Computed Tomography Enhancement Research Articles

Delayed Enhancement in Cardiac CT: A Potential Alternative to Cardiac MRI? Technical Updates and Clinical Considerations

Despite cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) being the current gold standard for non-invasive myocardial characterization and fibrosis quantification, its accessibility is limited, particularly in acute settings and in certain patient populations with contraindications to magnetic resonance imaging. Late iodine enhancement (LIE) in computed tomography (CT) imaging has emerged as a potential alternative, capitalizing on the similarities in the contrast kinetics between gadolinium and iodinated contrast agents. Studies have investigated LIE-CT’s effectiveness in myocardial infarction (MI) detection, revealing promising outcomes alongside some disparities compared to LGE-CMR. LIE-CT also proves beneficial in diagnosing non-ischemic heart diseases such as myocarditis, hypertrophic cardiomyopathy, and sarcoidosis. While LIE-CT demonstrates good accuracy in detecting certain myocardial pathologies, including acute MI and chronic fibrotic changes, it has limitations, such as the inability to detect diffuse myocardial enhancement. Nonetheless, thanks to the availability of optimized protocols with minimal radiation doses and contrast medium administration, integrating LIE-CT into cardiac CT protocols could enhance its clinical utility, particularly in acute settings, providing valuable prognostic and management insights across a spectrum of cardiac ischemic and non-ischemic conditions.

Characterization of tumors of jaw: Additive value of contrast enhancement and dual-energy computed tomography.

Currently, the differentiation of jaw tumors is mainly based on the lesion's morphology rather than the enhancement characteristics, which are important in the differentiation of neoplasms across the body. There is a paucity of literature on the enhancement characteristics of jaw tumors. This is mainly because, even though computed tomography (CT) is used to evaluate these lesions, they are often imaged without intravenous contrast. This study hypothesised that the enhancement characteristics of the solid component of jaw tumors can aid in the differentiation of these lesions in addition to their morphology by dual-energy CT, therefore improving the ability to differentiate between various pathologies. To evaluate the role of contrast enhancement and dual-energy quantitative parameters in CT in the differentiation of jaw tumors. Fifty-seven patients with jaw tumors underwent contrast-enhanced dual-energy CT. Morphological analysis of the tumor, including the enhancing solid component, was done, followed by quantitative analysis of iodine concentration (IC), water concentration (WC), HU, and normalized IC. The study population was divided into four subgroups based on histopathological analysis-central giant cell granuloma (CGCG), ameloblastoma, odontogenic keratocyst (OKC), and other jaw tumors. A one-way ANOVA test for parametric variables and the Kruskal-Wallis test for non-parametric variables were used. If significant differences were found, a series of independent t-tests or Mann-Whitney U tests were used. Ameloblastoma was the most common pathology (n = 20), followed by CGCG (n = 11) and OKC. CGCG showed a higher mean concentration of all quantitative parameters than ameloblastomas (P < 0.05). An IC threshold of 31.35 × 100 μg/cm3 had the maximum sensitivity (81.8%) and specificity (65%). Between ameloblastomas and OKC, the former showed a higher mean concentration of all quantitative parameters (P < 0.001), however when comparing unilocular ameloblastomas with OKCs, the latter showed significantly higher WC. Also, ameloblastoma had a higher IC and lower WC compared to "other jaw tumors" group. Enhancement characteristics of solid components combined with dual-energy parameters offer a more precise way to differentiate between jaw tumors.

APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF THYROID CANCER WITH ENHANCED COMPUTED TOMOGRAPHY

Thyroid Cancer (TC) is a common malignant tumor, head and neck in the incidence of malignant tumors in the seventh, ranked fourth in the incidence in women. There are several methods to diagnose and recognize TC, such as ultrasonic, computed tomography (CT) and other means. While, it is an important role for CT examination in the diagnosis of TC, because it has the characteristics of objectivity, repeatability, and multi-dimensional imaging, and can clearly understand the scope and spatial characteristics of the lesion. CT has unique advantages in showing lymph nodes and distant metastases, such as coarse-walled or thick-walled ring calcification. The early diagnosis of TC mainly relies on manual labor, which is very inefficient. With the continuous development of information science, the construction of TC diagnosis and recognition models based on artificial intelligence (AI) has gradually become a research hotspot. At present, research on AI-based thyroid screening models mainly focuses on four aspects: first, thyroid region segmentation and image denoising based on image-omics; second, the establishment of a high-precision TC risk prediction model based on multi-omics data; third, screening of biomarkers of TC for clinical diagnosis; fourth, establish the early screening model of TC based on AI. This paper reviews the research status of the AI-based thyroid screening model based on the above four aspects. In addition, this paper also summarizes the main challenges faced by the current AI -based TC recognition and detection model and it proposes a new research idea for the future TC early screening research based on enhanced CT.

Influence of Contrast Media Temperature and Concentration on Patient Comfort and Safety in Computed Tomography: CATCHY II Trial.

Previous research on the necessity to reduce the viscosity of contrast media (CM) by either prewarming CM before injection during computed tomography (CT) or by using less concentrated CM has yielded conflicting results. In addition, there is limited evidence on patient comfort. The aim of the study was to examine if prewarming CM, with varying CM concentrations, is superior to CM at room temperature, with respect to patient comfort and safety in CT. All elective patients scheduled for contrast-enhanced CT scans at Maastricht University Medical Center+ between October 27, 2021 and October 31, 2022 were eligible for inclusion when a questionnaire evaluating patient comfort was completed. This 1-year period was divided into 4 intervals (4 groups): group 1 (370 mg I/mL, 37°C), group 2 (370 mg I/mL, room temperature), group 3 (300 mg I/mL, 37°C), and group 4 (300 mg I/mL, room temperature). All CT scans were performed using state of the art equipment (Siemens Healthineers; SOMATOM Force and SOMATOM Definition AS, Forchheim, Germany). Contrast media injections were performed using a dual-head power injector (Stellant; Bayer Healthcare, Berlin, Germany) and individualized to body weight and/or tube voltage, depending on the CM protocols. After the CT scan, patients completed a questionnaire covering the primary outcomes comfort, pain, and adverse events such as feelings of heat, nausea, vomiting, itchiness, urticaria, difficulty breathing, dizziness, goosebumps, or an odd taste. Technicians were asked to report any adverse events, including extravasation and allergic-like reactions. The secondary outcome involved attenuation (in Hounsfield unit, HU), which was evaluated by assessing the HU of the coronary arteries for vascular CT, and liver enhancement in portal venous CT. The Kruskal-Wallis test was used for continuous scale outcomes and χ2 tests for examining adverse events. Results showed no significant differences examining comfort score (P = 0.054), pain sensation (P = 0.469), extravasation (P = 0.542), or allergic-like reaction (P = 0.253). Significant differences among the 4 groups were found with respect to heat sensation and dizziness (P = 0.005 and P = 0.047, respectively), showing small effect sizes. All other adverse effects showed no significant results. No significant differences were observed in coronary attenuation among the 4 groups in coronary CT angiography (P = 0.113). When analyzing attenuation in portal venous CT scans, significant differences were found among the 4 groups (P = 0.008). Administrating prewarmed CM is nonsuperior compared with CM at room temperature in relation to patient comfort and safety, regardless of CM concentration. These findings suggest that prewarming CM before usage is unnecessary, which will improve the efficiency of daily clinical workflow and brings environmentally friendly benefits.

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.

Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST. The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.

A computed tomography-based comprehensive standardized adrenal tumor scoring model for predicting the perioperative outcomes of retroperitoneal laparoscopic adrenal surgery.

