Contrast-enhanced Phase Research Articles

DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging.

Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.

Diagnostic Efficacy of Dynamic Maneuver in Contrast-Enhanced Computed Tomography Compared With Conventional Contrast-Enhanced Computed Tomography in Imaging the Neck Region.

Introduction Dynamic contrast-enhanced computed tomography (DCE-CT) and conventional contrast-enhanced computed tomography (CE-CT) are widely used to evaluate neck lesions, including lymph node metastases, thyroid nodules, salivary gland tumors, and other soft tissue masses. DCE-CT, which captures multiple phases of contrast enhancement over time, is hypothesized to provide superior diagnostic accuracy compared to the single-phase images obtained by CE-CT due to its ability to offer dynamic information about tissue perfusion, blood volume, and vascular permeability. Methods This retrospective observational diagnostic study included 100 patients who underwent neck imaging, divided equally into DCE-CT and CE-CT groups. Patient demographics (age, gender, body mass index) and lesion characteristics (type, location, size, enhancement pattern, margins) were recorded. Diagnostic performance metrics (sensitivity, specificity, accuracy, positive predictive value, negative predictive value) were evaluated alongside inter-observer variability using the kappa statistic. Clinical impact was assessed based on changes in treatment plans and improvements in patient outcomes. The radiation dose for each modality was documented. Statistical analysis was performed using SPSS software (IBM SPSS Statistics for Windows, IBM Corp., Armonk, NY) with chi-square tests for categorical variables and t-tests for continuous variables. Results The study included 58 males and 42 females with a mean age of 55.5 years. A total of 145 lesions were detected: 75 by DCE-CT and 70 by CE-CT. DCE-CT demonstrated higher sensitivity (93.33%) and specificity (96.00%) compared to CE-CT (sensitivity 86.67%, specificity 92.00%). The accuracy of DCE-CT was 94.00% versus 88.00% for CE-CT. Inter-observer agreement was higher for DCE-CT (kappa = 0.85) compared to CE-CT (kappa = 0.80). DCE-CT led to treatment plan changes in 40% of cases and resulted in a 75% improvement in outcomes compared to 25% and 60%, respectively, for CE-CT. The mean radiation dose was slightly higher for DCE-CT (8.5 mSv) compared to CE-CT (7.0 mSv). Conclusion DCE-CT offers superior diagnostic efficacy compared to CE-CT for imaging neck lesions with enhanced sensitivity, specificity, and accuracy. Its ability to capture multiple phases of contrast enhancement allows for detailed lesion characterization and provides crucial quantitative data on tissue perfusion and blood volume. These benefits lead to more frequent improvements in patient outcomes and changes in treatment plans. Despite the slightly higher radiation dose, the diagnostic advantages of DCE-CT outweigh the disadvantages, particularly in complex cases requiring detailed lesion analysis. Further prospective studies are recommended to validate these findings and explore the broader clinical benefits of DCE-CT.

Evaluation of pancreatic iodine uptake and related influential factors in multiphase dual-energy CT.

