Venous Phase Images Research Articles

Bolus-Tracked Biphasic Contrast-Enhanced CT Imaging Following Microwave Liver Ablation Improves Ablation Zone Conspicuity and Semi-automatic Segmentation Quality.

Contrast-enhanced CT (CECT) may be performed immediately following microwave liver ablation for assessment of ablative margins. However, practices and protocols vary among institutions. Here, we compare a standardized bolus-tracked biphasic CECT protocol and compare this with a single venous phase fixed delay protocol for ablation zone (AZ) assessment. An institutional review board approved study performed at a specialist cancer centre. A prospective cohort of patients undergoing bolus-tracked biphasic imaging was compared with a retrospective cohort of patients who underwent fixed delay venous phase imaging. AZ conspicuity and segmentation quality were semi-quantitatively scored using Five-point Likert scales. Time between ablation and image acquisition was recorded for each AZ and was correlated to AZ conspicuity and segmentation quality. Forty patients, median age 59years (IQR 48-66years), 24 men, underwent microwave ablation of 68 liver tumours. AZ conspicuity was higher in the bolus-tracked (n = 33) vs. fixed delay (n = 35) cohorts, 4.5 vs. 2.5, P < 0.0001. Commensurate segmentation quality was also higher, 5.0 vs. 3.0 respectively, P < 0.0001. Ordinal regression showed that image quality scores declined by 3-4% for each minute that passes after ablation, particularly for arterial phase images, where regression coefficients were - 0.04, P = 0.007, and -0.03, P = 0.012 for conspicuity and segmentation quality, respectively. Bolus-tracked biphasic contrast-enhanced CT protocols improve both conspicuity and semi-automatic segmentation quality of microwave liver ablation zones, particularly if imaged soon after ablation. Level 2b; exploratory prospective cohort study.

Assessment of vascular invasion of pancreatic ductal adenocarcinoma based on CE-boost black blood CT technique

ObjectivesTo explore the diagnostic efficacy of advanced intelligent clear-IQ engine (AiCE) and adaptive iterative dose reduction 3D (AIDR 3D), combination with and without the black blood CT technique (BBCT), for detecting vascular invasion in patients diagnosed with nonmetastatic pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 35 consecutive patients diagnosed with PDAC, proceeding with contrast-enhanced abdominal CT scans, were enrolled in this study. The arterial and portal venous phase images were reconstructed using AiCE and AIDR 3D. The corresponding BBCT images were established as AiCE–BBCT and AIDR 3D–BBCT, respectively. Two observers scored the image quality independently. Cohen’s kappa (k) value or intraclass correlation coefficient (ICC) was used to analyze consistency. The diagnostic performance of four algorithms in detecting vascular invasion in PDAC patients was assessed using the area under the curve (AUC).ResultsThe AiCE and AiCE–BBCT groups demonstrated superior image noise and diagnostic acceptability compared with AIDR 3D and AIDR 3D–BBCT groups (all p < 0.001), and the k value was 0.861–0.967 for both reviewers. In terms of diagnostic capability for vascular invasion in PDAC, the AiCE–BBCT group exhibited higher specificity (95.0%) and sensitivity (93.3%) compared to the AIDR 3D and AIDR 3D–BBCT groups, with an AUC of 0.942 (95% CI: 0.849–1.000, p < 0.05). Furthermore, all vascular evaluations conducted using AiCE–BBCT demonstrated better consistency (ICC: 0.847–0.935).ConclusionThe BBCT technique in conjunction with AiCE could lead to notable enhancements in both the image quality of PDAC images and the diagnostic performance for tumor vascular invasion.Critical relevance statementBetter diagnostic accuracy of vascular invasion of PDAC based on BBCT in combination with an AiCE is a critical factor in determining treatment strategies and patient outcomes.Key PointsIdentifying vascular invasion of PDAC is important for prognostication.Combined images provide improved image quality and higher diagnostic accuracy.Combined images can excellently display the vascular wall and invasion.Graphical

Clinical impact of magnetic resonance angiography in patients undergoing free fibular flap transfer

