Contrast-enhanced Imaging Research Articles

Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study

PurposeTo evaluate nnU-net’s performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.MethodsWe collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin’s concordance correlation coefficient (CCC).ResultsA total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80–0.82, PPV of 84.5–86.1%, and sensitivity of 77.6–81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland–Altman plots revealed minor tumor volume differences with 0.22–1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively.ConclusionThe nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

Supramolecular assembly of Polydopamine@Fe nanoparticles with near-infrared light-accelerated cascade catalysis applied for synergistic photothermal-enhanced chemodynamic therapy

Chemodynamic therapy (CDT) via Fenton-like reaction is greatly attractive owing to its capability to generate highly cytotoxic •OH radicals from tumoral hydrogen peroxide (H2O2). However, the antitumor efficacy of CDT is often challenged by the relatively low radical generation efficiency and the high levels of antioxidative glutathione (GSH) in tumor microenvironment. Herein, an innovative photothermal Fenton-like catalyst, Fe-chelated polydopamine (PDA@Fe) nanoparticle, with excellent GSH-depleting capability is constructed via one-step molecular assembly strategy for dual-modal imaging-guided synergetic photothermal-enhanced chemodynamic therapy. Fe(III) ions in PDA@Fe nanoparticles can consume the GSH overexpressed in tumor microenvironment to avoid the potential •OH consumption, while the as-produced Fe(II) ions subsequently convert tumoral H2O2 into cytotoxic •OH radicals through the Fenton reaction. Notably, PDA@Fe nanoparticles demonstrate excellent near-infrared light absorption that results in superior photothermal conversion ability, which further boosts above-mentioned cascade catalysis to yield more •OH radicals for enhanced CDT. Taken together with T1-weighted magnetic resonance imaging (MRI) contrast enhancement (r1 = 8.13 mM−1 s−1) and strong photoacoustic (PA) imaging signal of PDA@Fe nanoparticles, this design finally realizes the synergistic photothermal-chemodynamic therapy. Overall, this work offers a new promising paradigm to effectively accommodate both imaging and therapy functions in one well-defined framework for personalized precision disease treatment.

Image quality comparison of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients: multi-rater assessments including PI-QUAL version 2.

To compare the image quality of 1.5T and 3T prostate MRIs of the same post-hip arthroplasty patients, with a specific focus on the degree of susceptibility artifacts. This single-center retrospective study included post-hip arthroplasty patients who underwent 1.5T prostate MRIs between 2021 and 2023, as well as comparative 3T prostate MRIs. Three blinded abdominal radiologists retrospectively reviewed their diffusion-weighted imaging (DWI, 50s/mm2), T2-weighted imaging (T2WI), and dynamic contrast-enhanced imaging (DCE) to evaluate the image quality. The degree of susceptibility artifacts was categorized using a three-point scale, with 3 indicating the least artifact and 1 indicating the most. Image quality was also evaluated using Prostate Imaging Quality (PI-QUAL) version 2. The median of the three raters' scores was compared between 1.5T and 3T prostate MRIs using the Wilcoxon signed-rank test. The inter-rater agreement was evaluated using the multi-rater generalized kappa. Twenty pairs of 1.5T and 3T prostate MRI examinations from 20 unique patients were included. The DWI susceptibility artifact score at 1.5T was significantly higher than at 3T (mean score ± standard deviation, 2.80 ± 0.41 vs. 2.35 ± 0.93, p = 0.014). In contrast, no significant differences were observed in the susceptibility artifact scores in T2WI and DCE, or in the PI-QUAL score. The inter-reader agreement in the susceptibility artifact score was moderate (multi-rater generalized kappa: 0.60) in DWI, perfect in T2WI (not applicable), andsubstantial (0.65) in DCE. The inter-reader agreement wasfair (0.27) in the PI-QUAL score. Using 1.5T scanners may be preferable to reduce susceptibility artifacts from hip prostheses in DWI.

The Current Role of Contrast-Enhanced Ultrasound (CEUS) in the Diagnosis and Staging of Bladder Cancer: A Review of the Available Literature.