Many imaging scoring models have been developed for tumor surgery to provide critical guidance for the selection of surgical methods. However, little research has been aimed at developing scoring models for adrenal tumors and retroperitoneal laparoscopic adrenal surgery (RLAS), which has become the primary technique for treating adrenal tumors. The study set out to establish a computed tomography (CT)-based adrenal tumor scoring model for predicting perioperative outcomes in patients with adrenal tumors who have undergone RLAS. The retrospective analysis included 306 patients with adrenal tumors diagnosed by preoperative unenhanced or enhanced CT from January 2014 to August 2018 in the First Affiliated Hospital of Fujian Medical University. CT images were used to quantify the tumor location and size; the relationships of the tumors with the surrounding organs and tissues, the large abdominal blood vessels, and the upper poles of the kidneys and renal hila; the adhesion of periadrenal fat (PF); and the tumor CT enhancement value. We conducted multivariate ordinal logistic regression analysis to screen variables and performed principal component analysis to construct a novel scoring model for RLAS. The perioperative outcomes of RLAS were evaluated according to postoperative length of stay, operative time (OT), intraoperative blood loss (IBL), and postoperative complications. The final scoring model included tumor size; the relationships of the tumors with the surrounding organs and tissues, the large abdominal blood vessels, and the upper poles of the kidneys and renal hila; the tumor CT enhancement value; the adhesion of the PF; and the functional status of adrenal tumors. The total score had positive correlations with the OT (rs=0.431), IBL (rs=0.446), and postoperative length (rs=0.180) (all P values <0.001). Compared to any single metric, the total score provided better prediction of OT and IBL. The grading system for RLAS based on the scoring model also performed well in predicting the complexity and difficulty of RLAS. The coincidence rate for these factors was good (all P values <0.001). The developed model is feasible and repeatable in the prediction of the perioperative outcomes, complexity, and difficulty of RLAS.

Influence of splenomegaly on aortic and liver parenchymal CT numbers during contrast-enhance CT in patients with cirrhosis

IntroductionTo compare CT (computed tomography) values for enhancement of the abdominal aorta and liver parenchyma during dynamic contrast enhancement (CE) CT in cirrhotic patients with and without splenomegaly (SM). MethodsWe considered 258 patients (83 males and 46 females for the splenomegaly group, and 83 males and 46 females for the control group) for this retrospective study. We measured CT values in the abdominal aorta and hepatic parenchyma during the hepatic arterial (HAP) and portal venous (PVP) phases. The aortic CE at HAP and the hepatic parenchymal CE at PVP were compared between the two groups. For success rate of scans, we also calculated the optimal CE rates (>280 HU in the abdominal aorta and >50 HU in the hepatic parenchyma) for each group. ResultsIn the SM group, the CE for abdominal aorta was decreased during the aortic phase for a dynamic CE-CT (p < 0.05). When evaluating the success rates, they were found to be 65.1 % and 58.9 % in the SM group and 81.4 % and 72.3 % in the non-SM group (p < 0.05). ConclusionThe success rate of scans and CE for the abdominal aorta during the aortic phase exhibited a significant decrease during dynamic CE-CT scans on patients with SM. Patients with SM may have reduced diagnostic ability with typical contrast injection protocols. Implications for practiceIt may be necessary to change the injection rates and contrast medium volume during CE-CT depending on the presence or absence of SM.

Cardiac computed tomography angiography with and without bolus tracking methods in infants with congenital heart disease.

The study investigated radiation dose, vascular computed tomography (CT) enhancement and image quality of cardiac computed tomography angiography (CCTA) with and without bolus tracking (BT) methods in infants with congenital heart disease (CHD). The volume CT dose index (CTDIvol) and dose length product (DLP) were recorded for all CT scans, and the effective dose was obtained using a conversion factors. The CT number for the ascending aorta (AO) and pulmonary artery (PA), image noise of muscle tissue and contrast-to-noise ratio (CNR) were measured and calculated. The median values in the groups with and without BT were 2.20mGy versus 0.44mGy for CTDIvol, 8.10mGy·cm versus 6.20mGy·cm for DLP, and 0.66mSv versus 0.51mSv for effective dose (p<0.001). There were no statistical differences in vascular CT enhancement, image noise, and CNR. CCTA without BT methods can reduce the radiation dose while maintaining vascular CT enhancement and image quality compared to CCTA with BT methods.

Contrast-enhanced ultrasonography findings of LAMNs with peritoneal and splenic metastases: a case report and literature review.

