Multiphasic Computed Tomography Research Articles

Novel intravascular tantalum oxide-based contrast agent achieves improved vascular contrast enhancement and conspicuity compared to Iopamidol in an animal multiphase CT protocol

BackgroundTo assess thoracic vascular computed tomography (CT) contrast enhancement of a novel intravenous tantalum oxide nanoparticle contrast agent (carboxybetaine zwitterionic tantalum oxide, TaCZ) compared to a conventional iodinated contrast agent (Iopamidol) in a rabbit multiphase protocol.MethodsFive rabbits were scanned inside a human-torso-sized encasement on a clinical CT system at various scan delays after intravenous injection of 540 mg element (Ta or I) per kg of bodyweight of TaCZ or Iopamidol. Net contrast enhancement of various arteries and veins, as well as image noise, were measured. Randomized scan series were reviewed by three independent readers on a clinical workstation and assessed for vascular conspicuity and image artifacts on 5-point Likert scales.ResultsOverall, net vascular contrast enhancement achieved with TaCZ was superior to Iopamidol (p ≤ 0.036 with the exception of the inferior vena cava at 6 s (p = 0.131). Vascular contrast enhancement achieved with TaCZ at delays of 6 s, 40 s, and 75 s was superior to optimum achieved Iopamidol contrast enhancement at 6 s (p ≤ 0.036. Vascular conspicuity was higher for TaCZ in 269 of 300 (89.7%) arterial and 269 of 300 (89.7%) venous vessel assessments, respectively (p ≤ 0.005), with substantial inter-reader reliability (κ = 0.61; p < 0.001) and strong positive monotonic correlation between conspicuity scores and contrast enhancement measurements (ρ = 0.828; p < 0.001).ConclusionTaCZ provides absolute and relative contrast advantages compared to Iopamidol for improved visualization of thoracic arteries and veins in a multiphase CT protocol.Relevance statementThe tantalum-oxide nanoparticle is an experimental intravenous CT contrast agent with superior cardiovascular and venous contrast capacity per injected elemental mass in an animal model, providing improved maximum contrast enhancement and prolonged contrast conspicuity. Further translational research on promising high-Z and nanoparticle contrast agents is warranted.Key PointsThere have been no major advancements in intravenous CT contrast agents over decades.Iodinated CT contrast agents require optimal timing for angiography and phlebography.Tantalum-oxide demonstrated increased CT attenuation per elemental mass compared to Iopamidol.Nanoparticle contrast agent design facilitates prolonged vascular conspicuity.Graphical

An ensemble machine learning model assists in the diagnosis of gastric ectopic pancreas and gastric stromal tumors

ObjectiveTo develop an ensemble machine learning (eML) model using multiphase computed tomography (MPCT) for distinguishing between gastric ectopic pancreas (GEP) and gastric stromal tumors (GIST) in lesions < 3 cm.MethodsIn this study, we retrospectively collected MPCT images from 138 patients between April 2017 and June 2023 across two centers. Cohort 1 comprised 94 patients divided into a training cohort and an internal validation cohort, while the 44 patients from Cohort 2 constituted the external validation cohort. Deep learning (DL) models were constructed based on the lesion region, and radiomics features were extracted to develop radiomics models, which were later integrated into the fusion model. Model performance was assessed through the analysis of the area under the receiver operating characteristic curve (AUROC). The diagnostic efficacy of the optimal model was compared with that of a radiologist. Additionally, the radiologist with the assistance of the eML model provides a secondary diagnosis, to assess the potential clinical value of the model.ResultsAfter evaluation using an external validation cohort, the radiomics model demonstrated the highest performance in the venous phase, achieving AUROC of 0.87. The DL model showed optimal performance in the non-contrast phase, with AUROC of 0.81. The eML achieved the best performance across all models, with AUROC of 0.90. The use of eML-assisted analysis resulted in a significant improvement in the junior radiologist’s accuracy, rising from 0.77 to 0.93 (p < 0.05). However, the senior radiologist’s accuracy, while improving from 0.86 to 0.95, did not exhibit a statistically significant difference.ConclusioneML model based on MPCT can effectively distinguish between GEPs and GISTs < 3 cm.Critical relevance statementThe multiphase CT-based fusion model, incorporating radiomics and DL technology, proves effective in distinguishing between GEP and gastric stromal tumors, serving as a valuable tool to enhance diagnoses and offering references for clinical decision-making.Key PointsNo studies yet differentiated these tumors via radiomics or DL.Radiomics and DL methodologies unveil potentially distinct phenotypes within lesions.Quantitative analysis on CT for GIST and ectopic pancreas.Ensemble learning aids accurate diagnoses, assisting treatment decisions.Graphical