To establish normative values and identify potential factors influencing pancreatic iodine uptake using dual-energy CT (DECT). This retrospective study included participants without pancreatic diseases undergoing DECT at two institutions with different platforms. Their protocols both included arterial phase (AP), portal venous phase (PP), and equilibrium phase (EP), defined as 35 s-40 s, 60 s-70 s, and 150 s-180 s after injection of contrast agent, respectively. Both iodine concentration (IC) and normalised IC (NIC) were measured. Demographic features, local measurements of the pancreas and visceral fat area (VFA) were considered as potential factors influencing iodine uptake using multivariate linear regression analyses. A total of 562 participants (median age 58 years [interquartile range: 47-67], with 282 men) were evaluated. The mean IC differed significantly between two institutions (all p < 0.001) across three contrast-enhanced phases, while the mean NIC showed no significant differences (all p > 0.05). The mean values of NIC were 0.22 at AP, 0.43 at PP and 0.45 at EP. NICAP was independently affected by VFA (β = 0.362, p < 0.001), smoking (β = -0.240, p = 0.001), and type-II diabetes (β = -0.449, p < 0.001); NICPP by VFA (β = -0.301, p = 0.017) and smoking (β = -0.291, p < 0.001); and NICEP by smoking (β = -0.154, p = 0.10) and alcohol consumption (β = -0.350, p < 0.001) with statistical power values over 0.81. NIC values were consistent across institutions. Abdominal obesity, smoking, alcohol consumption, and diabetes are independent factors influencing pancreatic iodine uptake. This study has provided reference normative values, influential factors and effective normalisation methods of pancreatic iodine uptake in multiphase dual-energy CT for future studies in this area as a new biological marker. Evaluation of pancreatic iodine uptake measured by dual-energy CT is a promising method for future studies. Abdominal obesity, smoking, alcohol consumption, diabetes, and sex are independent factors influencing pancreatic iodine uptake. Utility of normalised iodine concentration is necessary to ensure the consistency across different institutions.

The texture analysis of computed tomography studies in clear cell renal cell carcinoma: reproducibility of 2D and 3D segmentation

Background: Differentiation of tumor grade at the preoperative stage is of utmost importance for the modification of the treatment strategy and the extent of operation. However, the routine analysis of computed tomography (CT) data in clear cell renal cell carcinoma (ccRCC) does not allow for reliable determination of the tumor grade.&#x0D; Aim: To assess the reproducibility of the results of 2D and 3D segmentation of a kidney tumor in the cortico-medullary and nephrographic phases of CT studies, as well as the reproducibility of the first order texture parameters for 2D and 3D tumor segmentation in patients with verified ccRCC.&#x0D; Materials and methods: This retrospective study included the CT data of 50 patients with morphologically verified ccRCC obtained before their surgical treatment. The first patient group included the patients with the renal tumor size in the axial plane of ≥ 4 cm (28 patients, 29 CT studies), and the second patient group included those with the renal tumor size in axial plane of 4 cm (22 patients, 23 CT studies). Two radiologists independently performed segmentation of the renal tumor in the cortico-medullary and nephrographic phases of CT procedures done under a standard protocol with the bolus intravenous contrast enhancement. A two-dimensional region of interest (2D ROI) was selected by the investigators on a subjectively selected axial slice, where the tumor had the largest size. When forming a three-dimensional region of interest (3D ROI), the entire tumor volume was segmented. Next, the statistical analysis of the segmentation results and the results of calculation of the first order texture indices was performed with calculation of the intra-class correlation coefficient (ICC) to assess the strength of the data correlation. The ICC of ≥ 0.75 demonstrated the reproducibility of the segmentation results and the first order texture indices.&#x0D; Results: The 3D segmentation method for ccRCC demonstrated the best ROI reproducibility results, regardless of the tumor size and the phase of contrast enhancement, with the ICC values of 0.961 (95% confidence interval: 0.946–0.971) for the cortico-medullary phase and 0.969 (95% CI: 0.958–0.977) for the nephrographic phase. The 2D tumor segmentation method showed unsatisfactory ROI reproducibility, with the ICC values of ≤ 0.058; however, the unsatisfactory reproducibility of the segmentation results in the patients with ccRCC tumor size of ≥ 4 cm did not significantly affect the reproducibility of the Entropy and Energy texture indices (good to excellent correlation). With the 3D segmentation of ccRCC, most first-order texture metrics were reproducible, with the exception of the Kurtosis parameter. The Entropy and Energy scores in both patient groups demonstrated a high degree of reproducibility. In the 2D tumor segmentation, high reproducibility of the first order texture metrics was obtained for the Entropy and Energy indices.&#x0D; Conclusion: The 3D segmentation of the CT data for ccRCC has high reproducibility, the most first-order textural features were excellently reproducible when segmentations were performed in 3D. The 2D CT data segmentation method for ccRCC demonstrated low reproducibility; however, some of the first order texture indices were reproducible. Both segmentation methods can be used for the texture analysis of CT images.