Low-grade appendiceal mucinous neoplasms (LAMNs) are rare appendiceal tumors that are primarily diagnosed using computed tomography(CT) enhancement and magnetic resonance imaging (MRI). Herein, we report the sonographic features, especially for contrast-enhanced ultrasound (CEUS), of a 70-year-old female with an unusual LAMN metastasizing to the peritoneum and spleen. The patient had a right pelvic mass 2 days prior to presentation. Two-dimensional (2D) ultrasound revealed a mixed cystic-solid mass in the right lower abdomen and spleen parenchyma; CEUS showed heterogeneous enhancement in both areas, suspected to be a mucinous mass. CT enhancement and MRI findings revealed concurrent findings. Histopathologically, LAMN lesions were confirmed in the appendix, spleen, and peritoneum of the specimens obtained during exploratory laparoscopy. No recurrences were reported at three years postoperatively. LAMN lesions may metastasize to abdominal organs, and imaging examinations are essential for diagnosis. This study presents major ultrasonography and CEUS findings for the diagnosis of LAMNs.

Imaging features of concurrent scirrhous hepatocellular carcinoma and primary clear cell carcinoma of the liver: A case report and literature review

Multiple lesions commonly appear in the liver of patients with hepatocellular carcinoma (HCC), and the prognosis is associated with the subtype. However, simultaneous occurrence of scirrhous HCC (SHCC) and primary clear cell carcinoma of the liver (PCCCL) has not been reported to date. The diagnosis of SHCC and PCCCL is uncommon using the traditional imaging examination methods such as ultrasound (US), computed tomography (CT), and magnetic resonance imaging. We herein summarize the imaging features of SHCC and PCCCL to provide a reference for accurate preoperative diagnosis and appropriate treatment. A 55-year-old male was diagnosed with two hepatic tumors by the US examination. On the 2nd day after the US examination, he was diagnosed with HCC with intrahepatic metastasis by enhanced CT examination. His alpha-fetoprotein level was 3272 ng/mL (reference range, 0–7 ng/mL). Two weeks after enhanced CT examination, the patient underwent hepatectomy and cholecystectomy. Pathological examination showed that the tumor in liver segments 2 and 3 was SHCC and that the tumor in liver segment 4 was PCCCL. One year postoperatively, digital subtraction angiography showed no obvious tumor staining. Five years postoperatively, enhanced CT examination showed no recurrence or metastasis. Radiologists usually diagnose multiple tumors in the liver as malignant tumors with intrahepatic metastasis. Simultaneous occurrence of SHCC and PCCCL in the liver is occasionally found. The US imaging features, CT enhancement types, and location of the tumors can help radiologists to accurately diagnose SHCC and PCCCL, allowing clinicians to choose appropriate treatment.

Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics

The purpose of this study was to differentiate the retroperitoneal paragangliomas and schwannomas using computed tomography (CT) radiomics. This study included 112 patients from two centers who pathologically confirmed retroperitoneal pheochromocytomas and schwannomas and underwent preoperative CT examinations. Radiomics features of the entire primary tumor were extracted from non-contrast enhancement (NC), arterial phase (AP) and venous phase (VP) CT images. The least absolute shrinkage and selection operator method was used to screen out key radiomics signatures. Radiomics, clinical and clinical-radiomics combined models were built to differentiate the retroperitoneal paragangliomas and schwannomas. Model performance and clinical usefulness were evaluated by receiver operating characteristic curve, calibration curve and decision curve. In addition, we compared the diagnostic accuracy of radiomics, clinical and clinical-radiomics combined models with radiologists for pheochromocytomas and schwannomas in the same set of data. Three NC, 4 AP, and 3 VP radiomics features were retained as the final radiomics signatures for differentiating the paragangliomas and schwannomas. The CT characteristics CT attenuation value of NC and the enhancement magnitude at AP and VP were found to be significantly different statistically (P < 0.05). The NC, AP, VP, Radiomics and clinical models had encouraging discriminative performance. The clinical-radiomics combined model that combined radiomics signatures and clinical characteristics showed excellent performance, with an area under curve (AUC) values were 0.984 (95% CI 0.952–1.000) in the training cohort, 0.955 (95% CI 0.864–1.000) in the internal validation cohort and 0.871 (95% CI 0.710–1.000) in the external validation cohort. The accuracy, sensitivity and specificity were 0.984, 0.970 and 1.000 in the training cohort, 0.960, 1.000 and 0.917 in the internal validation cohort and 0.917, 0.923 and 0.818 in the external validation cohort, respectively. Additionally, AP, VP, Radiomics, clinical and clinical-radiomics combined models had a higher diagnostic accuracy for pheochromocytomas and schwannomas than the two radiologists. Our study demonstrated the CT-based radiomics models has promising performance in differentiating the paragangliomas and schwannomas.

Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography-Based Radiomics Models Covering Tumor Per Se and Peritumoral Signatures.

We aimed to develop and validate a computed tomography (CT)-based radiomics model for early relapse prediction in patients with Ewing sarcoma (ES). We recruited 104 patients in this study. Tumor areas and areas with a tumor expansion of 3 mm were used as regions of interest for radiomics analysis. Six different models were constructed: Pre-CT, CT enhancement (CTE), Pre-CT +3 mm , CTE +3 mm , Pre-CT and CTE combined (ComB), and Pre-CT +3 mm and CTE +3 mm combined (ComB +3 mm ). All 3 classifiers used a grid search with 5-fold cross-validation to identify their optimal parameters, followed by repeat 5-fold cross-validation to evaluate the model performance based on these parameters. The average performance of the 5-fold cross-validation and the best one-fold performance of each model were evaluated. The AUC (area under the receiver operating characteristic curve) and accuracy were calculated to evaluate the models. The 6 radiomics models performed well in predicting relapse in patients with ES using the 3 classifiers; the ComB and ComB +3 mm models performed better than the other models (AUC -best : 0.820-0.922/0.823-0.833 and 0.799-0.873/0.759-0.880 in the training and validation cohorts, respectively). Although the Pre-CT +3 mm , CTE +3 mm, and ComB +3 mm models covering tumor per se and peritumoral CT features preoperatively forecasted ES relapse, the model was not significantly improved. The radiomics model performed well for early recurrence prediction in patients with ES, and the ComB and ComB +3 mm models may be superior to the other models.

Deep learning for predicting the human epidermal growth factor receptor 2 status of breast cancer liver metastases based on contrast-enhanced computed tomography: a development and validation study.

This study investigated the value of a deep learning (DL) model based on computed tomography (CT) enhancement for predicting human epidermal growth factor receptor 2 (HER2) expression in patients with liver metastasis from breast cancer. Data were collected for 151 female patients with liver metastasis from breast cancer who underwent abdominal enhanced CT examination in the Department of Radiology at the Affiliated Hospital of Hebei University between January 2017 and March 2022. Liver metastases were confirmed in all patients by pathology. The HER2 status of the liver metastases was assessed and enhanced CT examinations were performed before treatment. Of the 151 patients, 93 were HER2 negative and 58 were HER2 positive. Liver metastases were manually labeled with rectangular frames, layer by layer, and the labeled data were processed. Five basic networks (ResNet34, ResNet50, ResNet101, ResNeXt50, and Swim Transformer) were used for training and optimization, and the model's performance was tested. Receiver operating characteristic (ROC) curves were used to analyze the area under the curve (AUC), as well as the accuracy, sensitivity, and specificity of the networks in predicting HER2 expression in breast cancer liver metastases. Overall, ResNet34 demonstrated the best prediction efficiency. The accuracy of the validation and test set models in predicting HER2 expression in liver metastases was 87.4% and 80.5%, respectively. The AUC, sensitivity, and specificity of the test set model in predicting HER2 expression in liver metastases were 0.778, 77.0%, and 84.0%, respectively. Our DL model based on CT enhancement has good stability and diagnostic efficacy, and is a potential non-invasive method for identifying HER2 expression in liver metastases from breast cancer.

Diagnostic value of CT enhancement degree in lymph node metastasis of papillary thyroid cancer: A comparison of enhancement, ratio, and difference.