AbstractObjectiveThis retrospective study was carried out to assess the impact of dynamic time‐resolved magnetic resonance angiography (DTR‐MRA) in detecting lower extremity vascular abnormalities and its impact on deciding the harvest site in patients undergoing free fibular transfer.MethodsConsecutive patients who underwent DTR‐MRA imaging of lower extremities prior to free fibular flap transfer (FFFT) during the period of 2008–2020 were retrospectively included in the study. Retrospective review of medical records was performed to include the patients who had provisional side of harvest decided prior to DTR‐MRA based on clinical findings. The criteria for change in management included changing the harvest site, either to the contralateral leg or a different site based on DTR‐MRA.ResultsImaging findings in 103 patients who underwent DTR‐MRA prior to FFFT were included in the study. Overall incidence of vascular abnormalities of clinical significance for FFFT was 30/103 (29%), and site of harvest was changed in 30/103 (29%) patients after DTR‐MRA study. Addition of delayed venous phase imaging detected additional venous pathologies of clinical significance in 7/103 (6.8%) patients.ConclusionsDTR‐MRA as a preoperative study before FFFT has an excellent accuracy in detecting vascular abnormalities of clinical significance. Addition of delayed phase images can also detect clinically significant venous abnormalities.

A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study.

To develop and validate deep learning (DL) models using preoperative contrast-enhanced CT images for tumor auto-segmentation and microsatellite instability(MSI) prediction incolorectal cancer (CRC). Patients with CRC who underwent surgery or biopsy between January 2018 and April 2023 were retrospectively enrolled. Mismatch repair protein expression was determined via immunohistochemistry or fluorescence multiplex polymerase chain reaction-capillary electrophoresis. Manually delineated tumor contours using arterial and venous phase CT images by three abdominal radiologists are served as ground truth. Tumor auto-segmentation used nnU-Net. MSI prediction employed ViT or convolutional neural networks models, trained and validated with arterial and venous phase images (image model) or combined clinical-pathological factors (combined model). The segmentation model was evaluated using patch coverage ratio, Dice coefficient, recall, precision, and F1-score. The predictive models' efficacy was assessed using areas under the curves and decision curve analysis. Overall, 2180 patients (median age: 61years ± 17 [SD]; 1285 males) were divided into training (n = 1159), validation (n = 289), and independent external test (n = 732) groups. High-level MSI status was present in 435 patients (20%). In the external test set, the segmentation model performed well in the arterial phase, with patch coverage ratio, Dice coefficient, recall, precision, and F1-score values of 0.87, 0.71, 0.72, 0.74, and 0.71, respectively. For MSI prediction, the combined models outperformed the clinical model (AUC = 0.83 and 0.82 vs 0.67, p < 0.001) and two image models (AUC = 0.75 and 0.77, p < 0.001). Decision curve analysis confirmed the higher net benefit of the combined model compared to the other models across probability thresholds ranging from 0.1 to 0.45. DL enhances tumor segmentation efficiency and, when integrated with contrast-enhanced CT and clinicopathological factors, exhibits good diagnostic performance in predicting MSI in CRC.

Role of volumetric tumor enhancement on CT in predicting overall survival in patients with unresectable pancreatic ductal adenocarcinoma

PurposeTo assess the utility of volumetric tumor enhancement on CT to predict tumor treatment response and the overall survival (OS) of patients with PDAC undergoing FOLFIRINOX-based systemic chemotherapy. Additionally, we aim to explore the performance of a novel model that incorporates relevant volumetric CT-derived parameters to the established RECIST 1.1 in predicting both treatment response and OS. Material and methodsIn this retrospective single-institution study, 127 patients with PDAC who received FOLFIRINOX neoadjuvant chemotherapy between December 2012 and November 2021 were included. Manual volumetric segmentation of the single largest tumor was performed on portal venous phase images. Total and enhancing tumor volumes were calculated. Response by RECIST 1.1 was compared to response by tumor volume and enhancing tumor volume on follow-up CT. ResultsThere was no association between overall survival and RECIST 1.1 (p-value = 0.284), volumetric RECIST (p-value = 0.402), and other volumetric CT variables, except for a percentage reduction in enhancing tumor volume (p-value = 0.043). Using univariate survival analysis for categorical thresholds defined by CART, the percentage change in enhancing tumor volume was associated with OS (p-value = 0.018). There was also a significant association between baseline enhancing tumor volume and OS (p-value <0.0001). Using these two categories, we defined a multivariable model associated with OS (p-value <0.0001). ConclusionPercentage reduction in enhancing tumor volume was related to OS in non-surgical PDAC patients treated with FOLFIRINOX chemotherapy and could potentially be incorporated into patient survival prediction models.

Invasion in Advanced Gastric Cancer Based on Enhanced Computer Tomography Radiomics Nomogram.