Opportunistic screening with multiphase contrast-enhanced dual-layer spectral CT for osteoblastic lesions in prostate cancer compared with bone scintigraphy

Our study aimed to compare bone scintigraphy and dual-layer detector spectral CT (DLCT) with multiphase contrast enhancement for the diagnosis of osteoblastic bone lesions in patients with prostate cancer. The patients with prostate cancer and osteoblastic bone lesions detected on DLCT were divided into positive bone scintigraphy group (pBS) and negative bone scintigraphy group (nBS) based on bone scintigraphy. A total of 106 patients (57 nBS and 49 pBS) was included. The parameters of each lesion were measured from DLCT including Hounsfield unit (HU), 40–140 keV monochromatic HU, effective nuclear numbers (Zeff), and Iodine no water (InW) value in non-contrast phase (N), the arterial phase (A), and venous phase (V). The slope of the spectral curve at 40 and 100 keV, the different values of the parameters between A and N phase (A-N), V and N phase (V-N), and hybrid prediction model with multiparameters were used to differentiate pBS from nBS. Receiver operating characteristic analysis was performed to compare the area under the curve (AUC) for differentiating the pBS group from the nBS group. The value of conventional HU values, slope, and InW in A-N and V-N, and hybrid model were significantly higher in the pBS group than in the nBS group. The hybrid model of all significant parameters had the highest AUC of 0.988, with 95.5% sensitivity and 94.6% specificity. DLCT with arterial contrast enhancement phase has the potential to serve as an opportunistic screening tool for detecting positive osteoblastic bone lesions, corresponding to those identified in bone scintigraphy.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Common-Unique Decomposition Driven Diffusion Model for Contrast-Enhanced Liver MR Images Multi-Phase Interconversion.

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology.

The reproducibility and predictivity of radiomic features extracted from dynamic contrast-enhanced computed tomography of hepatocellular carcinoma.

To assess the reproducibility of radiomic features (RFs) extracted from dynamic contrast-enhanced computed tomography (DCE-CT) scans of patients diagnosed with hepatocellular carcinoma (HCC) with regards to inter-observer variability and acquisition timing after contrast injection. The predictive ability of reproducible RFs for differentiating between the degrees of HCC differentiation is also investigated. We analyzed a set of DCE-CT scans of 39 patients diagnosed with HCC. Two radiologists independently segmented the scans, and RFs were extracted from each sequence of the DCE-CT scans. The same lesion was segmented across the DCE-CT sequences of each patient's scan. From each lesion, 127 commonly used RFs were extracted. The reproducibility of RFs was assessed with regard to (i) inter-observer variability, by evaluating the reproducibility of RFs between the two radiologists; and (ii) timing of acquisition following contrast injection (inter- and intra-imaging phase). The reproducibility of RFs was assessed using the concordance correlation coefficient (CCC), with a cut-off value of 0.90. Reproducible RFs were used for building XGBoost classification models for the differentiation of HCC differentiation. Inter-observer analyses across the different contrast-enhancement phases showed that the number of reproducible RFs was 29 (22.8%), 52 (40.9%), and 36 (28.3%) for the non-contrast enhanced, late arterial, and portal venous phases, respectively. Intra- and inter-sequence analyses revealed that the number of reproducible RFs ranged between 1 (0.8%) and 47 (37%), inversely related with time interval between the sequences. XGBoost algorithms built using reproducible RFs in each phase were found to be high predictive ability of the degree of HCC tumor differentiation. The reproducibility of many RFs was significantly impacted by inter-observer variability, and a larger number of RFs were impacted by the difference in the time of acquisition after contrast injection. Our findings highlight the need for quality assessment to ensure that scans are analyzed in the same physiologic imaging phase in quantitative imaging studies, or that phase-wide reproducible RFs are selected. Overall, the study emphasizes the importance of reproducibility and quality control when using RFs as biomarkers for clinical applications.

Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases

ObjectivesTo evaluate and analyze radiomics models based on non-contrast-enhanced computed tomography (CT) and different phases of contrast-enhanced CT in predicting Ki-67 proliferation index (PI) among patients with pathologically confirmed gastrointestinal stromal tumors (GISTs).MethodsA total of 383 patients with pathologically proven GIST were divided into a training set (n = 218, vendor 1) and 2 validation sets (n = 96, vendor 2; n = 69, vendors 3–5). Radiomics features extracted from the most recent non-contrast-enhanced and three contrast-enhanced CT scan prior to pathological examination. Random forest models were trained for each phase to predict tumors with high Ki-67 proliferation index (Ki-67>10%) and were evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics on the validation sets.ResultsOut of 107 radiomics features extracted from each phase of CT images, four were selected for analysis. The model trained using the non-contrast-enhanced phase achieved an AUC of 0.792 in the training set and 0.822 and 0.711 in the two validation sets, similar to models trained on different contrast-enhanced phases (p > 0.05). Several relevant features, including NGTDM Busyness and tumor size, remained predictive in non-contrast-enhanced and different contrast-enhanced images.ConclusionThe results of this study indicate that a radiomics model based on non-contrast-enhanced CT matches that of models based on different phases of contrast-enhanced CT in predicting the Ki-67 PI of GIST. GIST may exhibit similar radiological patterns irrespective of the use of contrast agent, and such radiomics features may help quantify these patterns to predict Ki-67 PI of GISTs.Clinical relevance statementGIST may exhibit similar radiomics patterns irrespective of contrast agent; thus, radiomics models based on non-contrast-enhanced CT could be an alternative for risk stratification in GIST patients with contraindication to contrast agent.Key Points• Performance of radiomics models in predicting Ki-67 proliferation based on different CT phases is evaluated.• Non-contrast-enhanced CT–based radiomics models performed similarly to contrast-enhanced CT in risk stratification in GIST patients.• NGTDM Busyness remains stable to contrast agents in GISTs in radiomics models.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.

This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Exploring the Role of MRI in the Detection of Atypical Liver Hemangiomas and Exclusion of Metastases

Background: In this article, we highlight the importance of utilizing multiple imaging modalities, including CT, MRI, and abdominal Doppler ultrasound, to accurately differentiate between atypical liver hemangiomas (LH) and metastases. Case report: We introduce a 57-year-old male patient who presented with severe abdominal pain. Initial CT scan findings showed hypodense liver lesions and a well-defined hypodense mass in the sub-diaphragmatic region, raising suspicion for metastatic liver lesions, and prompting further evaluation. To differentiate between atypical hemangiomas and metastases, an MRI scan was performed, revealing a hyperintense signal on T2-weighted images and a hypointense signal on T1-weighted images, consistent with the characteristics of LH. These characteristic signal intensities aided in ruling out metastatic liver lesions, which typically present with a more solid and homogeneous appearance. Contrast-enhanced MRI played a crucial role in confirming the diagnosis. The liver lesions demonstrated moderate vascularization during the early phase of contrast enhancement, followed by progressive centripetal filling during the portal venous and delayed phases. This enhancement pattern is consistent with the slow flow within the dilated vascular spaces of liver hemangiomas. The lesion in the right liver lobe is almost completely filled with contrast, further supporting the diagnosis of hemangioma. Abdominal Doppler ultrasound can provide additional information regarding the vascularity of liver lesions. In this case, the Doppler examination likely helped to further confirm the presence of a hemangioma, as these lesions typically demonstrate increased vascularity compared to metastatic lesions. Conclusion: The comprehensive imaging evaluation utilizing CT, MRI, and abdominal Doppler ultrasound allowed for the confident differentiation of atypical liver hemangiomas from metastatic liver lesions in this case. This emphasizes the importance of a multimodal imaging approach in cases of liver lesions with overlapping features, leading to improved patient management and outcomes.