To evaluate the value of computed tomography (CT) enhancement degree in diagnosing lymph node (LN) metastasis in papillary thyroid carcinoma (PTC) by determining the ratio and difference between the Hounsfield units (HU) of CT enhancement and plain scan of the LNs, as well as between the HU of CT-enhanced LNs and the sternocleidomastoid muscle. The plain and enhanced CT findings of 114 metastasis-positive LNs in 89 cases and 143 metastasis-negative LNs in 114 cases of PTC were analyzed retrospectively. Plain HU of LNs (PNHU), enhanced HU of LNs (ENHU), and enhanced HU of the sternocleidomastoid muscle (EMHU) were measured. The ENHU, difference between ENHU and PNHU (EN-PNHU), ratio of ENHU to PNHU (EN/PNHU), difference between ENHU and EMHU (EN-EMHU), and ratio of ENHU to EMHU (EN/EMHU) in metastasis-positive and metastasis-negative LN groups were calculated, the corresponding diagnostic efficacy for differentiating metastasis-positive from metastasis-negative LNs in PTC were sought using the receiver-operating curve. The interobserver agreement between readers was assessed using the interobserver correlation coefficient (ICC). The ENHU of 114 metastasis-positive LNs and 143 metastasis-negative LNs was 113.39 ± 24.13 and 77.65 ± 15.93, EN-PNHU was 65.84 ± 21.72 HU and 34.07 ± 13.63 HU, EN/PNHU was 2.36 (1.98, 2.75) and 1.76 (1.54, 2.02), EN-EMHU was 49.42 ± 24.59 HU and 13.27 ± 15.41 HU, and EN/EMHU was 1.79 ± 0.40 and 1.21 ± 0.24, respectively (all P < 0.001). The area under the curve, cutoff value, sensitivity, specificity, and accuracy of ENHU for identifying metastasis-positive and metastasis-negative LNs were 0.895, 97.3 HU, 0.746, 0.895, and 0.829, EN-PNHU was 0.894, 47.8 HU, 0.807, 0.874, and 0.844, EN/PNHU was 0.831, 1.9, 0.877, 0.650, and 0.751, EN-EMHU was 0.890, 26.4 HU, 0.807, 0.839, and 0.825, and EN/EMHU was 0.888, 1.5, 0.728, 0.902, and 0.825, respectively. The readers had an excellent interobserver agreement on these five parameters (ICC = 0.874-0.994). In the preoperative evaluation of LN metastasis in PTC, ENHU, EN-PNHU, EN-EMHU, and EN/EMHU had similarly high diagnostic efficacy, with ENHU, EN-PNHU, and EN/EMHU having higher specificity and EN-PNHU and EN-EMHU having higher sensitivity.

Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?

This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT). We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)-based protocols (n = 100) by the prospective trial. The BW protocol was performed using routine protocol (600 mg/kg of iodine) by the prospective trial. The CT numbers of the abdominal aorta and hepatic parenchyma, CM dose, and injection rate were compared between each protocol using the paired t test. Equivalence tests were performed with equivalent margins of 100 and 20 Hounsfield units for the aorta and liver, respectively. The CM dose and injection rate for the ML and BW protocols were 112.3 mL and 3.7 mL/s, and 118.0 mL and 3.9 mL/s ( P < 0.05). There were no significant differences in the CT numbers of the abdominal aorta and hepatic parenchyma between the 2 protocols ( P = 0.20 and 0.45). The 95% confidence interval for the difference in the CT number of the abdominal aorta and hepatic parenchyma between 2 protocols was within the range of predetermined equivalence margins. Machine learning is useful for predicting the CM dose and injection rate required to obtain the optimal clinical contrast enhancement for hepatic dynamic CT without reducing the CT number of the abdominal aorta and hepatic parenchyma.