To evaluate the efficacy of an enhanced computed tomography (CT) radiomics nomogram in predicting preoperative lymphovascular invasion (LVI) or perineural invasion (PNI) in patients with advanced gastric cancer (GC). Data from 149 patients with GC from our hospital (January 2019 to December 2022) were analyzed. High throughput radiomics features were extracted from manually delineated volumes of interest on enhanced CT venous phase images. Optimal features were identified using intraclass correlation coefficient analysis and least absolute shrinkage and selection operator. Models were constructed using the radiomics score (Rad-score), the above features, and independent risk factors. Performance was assessed via the receiver operating characteristic, decision curve analysis and calibration curves. Eight radiomics features were deemed essential. Factors including history of alcohol consumption (P = 0.029), peritumor fatty infiltration (P = 0.046), degree of enhancement (P = 0.012), and Rad-score (P < 0.001) were significant predictors of LVI/PNI. The radiomics nomogram, which integrated these factors, showed superior prediction (the training group: area under the curve [AUC] = 0.917; the validation group: AUC = 0.925) compared with other models. The enhanced CT radiomics nomogram offers robust preoperative prediction for LVI/PNI in patients with GC.

Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features.

The aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC). A total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA). The model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit. A combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.

Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease

BackgroundCrohn’s disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography is commonly used to evaluate crohn’s disease, its imaging findings are often nonspecific and can overlap with other bowel diseases. Recent studies have explored the application of radiomics-based machine learning algorithms to aid in the diagnosis of medical images. This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn’s disease using CT enterography radiomics and machine learning algorithms.MethodsA total of 139 patients with pathologically confirmed Crohn’s disease were retrospectively enrolled in this study. Radiomics features were extracted from both arterial- and venous-phase CT enterography images, representing both bowel lesions with Crohn’s disease and segments of normal bowel. A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy.ResultsThe classification model demonstrated robust performance and high accuracy, with an area under the curve of 0.938 and 0.961 for the arterial- and venous-phase images, respectively. The model achieved an accuracy of 0.938 for arterial-phase images and 0.961 for venous-phase images.ConclusionsThis study successfully identified a radiomics machine learning method that effectively differentiates Crohn’s disease bowel lesions from normal bowel segments. Further studies with larger sample sizes and external cohorts are needed to validate these findings.

CT-based radiomics model for predicting perineural invasion status in gastric cancer.

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

The diagnosis value of dual-energy computed tomography (DECT) multi-parameter imaging in lung adenocarcinoma and squamous cell carcinoma.

Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung adenocarcinoma (AC), and lung squamous cell carcinoma (SC) are the two primary types of NSCLC. Determining the pathological type of NSCLC is important in establishing the most effective treatment method. Dual-energy computed tomography (DECT) multi-parameter imaging is an imaging technology that provides accurate and reliable disease diagnosis, and its uses are utilized for the combined diagnostic efficacy of AC and SC. The purpose of this study was to investigate the diagnostic value of spectral parameters of DECT in efficacy to AC and SC, and their combined diagnostic efficacy was also analyzed. We conducted a retrospective analysis of clinical and imaging data for 36 patients diagnosed with SC and 35 patients with AC. These patients underwent preoperative DECT chest scans, encompassing both arterial and venous phases, at our hospital from December 2020 to April 2022. The tumor diameter, water concentration (WC), iodine concentration (IC), normalized iodine concentration (NIC), Z effective (Zeff), and slope of the curve (K) in lesions were evaluated during two scanning phases in the two separate pathological types of lung cancers. The differences in parameters between these two types of lung cancers were statistically analyzed. In addition, receiver operating characteristic (ROC) curves were performed for these parameters to distinguish between SC and AC. In a univariate analysis involving 71 lung cancer patients, the results from Zeff, IC, NIC, and K from the AC's arterial and venous phase images were more elevated than those from the SC (P < 0.05). In contrast, the WC results were lower than those from SC (P < 0.05). The area under the ROC curve (AUC) for multi-parameter joint prediction typing was 0.831, with a corresponding sensitivity of 63.9% and specificity of 94.3%. It is possible to distinguish between central SC and AC using the spectrum characteristics of DECT-enhanced scanning (Zeff, IC, NIC, K, WC, and tumor diameter). Diagnostic effectiveness can be greatly improved when multiple variables are included.

Volumetric CT Assessment of In Situ Induced Hepatic Lesions in a Transgenic Swine Model.

