Contrast-enhanced Computed Tomography Imaging Research Articles

Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.

To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hybrid iterative reconstruction (HIR) algorithms. This retrospective study included 54 consecutive patients who underwent contrast-enhanced abdominal CT. Thin-slice images (0.5mm thickness) were reconstructed using SR-DLR, DLR, and HIR. Objective image noise and contrast-to-noise ratio (CNR) for liver parenchyma relative to muscle were assessed. Two radiologists independently graded image quality using a 5-point rating scale for image noise, sharpness, artifact/blur, and overall image quality. They also graded the visibility of small vessels, main pancreatic duct, ureters, adrenal glands, and right adrenal vein on a 5-point scale. SR-DLR yielded significantly lower objective image noise and higher CNR than DLR and HIR (P < .001). The visual scores of SR-DLR for image noise, sharpness, and overall image quality were significantly higher than those of DLR and HIR for both readers (P < .001). Both readers scored significantly higher on SR-DLR than on HIR for visibility for all structures (P < .01), and at least one reader scored significantly higher on SR-DLR than on DLR for visibility for all structures (P < .05). SR-DLR reduced image noise and improved image quality of thin-slice abdominal CT images compared to HIR and DLR. This technique is expected to enable further detailed evaluation of small structures.

Development and Clinical Evaluation of a Contrast Optimizer for Contrast-Enhanced CT Imaging of the Liver

Objective Patient characteristics, iodine injection, and scanning parameters can impact the quality and consistency of contrast enhancement of hepatic parenchyma in CT imaging. Improving the consistency and adequacy of contrast enhancement can enhance diagnostic accuracy and reduce clinical practice variability, with added positive implications for safety and cost-effectiveness in the use of contrast medium. We developed a clinical tool that uses patient attributes (height, weight, sex, age) to predict hepatic enhancement and suggest alternative injection/scanning parameters to optimize the procedure. Methods The tool was based on a previously validated neural network prediction model that suggested adjustments for patients with predicted insufficient enhancement. We conducted a prospective clinical study in which we tested this tool in 24 patients aiming for a target portal-venous parenchyma CT number of 110 HU ± 10 HU. Results Out of the 24 patients, 15 received adjustments to their iodine contrast injection parameters, resulting in median reductions of 8.8% in volume and 9.1% in injection rate. The scan delays were reduced by an average of 42.6%. We compared the results with the patients' previous scans and found that the tool improved consistency and reduced the number of underenhanced patients. The median enhancement remained relatively unchanged, but the number of underenhanced patients was reduced by half, and all previously overenhanced patients received enhancement reductions. Conclusions Our study showed that the proposed patient-informed clinical framework can predict optimal contrast enhancement and suggest empiric injection/scanning parameters to achieve consistent and sufficient contrast enhancement of hepatic parenchyma. The described GUI-based tool can prospectively inform clinical decision-making predicting optimal patient's hepatic parenchyma contrast enhancement. This reduces instances of nondiagnostic/insufficient enhancement in patients.

Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast-Enhanced CT Imaging.

The objective of the study is to assess the clinical value of machine learning radiomics based on contrast-enhanced computed tomography (CECT) images in preoperative prediction of perineural invasion (PNI) status in pancreatic ductal adenocarcinoma (PDAC). A total of 143 patients with PDAC were enrolled in this retrospective study (training group, n = 100; test group, n = 43). Radiomics features were extracted from CECT images and selected by the Mann-Whitney U-test, Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO). The logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) algorithms were trained to build radiomics models by radiomic features. Multivariate logistic regression was employed to identify independent predictors and establish clinical models. A combined model was constructed by integrating clinical and radiomics features. Model performances were assessed by receiver operating characteristic curves (ROCs) and decision curve analyses (DCAs). A total of 788 radiomics features were extracted from CECT images, of which 14 were identified as significant through the three-step selection process. Among the machine learning models, the SVM radiomics model exhibited the highest predictive performance in the test group, achieving an area under the curve (AUC) of 0.831, accuracy of 0.698, sensitivity of 0.677, and specificity of 0.750. After logistic regression screening, the clinical model was established using carbohydrate antigen 19-9 (CA199) levels as one independent predictor. In the test group, the clinical model demonstrated an AUC of 0.644, accuracy of 0.744, sensitivity of 0.871, and specificity of 0.417. The combined model showed improved performance compared to both the clinical and radiomics models in the test group, with an AUC of 0.844, accuracy of 0.767, sensitivity of 0.806, and specificity of 0.667. Subsequently, DCA of the combined model indicated optimal clinical value for predicting PNI status. Machine learning radiomics models can accurately predict PNI status in patients with pancreatic ductal adenocarcinoma. The combined model, which incorporates clinical and radiomics features, enhances preoperative diagnostic performance and aids in the selection of treatment methods.

Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches.

To compare the performance of whole tumor and habitats-based computed tomography (CT) radiomics for predicting immunophenotyping in laryngeal squamous cell carcinomas (LSCC) and further evaluate the stratified effect of the radiomics model on disease-free survival (DFS) and overall survival (OS) of LSCC patients. In all, 106 LSCC patients (40 with inflamed and 66 with non-inflamed immunophenotyping) were randomly assigned into a training (n = 53) and testing (n = 53) cohort. Briefly, 750 radiomics features from contrast-enhanced CT images were respectively extracted from the whole tumor and two Otsu method-derived subregions. Intraclass correlation coefficients (ICCs) were calculated to evaluate the reproducibility. The radiomics models for predicting immunophenotyping were respectively created using K-nearest neighbors (KNN), logistic regression (LR), and Naive bayes (NB) classifiers. The performance of models in the testing cohort were compared using area under the curve (AUC). The prognostic value of the optimal model was determined by survival analysis. The radiomics features derived from whole tumor showed better reproducibility than those derived from habitats. The best model for the whole tumor (LR classifier) showed superior performance than that for the habitats (KNN classifier) in the testing cohort, but there were no significant differences (AUC: 0.741 vs. 0.611, p = 0.112). Multivariable Cox regression analysis showed that the immunophenotyping predicted by the optimal model was an independent risk factor of unfavorable DFS (p = 0.009) and OS (p = 0.008) in LSCC patients. Whole tumor-based CT radiomics could serve as a potential predictive biomarker of immunophenotyping and outcome prediction in LSCC patients.

Preoperative assessment of pancreatic cancer with [68Ga]Ga-DOTA-FAPI-04 PET/MR versus [18F]-FDG PET/CT plus contrast-enhanced CT: a prospective preliminary study.

To assess the diagnostic performance of [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging in the preoperative evaluation of pancreatic cancer and compare it with that of [18F]-FDG PET/CT plus contrast-enhanced CT (CECT). Thirty-one patients with pancreatic cancer underwent preoperative [68Ga]Ga-DOTA-FAPI-04 PET/MR, [18F]-FDG PET/CT, and CECT imaging. Two nuclear medicine physicians independently reviewed two sets of images (set 1, [68Ga]Ga-DOTA-FAPI-04 PET/MR; set 2, [18F]-FDG PET/CT plus CECT) and reached a consensus on tumour resectability, N staging (N0 or N positive) and M staging (M0 or M1). Based on the above indices, the resectability of the tumour was determined according to a five-point scale. Clinical, operative, and pathological findings were used as a reference standard to compare the diagnostic performance of the two imaging sets via the McNemar test. The diagnostic performance of [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging was not significantly different from that of [18F]-FDG PET/CT plus CECT imaging in the assessment of tumour resectability (area under the receiver operating characteristic curve: 0.854 vs. 0.775, p = 0.192), N staging [accuracy: 82.4% (14 of 17 patients) vs. 58.8% (10 of 17 patients), p = 0.125] and M staging [accuracy: 100% (31 of 31 patients) vs. 90.3% (28 of 31 patients), p = 0.250]. However, compared with [18F]-FDG PET/CT plus CECT imaging, [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging changed the M stage in three patients by upstaging from M0 to M1 in 2 patients and downstaging from M1 to M0 in 2 patients. In 13 patients with liver metastases, the number of liver metastases detected via [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging was greater than that detected via [18F]-FDG PET/CT plus CECT imaging (324 vs. 240). In 3 patients with peritoneal metastases, [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging detected more peritoneal metastases than did [18F]-FDG PET/CT plus CECT imaging. [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging has diagnostic accuracy comparable to [18F]-FDG PET/CT plus CECT in terms of preoperative staging and assessment of resectability in pancreatic cancer; additionally, it exhibits superior capability in detecting liver and peritoneal metastases. Consequently, [68Ga]Ga-DOTA-FAPI-04 PET/MR has the potential to become a one-stop imaging tool for the preoperative evaluation of pancreatic cancer.