Contrast-enhanced ultrasound (CEUS) is an advanced imaging technique that integrates conventional US with the intravenous injection of specific US contrast agents (UCAs), combining the non-invasiveness of US with the higher accuracy of contrast-enhanced imaging. In contrast with magnetic resonance imaging (MRI), computed tomography (CT) and cystoscopy, CEUS has few contraindications, and UCAs are non-nephrotoxic agents that can be safely used in patients with kidney failure. CEUS is a well-established method for the detection of liver lesions and for echocardiography, and its indications are expanding. The updated 2018 WFUMB-EFSUMB guidelines have added the urinary bladder under non-hepatic applications of CEUS. The technique is able to distinguish between benign tissue, such as clots or hematoma, and malignant lesions by perfusing the mass with contrast agent. Thanks to the different perfusion rates of the various layers of the bladder wall, CEUS is also able to predict tumor invasion depth and stage. Despite that, current urological guidelines do not include CEUS as a plausible imaging technique for bladder urothelial carcinoma. The main reason for this omission might be the presence of scarce randomized evidence and the absence of large validated series. In this review, we describe the rationale behind the use of CEUS in bladder cancer and the added value of this imaging technique in the detection and staging of bladder lesions. In addition, we researched the available literature on the topic and then described the results of randomized clinical trials and a meta-analysis investigating the accuracy of CEUS in bladder cancer diagnosis and staging. The reported studies show that CEUS is a highly accurate diagnostic and staging tool for BC, reaching levels of specificity and sensitivity in differentiating between Ta-T1, or low-grade BC, and T2, or high-grade BC, that are comparable to those shown by the reference standard methods. Nonetheless, several limitations were found and are highlighted in this review. The aim of this study is to further validate and promote the use of CEUS as a quick, economic and effective diagnostic tool for this high-impact disease.

Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis

BackgroundDifferentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn’t been fully investigated for the differentiation and may have the potential to improve it.MethodsA total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance.ResultsHigher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256–0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25).ConclusionsThe frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.

MRI-based radiomics signatures for preoperative prediction of Ki-67 index in primary central nervous system lymphoma

PurposeThe aim of this study was to develop and validate radiomics signatures based on MRI for preoperative prediction of Ki-67 proliferative index (PI) expression in primary central nervous system lymphoma (PCNSL). MethodsA total of 341 patients with PCNSL were retrospectively analyzed, including 286 patients in one center as the training set and 55 patients in another two centers as the external validation set. Radiomics features were extracted and selected from preoperative contrast-enhanced T1-weighted images, fluid attenuation inversion recovery to build radiomics signatures according to the Ki-67 PI. The predictive performances of the radiomics model were evaluated using four classifiers including random forest, K-Nearest Neighbors, Neural Network and Decision Tree. A combined model was built by incorporating radiomics signature, clinical variables and MRI radiological characteristics using multivariate logistic regression analysis, and a nomogram was established to predict the expression of Ki-67 individually. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). ResultsRadiomics signatures were independent predictors of the expression level of Ki-67 (OR: 2.523, P < 0.001). RF radiomics models had the highest accuracy (0.934 in the training set and 0.811 in the external validation set) and F1 Score (0.920 in the training set and 0.836 in the external validation set). The clinic-radiologic-radiomics nomogram showed better predictive performance with AUCs of 0.877(95 % CI: 0.837–0.918) in the training set and 0.866(95 % CI: 0.774–0.957) in the external validation set. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram. ConclusionsNomograms integrating MRI-based radiomics and clinical-radiological characteristics could effectively predict Ki-67 PI in primary PCNSL.

Limited Field Images Concrete Crack Identification Framework Using PCA and Optimized Deep Learning Model

Concrete crack identification methods based on machine learning can greatly improve extraction efficiency and precision. However, in many cases, model training requires a large amount of sample data, and insufficient data makes it difficult to effectively obtain model parameters. This study introduces a deep learning framework that integrates filters, principal component analysis, and attention mechanisms suitable for small sample sizes. Firstly, the histogram equalization method is used for the raw images, which can effectively enhance image contrast. Then, to acquire effective images of the crack, different methods are employed for crack detection, which are subsequently handled by principal component analysis (PCA) for optimal feature choice. Att-Unet and Att-Mask R-cnn segmentation models are used to design the detection for concrete cracks. To raise the learning ability of the segmentation models, an attention mechanism is applied to each feature layer of the decoder, and the loss function is evaluated using a combination of the Focal function and Cross Entropy. To verify the effectiveness of the proposed method, Deep Crack datasets and 76 sets of concrete crack data were collected for testing. Experimental results have shown that the method proposed can significantly reduce the model’s demand for data volume and improve training speed, which provides a new direction for small-sample crack extraction.