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality

Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications.

Comparative Evaluation of Imaging Modalities for Eligibility in Endovascular Treatment of Delayed Onset Acute Anterior Circulation Ischemic Stroke in Siriraj Hospital: A Retrospective Analysis

Objective: The goal of this study is to evaluate the consistency between CTP according to the endovascular therapy following imaging evaluation for ischemic stroke (DEFUSE-3) criteria and other standard computed tomography (CT) imaging modalities, such as multi-phase CT angiography (MCTA) and unenhanced computed tomography (UECT), in assessing patient eligibility for EVT as determined by neurointerventionists evaluations. Materials and Methods: This retrospective analysis included 64 patients with anterior circulation stroke and onset between 6 to 12 hours or unknown onset. Two neuro-interventionalists independently reviewed images and assessed eligibility for EVT based on the Alberta stroke program early CT score (ASPECTS) derived from UECT and collateral score obtained from MCTA. The results were then compared to CTP, utilizing the DEFUSE-3 criteria. Results: Out of the 64 cases analyzed (mean age: 69 years ± 13.9 [SD]), 61 met DEFUSE-3 criteria for EVT by CTP, while 54 were deemed eligible based on an ASPECTS ≥ 6 and collateral score ≥ 3. Agreement between the modalities was moderate (Kappa coefficient score 0.4). When patients with ASPECTS score &lt; 6 were excluded, concordance improved to perfect (Kappa coefficient score 1.0). Hence, concordance was significantly associated with ASPECTS scores ≥ 6 (P &lt; 0.001). Conclusion: In patients experiencing anterior circulation stroke with onset between 6 to 12 hours or unknown onset, excluding an ASPECTS score of 6 or higher, MCTA and UECT proved to be reliable for assessing endovascular treatment eligibility. These modalities may serve as substitutes for CTP and offer support in the clinical decisionmaking process.

The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma.

To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation of pancreatic ductal adenocarcinoma (PDAC) compared to conventional hybrid iterative reconstruction (HIR). Thirty-five patients with PDAC who underwent multiphasic CT were retrospectively evaluated. Raw data were reconstructed with two energy levels (40keV and 70keV) of virtual monochromatic imaging (VMI) using HIR (ASiR-V50%) and DLIR (TrueFidelity-H). Contrast-to-noise ratio (CNRtumor) was calculated from the CT values within regions of interest in tumor and normal pancreas in the pancreatic parenchymal phase images. Lesion conspicuity of PDAC in pancreatic parenchymal phase on 40-keV HIR, 40-keV DLIR, and 70-keV DLIR images was qualitatively rated on a 5-point scale, using 70-keV HIR images as reference (score 1 = poor; score 3 = equivalent to reference; score 5 = excellent) by two radiologists. CNRtumor of 40-keV DLIR images (median 10.4, interquartile range (IQR) 7.8-14.9) was significantly higher than that of the other VMIs (40keV HIR, median 6.2, IQR 4.4-8.5, P < 0.0001; 70-keV DLIR, median 6.3, IQR 5.1-9.9, P = 0.0002; 70-keV HIR, median 4.2, IQR 3.1-6.1, P < 0.0001). CNRtumor of 40-keV DLIR images were significantly better than those of the 40-keV HIR and 70-keV HIR images by 72 ± 22% and 211 ± 340%, respectively. Lesion conspicuity scores on 40-keV DLIR images (observer 1, 4.5 ± 0.7; observer 2, 3.4 ± 0.5) were significantly higher than on 40-keV HIR (observer 1, 3.3 ± 0.9, P < 0.0001; observer 2, 3.1 ± 0.4, P = 0.013). DLIR is a promising reconstruction method to improve PDAC delineation in 40-keV VMI at the pancreatic parenchymal phase compared to conventional HIR.