The growth rate of in situ-induced hepatic lesions in an Oncopig large animal model is quantitatively assessed. Oncopigs (n = 9) received baseline triple-phase CT scans prior to lesion induction. Lesions were subsequently induced by delivering the Ad-Cre vector to four locations in the liver. Triple-phase CT scans were obtained weekly to track the growth of the lesions. Animals were sacrificed at 14, 21, or 28 days (n = 3 in each group). The overall success rate of lesion generation was ~78%. Histopathology sections consistently revealed lesions that were highly inflammatory and consisted of a large leukocyte population without clear evidence of carcinomas. Lesions presented within imaging as hypovascular, low attenuating masses with slight contrast enhancement around the margins but little to no enhancement within the lesions themselves. The observed lesions were manually segmented on the venous phase image. Segmentation volumes were fitted to a logistic growth and decay model. Several lesions observed at earlier time points in the 28-day group had fully regressed by the time of the necropsy. The overall trend of rapid growth for the first 21 days, with spontaneous regression of the lesions being observed from day 21 to 28, suggests that the optimal window for experimental studies may be from days 14 to 21. The data and mathematical models generated from this study may be used for future computational models; however, the current model presented has moderate clinical relevance because many induced tumors resolved spontaneously within a few weeks. Awareness and careful consideration of the modest relevance and limitations of the model are advisable for each specific use case.

Differential diagnosis of gastric submucosal masses and external pressure lesions.

Lesions of the left triangular ligament of the liver are rare, and there are even fewer cases of vascular tumors misdiagnosed as gastrointestinal stromal tumors. We comment on the two cases reported in the article. The article did not include pictures of laparoscopic surgery, making it unconvincing. For gastric submucosal lesions, enhanced computed tomography venous phase imaging may be beneficial for differential diagnosis. Although endoscopic ultrasound is an effective tool for diagnosing submucosal lesions of the stomach, due to various factors, it cannot achieve an accurate diagnosis. During endoscopic examination, a more accurate diagnosis can be made depending on the personal experience of the operators.

Evaluation of colorectal liver metastases using virtual monoenergetic images obtained from dual-layer spectral computed tomography.

To assess the potential of virtual monoenergetic images in assessing colorectal liver metastasis (CRLM) compared with conventional CT images. This single-center, retrospective study included 173 consecutive patients (mean age, 65.5 ± 10.6years; 106 men) who underwent dual-layer spectral CT (DLSCT) between November 2016 and April 2021. Portal venous phase images were reconstructed using hybrid iterative reconstruction (iDose) and virtual monoenergetic imaging at 50keV. Four radiologists independently and randomly reviewed the de-identified iDose and 50keV images. Lesion detection, CRLM conspicuity, and CRLM diagnosis were compared between these images using a generalized estimating equation analysis. The reference standards used were histopathology and follow-up imaging findings. The study included 797 focal liver lesions, including 463 CRLMs (median size, 18.1mm [interquartile range, 10.9-37.7mm]). Lesion detection was better with 50keV images than with iDose images (45.0% [95% confidence interval [CI]: 39-50] vs 40.0% [95% CI: 34-46], P = 0.003). CRLM conspicuity was higher in the 50keV images than in the iDose images (3.27 [95% CI: 3.09-3.46] vs 3.09 [95% CI: 2.90-3.28], P < 0.001). However, the specificity for diagnosing CRLM was lower with 50keV images than with iDose images (94.5% [95% CI: 91.6-96.4] vs 96.0% [95% CI: 93.2-98.1], P = 0.022), whereas sensitivity did not differ significantly (77.6% [95% CI: 70.3-83.5] vs 76.9% [95% CI: 70.0-82.7], P = 0.736). Indeterminate lesions were more frequently noted in 50keV images than in iDose images (13% [445/3188] vs 9% [313/3188], P = 0.005), and 56% (247/445) of the indeterminate lesions at 50keV were not CRLMs. The 50keV images obtained from DLSCT were better than the iDose images in terms of CRLM conspicuity and lesion detection. However, 50keV images did not improve CRLM diagnosis but slightly increased the reporting of indeterminate focal liver lesions associated with CRLMs.

Radiomic Features at Contrast-Enhanced CT Predict Virus-Driven Liver Fibrosis: A Multi-Institutional Study.

Liver fibrosis is a major cause of morbidity and mortality among in patients with chronic hepatitis. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. In this retrospective study, we developed 3 radiomics models using contrast-enhanced computed tomography scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest and the least absolute shrinkage and selection operator logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots, and decision curve analysis. External validation was performed on 114 patients from 2 institutions. Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (Random Forest score and support vector machine based radiomics score) providing more significant advantages. Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

Comparison of Conventional and Virtual Non-contrast Abdominal Images Using the Third-Generation Dual-Source Dual-Energy Computed Tomography.