Imaging features of primary hepatic sarcomatoid carcinoma: Differentiation from hepatocellular carcinoma and intrahepatic cholangiocarcinoma on CT: A preliminary study

PurposePrimary hepatic sarcomatoid carcinoma (PHSC) is a rare type of malignant tumor in the liver. Nevertheless, few studies have focused on the imaging diagnosis of PHSC. In this study, we collected clinical and computed tomography (CT) imaging data of PHSC from two institutions, aiming to investigate the clinical and radiological characteristics of PHSC. MethodsWe retrospectively investigated the clinical characteristics and CT features of 22 PHSC patients (19 males and 3 females; mean age, 63.4 years; range, 49 to 76 years), 95 hepatocellular carcinoma (HCC) patients and 50 intrahepatic cholangiocarcinoma (ICC) patients. Two radiologists independently evaluated the CT features of the three groups. Subsequently, we analyzed the differences in the clinical characteristics and CT features between the PHSC and control groups. ResultsMost PHSCs were larger than 5 cm (72.7%). PHSC mainly showed irregular (81.8%), heterogeneous (100%) masses with ill-defined (72.7%) borders with necrosis (86.4%) on CT, which are more common CT features versus HCC (p < 0.001). In the arterial phase, PHSC always showed noticeable heterogeneous enhancement (100.0%), mainly manifesting as partial arterial phase hyperenhancement (APHE) (86.4%). The enhancement patterns of PHSC mainly included delayed progressive enhancement (72.7%), nonperipheral washout (22.7%), and unclassified enhancement (4.5%), which were significantly different from the HCC enhancement pattern but similar to the enhancement pattern of ICC. In addition, vein tumor thrombus (18.2%), intrahepatic metastasis (27.3%), and lymphadenopathy (27.3%) were relatively common in PHSC. Furthermore, most PHSC tumors classified as LR-M (66.7%) were similar to ICC. ConclusionsPHSC generally presents as irregularly large masses with necrosis, intrahepatic metastasis, and lymphadenopathy. The CT enhancement of PHSC is mainly part of APHE and delayed progressive enhancement.

Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data.

Objective: To collect the clinical, pathological, and computed tomography (CT) data of 143 accepted surgical cases of pancreatic body tail cancer (PBTC) and to model and predict its prognosis. Methods: The clinical, pathological, and CT data of 143 PBTC patients who underwent surgical resection or endoscopic ultrasound biopsy and were pathologically diagnosed in Xiangyang No.1 People's Hospital Hospital from December 2012 to December 2022 were retrospectively analyzed. The Kaplan-Meier method was adopted to make survival curves based on the 1 to 5 years' follow-up data, and then the log-rank was employed to analyze the survival. According to the median survival of 6 months, the PBTC patients were divided into a group with a good prognosis (survival time ≥ 6 months) and a group with a poor prognosis (survival time < 6 months), and further the training set and test set were set at a ratio of 7/3. Then logistic regression was conducted to find independent risk factors, establish predictive models, and further the models were validated. Results: The Kaplan-Meier analysis showed that age, diabetes, tumor, node, and metastasis stage, CT enhancement mode, peripancreatic lymph node swelling, nerve invasion, surgery in a top hospital, tumor size, carbohydrate antigen 19-9, carcinoembryonic antigen, Radscore 1/2/3 were the influencing factors of PBTC recurrence. The overall average survival was 7.4 months in this study. The multivariate logistic analysis confirmed that nerve invasion, surgery in top hospital, dilation of the main pancreatic duct, and Radscore 2 were independent factors affecting the mortality of PBTC (P < .05). In the test set, the combined model achieved the best predictive performance [AUC 0.944, 95% CI (0.826-0.991)], significantly superior to the clinicopathological model [AUC 0.770, 95% CI (0.615-0.886), P = .0145], and the CT radiomics model [AUC 0.883, 95% CI (0.746-0.961), P = .1311], with a good clinical net benefit confirmed by decision curve. The same results were subsequently validated on the test set. Conclusion: The diagnosis and treatment of PBTC are challenging, and survival is poor. Nevertheless, the combined model benefits the clinical management and prognosis of PBTC.

Retrograde venous coil embolization prior to transarterial chemoembolization in hepatocellular carcinoma with arterio-hepatic venous shunts.