Lesion-aware cross-phase attention network for renal tumor subtype classification on multi-phase CT scans

Multi-phase computed tomography (CT) has been widely used for the preoperative diagnosis of kidney cancer due to its non-invasive nature and ability to characterize renal lesions. However, since enhancement patterns of renal lesions across CT phases are different even for the same lesion type, the visual assessment by radiologists suffers from inter-observer variability in clinical practice. Although deep learning-based approaches have been recently explored for differential diagnosis of kidney cancer, they do not explicitly model the relationships between CT phases in the network design, limiting the diagnostic performance. In this paper, we propose a novel lesion-aware cross-phase attention network (LACPANet) that can effectively capture temporal dependencies of renal lesions across CT phases to accurately classify the lesions into five major pathological subtypes from time-series multi-phase CT images. We introduce a 3D inter-phase lesion-aware attention mechanism to learn effective 3D lesion features that are used to estimate attention weights describing the inter-phase relations of the enhancement patterns. We also present a multi-scale attention scheme to capture and aggregate temporal patterns of lesion features at different spatial scales for further improvement. Extensive experiments on multi-phase CT scans of kidney cancer patients from the collected dataset demonstrate that our LACPANet outperforms state-of-the-art approaches in diagnostic accuracy.

A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging

BackgroundPredicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. Computed tomography (CT) imaging, analyzed by deep learning (DL) models combining domain knowledge, has been employed for the prediction. However, these DL models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability. MethodsWe propose a novel Vision Transformer (ViT)-based DL network, referred to as Dual-Style ViT (DSViT), to augment the interaction between domain knowledge and images and the effective fusion among multi-phase CT images for improving both predictive performance and interpretability. We apply the DSViT to develop pre-/post-operative models for predicting ER. Within DSViT, to balance the utilization between domain knowledge and images within DSViT, we propose an adaptive self-attention mechanism. Moreover, we present an attention-guided supervised learning module for balancing the contributions of multi-phase CT images to prediction and a domain knowledge self-supervision module for enhancing the fusion between domain knowledge and images, thereby further improving predictive performance. Finally, we provide the interpretability of the DSViT decision-making. ResultsExperiments on our multi-phase data demonstrate that DSViTs surpass the existing models across multiple performance metrics and provide the decision-making interpretability. Additional validation on a publicly available dataset underscores the generalizability of DSViT. ConclusionsThe proposed DSViT can significantly improve the performance and interpretability of ER prediction, thereby fortifying the trustworthiness of artificial intelligence tool for HCC ER prediction in clinical settings.

A landmark-supervised registration framework for multi-phase CT images with cross-distillation