Segmentation of Brain MRI Images using Multi-Kernel FCM EHO Method.

In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field. The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels. In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation. Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods. Our proposed methodology was reported to be a more effective technique than existing techniques.

Breast DWI Analyzed Before and After Gadolinium Contrast Administration-An Intrapatient Analysis on 1.5 T and 3.0 T.

Diffusion-weighted magnetic resonance imaging (MRI) is gaining popularity as an addition to standard dynamic contrast-enhanced breast MRI. Although adding diffusion-weighted imaging (DWI) to the standard protocol design would require increased scanning-time, implementation during the contrast-enhanced phase could offer a multiparametric MRI protocol without any additional scanning time. However, gadolinium within a region of interest (ROI) might affect assessments of DWI. This study aims to determine if acquiring DWI postcontrast, incorporated in an abbreviated MRI protocol, would statistically significantly affect lesion classification. In addition, the effect of postcontrast DWI on breast parenchyma was studied. Screening or preoperative MRIs (1.5 T/3 T) were included for this study. Diffusion-weighted imaging was acquired with single-shot spin echo-echo planar imaging before and at approximately 2 minutes after gadoterate meglumine injection. Apparent diffusion coefficients (ADCs) based on 2-dimensional ROIs of fibroglandular tissue, as well as benign and malignant lesions at 1.5 T/3.0 T, were compared with a Wilcoxon signed rank test. Diffusivity levels were compared between precontrast and postcontrast DWI with weighted κ. An overall P ≤ 0.05 was considered statistically significant. No significant changes were observed in ADC mean after contrast administration in 21 patients with 37 ROI of healthy fibroglandular tissue and in the 93 patients with 93 (malignant and benign) lesions. This effect remained after stratification on B 0 . In 18% of all lesions, a diffusion level shift was observed, with an overall weighted κ of 0.75. This study supports incorporating DWI at 2 minutes postcontrast when ADC is calculated based on b150-b800 with 15 mL 0.5 M gadoterate meglumine in an abbreviated multiparametric MRI protocol without requiring extra scan time.

The ‘Zebra Spleen’: CT enhancement pattern of the spleen

Zebra spleen, also referred to as psychedelic spleen is a radiological sign reflecting the transient heterogeneous parenchymal enhancement of the spleen during the arterial or early portal venous phases of contrast enhancement in CT, MRI, or ultrasound imaging

CT features of pulmonary ACTH-Secreting Tumor

The purpose of the study is to improve the accuracy of CT diagnostics of pulmonary ACTH-Secreting Tumor.Material and methods. During the period from 2010 to 2022 at the Endocrinology Research Centre and the National Medical Research Center of Surgery named after A. Vishnevsky of the Ministry of Health of Russia 23 patients with pulmonary ACTH-Secreting Tumor were examined and treated. CT-features were analyzed: localization, shape, contours, connection of the formation with the bronchus, connection of the formation with the vessel, adherence to the vessel and bronchus along the long axis, structure of the neoplasm. We also analyzed the largest size of the formation, the size of the largest regional lymph node, the density of the tumor on axial sections in the native, arterial, venous and delayed phases of the study.Results. In the study, we identified a new phenotypic CT sign – the relationship with the bronchovascular bundle. The connection with the bronchus and vessel was assessed in detail. In the majority of patients, the connection with the pulmonary vessels (in most cases with small bronchial arteries) was especially clearly noted: an intimate attachment of the tumor along its long axis to the vessel. Less often it was possible to trace the connection with the bronchus.Conclusion. ACTH-producing NET of the lung is most often a peripherally located oval-shaped formation with even/ lobulated contours, adjacent to the bronchovascular bundle along the long axis of the tumor, intensively enchancing (increase of density parameters by more than 40 HU compared with the unenhanced phase) in the venous, less often the arterial phase of bolus contrast enhancement.