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.

Delta-radiomics Approach Using Contrast-enhanced and Non-contrast-enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients with Borderline Resectable Pancreatic Carcinoma

PurposeTo predict distant metastasis (DM) in patients with borderline resectable pancreatic carcinoma (BRPC) using delta-radiomics features calculated from contrast-enhanced computed tomography (CECT) and non-CECT images. MethodsAmong 250 patients who underwent radiotherapy at our institution between February 2013 and December 2021, 67 patients were deemed eligible. A total of 11 clinical features and 3,906 radiomics features were incorporated. Radiomics features were extracted from CECT and non-CECT images and the differences between these features were calculated, resulting in delta-radiomics features. The patients were randomly divided into the training (70%) and test (30%) datasets for model development and validation. Predictive models were developed with clinical features (clinical model), radiomics features (radiomics model), and a combination of the abovementioned features (hybrid model) using Fine–Gray regression (FG) and random survival forest (RSF). Optimal hyperparameters were determined using stratified five-fold cross-validation. Subsequently, the developed models were applied to the remaining test datasets, and the patients were divided into the high- or low-risk groups based on their risk scores. Prognostic power was assessed using the concordance index (C-index) with 95% confidence intervals obtained through 2,000 bootstrapping iterations. Statistical significance between the two groups was assessed using Gray's test. ResultsAt a median follow-up period of 23.8 months, 47 (70.1%) patients developed DM. The C-indices of the FG-based clinical, radiomics, and hybrid models were 0.548, 0.603, and 0.623, respectively, in the test dataset, whereas those of the RSF-based models were 0.598, 0.680, and 0.727, respectively. The RSF-based model, including delta-radiomics features, significantly divided the cumulative incidence curves into two groups (P<0.05). The feature map of the gray-level size-zone matrix showed that the difference in feature values between CECT and non-CECT images correlated with the incidence of DM. ConclusionsDelta-radiomics features obtained from CECT and non-CECT images using RSF successfully predict the incidence of DM in patients with BRPC.

Value of Radiomics Features Extracted from Baseline CT Images in Predicting Overall Survival in Patients with Non-Surgical Pancreatic Ductal Adenocarcinoma: Incorporation of a Radiomics Score to a Multi-Parametric Nomogram to Predict One-Year Overall Survival