Objective. Multi-phase computed tomography (CT) has become a leading modality for identifying hepatic tumors. Nevertheless, the presence of misalignment in the images of different phases poses a challenge in accurately identifying and analyzing the patient’s anatomy. Conventional registration methods typically concentrate on either intensity-based features or landmark-based features in isolation, so imposing limitations on the accuracy of the registration process. Method. We establish a nonrigid cycle-registration network that leverages semi-supervised learning techniques, wherein a point distance term based on Euclidean distance between registered landmark points is introduced into the loss function. Additionally, a cross-distillation strategy is proposed in network training to further improve registration performance which incorporates response-based knowledge concerning the distances between feature points. Results. We conducted experiments using multi-centered liver CT datasets to evaluate the performance of the proposed method. The results demonstrate that our method outperforms baseline methods in terms of target registration error. Additionally, Dice scores of the warped tumor masks were calculated. Our method consistently achieved the highest scores among all the comparing methods. Specifically, it achieved scores of 82.9% and 82.5% in the hepatocellular carcinoma and the intrahepatic cholangiocarcinoma dataset, respectively. Significance. The superior registration performance indicates its potential to serve as an important tool in hepatic tumor identification and analysis.

Childhood Solid Pseudopapillary Neoplasm of the Pancreas: Acute Presentation of an Uncommon Tumor

AbstractSolid pseudopapillary tumor is a mixed solid and cystic neoplasm of the pancreas which occurs most commonly in females in second or third decade of life with few reported pediatric cases. It has a low malignancy potential with good prognosis after surgical resection.We present a case of an 11-year-old male child with solid pseudopapillary tumor of pancreas who presented with clinical features of acute pancreatitis. He was initially evaluated with ultrasound, followed by computed tomography (CT) and magnetic resonance imaging (MRI).Solid pseudopapillary tumor of pancreas presenting as acute pancreatitis is rarely reported in literature. This case is a unique presentation of a pancreatic tumor and highlights the value of multiphase CT imaging and MRI in the diagnosis.

Effect of contrast phase on quantitative analysis of skeletal muscle and adipose tissue by computed tomography

ObjectivesSignificant variability exists in the contrast phases applied during computed tomography (CT) studies when assessing morphometric measurements of muscle area (CT-assessed sarcopenia) and density (CT-assessed myosteatosis) and visceral adipose tissue area (CT-assessed visceral obesity). This study explored the impact of contrast phase timing on changes in morphometric measurements of body composition. MethodsThis single-center retrospective cohort study included 459 patients undergoing a multiphase CT scan. Morphometric measurements were obtained at the third lumbar vertebra level. Patients were classified as sarcopenic, myosteatotic, or visceral obese using predefined cutoff values. The intraclass correlation coefficient was used to assess correlations across different enhancement phases, and Cohen's κ measured the inter-enhancement agreement for sarcopenia, myosteatosis, and visceral obesity. ResultsSignificant differences were observed in mean visceral adipose tissue area, muscle density, and muscle area (P < 0.001). The intraclass correlation coefficient between unenhanced and arterial phases was 0.987 (95% confidence interval [CI], 0.759–0.996) for adipose tissue, 0.995 (95% CI, 0.989–0.997) for muscle area, and 0.850 (95% CI, 0.000–0.956) for muscle density. However, when morphometric measurements were categorized using predefined cutoffs, the κ agreement was considerably lower, particularly for CT-assessed myosteatosis, ranging from 0.635 (unenhanced to arterial) to 0.331 (unenhanced to late venous phase). ConclusionsDifferent CT contrast phases induce small but clinically significant alterations in the measurements of muscle area and density and visceral fat. Such minor changes can result in misclassification issues when fixed cutoff values are used to diagnose myosteatosis with CT. This underscores the importance of reporting absolute values and the specific contrast phase used in future studies.

Prognostic predictive value of urothelial carcinoma of the bladder after TURBT based on multiphase CT radiomics.

