Arterial Phase Research Articles

Hemangioendothelioma of the spleen and liver in a 1-year-old child.

Concurrent occurrence of hemangioendothelioma in the spleen and liver of a 1-year-old child is a rare clinical case.She had a history of good health. Laboratory tests showed elevated inflammatory markers, but all other parameters were within normal range.In this article, we exhibit the typical clinical presentation and Pathology imaging features of this disease.CT imaging revealed a 9.8 cm×8.6 cm mass in the spleen, with areas of non-enhancing liquefactive necrosis. The mass exhibited heterogeneous enhancement during the arterial phase and progressive enhancement during the venous phase. Multiple nodules were observed in liver segments S2 and S4, the largest being approximately 3.7 cm×3.5 mm, with enhancement patterns similar to the splenic lesion. The patient underwent splenectomy, left hemihepatectomy, and cholecystectomy. Intraoperatively, the splenic mass exhibited some areas appearing spongy. Multiple nodules were observed in the left liver, presenting as gray-brown solid masses with a spongy texture. Pathology confirmed a diagnosis of hemangioendothelioma, supported by positive immunohistochemical staining for CD31, CD34, ERG, and FLI-1.

CT and MR imaging characteristics of primary hepatic lymphoma

BackgroundPrimary hepatic lymphoma (PHL) is a rare primary liver tumor. The purpose of this study was to investigate the crucial imaging characteristics of PHL.MethodsCT/MR data and clinical features of 16 patients with pathologically proven PHL were retrospectively reviewed. The assessed imaging characteristics included lesion distribution, growth appearance, density or signal characteristics, and typical signs during the dynamic enhancement.ResultsA total of sixteen patients were included in this study, ranging in age from 27 to 86 years (nine men and seven women; mean age, 62 years). Right upper abdominal pain, malaise, or first detection during physical examination were the chief complaint. Dynamic contrast-enhanced CT and MRI demonstrated focal masses in thirteen patients (13/16, 81%), and multiple nodules in three patients (3/16, 19%). The lesions showed vascular floating sign in 10 patients (10/16, 63%) and biliary pass-through sign in 6 patients (6/16, 37%). Necrosis in PHLs were observed in 10 patients (10/16, 63%) and presented target sign on T2WI in 5 out of 11 patients (5/11, 45%) undergoing MRI. After enhancement, PHLs had multinodular sign in the arterial phase in 10 patients (10/16, 63%), and presented washout of contrast medium in the portal-venous or delayed phase in 12 patients (12/16, 75%), pseudocapsule in the delayed phase in 15 patients (15/16, 94%), and double-ring sign in the delayed phase in 8 patients (8/16, 50%).ConclusionPHL is generally seen in elderly patients with elevated beta-2-microglobulin levels. Its distinctive imaging features including obviously low ADC value, biliary/vascular floating sign, multinodular sign and double-ring sign in the delayed phase, may help to diagnose and differentiate PHL.

The reliability of virtual non-contrast reconstructions of photon-counting detector CT scans in assessing abdominal organs

BackgroundSpectral imaging of photon-counting detector CT (PCD-CT) scanners allows for generating virtual non-contrast (VNC) reconstruction. By analyzing 12 abdominal organs, we aimed to test the reliability of VNC reconstructions in preserving HU values compared to real unenhanced CT images.MethodsOur study included 34 patients with pancreatic cystic neoplasm (PCN). The VNC reconstructions were generated from unenhanced, arterial, portal, and venous phase PCD-CT scans using the Liver-VNC algorithm. The observed 11 abdominal organs were segmented by the TotalSegmentator algorithm, the PCNs were segmented manually. Average densities were extracted from unenhanced scans (HUunenhanced), postcontrast (HUpostcontrast) scans, and VNC reconstructions (HUVNC). The error was calculated as HUerror=HUVNC–HUunenhanced. Pearson’s or Spearman’s correlation was used to assess the association. Reproducibility was evaluated by intraclass correlation coefficients (ICC).ResultsSignificant differences between HUunenhanced and HUVNC[unenhanced] were found in vertebrae, paraspinal muscles, liver, and spleen. HUVNC[unenhanced] showed a strong correlation with HUunenhanced in all organs except spleen (r = 0.45) and kidneys (r = 0.78 and 0.73). In all postcontrast phases, the HUVNC had strong correlations with HUunenhanced in all organs except the spleen and kidneys. The HUerror had significant correlations with HUunenhanced in the muscles and vertebrae; and with HUpostcontrast in the spleen, vertebrae, and paraspinal muscles in all postcontrast phases. All organs had at least one postcontrast VNC reconstruction that showed good-to-excellent agreement with HUunenhanced during ICC analysis except the vertebrae (ICC: 0.17), paraspinal muscles (ICC: 0.64–0.79), spleen (ICC: 0.21–0.47), and kidneys (ICC: 0.10–0.31).ConclusionsVNC reconstructions are reliable in at least one postcontrast phase for most organs, but further improvement is needed before VNC can be utilized to examine the spleen, kidneys, and vertebrae.