PurposeTo determine the value of radiomics features derived from baseline CT scans and volumetric measurements to predict overall survival in patients with non-surgical pancreatic ductal adenocarcinoma (PDAC) treated with FOLFIRINOX. MethodsIn this retrospective single-institution study, 131 patients with non-surgical PDAC who received FOLFIRINOX neoadjuvant chemotherapy between December 2012 and November 2021 were included. Pretreatment contrast-enhanced CT imaging was obtained for all patients prior to inclusion. The primary tumor was contoured by an expert radiologist with 25 years of experience. A total of 845 radiomics features, including first, second, and higher-order features, were extracted from the total tumor volume. A feature reduction pipeline was used to reduce the dimensionality of the data. The selected features were used to generate a radiomics score based on LASSO coefficients. A high-dimensional Cox model was generated based on the radiomics score and other quantitative and semantic imaging findings. ResultsFrom the 845 radiomics features extracted, 45 were significantly different between the tertiles. The following equation was used to generate a radiomics score: = SmallAreaEmphasis⁎-66.87801+LargeDependenceEmphasis ⁎-0.2345916. The radiomics score was significantly different between the three groups of the radiomics features (P = 0.034). The overall difference in survival was significant between the three groups (p = 0.02). The nomogram showed good calibration and showed significant differences among the patients when they were classified as tertiles (P <0.00). ConclusionRadiomics approaches have the potential to predict overall survival in non-surgical patients with pancreatic ductal adenocarcinoma, and the inclusion of semantic imaging findings and pathological data could further enhance prognostication in patients with PDAC.

Prevalence of high-risk plaque features on cardiac computed tomography scans in patients with a coronary artery calcium score of zero

Abstract Introduction Coronary artery calcium (CAC) score of zero (so-called "power of zero") is associated with a very low rate of major adverse cardiac events (MACE), especially during short and midterm follow-up. However, a CAC score of zero does not definitively exclude the presence of atherosclerotic plaques or obstructive coronary artery disease (CAD). The composition of atherosclerotic plaques and the frequency of high-risk plaque features in this population are not well described. Purpose We sought to characterize the composition of atherosclerotic plaques and assess the frequency of high-risk plaque features on contrast-enhanced computed tomography (CT) images in patients with a zero CAC score. Methods The study enrolled patients with stable chest pain suspected of CAD referred for coronary CT imaging and having a CAC score of zero. Coronary CT angiography images were assessed by an expert reader to identify atherosclerotic plaques and high-risk plaque features in all coronary arteries with a diameter ≥1.5mm. The coronary artery lumen and plaques were manually segmented, and the degree of stenosis was evaluated following the Coronary Artery Disease Reporting and Data System (CAD-RADS) criteria. The presence of four high-risk plaque features was identified: positive remodeling, spotty calcification, low-attenuation plaque, and napkin ring sign. Total plaque volume, percent atheroma volume, and plaque composition were quantified using dedicated software (Figure 1). Results A total of 145 patients (mean age 54.2±12.1 years, 35.9% males) were included in the analysis. At least one coronary artery plaque was identified in 30 individuals (20.7%). 22 (15.2%), 7 (4.8%), and 1 (0.7%) patients were categorized into CAD-RADS 1, CAD-RADS 2, and CAD-RADS 3 categories, respectively. Among 30 patients with any coronary artery plaque, at least one high-risk feature was found in 10 individuals (33.3%), while 3 individuals (10.0%) had two features. Positive remodeling was found in 7 (23.3%), spotty calcification in 3 (10.0%), low-attenuation plaque in 3 (10.0%), and napkin ring sign in 0 patients, respectively (Figure 1). The mean total plaque volume was 48.2±54.3mm³, and the mean percent atheroma volume was 1.2±0.8%. Total plaque volume comprised low-attenuation (0.4%), fibro-fatty (58.9%), fibrous (39.4%), and calcified (1.3%) plaque volumes. Notably, in 3 (10.0%) individuals, the presence of calcified plaque was identified, which on non-contrast CT images did not meet the Agatston criteria for the CAC score. Conclusions Our study demonstrates a significant prevalence of atherosclerotic plaques and high-risk plaque features in patients with a CAC score of zero. Specifically, one in every five study participants had identifiable atherosclerotic plaque, with one in every three of those exhibiting one or more high-risk plaque features. The prognostic significance of these findings warrants further evaluation in studies with longitudinal follow-up.

Synergizing traditional CT imaging with radiomics: a novel model for preoperative diagnosis of gastric neuroendocrine and mixed adenoneuroendocrine carcinoma.

To develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features. We retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong's test. The models' diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases. Tumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P < 0.05). The clinical model's AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC of 0.809 (training) and 0.802 (validation). The combined model's AUCs were 0.853 (training) and 0.812 (validation), significantly outperforming the clinical model (P < 0.05). Subgroup analysis revealed that the combined model exhibited acceptable performance in differentiating g-NEC from g-ADC and g-MANEC from g-ADC, with AUCof0.887 and 0.823 in the training cohort and 0.852 and 0.762 in the validation cohort. A combined model based on traditional CT imaging and radiomic features provides a non-invasive and effective preoperative diagnostic method for differentiating g-(MA)NEC from g-ADC.

Computed tomography for the detection of myocardial hypoperfusion in acute myocardial infarction and the associated CT-to-catheter time

Emergency computed tomography (CT) often does not allow for comprehensive coronary artery assessment. However, CT may reveal pathological myocardial hypoperfusion suggestive of acute myocardial infarction (AMI), especially in patients presenting with a different diagnostic hypothesis. CT hypoperfusion is known to be associated with myocardial infarction, however the diagnostic value of CT hypoperfusion for the detection of AMI is still not well evaluated. This was a single-centre retrospective study including patients who underwent invasive coronary angiography (ICA) due to suspected AMI based on incidental perfusion defects upon emergency CT imaging between 2018 and 2023. A total of 22 patients (mean age 66.3 ± 10.8 years, 11 female) were included in this analysis. The diagnosis of AMI was established in all cases leading to ICA. Culprit coronary artery lesions with an indication of percutaneous coronary intervention were detected in all patients who underwent ICA. Spearmann correlation for hypoperfused segments on CT imaging and the corresponding vascular territory upon ICA was significantly substantial (ρ = 0.73, p = < 0.001). The higher the number of affected myocardial segments, the faster ICA was initiated. Mean time between the suspicion of AMI on CT imaging and ICA was 196 (29–4044) minutes. Myocardial hypoperfusion on emergency CT imaging should be considered as AMI until proven otherwise, independent of the clinical scenario leading to performance of CT imaging and whether imaging was performed for the exclusion of non-cardiac pathologies. Early initiation of further diagnostic workup may potentially avoid delays to invasive treatment and reduce the CT-to-catheter-time. Our study explicitly underlines that myocardial hypoperfusion upon contrast enhanced CT imaging needs to be considered as sign of acute myocardial infarction and indicates targeted clinical workup to rule out this diagnosis and to shorten the timeframe from imaging diagnosis to interventional treatment.

Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?

BackgroundTo compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).MethodsTwo hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors’ 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).ResultsThere were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.ConclusionsBoth 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.

Practical Step-by-step SYNAPSE VINCENT Rendering of Three-dimensional Graphics in Horseshoe Kidney with Bilateral Varicoceles.

Medical illustration serves as a cornerstone for understanding intricate anatomical anomalies, with three-dimensional (3D) rendering emerging as a pioneering tool for emphasizing basic medical concepts and clinical practices. In Japan, the SYNAPSE VINCENT software package (SVSP; Fujifilm Medical Co., Ltd., Tokyo, Japan), internationally known as "SYNAPSE 3D," is a widely embraced solution for 3D rendering. However, despite its prevalence, resources elucidating its practical usage and offering insightful tips are scarce. In this review, we focus on the use of SVSP for 3D rendering of complex anatomical anomalies, particularly in the field of urology. We demonstrate a step-by-step process of 3D rendering. 3D rendering was performed in a sample case of a patient with horseshoe kidney and coexisting bilateral varicoceles through inputting of multiphase contrast-enhanced CT images into the application, followed by segmentation of the renal parenchyma, image registration, and segmentation of the arterial and venous systems as well as the upper urinary tract. Manual adjustments were made using the "Mask edit" and "Diameter setting" tools to ensure accuracy, particularly in cases of significant anomalies. Then, color-coded structures appeared, including the renal parenchyma, arterial and venous systems, and upper urinary tract, which provided a comprehensive visualization of the anatomical anomalies. This review highlights the effectiveness of the SVSP in visualizing complex anatomical abnormalities and detailing the practical rendering process, which could promote wider adoption of the application among urologists despite the challenges associated with the software.