Improving Glioblastoma Multiforme Recurrence Prediction through Integrated Radiomics and Deep Learning Techniques

Glioblastoma multiforme (GBM) is one of the most lethal forms of brain cancer, with a five-year survival rate of only 4% to 5%. The recurrence rate is alarmingly high, reaching up to 90%. While tumor-treating fields have shown potential in extending survival, their efficacy in treating recurrent GBM remains limited. This study aims to leverage Deep Learning (DNN) to predict the recurrence of GBM in patients, both pre- and post-surgery. Utilizing advanced computational techniques, this research employs radiomics to analyze brain tumor images, aiding clinicians in identifying tumor spread, predicting post-surgical recurrence, and estimating patient survival. Pre-surgery, Multi-Parametric Magnetic Resonance Imaging (MP-MRI) scans are used to detect tumor locations and forecast potential recurrences. To enhance image processing, Z-score normalization and spatial resampling are applied. Additionally, a model was developed to address the issue of imbalanced data in medical imaging. The study utilized Contrast-Enhanced T1-Weighted Imaging (CE-T1WI) MRI to assess treatment effectiveness and predict recurrence-free survival. A Deep Neural Network was trained to forecast tumor recurrence, identifying patients at risk of early recurrence. Feature extraction from brain images was performed using the Inheritable Bi-Objective Combinatorial Genetic Algorithm. The accuracy of the recurrence predictions was validated and compared against other models, including CNN Inception-V3, CNN AlexNet, and VGG16, using the Python programming language. Results indicate that the proposed method surpasses existing techniques by 3%, 4%, and 5% in accuracy, specificity, and sensitivity, respectively. This research demonstrates that in a retrospective patient population, predictions of patient survival and time to recurrence exhibit high sensitivity, specificity, and accuracy, offering a promising tool for improving GBM management and patient outcomes.

Assessment of NIR-triggered PEG-coated NaGdF4:Tm3+/Yb3+ bio-compatible upconversion nanoparticles for contrast enhancement in OCT imaging and optical thermometry

In this investigation, we embarked on the synthesis of polyethylene glycol coated NaGdF4:Tm3+/Yb3+ upconversion nanoparticles (UCNPs), aiming to assess their utility in enhancing image contrast within the context of swept source optical coherence tomography (OCT) and photo-thermal OCT imaging. Our research unveiled the remarkable UC emissions stemming from the transitions of Tm3+ ions, specifically the 1G4 → 3H6 transitions, yielding vibrant blue emissions at 472 nm. We delved further into the UC mechanism, meticulously scrutinizing decay times and the nanoparticles′ capacity to convert radiation into heat. Notably, these nanoparticles exhibited an impressive photo-thermal conversion efficiency of 37.5%. Furthermore, our investigations into their bio-compatibility revealed a promising outcome, with more than 90% cell survival after 24 h of incubation with HeLa cells treated with UCNPs. The nanoparticles demonstrated a notable thermal sensitivity of 4.7 × 10−3 K−1 at 300 K, signifying their potential for precise temperature monitoring at the cellular level.

Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer.

The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.

Tenfold Increase: Acute Pediatric Mastoiditis Before, During, and After COVID-19 Restrictions.