Noninvasive visualization of the midline-crossing arterial variation in the deep inferior epigastric artery perforator flap using photoacoustic tomography for application in patients with abdominal scars

BackgroundThe importance of the subcutaneous arterial network crossing the midline in transverse abdominal flaps has been reported. Photoacoustic tomography can be used to noninvasively visualize subcutaneous vascular networks. We applied this novel technology preoperatively in patients undergoing breast reconstruction to detect individual variations in the midline-crossing arteries. MethodsSix patients scheduled to undergo breast reconstruction using free deep inferior epigastric artery perforator flaps were examined. Each scan of the 12×8-cm region took approximately 8min. The accuracy of the tentative arteries evaluation defined by photoacoustic tomography was compared with the arterial phase detected by intraoperative indocyanine green angiography. The number of perforator vessels used for the flap, surgical time for flap elevation, and perfusion area ratio were compared with those of the control group. ResultsThe average match rate between tentative arteries prediction by photoacoustic tomography and arterial-phase assessment by intraoperative angiography in 5 patients was 79.8%. Each midline crossing artery showed individual variations. The photoacoustic tomography group (PAT-1 to 5) showed 1.8 perforators per flap, 163minutes for flap elevation, and 93% perfusion area, with no significant differences from the control group (N=5). A 63-year-old woman (PAT-6) with abdominal scars, including a midline abdominal incision, showed a preserved midline crossing artery. The planned single perforator deep inferior epigastric perforator flap was successfully applied to the contralateral perfusion area. ConclusionsPhotoacoustic tomography noninvasively visualizes the subcutaneous midline-crossing arterial networks. Understanding individual vascular variations can support preoperative planning and surgical indication of abdominal flaps, especially in patients with postsurgical scars.

The role of radiomics analysis in the assessment of renal nodules on CT

PurposeTo develop a radiomics model for the characterization of renal nodules on CT.MethodsPatients who underwent surgical resection of renal nodules, with preoperative CT (LightSpeed VCT, GE) with contrast agent (Iopamidol 370; 1.5 ml/kg) and availability of a histopathological report, were retrospectively included. Renal lesions were segmented by two radiologists in consensus, in the arterial phase on the axial section with greater diameter. The radiomics analysis was performed with validated software (PyRadiomics on Syngo.via Frontier, Siemens) by applying normalization, resampling (1,1,1), setting bin width (15), and applying padding (2). The extracted features were used for training and testing machine learning models (random forest and support vector machine (end point: clear cell carcinoma, ccRCC).Results45 patients were included (mean age 56 years; 34/45 ccRCC). SVM e RF models obtained an AUC of 0.84 and accuracy of 0.87 and 0.80, respectively, in the testing phase.ConclusionRadiomics analysis is feasible and effective in the characterization of renal lesions on CT.

Diagnostic value of Gd-EOB-DTPA-enhanced MRI radiomics models for dual-phenotype hepatocellular carcinoma

