaging in Peripheral Pulmonary Lesions: The Role in US-guided Biopsies.

Purpose To compare the tissue adequacy and diagnostic accuracy of US-guided biopsies of peripheral pulmonary lesions (PPLs) with and without contrast agents. Materials and Methods A retrospective study was conducted at four medical centers in patients with PPLs who underwent US-guided percutaneous transthoracic needle biopsy (PTNB) between January 2017 and October 2022. The patients were divided into contrast-enhanced US (CEUS) and US groups based on whether prebiopsy CEUS evaluation was performed. Tissue adequacy and the diagnostic accuracy of PTNB, stratified by lesion size, were analyzed and compared between groups. A propensity score matching (PSM) analysis was conducted using the nearest-neighbor matching method. Results A total of 1027 lesions were analyzed, with 634 patients (mean age, 59.4 years ± 13.0 [SD]; 413 male) in the US group and 393 patients (mean age, 61.2 years ± 12.5; 270 male) in the CEUS group. The CEUS group produced more acceptable samples than the US group (98.2% vs 95.7%; P = .03) and achieved higher diagnostic accuracy (96.9% vs 94.2%; P = .04), with no evidence of a difference in sensitivity (96.7% vs 94.0%; P = .06). PSM and stratified analyses (n = 358 per group) indicated higher tissue adequacy (99.0% vs 95.7%; P = .04) and diagnostic accuracy (98.5% vs 92.9%; P = .006) in the CEUS group compared with the US group for 2-7-cm PPLs but not for lesions larger than 7 cm. Conclusion PTNB with prebiopsy CEUS evaluation demonstrated significantly better tissue adequacy and diagnostic accuracy compared with US guidance alone for PPLs ranging from 2 to 7 cm, with similar biopsy performance achieved between groups for lesions larger than 7 cm. Keywords: Contrast Material, Thoracic Diseases, Ultrasonography, Image-Guided Biopsy © RSNA, 2024.

The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.

Radio genomics is an exciting new area that uses diagnostic imaging to discover genetic features of diseases. In this review, we carefully examined existing literature to evaluate the role of artificial intelligence (AI) and machine learning (ML) on dynamic contrastenhanced MRI (DCE-MRI) data to distinguish molecular subtypes of breast cancer (BC). Implications to noninvasive assessment of molecular subtype include reduction in procedure risks, tailored treatment approaches, ability to examine entire lesion, follow-up of tumour biology in response to treatment and evaluation of treatment resistance and failure secondary to tumour heterogeneity. Recent studies leverage radiomics and AI on DCE-MRI data for reliable, non-invasive breast cancer subtype classification. This review recognizes the potential of AI to predict the molecular subtypes of breast cancer non-invasively.

Time conditioning for arbitrary contrast phase generation in interventional computed tomography.

Minimally invasive ablation techniques for renal cancer are becoming more popular due to their low complication rate and rapid recovery period. Despite excellent visualisation, one drawback of the use of computed tomography (CT) in these procedures is the requirement for iodine-based contrast agents, which are associated with adverse reactions and require a higher x-ray dose. The purpose of this work is to examine the use of time information to generate synthetic contrast enhanced images at arbitrary points after contrast agent injection from non-contrast CT images acquired during renal cryoablation cases. To achieve this, we propose a new method of conditioning generative adversarial networks with normalised time stamps and demonstrate that the use of a HyperNetwork is feasible for this task, generating images of competitive quality compared to standard generative modelling techniques. We also show that reducing the receptive field can help tackle challenges in interventional CT data, offering significantly better image quality as well as better performance when generating images for a downstream segmentation task. Lastly, we show that all proposed models are robust enough to perform inference on unseen intra-procedural data, while also improving needle artefacts and generalising contrast enhancement to other clinically relevant regions and features.

Naturally biocompatible melanin based iron-complex nanoparticles for pH-responsive magnetic resonance imaging.

The sensitivity and diagnostic accuracy of magnetic resonance imaging mainly depend on the relaxation capacity of contrast agents (CAs) and their accumulated amount at the pathological region. Due to the better biocompatibility and high-spin capacity, Fe-complexes have been studied widely as an alternative to replace popular Gd-based CAs associated with potential biotoxicity. Compared with a variety of Fe complex-based CAs, such as small molecular, macrocyclic, multinuclear complexes, the form of nanoparticle exhibits outstanding longitudinal relaxation, but the clinical transformation was still limited by the inconspicuous difference of contrast between tumor and normal tissue. The enhanced effect of contrast is a positive relation as relaxation of CAs and their concentration in desired region. To specifically improve the amount of CAs accumulated in the tumor, pH-responsive polymer poly(2-ethyl-2-oxazoline) (PEOz) was modified on melanin, a ubiquitous natural pigment providing much active sites for chelating with Fe(III). The Fe(III)-Mel-PEOz we prepared could raise the tumor cell endocytosis efficiency via switching surface charge from anion to cation with the stimuli of the decreasing pH of tumor microenvironment. The change of pH has negligible effect on ther1of Fe(III)-Mel-PEOz, which is always maintained at around 1.0 mM-1s-1at 0.5 T. Moreover, Fe(III)-Mel-PEOz exhibited low cytotoxicity, and satisfactory enhancement of positive contrast effectin vivo. The excellent biocompatibility and stable relaxation demonstrate the high potential of Fe(III)-Mel-PEOz in the diagnosis of tumor.

Berberine alleviated contrast-induced acute kidney injury by mitophagy-mediated NLRP3 inflammasome inactivation in a mice model.

The incidence of contrast-induced acute kidney injury (CI-AKI) has escalated to become the third most prevalent cause of hospital-acquired AKI, with a lack of efficacious interventions. Berberine (BBR) possesses diverse pharmacological effects and exhibits renoprotective properties; however, limited knowledge exists regarding its impact on CI-AKI. Therefore, our study aimed to investigate the protective effects and underlying mechanisms of BBR on CI-AKI in a mice model, focusing on the nucleotide-binding oligomerization domain-like pyrin domain-containing protein 3 (NLRP3) inflammasome and mitophagy. The CI-AKI mice model was established by administering NG-nitro-L-arginine methyl ester (L-NAME) (10 mg/kg), indomethacin (10 mg/kg), and iohexol (11 g/kg) following water deprivation. A pretreatment of 100 mg/kg of BBR was orally administered to the mice for two weeks. Renal injury markers, damage-associated molecular patterns (DAMPs), renal histopathology, mitochondrial morphology, autophagosomes, and potential mechanisms were investigated. BBR effectively reduced levels of renal injury biomarkers such as serum cystatin C, urea nitrogen, and creatinine, downregulated the protein level of kidney injury molecule 1 (KIM1), and mitigated renal histomorphological damage. Moreover, BBR reduced DAMPs, including high mobility group box-1 (HMGB1), heat shock protein 70 (HSP70), and uric acid (UA). It also alleviated oxidative stress and inflammatory factors such as monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1 beta (IL-1ß). Furthermore, the activation of NLRP3 inflammasome was attenuated in the BBR pretreatment group, as evidenced by both mRNA and protein levels. Electron microscopy and western blotting examination revealed that BBR mitigated mitochondrial damage and enhanced mitophagy. Additionally, BBR increased the P-AMPK/AMPK ratio. These findings indicated that BBR exerted a protective effect against CI-AKI by suppressing NLRP3 inflammasome activation and modulating mitophagy, providing a potential therapeutic strategy for its prevention.