To determine the efficacy and safety of virtual unenhanced imaging by comparing the attenuation values of virtual and true unenhanced images acquired using third-generation dual-source dual-energy computed tomography (dsDECT). Single-energy non-contrast and dual-energy arterial and venous phase images of 97 patients who underwent triphasic abdominal computed tomography (CT) were included in this retrospective study. Virtual unenhanced images were generated for the arterial (a) and venous (v) phases using two dsDECT algorithms. The attenuation values were measured on the true and virtual unenhanced images of the liver, spleen, kidney, gallbladder, paraspinal muscle, aorta, subcutaneous fat, retroperitoneal fat, and renal cysts. A statistically significant difference was observed between the attenuation values ​​of true and virtual unenhanced images for all tissues (p < 0.001-0.025), except the venous phase virtual unenhanced images of the kidney, renal cysts, and gallbladder (p = 0.061-0.325). The proportion of cases with differences of ≥ 10 Hounsfield unit (HU) in the attenuation values between the virtual and true unenhanced images ranged from 3% to 8% for renal parenchyma, renal cysts, and gallbladder using this algorithm; however, this proportion was up to 90% for adipose tissue. No significant correlation was observed between the body mass index and attenuation differences between the true and virtual unenhanced images, except for those of the aorta and paraspinal muscle. Virtual unenhanced images acquired using third-generation dsDECT cannot replace true unenhanced images in clinical practice owing to the difference between the attenuation values and variability of attenuation between true and virtual unenhanced images.

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.

Comparative study of image quality and radiation dose in thoracic-abdominal-pelvic CT Enhancement with different tube voltages and reconstruction algorithms

ObjectivesTo evaluate the image quality and radiation dose in thoracic-abdominal-pelvic computed tomography enhancement using ATVM (Automatic Tube Voltage Modulation)coupled with AIIR versus routine tube voltage combined with Karl-3D IR. The optimal noise level for the AIIR in thoracic-abdominal-pelvic CT enhancement was also determined. MethodsGroup A was scanned using ATVM, and images were reconstructed using AIIR with 1–5 noise levels. Group B was scanned and reconstructed using the fixed tube voltage (120 kVp) combined with Karl 3D level 5 IR. The image quality of the reconstructed images of AIIR with 1–5 noise levels were compared and the best image reconstruction noise level for AIIR was preferred. Image quality and radiation dose were statistically analyzed for group A (best image reconstruction noise level for AIIR) and group B. ResultsAIIR level 3 is the optimal noise level for CT-enhanced image reconstruction of the thorax, abdomen, and pelvis. The mean SNR, CNR, and subjective evaluation of AIIR level 3 Group A images were better than those of Karl 3D level 5 Group B images (p < 0.05). The mean SSDE and the mean ED of the AIIR Group A patients were reduced by 46% and 41%, respectively, compared with those of Group B. ConclusionsATVM technology combined with the AIIR algorithm improved image quality and reduced patient radiation dose in thoracic-abdominal-pelvic CT scans. The optimal noise level for the reconstruction of high-quality arterial and venous-phase images was AIIR level 3.

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.

The influence of contrast media on calcium-based imaging of the spine in dual-layer CT

This study aimed to evaluate the impact of contrast media application on CT attenuation of the bone using a novel calcium-only imaging technique (VCa) from dual-layer spectral detector CT (DLCT), which enables CT-based bone mineral density measurement unimpeded by soft tissue components. For this, true non-contrast (TNC) and venous phase images (VP) of n = 97 patients were acquired. CT attenuation of the first lumbar vertebra (L1) was measured in TNC-VCa, VP-VCa, and in virtual non-contrast images (VNC). CT attenuation was significantly higher in VP-VCa than in TNC-VCa (p < 0.001), although regression analyses revealed a strong linear association between these measures (R2 = 0.84). A statistical model for the prediction of TNC-VCa CT attenuation was established (TNC-VCa[HU] = − 6.81 + 0.87 × VP-VCa[HU]-0.55 × body weight[kg]) and yielded good agreement between observed and predicted values. Furthermore, a L1 CT attenuation threshold of 293 HU in VP-VCa showed a sensitivity of 90% and a specificity of 96% for detecting osteoporosis. The application of contrast media leads to an overestimation of L1 CT attenuation in VCa. However, CT attenuation values from VP-VCa can be used within CT-based opportunistic osteoporosis screening eighter by applying a separate threshold of 293 HU or by converting measured data to TNC-VCa CT attenuation with the given regression equation.