Diagnostic performance of multiparametric magnetic resonance imaging in the differentiation of clear cell renal cell cancer.

This study aimed to evaluate the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) in the differentiation of renal cell carcinoma (RCC) subtypes. This is a retrospective diagnostic performance study, in which the diagnostic performances of mpMRI features were evaluated to differentiate clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC). Adult patients who were evaluated using a 3-Tesla dynamic contrast-enhanced mpMRI before undergoing partial or radical nephrectomy for possible malignant renal tumors were included in the study. Signal intensity change percentages (SICP) between contrast-enhanced phases and pre-administration period for both the tumor and normal renal cortex, and tumor-to-cortex enhancement index (TCEI); tumor apparent diffusion coefficient (ADC) values; tumor-to-cortex ADC ratio; and a scale which was developed according to the tumor signal intensities on the axial fat-suppressed T2-weighted Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images were used in ROC analysis to estimate the presence of ccRCC in the patients. The reference test positivity was the histopathologic examination of the surgical specimens. Ninety-eight tumors from 91 patients were included in the study, and 59 of them were ccRCC, 29 were pRCC, and 10 were chRCC. The mpMRI features that had the three highest sensitivity rates were excretory phase SICP, T2-weighted HASTE scale score, and corticomedullary phase TCEI (93.2%, 91.5%, and 86.4%, respectively). However, those with the three highest specificity rates were nephrographic phase TCEI, excretory phase TCEI, and tumor ADC value (94.9%, 94.9%, and 89.7%, respectively). Several parameters on mpMRI showed an acceptable performance to differentiate ccRCC from non-ccRCC.

The effect of atypical arterial vascularization of the liver on computed tomography signs of hemangiomas

Introduction. The article describes the role of computed tomography in the diagnosis of hemangiomas in various types of hepatic arterial circulation. Aim. To determine the features of contrast enhancement of hemangiomas in multislice computed tomography with multiphase contrast enhancement in patients with abnormalities of the arterial supplement of the liver. Materials and methods. The results of 60 computed tomographic angiographies of patients with hemangiomas of various sizes (mean age of patients 59.2±12.3 years, the study included 34 (57 %) men, 26 (43 %) women) were analyzed. Results. The most common anatomical variants of the vasculature, concomitant anomalies of the arterial supply of other abdominal organs, as well as features of contrast enhancement of hemangiomas depending on their size are described. The analysis of vascular architectonics revealed that the first type of arterial supply according to Mitchels (p &lt;0.05) ) is prevailed, however, 3 (3.3 %), 5 (3.3 %), 6 (6.6 %) types were also determined. Thus, a correlation analysis using Fisher’s exact test was not detected a significant correlation between the presence of hemangiomas in the liver parenchyma and the presence of aberrant (r=0.11, p=0.36) or replacement arterial vessels (r=0.14, p=0.35). At the same time, when visually assessing the contrast enhancement of hemangiomas in different phases of contrast enhancement, both in the presence of aberrant and replacing vessels and without them, the main radiation patterns of changing hemangiomas density remained unchanged. Conclusion. Based on the obtained data on the liver arterial system, the absence of a significant correlation between the type of arterial blood supply, the presence of an additional or replacement vessel, and the type of contrast enhancement of hemangiomas during multiphase computed tomography of the abdominal organs was demonstrated.

Effect of contrast medium on early detection and analysis of mediastinal lymph nodes in computed tomography.