To investigate multiphase computed tomography (CT) radiomics-based combined with clinical factors to predict overall survival (OS) in patients with bladder urothelial carcinoma (BLCA) who underwent transurethral resection of bladder tumor (TURBT). Data were retrospectively collected from 114 patients with primary BLCA from February 2016 to February 2018. The regions of interest (ROIs) of the plain, arterial, and venous phase images were manually segmented. The Cox regression algorithm was used to establish 3 basic models for the plain phase (PP), arterial phase (AP), and venous phase (VP) and 2 combination models (AP + VP and PP + AP + VP). The highest-performing radiomics model was selected to calculate the radiomics score (Rad-score), and independent risk factors affecting patients' OS were analyzed using Cox regression. The Rad-score and clinical risk factors were combined to construct a joint model and draw a visualized nomogram. The combined model of PP + AP + VP showed the best performance with the Akaike Information Criterion (AIC) and Consistency Index (C-index) in the test group of 130.48 and 0.779, respectively. A combined model constructed with two independent risk factors (age and Ki-67 expression status) in combination with the Rad-score outperformed the radiomics model alone; AIC and C-index in the test group were 115.74 and 0.840, respectively. The calibration curves showed good agreement between the predicted probabilities of the joint model and the actual (p < 0.05). The decision curve showed that the joint model had good clinical application value within a large range of threshold probabilities. This new model can be used to predict the OS of patients with BLCA who underwent TURBT.

Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma

ObjectivesCombination therapy of lenvatinib and immune checkpoint inhibitors (CLICI) has emerged as a promising approach for managing unresectable hepatocellular carcinoma (HCC). However, the response to such treatment is observed in only a subset of patients, underscoring the pressing need for reliable methods to identify potential responders. Materials & methodsThis was a retrospective analysis involving 120 patients with unresectable HCC. They were divided into training (n = 72) and validation (n = 48) cohorts. We developed an interpretable deep learning model using multiphase computed tomography (CT) images to predict whether patients will respond or not to CLICI treatment, based on the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1). We evaluated the models' performance and analyzed the impact of each CT phase. Critical regions influencing predictions were identified and visualized through heatmaps. ResultsThe multiphase model outperformed the best biphase and uniphase models, achieving an area under the curve (AUC) of 0.802 (95% CI = 0.780–0.824). The portal phase images were found to significantly enhance the model's predictive accuracy. Heatmaps identified six critical features influencing treatment response, offering valuable insights to clinicians. Additionally, we have made this model accessible via a web server at http://uhccnet.com/ for ease of use. ConclusionsThe integration of multiphase CT images with deep learning-generated heatmaps for predicting treatment response provides a robust and practical tool for guiding CLICI therapy in patients with unresectable HCC.

Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module

Liver cancer is one of the dominant causes of cancer death worldwide. Computed Tomography (CT) is the commonly used imaging modality for diagnosing it. Computer-based liver cancer diagnosis systems can assist radiologists in image interpretation and improve diagnosis speed and accuracy. Since liver segmentation is crucial to such systems, researchers are relentlessly pursuing various segmentation approaches. A clinically viable computer-aided system requires examining multiphase CT images. However, most of the research focuses only on the portal venous phase. In this work, we developed an automatic and efficient Deep Learning (DL) method using SegNet, atrous spatial pyramid pooling module and leaky ReLU layers for liver segmentation from quadriphasic abdominal CT volumes. The proposed method was validated on two datasets, an internal institutional dataset consisting of multiphase CT and a public dataset of portal venous phase CT volumes. The Dice Coefficients (DC) obtained were greater than 96% for the latter dataset and the portal venous phase of the former. For arterial, delayed and plain CT phases of the former dataset, the DC achieved were 94.61%, 95.01% and 93.23%, respectively. Experiments showed that our model performed better than the other state-of-the-art DL models. Ablation studies have revealed that the proposed model leverages the strengths of all the three components that make it up. The promising performance of the proposed method suggests that it is appropriate for incorporation in hepatic cancer diagnosis systems.

Institutional clinical indication-based typical dose values of multiphasic abdominopelvic computed tomography examinations.