Delta-CT radiomics based model for predicting postoperative anastomotic leakage following radical resection of esophageal squamous cell carcinoma.

To predict postoperative anastomotic leakage (AL) following radical resection of esophageal squamous cell carcinoma (ESCC) based on clinical data and preoperative enhanced Computed tomography(CT) radiomics of the esophagus. We retrospectively analyzed the clinicopathological and radiological data of 213 patients with ESCC who received radical resection at Xiangyang No.1 People's Hospital from July 2011 to February 2024. 3D slicer software was used in combination with Lasso extraction and 10-fold cross-validation to extract texture parameters from contrast-enhanced CT images and generate Delta-Radscores. Several models were built using logistic regression to predict postoperative AL in ESCC. In the training set, the univariate analysis confirmed that duration of surgery, surgical method, delta radscore 1, delta radscore 2, contrast enhancement patterns, peripheral lymph node metastasis, post thoracotomy pulmonary infection(PTPI), and hot pot were risk factors for ESCC-AL (P<0.05 for both). The multivariate analysis showed that delta radscore 1, delta radscore 2, PTPI, and hot pot were independent risk factors for AL (P<0.05 for all). These results were verified by the XGboost machine learning model. The combinational model based on all of the above risk factors [AUC 0.900, OR 0.0282, 95%CI 0.841-0.943] outperformed either the clinical model[AUC 0.759, OR 0.0392, 95%0.683-0.825,P<0.05] or the imaging model[AUC 0.869, OR 0.0335, 95%0.804-0.918,P=0.1277] alone in predictive efficacy. The decision curve proved that the combinational model had a higher clinical net benefit. The nomogram generated via the combinational model simplified the predictive process. The same predictions were verified in the testing set. Delta radscore 1, delta radscore 2, PTPI, and hot pot were related to ESCC-AL. The novel nomogram created using enhanced CT radiomics informed perioperative management and improved the survival quality of ESCC patients.

Key Factors for Predicting Appendicitis from Contrast-Enhanced Computed Tomography Images Obtained Through Multiplanar Reconstruction

Background: The diameter of the appendix is a key parameter in diagnosing appendicitis. The diagnostic threshold for this parameter is 6 mm, originally established through graded compression sonography of the right lower quadrant (RLQ) of the abdomen. However, without corroborative findings from computed tomography (CT), this threshold may not be a reliable indicator of appendicitis. To ensure accurate diagnosis, clinicians should perform a comprehensive, multiparameter imaging assessment of the appendix, rather than relying solely on appendix diameter. Objectives: This study aimed to identify key factors for predicting appendicitis using contrast-enhanced coronal and sagittal CT images obtained through multiplanar reconstruction. Patients and Methods: This single-center, retrospective, cross-sectional study included patients who presented to our emergency department (ED) with RLQ abdominal pain and subsequently underwent contrast-enhanced CT between July 2019 and September 2020. The primary study outcome was pathologically confirmed appendicitis. Two experienced radiologists assessed parameters such as appendix diameter, wall thickness, abnormal appendix enhancement, abnormal appendix content, appendix erection, and periappendiceal fat stranding. Multivariate logistic regression was performed to identify significant predictive factors for appendicitis. Results: The study included 173 patients (median age: 37 years; women: 86). They were divided into appendicitis (n = 102) and alternative diagnosis (n = 71) groups. Significant differences were observed between the groups in terms of appendix diameter, wall thickness, wall enhancement, luminal content, appendix erection, and periappendiceal fat stranding (P &lt; 0.001). The diagnostic sensitivity and specificity values for an appendix diameter threshold of 7.7 mm were 91% and 82%, respectively. An appendix diameter of &gt; 7.7 mm (OR: 15.3; P &lt; 0.001), abnormal appendix enhancement (OR: 12.5; P &lt; 0.001), and appendix erection (OR: 6.1; P = 0.004) emerged as significant independent predictors of appendicitis. Conclusion: An appendix diameter of 7.7 mm appears to be the optimal threshold for diagnosing appendicitis. Additionally, the detection of abnormal appendix enhancement and appendix erection on contrast-enhanced CT images holds considerable diagnostic value.

