Preprocedural fasting for contrast-enhanced CT: when experience meets evidence.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu H
PROVIDER: S-EPMC8643287 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ObjectivesTo analyze the relationship between the dietary preparation status prior to contrast-enhanced CT (CECT) and adverse drug reactions (ADR) and emetic complications.MethodsNon-emergency adult patients who underwent routine CECT in our hospital from January 2019 to December 2020 were retrospectively analyzed. Stratified dietary preparation regimens were implemented for different clinical scenarios. The relationship between actual dietary preparation status and ADR and emetic complications was analyzed.ResultsA total of 127,200 cases were enrolled, including 49,676 cases in the fasting group (57 years ± 13, 56.79% men) and 77,524 cases in the non-fasting group (60 years ± 13, 54.55% men). No statistical difference was found in the overall incidence of ADR (0.211% vs. 0.254%, p = 0.126) or emetic complications (0.030% vs. 0.046%, p = 0.158) between the two groups, and no aspiration pneumonia or death occurred. For patients with an ICM-ADR history, the ADR incidence in non-fasting group was significantly lower than fasting group (2.424% vs. 12.371%, p = 0.002). For patients with hypertension, injection dose ≥ 100 mL, injection rate ≥ 5 mL/s, and Iopromide 370 usage, non-fasting was associated with higher ADR incidence (p < 0.05). 36.67% of the patients experienced unnecessary excessive fasting in practice. Excessive fasting (≥ 10 h) and more water ingestion (≥ 500 mL) within 1 h prior to CECT were associated with higher ADR incidence (p < 0.05).ConclusionUnrestricted food ingestion would not increase the overall risk of ADR and emetic complications. For some special patient subgroups, non-fasting, excessive fasting, and more water ingestion were associated with higher ADR incidence.
| S-EPMC8873329 | biostudies-literature
Project description:To quantify contrast material enhancement of breast lesions scanned with dedicated breast computed tomography (CT) and to compare their conspicuity with that at unenhanced breast CT and mammography.Approval of the institutional review board and the Radiation Use Committee and written informed consent were obtained for this HIPAA-compliant study. Between September 2006 and April 2009, 46 women (mean age, 53.2 years; age range, 35-72 years) with Breast Imaging Reporting and Data System category 4 or 5 lesions underwent unenhanced breast CT and contrast material-enhanced breast CT before biopsy. Two radiologists independently scored lesion conspicuity for contrast-enhanced breast CT versus mammography and for contrast-enhanced breast CT versus unenhanced breast CT. Mean lesion voxel intensity was measured in Hounsfield units and normalized to adipose tissue intensity on manually segmented images obtained before and after administration of contrast material. Regression models focused on conspicuity and quantified enhancement were used to estimate the effect of pathologic diagnosis (benign vs malignant), lesion type (mass vs calcifications), breast density, and interradiologist variability.Fifty-four lesions (25 benign, 29 malignant) in 46 subjects were analyzed. Malignant lesions were seen significantly better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) or mammography (P < .001). Malignant calcifications (malignant lesions manifested mammographically as microcalcifications only, n = 7) were seen better at contrast-enhanced breast CT than at unenhanced breast CT (P < .001) and were seen similarly at contrast-enhanced breast CT and mammography. Malignant lesions enhanced 55.9 HU +/- 4.0 (standard error), whereas benign lesions enhanced 17.6 HU +/- 6.1 (P < .001). Ductal carcinoma in situ (n = 5) enhanced a mean of 59.6 HU +/- 2.8. Receiver operating characteristic curve analysis of lesion enhancement yielded an area under the receiver operating characteristic curve of 0.876.Conspicuity of malignant breast lesions, including ductal carcinoma in situ, is significantly improved at contrast-enhanced breast CT. Quantifying lesion enhancement may aid in the detection and diagnosis of breast cancer.
| S-EPMC2923727 | biostudies-other
Project description:PurposeTo evaluate the feasibility of using contrast material-enhanced computed tomographic (CT) measurements of hepatic fractional extracellular space (fECS) and macromolecular contrast material (MMCM) uptake to measure severity of liver fibrosis.Materials and methodsAll procedures were approved by and executed in accordance with University of California, San Francisco, institutional animal care and use committee regulations. Twenty-one rats that received intragastric CCl(4) for 0-12 weeks were imaged with respiratory-gated micro-CT by using both a conventional contrast material and a novel iodinated MMCM. Histopathologic hepatic fibrosis was graded qualitatively by using the Ishak fibrosis score and quantitatively by using morphometry of the fibrosis area. Hepatic fECS and MMCM uptake were calculated for each examination and correlated with histopathologic findings by using uni- and multivariate linear regressions.ResultsIshak fibrosis scores ranged from a baseline of 0 in untreated animals to a maximum of 5. Histopathologic liver fibrosis area increased from 0.46% to 3.5% over the same interval. Strong correlations were seen between conventional contrast-enhanced CT measurements of fECS and both the Ishak fibrosis scores (R(2) = 0.751, P < .001) and the fibrosis area (R(2) = 0.801, P < .001). Strong negative correlations were observed between uptake of MMCM in the liver and Ishak fibrosis scores (R(2) = 0.827, P < .001), as well as between uptake of MMCM in the liver and fibrosis area (R(2) = 0.643, P = .001). Multivariate linear regression analysis showed a trend toward independence for fECS and MMCM uptake in the prediction of Ishak fibrosis scores, with an R(2) value of 0.86 (P = .081 and P = .033, respectively).ConclusionContrast-enhanced CT measurements of fECS and MMCM uptake are individually capable of being used to estimate the degree of early hepatic fibrosis in a rat model.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112452/-/DC1.