Our study aimed to obtain clinical indication-based typical dose values and size-specific dose estimates (SSDEs) for multiphasic abdominopelvic computed tomography (CT) examinations and to review our data with published diagnostic reference levels (DRLs). In this retrospective study, multiphasic liver, kidney, pancreas, and mesenteric ischemia protocol CT scans performed at our center between January 2018 and December 2021 were analyzed. The clinical indications were hepatocellular carcinoma, renal cell carcinoma, pancreas adenocarcinoma, and mesenteric ischemia. The computed tomography dose index volume (CTDIvol) and dose-length product (DLP) values were recorded, and the SSDE and effective dose (ED) values were calculated. The water-equivalent diameter (Dw) value required for the SSDE calculation was measured using the automated calculation of the Dw program. The total number of patients was 514, with 86 patients excluded from this study. The dose values were calculated for 426 patients (183 female and 243 male; 111 liver, 120 kidney, 85 pancreas, and 110 mesenteric). The median values for the CTDIvol, DLP, SSDE, and ED were 6.86 mGy, 683.02 mGy. cm, 8.75 mGy, and 10.45 mSv for the liver CT; 8.37 mGy, 908.37 mGy.cm, 10.37 mGy, and 13.89 mSv for the kidney CT; 7.82 mGy, 517.98 mGy.cm, 10.01 mGy, and 7.92 mSv for the pancreas CT; and 9.48 mGy, 983.68 mGy.cm, 12.78 mGy, and 13.86 mSv for the mesenteric CT, respectively. All dose values were lower than the published DRLs. The literature reveals large differences in the multiphasic abdominopelvic CT protocols, especially in the number of phases and scan length. This situation makes comparing dose values difficult. Dose studies revealing the protocol parameters in detail are needed so that institutions can compare and optimize their own protocols. Additionally, users should periodically check the dose values in their own institutions.

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

Customised weight-based volume contrast media protocol for multiphase abdominal computed tomography.

Multiphase computed tomography (CT) using fixed volume contrast media may lead to high radiation exposure and toxicity in patients with low body weight. We evaluated a customised weight-based protocol for multiphase CT in terms of radiation exposure, image quality and cost savings. A total of 224 patients were recruited. An optimised CT protocol was applied using 100 kV and 1 mL/kg of contrast media dosing. The image quality and radiation dose exposure of this CT protocol were compared to those of a standard 120 kV, 80 mL fixed volume protocol. The radiation dose information and CT Hounsfield units were recorded. The signal-to-noise ratio, contrast-to-noise ratio (CNR) and figure of merit (FOM) were used as comparison metrics. The images were assessed for contrast opacification and visual quality by two radiologists. The renal function, contrast media volume and cost were also evaluated. The median effective dose was lowered by 16% in the optimised protocol, while the arterial phase images achieved significantly higher CNR and FOM. The radiologists' evaluation showed more than 97% absolute agreement with no significant differences in image quality. No significant differences were found in the pre- and post-CT estimated glomerular filtration rate. However, contrast media usage was significantly reduced by 1,680 mL, with an overall cost savings of USD 421 in the optimised protocol. The optimised weight-based protocol is cost-efficient and lowers radiation dose while maintaining overall contrast enhancement and image quality.

Systematic training of LI-RADS CT v2018 improves interobserver agreements and performances in LR categorization for focal liver lesions.

To retrospectively explored whether systematic training in the use of Liver Imaging Reporting and Data System (LI-RADS) v2018 on computed tomography (CT) can improve the interobserver agreements and performances in LR categorization for focal liver lesions (FLLs) among different radiologists. A total of 18 visiting radiologists and the liver multiphase CT images of 70 hepatic observations in 63 patients at high risk of HCC were included in this study. The LI-RADS v2018 training procedure included three thematic lectures, with an interval of 1 month. After each seminar, the radiologists had 1 month to adopt the algorithm into their daily work. The interobserver agreements and performances in LR categorization for FLLs among the radiologists before and after training were compared. After training, the interobserver agreements in classifying the LR categories for all radiologists were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.053). After systematic training, the areas under the curve (AUCs) for LR categorization performance for all participants were significantly increased for most LR categories (P < 0.001), except for LR-1 (P = 0.062). Systematic training in the use of the LI-RADS can improve the interobserver agreements and performances in LR categorization for FLLs among radiologists with different levels of experience.