Objective: To develop and validate clinical and radiomics models based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI of dual-phenotype hepatocellular carcinoma (DPHCC) for preoperative differential diagnosis. Methods: Two hundred and fifty inpatients of hepatocellular carcinoma (HCC) confirmed by postoperative pathology, who underwent Gd-EOB-DTPA-enhanced MRI were retrospectively included. A total of 172 inpatients (72 DPHCC and 100 non-DPHCC) were included in Institution 1 (the First Affiliated Hospital of Soochow University) as a training cohort (between January 2020 and July 2023) and 78 inpatients (44 DPHCC and 34 non-DPHCC) were included in Institution 2 (the Third People's Hospital of Nantong) as an external validation cohort(between January 2019 and July 2023). The regions of interest of the tumor were delineated layer by layer in noncontrast phase, arterial phase (AP), portal venous phase (PP) and hepatobiliary phase (HBP) images. The software of FAE was used to extract the radiomics features of the images. Pearson correlation analysis and recursive feature elimination were used for feature selection. Each phase and combined radiomics models were established using logistic regression, linear discriminant analysis and support vector machine. Receiver operating characteristic curve and the areas under the curve (AUC) were used to evaluate and select the dominant radiomics model. The dominant radiomics model was combined with clinically independent predictors to construct a clinical radiomics model. Delong test was used to compare the performance of the models. Results: The age of the training cohort was (59.6±10.4) years, in which there were 135 men (78.5%). In the external validation cohort, the age was (57.8±9.2) years, including 56 men (71.8%). The maximum diameters of the lesions [M (Q1, Q3), 4.7 (2.6, 7.5) vs 2.7 (1.8, 4.4) cm, P<0.001] and the proportion of the multiple lesions (39.5% vs 16.7%, P<0.001) in the training cohort were higher than those in the external validation cohort. In the training group, the proportion of patients with hepatitis B virus (HBV) infection in the DPHCC subgroup (66.7%,48/172) was higher than that in non-DPHCC subgroup (49.0%,49/78,P=0.021). In the external validation cohort, the AUC (95%CI) of the PP [0.835 (0.733-0.937)] and combined radiomics models [0.786 (0.681-0.891)] were significantly higher than that of noncontrast phase [0.451 (0.319-0.584)], AP [0.566 (0.435-0.696)] and HBP models [0.496 (0.363-0.629)] (all P<0.05). There was no significant difference in AUC between PP radiomics model and combined radiomics model (P=0.189). The AUC between the radiomics models and clinical-radiomics models, which were brought into clinically independent variable HBV, showed no significant difference (all P>0.05). Conclusion: Gd-EOB-DTPA-enhanced MRI radiomics model based on portal venous phase may be available for discriminating DPHCC from non-DPHCC before operation.

Comparison of virtual and true non-contrast images from dual-layer spectral detector computed tomography (CT) in patients with colorectal cancer.

Colorectal cancer (CRC) is commonly assessed by computed tomography (CT), but the associated radiation exposure is a major concern. This study aimed to quantitatively and qualitatively compare the image quality of virtual non-contrast (VNC) images reconstructed from arterial and portal venous phases with that of true non-contrast (TNC) images in patients with CRC to assess the potential of TNC images to replace VNC images, thereby reducing the radiation dose. A total of 69 patients with postoperative pathologically confirmed CRC at the West China Hospital of Sichuan University between May 2022 and April 2023 were enrolled in this cross-sectional study. The CT protocol included the acquisition of TNC images, arterial and portal venous phase images; the VNC images were reconstructed from the two postcontrast phase images. Several parameters, including the CT attenuation value, absolute attenuation error, imaging noise [standard deviation (SD)], signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were measured in multiple abdominal structures for both the TNC and VNC images. Two blinded readers assessed the subjective image quality using a five-point scale. Interobserver agreement was evaluated using interclass correlation coefficients (ICCs). The paired t-test and Wilcoxon signed-rank test were used to compare the objective and subjective results between the TNC and VNC images. Individual measurements of radiation doses for the TNC scan and contrast scan protocols were recorded. A total of 2,070 regions of interest (ROIs) of the 69 patients were analyzed. Overall, the VNC images exhibited significantly lower attenuation values and SD values than the TNC images in all tissues, except for the abdominal aorta, portal vein, and spleen. The mean absolute attenuation errors between the VNC and TNC images were all less than 10 Hounsfield units (HU). The percentages of absolute attenuation errors less than 5 and 10 HU in the VNC images from the arterial phase (VNCa) were 78.99% and 97.97%, respectively, while those from the portal venous phase (VNCp) were 81.59% and 96.96%, respectively. The absolute attenuation errors between the TNC and VNCa images were smaller than those between the TNC and VNCp images for tumors [VNCaerror: 2.77, interquartile range (IQR) 1.77-4.22; VNCperror: 3.27, IQR 2.68-4.30; P=0.002]. The SNR values and CNR values in the VNC images were significantly higher than those in the TNC images for all tissues, except for the portal vein and spleen. The image quality was rated as excellent (represented by a score of 5) in the majority of the TNC and VNC images; however, the VNC images scored lower than the TNC images. Eliminating the TNC phase resulted in a reduction of approximately 37.99% in the effective dose (ED). The VNC images provided accurate CT attenuation, good image quality, and lower radiation doses than the TNC images in CRC, and the VNCa images showed minimal differences in the CT attenuation of the tumors.

Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT

PurposeThe purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening. Materials and methodsPatients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses. ResultsA total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 ± 11.6 (standard deviation) years (range: 20–85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5–1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90 % sensitivity (95 % confidence interval [CI]: 80–98), 76 % specificity (95 % CI: 62–88) and an AUC of 0.87 (95 % CI: 0.79–0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95 % CI: 0.79–0.93). In comparison, the two radiologists achieved a mean 50 % sensitivity and 100 % specificity using arterial phase CT images. ConclusionRadiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.

Dual-energy CT Radiomics Combined with Quantitative Parameters for Differentiating Lung Adenocarcinoma From Squamous Cell Carcinoma: A Dual-center Study

Rationale and ObjectivesTo evaluate the ability of dual-energy CT(DECT)-based quantitative parameters and radiomics features to differentiate solid lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC). MethodsThis study included 213 patients diagnosed with ADC and SCC who underwent DECT scans at two centers from November 2022 to December 2023. Patients at center 1 were randomly divided into training(n=114) and internal test set (n=50) in a 7:3 ratio, with center 2 serving as the external test set (n=49). Radiologic and clinical data were combined to establish a clinical-radiologic model. Ten types of DECT energy images including conventional images, iodine density (ID), effective atomic number (Zeff), electron density, and virtual monoenergetic images (VMI) were reconstructed in both arterial phases (AP) and venous phases (VP). Quantitative parameters were measured at the uniform enhanced solid portion of the tumor and normalized to the aorta, used to develop a quantification model and calculate the quantitative score (quantscore). Radiologists manually delineated the tumor ROI at the largest level for extracting radiomics features in these 10 energy images. These features were used to establish ten uni-energy models from which the best-performing features were selected to construct the final radiomics model and calculate a radiomics score (radscore). Then, a combined model was developed using the akaike information criterion(AIC) and compared with the clinical-radiological model to test its diagnostic validity. ResultsThe independent predictors of the clinical-radiological model included age, gender, and central or peripheral location, and the AUCs for the training set, internal test set, and external test set were 0.808, 0.837, and 0.802. The quantification model incorporated 40keV CT values, Zeff, normalized Zeff, and the slope of the spectral attenuation curve (λHU) in the AP and normalized ID, Zeff, and λHU in the VP. Uni-energy models based on AP ID maps, AP Zeff maps, and VP VMI 65keV significantly outperformed AUC = 0.5, and 11 radiomics features were selected from these three models to construct the final radiomics model. The combined model, incorporating age, gender, quantscore, and radscore, significantly outperformed the clinical-radiological model in the training set (AUC = 0.952 vs 0.808, P < 0.001), and demonstrated higher performance in both the internal and external test sets, although these differences did not reach statistical significance (AUC = 0.870 vs 0.837, for the internal test set [P = 0.542], 0.888 vs 0.802 for the external test sets [P = 0.128]). The evaluation of the combined model demonstrated good discriminative ability and potential for generalization. ConclusionsThe combined model, integrating quantitative parameters and radiomics features from DECT multi-energy images with clinical-radiological characteristics, can be used as a non-invasive tool to differentiate ADC from SCC.

The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.