Segmentation-based quantitative measurements in renal CT imaging using deep learning

BackgroundRenal quantitative measurements are important descriptors for assessing kidney function. We developed a deep learning-based method for automated kidney measurements from computed tomography (CT) images.MethodsThe study datasets comprised potential kidney donors (n = 88), both contrast-enhanced (Dataset 1 CE) and noncontrast (Dataset 1 NC) CT scans, and test sets of contrast-enhanced cases (Test set 2, n = 18), cases from a photon-counting (PC)CT scanner reconstructed at 60 and 190 keV (Test set 3 PCCT, n = 15), and low-dose cases (Test set 4, n = 8), which were retrospectively analyzed to train, validate, and test two networks for kidney segmentation and subsequent measurements. Segmentation performance was evaluated using the Dice similarity coefficient (DSC). The quantitative measurements’ effectiveness was compared to manual annotations using the intraclass correlation coefficient (ICC).ResultsThe contrast-enhanced and noncontrast models demonstrated excellent reliability in renal segmentation with DSC of 0.95 (Test set 1 CE), 0.94 (Test set 2), 0.92 (Test set 3 PCCT) and 0.94 (Test set 1 NC), 0.92 (Test set 3 PCCT), and 0.93 (Test set 4). Volume estimation was accurate with mean volume errors of 4%, 3%, 6% mL (contrast test sets) and 4%, 5%, 7% mL (noncontrast test sets). Renal axes measurements (length, width, and thickness) had ICC values greater than 0.90 (p < 0.001) for all test sets, supported by narrow 95% confidence intervals.ConclusionTwo deep learning networks were shown to derive quantitative measurements from contrast-enhanced and noncontrast renal CT imaging at the human performance level.Relevance statementDeep learning-based networks can automatically obtain renal clinical descriptors from both noncontrast and contrast-enhanced CT images. When healthy subjects comprise the training cohort, careful consideration is required during model adaptation, especially in scenarios involving unhealthy kidneys. This creates an opportunity for improved clinical decision-making without labor-intensive manual effort.Key PointsTrained 3D UNet models quantify renal measurements from contrast and noncontrast CT.The models performed interchangeably to the manual annotator and to each other.The models can provide expert-level, quantitative, accurate, and rapid renal measurements.Graphical

Texture analysis can predict response to etoposide-doxorubicin-cisplatin in patients with adrenocortical carcinoma.

The adrenocortical carcinoma (ACC) is a rare and highly aggressive malignancy originating from the adrenal cortex. These patients usually undergo chemotherapy with etoposide, doxorubicin, cisplatin and mitotane (EDP-M) in case of locally advanced or metastatic ACC. Computed tomography (CT) radiomics showed to be useful in adrenal pathologies. The study aimed to analyze the association between response to EDP-M treatment and CT textural features at diagnosis in patients with locally advanced or metastatic ACCs. We enrolled 17 patients with advanced or metastatic ACC who underwent CT before and after EDP-M therapy. The response to treatment was evaluated according to RECIST 1.1, Choi, and volumetric criteria. Based on the aforementioned criteria, the patients were classified as responders and not responders. Textural features were extracted from the biggest lesion in contrast-enhanced CT images with LifeX software. ROC curves were drawn for the variables that were significantly different (p < 0.05) between the two groups. Long-run high grey level emphasis (LRHGLE_GLRLM) and histogram kurtosis were significantly different between responder and not responder groups (p = 0.04) and the multivariate ROC curve combining the two features showed a very good AUC (0.900; 95%IC: 0.724-1.000) in discriminating responders from not responders. More heterogeneous tissue texture of initial staging CT in locally advanced or metastatic ACC could predict the positive response to EDP-M treatment. Adrenal texture is able to predict the response to EDP-M therapy in patients with advanced ACC.