Acute mastoiditis (AM) is a relatively rare complication arising from acute otitis media, a common condition among children. The COVID-19 pandemic has significantly impacted AM cases. We noted a surge in pediatric AM cases in Germany after COVID-19 restrictions were lifted in 2022. This study assesses AM incidence and the clinical course in children before, during, and after the pandemic. The study also explores complication rates and microbial changes. Participants: We included children (0-18 yr) diagnosed with AM who underwent mastoidectomy at a tertiary-care university hospital from January 2012 to June 2023.Objectives: We aimed to evaluate AM incidence during pre-COVID, COVID, and post-COVID periods; assess complications; and analyze the microbial spectrum.Data Analysis: Incidence and complication rates were compared between periods, along with the microbial spectrum. Population: 75 children were included (median age, 3.3 yr).Incidence: Significant increases in AM cases occurred in the post-COVID period compared to pre-COVID and COVID periods. No significant difference was observed between pre-COVID and COVID periods.Complications: Complication rates increased notably in the post-COVID period compared to pre-COVID and COVID periods with respect to more sensitive imaging methods being used in the post-COVID period. No significant difference was observed between pre-COVID and COVID periods.Spectrum of Pathogens: No significant differences were found in pathogen distribution between periods. Streptococcus pyogenes and Streptococcus pneumoniae were common throughout. The study highlights a substantial rise in AM cases and complications after COVID-19 restrictions were lifted in Germany. This underscores the importance of monitoring infectious diseases and their complications during health crises. Additionally, the study highlights the importance of contrast-enhanced imaging. Further research is needed to explore the mechanisms behind this trend. The study reveals a significant increase in pediatric AM cases and complications following the COVID-19 pandemic in Germany. Adequate computed tomographic or magnetic resonance imaging, including contrast enhancement, is shown to be a very important parameter beside clinical symptoms in deciding for the right therapy. Thus, surgical treatment became more important. Continuous monitoring and adaptive healthcare strategies during health crises are vital for optimal patient care. Further research is warranted to understand the reasons behind these trends and to inform future pandemic preparedness efforts.

The Impact of Hydration and Other Strategies in prevention of Contrast-Induced Nephropathy

Introduction and purpose Contrast-induced nephropathy (CIN) remains a significant concern in clinical practice, particularly among patients undergoing contrast-enhanced imaging or interventional procedures. Characterized by an acute decline in renal function following exposure to contrast media, CIN not only poses substantial morbidity and mortality risks but also presents challenges in patient management and healthcare resource utilization. Despite extensive research efforts, the precise pathophysiology of CIN remains incompletely understood, with multifactorial mechanisms involving renal ischemia, oxidative stress, and direct tubular toxicity implicated in its development. This review explores the impact of hydration and other prophylactic strategies on the prevention of CIN. Numerous studies have demonstrated the effectiveness of various hydration protocols, including isotonic saline and bicarbonate solutions, in reducing the incidence of CIN. Additionally, the review evaluates the efficacy of pharmacological agents such as N-acetylcysteine, statins, and ascorbic acid, as well as emerging techniques like remote ischemic preconditioning.

The American College of Radiology contrast-enhanced ultrasound Liver Imaging Reporting and Data System and its modified version in diagnosing hepatocellular carcinoma via Sonazoid: a meta-analysis.

The American College of Radiology (ACR) developed the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) for pure blood contrast agents, but Sonazoid was not included. Modifications to LI-RADS have been proposed for Sonazoid. The purpose of this meta-analysis was to identify and compare the diagnostic efficacy of the two LI-RADS algorithms of Sonazoid. We searched the PubMed, MEDLINE, Web of Science, Embase, and Cochrane Library databases from databases inception to August 31, 2023, to find original studies on the ACR LI-RADS and/or modified LI-RADS algorithm with Sonazoid used as the contrast agent in patients with high-risk hepatocellular carcinoma (HCC). A bivariate random-effects model was used. Data pooling, meta-regression, and sensitivity analysis were performed for meta-analysis. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was used to assess the methodological quality, and the Deeks funnel plot asymmetry test was used to evaluate the publication bias. A meta-analysis of 10 studies with 1,611 observations was conducted. The pooled data for ACR LI-RADS category 5 (LR-5) and modified LR-5 were respectively as follows: pooled sensitivity, 0.70 [95% confidence interval (CI): 0.64-0.75] and 0.81 (95% CI: 0.76-0.86) (P<0.05); pooled specificity, 0.90 (95% CI: 0.82-0.94) and 0.87 (95% CI: 0.81-0.91) (P>0.05); and pooled area under the summary receiver operating characteristic curve, 0.84 and 0.91. The diagnostic performance of LI-RADS category M (LR-M) of the two algorithms was comparable. Study heterogeneity was observed. The results indicated that modified LR-5 algorithm demonstrated improved diagnostic sensitivity compared with the ACR LR-5 algorithm of Sonazoid, with differences observed between the different versions. Further research is needed to validate and explore the optimal diagnostic criteria for HCC using Sonazoid. Before the database search was conducted, this study was registered on PROSPERO (International Prospective Register of Systematic Reviews; CRD42023455220).

Dual-Drug Loaded Nanobubbles Combined with Sonodynamic and Chemotherapy for Hepatocellular Carcinoma Therapy.