The incidence and mortality rate of lung cancer are the highest in the world among all malignant tumors. Accurate assessment of ground-glass nodules (GGNs) is crucial in reducing lung cancer mortality. This study aimed to explore the value of computed tomography (CT) features and quantitative parameters in predicting the invasiveness and degree of infiltration of GGNs. Lesions were classified into three groups based on pathological types: the precursor glandular lesion (PGL) group, including atypical adenomatoid hyperplasia and adenocarcinoma in situ; the minimally invasive adenocarcinoma group; and the invasive adenocarcinoma group. Quantitative and qualitative data of the nodules were compared, and receiver operating characteristic (ROC) curve analysis was performed for each quantitative parameter. Binary logistic regression analysis was used to evaluate independent predictors of GGN invasiveness. There were significant differences in lesion size, morphology, nodule type, bronchial abnormality, internal vascular sign and pleural retraction among the three groups (P<0.05). There were significant differences in all CT quantitative parameters (CT attenuation value in the plain phase, CT attenuation value in the arterial phase, CT attenuation value in the venous phase, arterial phase enhancement difference, venous phase enhancement difference, arterial phase enhancement index and venous phase enhancement index) among the three groups (P<0.001). The ROC curve analysis showed that the CT attenuation value in the plain phase, CT attenuation value in each enhanced phase, enhancement difference and enhancement index had good discriminatory power. Binary logistic regression analysis revealed that nodule type and internal vascular sign were independent risk factors for GGN invasiveness. CT features combined with enhanced scanning and quantitative analysis have important value in predicting the invasiveness of GGNs. The type of pulmonary nodule detected on CT (pure GGN or mixed GGN) and the presence of internal vascular signs are independent risk factors for GGN invasiveness.

Comparison between pancreatoblastoma (PB) and solid pseudopapillary neoplasm (SPN) in pediatric patients with enhanced CT

Rationale and objectivesTo investigate whether computed tomography features can differentiate pancreatoblastoma (PB) from solid pseudopapillary tumor (SPN) in children. Materials and methodsClinical and imaging data of 18 cases of PB and 61 cases of SPN confirmed by surgery or biopsy were retrospectively analyzed. All enrolled patients underwent 3 phases (non-contrast, arterial, and portal venous phases) of CT scanning. Qualitative CT analysis (location, margin, solid/cystic component proportion, calcification, hemorrhage, peritumoral vascularity, bile duct dilatation, pancreatic duct dilatation, pancreatic atrophy, vascular invasion, peripancreatic invasion, and distant metastases) and quantitative analysis (maximum tumor diameter, interface between tumor and parenchyma [delta], arterial enhancement ratio [AER], and portal enhancement ratio [PER]) were performed. The general CT morphologic features, age and tumor markers were compared also compared between the groups. Univariate analysis and the F test were conducted to identify features of PB. Then logistic Regression classifier was trained using the top five features with the highest F-value. Moreover, we used 5-fold cross-validation techniques for the validation of our model. ResultsPB exhibited a significantly higher frequency of location in the body/tail, larger tumor size, poorly defined margins, calcification, peritumoral vascularity, pancreatic atrophy, and less hemorrhage. In addition, PB had higher AER, PER and lower delta relative to SPN (p < 0.05). PB presented a younger age and higher levels of AFP. Results of the F test indicated that AFP, AER, Age, calcification and pancreatic atrophy were the top five features included in the model that could differentiate pediatric PB from SPN. The combined model of CT and clinical features performed well in differentiating PB from SPN, with an AUC of 0.981 in the training cohort and 0.953 in the validation cohort. ConclusionsAFP, AER, age, calcification and pancreatic atrophy are robust CT and clinical features for differentiating pediatric PB from SPN. A combination of qualitative and quantitative CT features may provide good diagnostic accuracy in differentiating PB from SPN in children.

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.

Adjustment of scan delay for bolus tracking with cardiothoracic ratio of CT scout image for hepatic artery phase of hepatic dynamic CT.