This study aimed to evaluate the diagnostic efficiency of contrast-to-noise and signal-to-noise ratios created by the contrast medium in detecting lymph nodes. In this study, 57 short-axis subcentimeter lymph nodes in 40 cardiac computed tomography patients with noncontrast- and contrast-enhanced phases were evaluated. The contrast-to-noise ratios and signal-to-noise ratios of noncontrast- and contrast-enhanced lymph node-mediastinal fat and aortic-mediastinal fat tissues were determined. In addition, lymph nodes in noncontrast- and contrast-enhanced series were evaluated subjectively. There was a significant difference in lymph node-mediastinal fat signal-to-noise values between the contrast and noncontrast phases (p=0.0002). In the contrast phase, aortic density values were found to be 322.04±18.51 HU, lymph node density values were 76.41±23.41 HU, and mediastinal adipose tissue density values were -65.73±22.96 HU. Aortic-mediastinal fat contrast-to-noise ratio value was 20.23±6.92 and the lymph node-mediastinal fat contrast-to-noise ratio value was 6.43±2.07. A significant and moderate correlation was observed between aortic-mediastinal fat and lymph node-mediastinal fat contrast-to-noise ratio values in the contrast phase (r=0.605; p<0.001). In the contrast-enhanced series, there was a significant increase in the subjective detection of lymph nodes (p=0.0001). In the detection of paratracheal lymph nodes, the contrast agent increases the detection of short-axis subcentimeter lymph nodes quantitatively and qualitatively. Contrast enhances and facilitates the detection of paratracheal lymph nodes.

Differentiating Oncocytic Renal Tumors from Chromophobe Renal Cell Carcinoma: Comparison of Peak Early-phase Enhancement Ratio to Clinical Risk Factors and Rater Predictions

BackgroundMost surgically resected benign renal tumors are found to be oncocytomas or indolent hybrid oncocytic tumors, which are difficult to differentiate from chromophobe renal cell carcinoma (chRCC) on renal mass biopsy. Both often exhibit CD117+ staining. ObjectiveTo evaluate the ability of the peak early-phase enhancement ratio (PEER) to distinguish oncocytomas from chRCC and compare its discrimination to traditional clinical risk factors and blinded clinical raters. Design, setting, and participantsThis was a diagnostic case-control study of patients (2006–2020) with oncocytoma or chRCC according to surgical pathology. InterventionPartial or radical nephrectomy. Outcome measurements and statistical analysisThree clinical raters blinded to histology measured the PEER and the presence of stellate scar and predicted the final histology for each tumor. Averaged and individual PEER values were compared to surgical pathology and assessed for interobserver variability. Subanalyses were conducted for patients with confirmed CD117+ status. Results and limitationsFor the 76 patients identified, PEER was higher among the 32 (42.1%) oncocytomas than among the 44 (57.9%) chRCCs (median 0.81 vs 0.43; p < 0.001), with high correlation across raters (correlation coefficients ≥0.85). A PEER cutoff of <0.60 was strongly associated with identification of chRCC (OR 95.7 (95% CI 19.9–460.8), p < 0.001). In the overall and CD117+ cohorts, sensitivity was 93.2% and 97.0%, the negative predictive value was 90.3% and 95.5%, and the area under the receiver operating characteristic curve (AUC) on multivariable modeling was 95.0% and 98.1%, respectively. PEER outperformed models with clinical risk factors alone (AUC 70.4%) and histology predictions by three raters (AUC 51.6%, 62.5%, and 63.1%). Limitations include reliance on surgical pathology and inclusion of a mix of early contrast-enhanced phases. ConclusionsPEER reliably differentiated benign renal oncocytomas and indolent hybrid tumors from malignant chRCC with excellent diagnostic performance. A diagnostic pathway with biopsy, CD117 staining, and PEER deserves further study to potentially avoid unnecessary surgery for oncocytic renal tumors. Patient summaryWe assessed a measurement called PEER on computed tomography (CT) scans and found higher values for benign and lower values for malignant kidney masses, so we were able to tell these apart. PEER was reliable for identifying tumors with positive staining for the CD117 protein biomarker as well as in the overall patient group. Our results show that PEER could be considered for use with biopsy and CD117 staining to potentially avoid unnecessary surgery for benign kidney masses.