PURPOSE This study explored the clinical efficacy of transcatheter retrograde shunt occlusion with coils to prevent pulmonary oil or particle embolization prior to transarterial chemoembolization (TACE) in patients with artero-hepatic venous shunts (AHVS) secondary to hepatocellular carcinoma (HCC). METHODS From July 2017 to January 2021, 6 patients with advanced, unresectable HCC were found to have an AHVS by hepatic arteriography at the time of attempted TACE. The AHVS was embolized ret rogradely with metal coils through a transfemoral or transjugular venous approach. After venous embolization and confirmation of the absence of the AHVS, TACE was performed using an emul sion of iodized oil and doxorubicin or drug-eluting beads. Follow-up computed tomography (CT) was performed within 1 month after the first TACE to evaluate the results and complications. RESULTS Hepatic angiography after venous embolization showed that AHVS had utterly disappeared in all patients during the operation. The immediate technical success of the retrograde venous embo lization was 100%. The AHVS had disappeared entirely during the follow-up period through triple-phase enhancement CT scanning. According to the modified response evaluation criteria in solid tumors, TACE in all 6 patients had a disease control response rate of 100% (6/6) with complete response in 2 patients and partial response in 4 patients. One patient died during the 6-month follow-up, and the other 5 were still alive. No complications related to pulmonary embolism occurred. CONCLUSION Retrograde venous coil embolization of AHVS via the draining hepatic vein appears to be a safe, feasible, and effective treatment to allow TACE treatment without pulmonary embolic events. This approach appears to provide better tumor control and effectively decreases the occurrence of pulmonary embolism.

Quantification of aortic valve calcification in contrast enhanced computer tomography

Abstract Introduction Aortic valve calcification (AVC) is linked to higher gradients and worse prognosis in aortic valve stenosis. Current guidelines recommend calcium scoring of the aortic valve as additional factor for decision making in low-flow, low-gradient aortic stenosis. Calcium Scoring is performed by native computed tomography (CT). For planning of interventional valve replacement (TAVR) a second scan with contrast agent (CECT) is needed at the cost of additional radiation exposure and work up time. The aim of this study is to assess and validate an estimate conversion factor of aortic valve quantification by using CECT without native imaging. Methods The Agatston score was measured on native CT in 45 patients and used as a reference. In contrast enhanced CT, mean aortic attenuation values in Hounsfield units (HU) were determined by placement of a region of interest in the ascending aorta with a standard deviation of 4xHU for calcification threshold. AVC was segmented semi-automatically. A conversion formula was created by plotting of native Agatston score versus aortic valve calcification segmented on contrast enhanced CT and linear regression. Validation of calculated conversion formula was established by Intra-class coefficient (ICC) and Bland Altman analysis. Results The linear regression model yielded an Agatston score conversion formula of 691 + 1.83 × AVCCECT. Validation of the formula in a validation cohort (n=20) showed high agreement (r2=0.802, ICC 0.915 (CI 95%: 0.786–0.966) p&amp;lt;0.001, p=0.055). Conclusion Approximation by our calculated conversion factor from contrast enhanced CT has shown excellent reliability to determine aortic valve calcification. Further studies are needed to extend this finding to larger cohorts and diverse clinical environments. Funding Acknowledgement Type of funding sources: None.

Radiological evaluation of ovarian mass by contrast-enhanced computed tomography abdomen with clinicopathological correlation in Eastern Indian population

Background: Adenexal masses present diagnostic challenges. After identification of the ovarian mass, the most critical step is to determine the degree of suspicion for malignancy. It is mainly based on imaging modalities and clinicopathological correlation. Aims and Objectives: This radiological study was designed to differentiate benign and malignant ovarian masses by contrast enhanced-computed tomography (CECT) of abdomen and correlation of the radiological findings with histopathological report. Materials and Methods: This was a hospital-based prospective study conducted in Department of Radiodiagnosis, Nilratan Sircar Medical College and Hospital, Kolkata during January 2019–June 2020. The study was done on clinically suspected 50 patients of 15–50 years with ovarian mass having CECT diagnosed solid/complex solid cystic mass. Results: Among 50 cases under study, 37(74.0%) were benign which was significantly higher than that of malignant cases (26.0%). Malignancy was significantly associated with wall irregularity, solid-cystic composition, presence of thick septations, septal enhancement, presence of both septal, and solid enhancement pattern in CECT. Conclusion: Based on the results of the present study, multi-detector contrast-enhanced CT could be a rapid non-invasive and cost-effective step in the evaluation of ovarian masses.