This study aimed to determine the scan delay for bolus tracking in the hepatic artery phase (HAP) of hepatic dynamic computed tomography (CT) using the cardiothoracic ratio (CTR) from CT scout images. We retrospectively studied 188 patients who underwent hepatic dynamic CT, 24 of whom had scan delays adjusted for CTR. The contrast enhancement of the abdominal aorta, portal vein, hepatic vein, and hepatic parenchyma was calculated for HAP. The adequacy of the scan timing for HAP was assessed using three classifications: early, appropriate, or late. The effect of HAP on scan timing adequacy was determined using multivariate logistic regression analysis, and the optimal cutoff value of CTR was evaluated using receiver operating characteristic analysis. The trigger times for bolus tracking (odds ratio: 1.58) and CTR (odds ratio: 1.23) were significantly affected by the appropriate scan timing of the HAP. The optimal cutoff value of CTR was 59.3%. The scan timing of HAP with a scan delay of 15s was 14% of early and 86% of appropriate, and the proportion of early in CTR ≥ 60% (early, 52%; appropriate, 48%) was higher than that in CTR < 60% (early, 6%; appropriate, 94%). Adjusting the scan delay to 20s in CTR ≥ 60% increased the proportion of appropriate (early, 4%; appropriate, 96%). The CTR of a CT scout image is an effective index for determining the scan delay for bolus tracking. Adjusting the scan delay by CTR can provide appropriate HAP images in more patients. Trial registration number: R-080; date of registration: 9 March 2023, retrospectively registered.

Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features

PurposeThis study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making. Materials and methodsClinical data and contrast-enhanced CT images of 144 patients with parotid gland tumors from the Peking University School of Stomatology Hospital, collected from January 2019 to December 2022, were gathered. The 3D slicer software was utilized to accurately annotate the tumor regions, followed by exploring the correlation between multiple preoperative contrast-enhanced CT imaging features and the benign or malignant nature of the tumor, as well as the type of benign tumor. A prediction model was constructed using the k-nearest neighbors (KNN) algorithm. ResultsThrough feature selection, four key features—morphology, adjacent structure invasion, boundary, and suspicious cervical lymph node metastasis—were identified as crucial in preoperative discrimination between benign and malignant tumors. The KNN prediction model achieved an accuracy rate of 94.44 %. Additionally, six features including arterial phase CT value, age, delayed phase CT value, pre-contrast CT value, venous phase CT value, and gender, were also significant in the classification of benign tumors, with a KNN prediction model accuracy of 95.24 %. ConclusionThe machine learning model based on preoperative contrast-enhanced CT imaging features can effectively discriminate between benign and malignant parotid gland tumors and classify benign tumors, providing valuable reference information for clinicians.

Head-to-head comparison of contrast-enhanced CT, dual-layer spectral-detector CT, and Gd-EOB-DTPA-enhanced MR in detecting neuroendocrine tumor liver metastases

PurposeTo explore the optimal of kiloelectron voltage (keV) of virtual monoenergetic imaging (VMI) of dual-layer spectral-detector CT (DLCT) in detecting neuroendocrine tumor liver metastases (NETLM) and to investigate diagnostic performance of polyenergetic images (PEI), DLCT, and Gd-EOB-DTPA-enhanced MR. MethodsSeventy-two patients with suspected NETLM who underwent DLCT and Gd-EOB-DTPA-enhanced MR were retrospectively enrolled. Tumor signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between PEI and VMI at 40–140 keV. Two radiologists read the CT examinations with and without VMI separately in consensus. Two other radiologists read the Gd-EOB-DTPA-enhanced MR in consensus. The diagnostic performance was evaluated. Reference standard was histopathology, follow-up, and interpretation of all available imaging. ResultsThe highest SNR and CNR were observed at VMI40keV, significantly higher than PEI in the arterial and venous phases (all P<0.01). A total of 477 lesions were identified (396 metastases, 81 benign lesions). Per-lesion AUC was 0.86, 0.91, and 0.97 (PEI, DLCT, and Gd-EOB-DTPA-enhanced MR, respectively). Sensitivity of PEI, DLCT, and Gd-EOB-DTPA-enhanced MRI were 0.76, 0.86, and 0.95, respectively. DLCT significantly improved sensitivity compared to PEI. MR had significantly higher sensitivity than DLCT and PEI. Subgroup analysis demonstrated that the difference in diagnostic performance was concentrated on lesions < 10 mm. ConclusionThe image quality of VMI40keV is higher than that of PEI. DLCT with VMI40keV provides better diagnostic sensitivity for NETLM detection than PEI. Gd-EOB-DTPA-enhanced MR yielded the best diagnostic performance for NETLM detection.

Prognostic Prediction of Multiterritory Flap: A Preliminary Clinical Study on the Classification of Priority Developing Artery and Arterial Anastomosis Utilizing Indocyanine Green Angiography.