The incremental value of Histogram Analysis in the differentiation between hyperdense cysts and solid renal masses on unenhanced CT images.

To investigate the utility of voxel histogram analysis (HA) for differentiating hyperdense renal cysts from small solid masses on unenhanced computed tomography (CT) scans. A retrospective analysis of 99 hyperdense cystic lesions and 28 solid malignant lesions was conducted using a radiological database (from 2015 to 2021) and a pathological database (from 2010 to 2020). The study investigated the distribution of voxel attenuation values using percentiles to establish reliable criteria for differentiation after drawing a region of interest (ROI) in the centre of the lesions. The standard of reference was a histopathological diagnosis for malignant masses and contrast-enhanced CT or magnetic resonance imaging (MRI) for cysts. HA provided higher diagnostic accuracy than the conventional mean attenuation value of 70 Hounsfield Units (HU). For the 75th and 90th percentiles ± 1 standard deviation, accuracies of 77.2% (95% confidence interval 68.9-84.2%) for the 75th and 68.5% (59.7-76.4%) for the 90th were found, versus 37.0% (28.6-46.0%) for the 70 HU threshold criterion. A Gaussian distribution of voxel attenuation values was observed in 88.9% of the lesions, suggesting that it is feasible to calculate these parameters from a single measurement. The study underscores the potential of HA as a valuable tool for characterizing hyperdense cysts on unenhanced CT by using the same ROI for measuring lesion attenuation. HA could offer additional value beyond the 70 HU criterion and possibly influence clinical decisions. Multi-institutional studies are necessary for external validation to confirm its generalizability and more extensive applicability.

Multi-stage cascade GAN for synthesis of contrast enhancement CT aorta images from non-contrast CT

Recently in diagnosis of Aortic dissection (AD), the synthesis of contrast enhanced CT (CE-CT) images from non-contrast CT (NC-CT) images is an important topic. Existing methods have achieved some results but are unable to synthesize a continuous and clear intimal flap on NC-CT images. In this paper, we propose a multi-stage cascade generative adversarial network (MCGAN) to explicitly capture the features of the intimal flap for a better synthesis of aortic dissection images. For the intimal flap with variable shapes and more detailed features, we extract features in two ways: dense residual attention blocks (DRAB) are integrated to extract shallow features and UNet is employed to extract deep features; then deep features and shallow features are cascaded and fused. For incomplete flaps or lack of details, we use spatial attention and channel attention to extract key features and locations. At the same time, multi-scale fusion is used to ensure the continuity of the intimal flap. We perform the experiment on a set of 124 patients (62 with AD and 62 without AD). The evaluation results show that the synthesized images have the same characteristics as the real images and achieves better results than the popular methods.

Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma

BackgroundConcurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways.MethodsOverall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing.ResultsThe DL model exhibited favorable predictive performance, with an area under the curve of 0.86 (95% confidence interval [CI] 0.79–0·92) in the training cohort and 0.84 (95% CI 0.75–0.94) in the validation cohort. The DL model (low score vs. high score) was an independent predictive factor; it was significantly associated with progression-free survival and overall survival in both the training (hazard ratio [HR] = 0.54 [0.36−0.80], P = 0.002; 0.44 [0.28−0.68], P < 0.001) and validation cohorts (HR = 0.46 [0.24−0.88], P = 0.008; 0.30 [0.14−0.60], P < 0.001). The DL model was also positively related to the cell adhesion molecules, the P53 signaling pathway, and natural killer cell-mediated cytotoxicity. Single-cell analysis revealed that differentially expressed genes were enriched in different immune cells.ConclusionThe DL model demonstrated a strong predictive ability for determining the response in patients with NSCLC undergoing CCRT. Our findings contribute to understanding the potential biological mechanisms underlying treatment responses in these patients.