Enhancing preoperative diagnosis of microvascular invasion in hepatocellular carcinoma: domain-adaptation fusion of multi-phase CT images

ObjectivesIn patients with hepatocellular carcinoma (HCC), accurately predicting the preoperative microvascular invasion (MVI) status is crucial for improving survival rates. This study proposes a multi-modal domain-adaptive fusion model based on deep learning methods to predict the preoperative MVI status in HCC.Materials and methodsFrom January 2008 to May 2022, we collected 163 cases of HCC from our institution and 42 cases from another medical facility, with each case including Computed Tomography (CT) images from the pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP). We divided our institution’s dataset (n=163) into training (n=119) and test sets (n=44) in an approximate 7:3 ratio. Additionally, we included cases from another institution (n=42) as an external validation set (test1 set). We constructed three single-modality models, a simple concatenated multi-modal model, two current state-of-the-art image fusion model and a multi-modal domain-adaptive fusion model (M-DAFM) based on deep learning methods. We evaluated and analyzed the performance of these constructed models in predicting preoperative MVI using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI) methods.ResultsIn comparison with all models, M-DAFM achieved the highest AUC values across the three datasets (0.8013 for the training set, 0.7839 for the test set, and 0.7454 for the test1 set). Notably, in the test set, M-DAFM’s Decision Curve Analysis (DCA) curves consistently demonstrated favorable or optimal net benefits within the 0-0.65 threshold probability range. Additionally, the Net Reclassification Improvement (NRI) values between M-DAFM and the three single-modal models, as well as the simple concatenation model, were all greater than 0 (all p &amp;lt; 0.05). Similarly, the NRI values between M-DAFM and the two current state-of-the-art image fusion models were also greater than 0. These findings collectively indicate that M-DAFM effectively integrates valuable information from multi-phase CT images, thereby enhancing the model’s preoperative predictive performance for MVI.ConclusionThe M-DAFM proposed in this study presents an innovative approach to improve the preoperative predictive performance of MVI.

Computed tomography for visible haematuria - a single nephrographic phase is sufficient for detecting renal cell carcinoma.

No previous studies have compared two computed tomography (CT) protocols in patients presenting with visible haematuria, and most patients undergo a multiphase CT in order todetect upper tract malignancies. We aimed to prospectively compare the diagnostic performance ofsingle- and four-phase CT for detecting renal cell carcinoma (RCC) in patients with visible haematuria. 'A Prospective Trial for Examining Hematuria using Computed Tomography' (PROTEHCT) was a single-centre prospective paired diagnostic study in patients referred for CT due to painless visible haematuria between September 2019 and June 2021. All patients underwent four-phase CT (control) from which a single nephrographic phase dual energy CT (experimental) was extracted. Both were independently assessed for RCC by randomised radiologists. Histologically verified RCC defined a positive reference standard. Follow-up ascertainment of RCC diagnosis was completed in May 2022. Descriptive statistics were used to calculate the accuracies. Inter-reader agreement was assessed by kappa statistics. A total of 308 patients (median age, 68 years [interquartile range 53-77, range 18-96], 250 males) were included for analysis. RCC was diagnosed in seven (2.3%) patients during a median follow-up time of 19 months (interquartile range: 15-25). For the control and experimental CT, sensitivity was 100% versus 100%, specificity was 97% versus 98% and accuracy 97% versus 97%. The positive predictive value was 44% versus 50%, and the negative predictive value was 100% versus 100%. The agreement between the control and experimental CT was 98% (k = 0.79). A single nephrographic phase dual energy CT is sufficient for detecting RCC in patients with visible haematuria.

Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.