Congestion and necrosis are frequent complications in multiterritory flaps. Indocyanine green angiography (ICGA) is a commonly utilized tool for evaluating blood flow and perforator location within the flap; however, there is currently no existing research investigating its potential to predict flap prognosis before surgery. The forehead skin of 50 surgical patients was assessed using preoperative ICGA, enabling observation and classification of the priority developing artery and arterial anastomosis among adjacent arterial perfusion territories during the arterial phase. Subsequently, 5 clinical cases of forehead flap transfer were studied to validate the classification method. First, the priority developing artery can be classified into 4 types and 5 subtypes, encompassing type Ⅰa: Bilateral ST-As equalization type (9/50), type Ⅰb: unilateral ST-A dominance type (11/50), type II: SOT-As dominance type (14/50), type III: unilateral ST-A plus SOT-A dominance type (6/50), and type IV: bilateral ST-As plus SOT-As equilibrium type (10/50). Second, arterial anastomosis can be divided into 5 types: type I: complete choke anastomosis type (13/50), type II: complete true anastomosis type (7/50), type III: central choke anastomosis type (10/50), type IV: bilateral choke anastomosis type (8/50), and type V: unilateral choke anastomosis type (12/50). Finally, the clinical flap outcomes showed that the ICGA classification could serve as a good prognostic indicator. The hemodynamic classification of priority developing artery and arterial anastomosis employed by ICGA has the potential to predict flap prognosis and offer valuable insights for preoperative design and perioperative treatment strategies. More sample size is needed to optimize and validate this classification.

Energy spectrum computed tomography multi-parameter imaging in preoperative assessment of vascular and neuroinvasive status in gastric cancer.

Vascular and nerve infiltration are important indicators for the progression and prognosis of gastric cancer (GC), but traditional imaging methods have some limitations in preoperative evaluation. In recent years, energy spectrum computed tomography (CT) multiparameter imaging technology has been gradually applied in clinical practice because of its advantages in tissue contrast and lesion detail display. To explore and analyze the value of multiparameter energy spectrum CT imaging in the preoperative assessment of vascular invasion (LVI) and nerve invasion (PNI) in GC patients. Data from 62 patients with GC confirmed by pathology and accompanied by energy spectrum CT scanning at our hospital between September 2022 and September 2023, including 46 males and 16 females aged 36-71 (57.5 ± 9.1) years, were retrospectively collected. The patients were divided into a positive group (42 patients) and a negative group (20 patients) according to the presence of LVI/PNI. The CT values (CT40 keV, CT70 keV), iodine concentration (IC), and normalized IC (NIC) of lesions in the upper energy spectrum CT images of the arterial phase, venous phase, and delayed phase 40 and 70 keV were measured, and the slopes of the energy spectrum curves [K (40-70)] from 40 to 70 keV were calculated. Arterial phase combined parameter, venous phase combined parameters (VP-ALLs), and delayed phase association parameters were calculated for patients with late-stage disease. The differences in the energy spectrum parameters between the positive and negative groups were compared, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC), sensitivity, specificity, and optimal threshold were calculated to measure the diagnostic efficiency of each parameter. In the delayed phase, the CT40 keV, CT70 keV, K (40-70), IC, NIC, and CT70 keV and the NIC in the upper arterial and venous phases of energy spectrum CT were greater in the LVI/PNI-positive group than in the LVI-negative group. The representative parameters for the arterial phase NIC were 0.14 ± 0.04 in the positive group and 0.12 ± 0.04 in the negative group. The venous phase NIC was 0.5 (0.5, 0.6) in the positive group and 0.4 (0.4, 0.5) in the negative group. Last, for the delayed phase NIC, it was 0.6 ± 0.1 in the positive group and 0.5 ± 0.1 in the negative group (all P values are less than 0.05). ROC curve analysis demonstrated that the diagnostic efficacy of each parameter during the venous stage was superior to that during the arterial and delayed stages. Furthermore, the diagnostic efficacy of the combined parameter throughout all three stages was superior to that of any single parameter. The AUC, sensitivity, and specificity of the optimal parameter, VP-ALL, were 0.931 (95% confidence interval: 0.872-0.990), 80.95%, and 95.00%, respectively. When assessing the condition of LVI and PNI (perineural invasion) in patients with GC prior to surgery, the ability to diagnose these conditions using venous stage parameters was superior to that using arterial stage and delayed stage parameters. Furthermore, the diagnostic accuracy of using a combination of parameters was better than that of using individual parameters alone.