| S-EPMC3528968 | biostudies-literature
Project description:Secondary osseous involvement in lymphoma is more common compared to primary bone lymphoma. The finding of osseous lesion can be incidentally discovered during the course of the disease. However, osseous metastases are infrequently silent. Detection of osseous metastases is crucial for accurate staging and optimal treatment planning of lymphoma. The aim of imaging is to identify the presence and extent of osseous disease and to assess for possible complications such as pathological fracture of the load-bearing bones and cord compression if the lesion is spinal. We are presenting two patients with treated lymphoma who were in complete remission. On routine follow-up contrast enhanced CT, there were new osteoblastic lesions in the spine worrisome for metastases. Additional studies were performed for further evaluation of both of them which did not demonstrate any corresponding suspicious osseous lesion. The patients have a prior history of chronic venous occlusive thrombosis that resulted in collaterals formation. Contrast enhancement of the vertebral body marrow secondary to collaterals formation and venous flow through the vertebral venous plexus can mimic the appearance of spinal osteoblastic metastases.
| S-EPMC5549491 | biostudies-other
Project description:Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.
| S-EPMC11291756 | biostudies-literature
Project description:ObjectivesTo investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.MethodsThe retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsThe SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively.ConclusionThe machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.
| S-EPMC8212783 | biostudies-literature
Project description:BackgroundComputed tomography (CT) imaging has been widely used for the diagnosis and surveillance of diseases. Although CT is attracting attention due to its reasonable price, short scan time, and excellent diagnostic ability, there are severe drawbacks of conventional CT contrast agents, such as low sensitivity, serious toxicity, and complicated synthesis process. Herein, we describe iodine-doped carbon dots (IDC) for enhancing the abilities of CT contrast agents.MethodIDC was synthesized by one-pot hydrothermal synthesis for 4 h at 180 ℃ and analysis of its structure and size distribution with UV-Vis, XPS, FT-IR, NMR, TEM, and DLS. Furthermore, the CT values of IDC were calculated and compared with those of conventional CT contrast agents (Iohexol), and the in vitro and in vivo toxicities of IDC were determined to prove their safety.ResultsIDC showed improved CT contrast enhancement compared to iohexol. The biocompatibility of the IDC was verified via cytotoxicity tests, hemolysis assays, chemical analysis, and histological analysis. The osmotic pressure of IDC was lower than that of iohexol, resulting in no dilution-induced contrast decrease in plasma.ConclusionBased on these results, the remarkable CT contrast enhancement and biocompatibility of IDC can be used as an effective CT contrast agent for the diagnosis of various diseases compared with conventional CT contrast agents.
| S-EPMC9233767 | biostudies-literature
Project description:PurposeNon-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep-learning approach to generate NCECT directly from CECT. This method could be useful to avoid the NCECT scan, reduce CT simulation time and imaging dose, and decrease the uncertainties caused by tissue motion between otherwise two different CT scans.MethodsA deep network was developed to convert CECT to NCECT. The network receives a 3D image from CECT images as input and generates a corresponding contrast-removed NCECT image patch. Abdominal CECT and NCECT image pairs of 20 patients were deformably registered and 8000 image patch pairs extracted from the registered image pairs were utilized to train and test the model. CTs of clinical proton patients and their treatment plans were employed to evaluate the dosimetric impact of using the generated NCECT for proton dose calculation.ResultsOur approach achieved a Cosine Similarity score of 0.988 and an MSE value of 0.002. A quantitative comparison of clinical proton dose plans computed on the CECT and the generated NCECT for five proton patients revealed significant dose differences at the distal of beam paths. V100% of PTV and GTV changed by 3.5% and 5.5%, respectively. The mean HU difference for all five patients between the generated and the scanned NCECTs was ∼4.72, whereas the difference between CECT and the scanned NCECT was ∼64.52, indicating a ∼93% reduction in mean HU difference.ConclusionsA deep learning approach was developed to generate NCECTs from CECTs. This approach could be useful for the proton dose calculation to reduce uncertainties caused by tissue motion between CECT and NCECT.
| S-EPMC10860532 | biostudies-literature
Project description:BackgroundSolitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.Research questionWhat is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?Study design and methodsThis was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.ResultsTwo hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).InterpretationAn LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.Trial registrationClinicalTrials.gov Identifier; No.: NCT02013063.
| S-EPMC9899635 | biostudies-literature
Project description:Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media.A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level.The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.
| S-EPMC5523753 | biostudies-other