Chemotherapy remains the primary therapeutic approach for advanced Hepatocellular Carcinoma (HCC). The therapeutic effect of chemotherapy is limited and the toxic side effects are serious. The aim of this study is to develop a nanobubble that is ultrasonically responsive to reduce the toxic side effects of direct chemotherapy. We developed curcumin/doxorubicin-cis-aconitic anhydride-polyethylene glycol nanobubble (C/DCNB) surface modified with acid-sensitive polyethylene glycol (PEG). And it is loaded with curcumin (CUR) and doxorubicin (DOX), as liposomes at the nanoscale for diagnosis and therapy of tumors. In this study, the acid-sensitive PEG on the surface layer of nanobubbles serves to stabilize them in the blood circulatory system and in normal tissues, while peeling off in the acidic tumor microenvironment (pH 6.8). C/DCNB can identify tumor sites through contrast-enhanced ultrasound (CEUS). And ultrasound-mediated nanobubbles promote permeability of the tumor vascular, thus improving the enhanced permeability and retention (EPR) effects in the tumor, leading to the accumulation of nanobubbles in the tumor. After endocytosis of nanobubbles, drugs are released and curcumin generates reactive oxygen species (ROS) under ultrasound conditions. CUR can enhance the sensitivity of tumor cells to DOX by inhibiting the expression of P-glycoprotein. In vitro and vivo experiments demonstrate that C/DCNB can facilitate contrast-enhanced ultrasound imaging while simultaneously delivering drugs, enabling both imaging and treatment. The combination of C/DCNB and ultrasound provides an effective strategy for improving the efficiency of HCC therapy and imaging.

Value of ultrasound and contrast-enhanced ultrasound imaging in renal damage and treatment follow up of diabetic patients

Value of ultrasound and contrast-enhanced ultrasound imaging in renal damage and treatment follow up of diabetic patients

Radiologic-Pathologic Correlation: Is There an Association Between Contrast-Enhanced Mammography Imaging Features and Molecular Subtypes of Breast Cancer?

This study aims to assess the correlation between imaging features of contrast-enhanced mammography (CEM) and molecular subtypes of breast cancer. This is a retrospective single-institution study of patients who underwent CEM from December 2019 to August 2023. Each patient had at least one histologically proven invasive breast cancer with a core biopsy performed. Patients with a history of breast cancer treatment and lesions not entirely included in the CEM images were excluded. The images were interpreted using the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) lexicon for CEM, published in 2022. Different imaging features, including the presence of calcifications, architectural distortion, non-mass enhancement, mass morphology, internal enhancement pattern, the extent of enhancement, and lesion conspicuity, were analyzed. The molecular subtypes were studied as dichotomous variables, including luminal A, luminal B, HER2, and basal-like. The association between the imaging features and molecular subtypes was analyzed with a Fisher's exact test. Statistical significance was assumed when the p-value was <0.05. A total of 31 patients with 36 malignant lesions were included in this study. Sixteen lesions (44.4%) were luminal A, four lesions (11.1%) were luminal B, 10 lesions (27.8%) were HER2, and six (16.7%) were basal-like subtypes. The presence of calcifications was associated with the HER2 subtype (p=0.024). Rim-enhancement on recombined images was associated with a basal-like subtype (p=0.001). Heterogeneous enhancement on recombined images was associated with non-basal-like breast cancer (p=0.027). No statistically significant correlation was found between other analyzed CEM imaging features and molecular subtypes. CEM imaging features, including the presence of calcifications and certain internal enhancement patterns, were correlated with distinguishing breast cancer molecular subtypes and thus may further expand the role of CEM.

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Intracavitary Applications for CEUS in PTCD.

Intracavitary contrast-enhanced ultrasound is widely accepted as a highly informative, safe, and easily reproducible technique for the diagnosis, treatment, and follow-up of different pathologies of the biliary tree. This review article describes the diverse applications for CEUS in intracavitary biliary scenarios, supported by a literature review of the utilization of the method in indications like biliary obstruction by various etiologies, including postoperative strictures, evaluation of the biliary tree of liver donors, and evaluation of the localization of a drainage catheter. We also provide pictorial examples of the authors' personal experience with the use of intracavitary CEUS in cases of PTCD as a palliative intervention. Intracavitary CEUS brings all the positive features of US together with the virtues of contrast-enhanced imaging, providing comparable accuracy to the standard techniques for diagnosing biliary tree diseases.