Discontinuous peripheral enhancement of focal liver lesions on CT and MRI: outside the box of typical cavernous hemangioma.

The discontinuous peripheral enhancement is a pattern of enhancement usually attributed to typical cavernous hemangioma, that is the most common benign solid lesion of the liver. The discontinuous peripheral enhancement, however, may be encountered in many other benign and malignant focal liver lesions as an atypical presentation or evolution, and hemangiomas with discontinuous peripheral hyperenhancement on hepatic arterial phase may not always have the typical post-contrast pattern on portal venous and delayed phases. Therefore, abdominal radiologists may be challenged in their practice by lesions with discontinuous peripheral enhancement. This pictorial essay aims to review the spectrum of benign and malignant focal liver lesions that may show discontinuous peripheral enhancement. A particular point of interest is the diagnostic tree pathway that may guide the radiologists in the differential diagnosis.

Predictive Value of the Nomogram Model Based on Multimodal Ultrasound Features for Benign and Malignant Thyroid Nodules of C-TIRADS Category 4.

To explore the predictive value of the nomogram model based on multimodal ultrasound features for benign and malignant thyroid nodules of C-TIRADS category 4. A retrospective analysis was conducted on the general conditions and ultrasound features of patients who underwent thyroid ultrasound examination and fine needle aspiration biopsy (FNA) or thyroidectomy at the Affiliated Hospital of Zunyi Medical University from April 2020 to April 2023. Predictive signs for benign and malignant nodules of thyroid C-TIRADS category 4 were screened through LASSO regression and multivariate logistic regression analysis to construct a nomogram prediction model. The predictive efficiency and accuracy of the model were assessed through ROC curves and calibration curves. Seven independent risk factors in the predictive model for benign and malignant thyroid nodules of C-TIRADS category 4 were growth pattern, morphology, microcalcifications, SR, arterial phase enhancement intensity, initial perfusion time, and PE [%]. Based on these features, the area under the curve (AUC) of the constructed prediction model was 0.971 (p ＜ .001, 95% CI: 0.952-0.989), with a prediction accuracy of 93.1%. Internal validation showed that the nomogram calibration curve was consistent with reality, and the decision curve analysis indicated that the model has high clinical application value. The nomogram prediction model constructed based on the multimodal ultrasound features of thyroid nodules of C-TIRADS category 4 has high clinical application value.

Radiomics analysis in predicting vascular invasion in gastric cancer based on enhanced CT: a preliminary study

BackgroundVascular invasion (VI) is closely related to the metastasis, recurrence, prognosis, and treatment of gastric cancer. Currently, predicting VI preoperatively using traditional clinical examinations alone remains challenging. This study aims to explore the value of radiomics analysis based on preoperative enhanced CT images in predicting VI in gastric cancer.MethodsWe retrospectively analyzed 194 patients with gastric adenocarcinoma who underwent enhanced CT examination. Based on pathology analysis, patients were divided into the VI group (n = 43) and the non-VI group (n = 151). Radiomics features were extracted from arterial phase (AP) and portal venous phase (PP) CT images. The radiomics score (Rad-score) was then calculated. Prediction models based on image features, clinical factors, and a combination of both were constructed. The diagnostic efficiency and clinical usefulness of the models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).ResultsThe combined prediction model included the Rad-score of AP, the Rad-score of PP, Ki-67, and Lauren classification. In the training group, the area under the curve (AUC) of the combined prediction model was 0.83 (95% CI 0.76–0.89), with a sensitivity of 64.52% and a specificity of 92.45%. In the validation group, the AUC was 0.80 (95% CI 0.67–0.89), with a sensitivity of 66.67% and a specificity of 88.89%. DCA indicated that the combined prediction model might have a greater net clinical benefit than the clinical model alone.ConclusionThe integrated models, incorporating enhanced CT radiomics features, Ki-67, and clinical factors, demonstrate significant predictive capability for VI. Moreover, the radiomics model has the potential to optimize personalized clinical treatment selection and patient prognosis assessment.