Contrast-enhanced Computed Tomography Imaging Research Articles

An Investigative Analysis of Therapeutic Strategies in Hepatocellular Carcinoma: A Raetrospective Examination of 23 Biopsy-Confirmed Cases Emphasizing the Significance of Histopathological Insights

Background: The Liver Imaging Reporting and Data System (LI-RADS) combines standardized terminology with a classification system for imaging findings in patients with HCC, therefore rendering diagnostic biopsy unnecessary in many cases. This retrospective study included 23 patients with a biopsy diagnosis of HCC, performed either before or after local interventional procedures, in order to evaluate the histopathologic changes induced by previous procedures and their potential influence on the response to immune therapy. Material and Methods: The study encompassed a cohort of patients diagnosed with Hepatocellular Carcinoma (HCC). Diagnosis was established via contrast-enhanced computer tomography or magnetic resonance imaging that identified LI-RADS-5 nodules in conjunction with historical liver disease and elevated alpha-fetoprotein (AFP) levels or via histological examination confirming positivity for glypican3, heat shock protein 70, and glutamine synthetase. The study detailed the liver disease etiology, LI-RADS scores, characteristics and dimensions of HCC nodules, serum AFP concentrations, Edmondson–Steiner grading, and the expression of programmed cell death ligand 1 (PD-L1) in the tumor cells. Results: Among the study’s cohort of Hepatocellular Carcinoma (HCC) patients, a portion had not received any prior treatments, while the remainder experienced local HCC recurrence following trans-arterial chemoembolization or radiofrequency ablation. Observations indicated elevated alpha-fetoprotein (AFP) levels in those who had not undergone any previous interventions, showing statistical significance. The Edmondson–Steiner classification predominantly identified grade III differentiation across patients, irrespective of their treatment history. Furthermore, an increase in intra-tumoral programmed cell death ligand 1 (PD-L1) expression was noted in patients who had not been subjected to previous therapies. Conclusion: Liver biopsy offers valuable insights for patients with Hepatocellular Carcinoma (HCC), assisting in the tailoring of immune therapy strategies, particularly in cases of recurrence following prior local interventions.

Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study.

To develop and compare machine learning models based on triphasic contrast-enhanced CT (CECT) for distinguishing between benign and malignant renal tumors. In total, 427 patients were enrolled from two medical centers: Center 1 (serving as the training set) and Center 2 (serving as the external validation set). First, 1781 radiomic features were individually extracted from corticomedullary phase (CP), nephrographic phase (NP), and excretory phase (EP) CECT images, after which 10 features were selected by the minimum redundancymaximum relevance method. Second, random forest (RF) models were constructed from single-phase features (CP, NP, and EP) as well as from the combination of features from all three phases (TP). Third, the RF models were assessed in the training and external validation sets. Finally, the internal prediction mechanisms of the models were explained by the SHapley Additive exPlanations (SHAP) approach. A total of 266 patients with renal tumors from Center 1 and 161 patients from Center 2 were included. In the training set, the AUCs of the RF models constructed from the CP, NP, EP, and TP features were 0.886, 0.912, 0.930, and 0.944, respectively. In the external validation set, the models achieved AUCs of 0.860, 0.821, 0.921, and 0.908, respectively. The "original_shape_Flatness" feature played the most important role in the prediction outcome for the RF model based on EP features according to the SHAP method. The four RF models efficiently differentiated benign from malignant solid renal tumors, with the EP feature-based RF model displaying the best performance.

Diagnostic utility of contrast-enhanced computed tomography for ectopic pregnancy.

Contrast-enhanced computed tomography (CECT) is an emergent diagnostic imaging modality to identify the bleeding site and survey the abdominal cavity. The diagnostic utility of CECT for ectopic pregnancy (EP) has not been well-investigated. The objective of this study was to evaluate the characteristics of CECT findings in patients with EP and extract specific findings that could contribute to the identification of implantation sites. We conducted a retrospective study, reviewing suspected EP cases between April 2015 and March 2018 in our hospital. Clinical symptoms, blood test results, transvaginal sonography findings, and surgical and pathologic findings from the medical records were assessed. CECT images were evaluated by a certified radiologist and gynecologist retrospectively in consensus. The following were selected as positive findings for specific determination of the ectopic implantation site: the ectopic gestational sac, lateralization of the hemoperitoneum around the adnexa on either side, and extravascular leakage of the contrast agent outside the uterine cavity. CECT was performed in 41 women with an EP. The ectopic implantation site was detectable on CECT in 90.2% (37/41), whereas it was noted in 70.0% (32/41) on transvaginal ultrasonography (TVS). Of nine patients with an EP with an undetectable implantation site on TVS, six were positive for the specific determination of the ectopic implantation site on CECT. CECT has the potential to predict ectopic implantation sites with high-level sensitivity. As CECT is an urgent diagnostic imaging tool to be used in an emergent setting, it may be a good option for EP diagnosis when the availability of magnetic resonance imaging is limited.

Dual-energy CT in acute cholecystitis- features predicting culture-positive bile and outcome

PurposeLow mono-energetic CT has been shown to improve visualization of acute abdominal inflammatory processes. We aimed to determine its utility in patients with acute cholecystitis and potential added value in clinical decision making. MethodsSixty-seven consecutive patients with radiological signs of cholecystitis on contrast-enhanced dual-layer CT imaging were retrospectively identified over a four-year period (2/17–8/21). A ranked Likert scale was created for imaging findings present in acute cholecystitis, including gallbladder mucosal integrity and enhancement and pericholecystic liver parenchymal enhancement. These rankings were correlated with laboratory data, followed by sensitivity, specificity, and odds-ratios calculations. ResultsMucosal integrity and pericholecystic liver enhancement were better seen on low-energetic images by unanimous consensus. Presence of pericholecystic liver enhancement and poorer mucosal wall integrity correlated with positive bile cultures (sensitivity: 93.8 % and 96.9 %, specificity: 37.5 and 50.0 %; odds-ratio: 9.0[1.1–68.1 95 %CI] and 31.0 [2.7–350.7 95 %CI], p = 0.017 and p ≤ 0.001) in patients undergoing cholecystostomy (n = 40/67). Moreover, binary regression modeling showed that the strongest predictor variable for bile culture positivity was the score for pericholecystic liver enhancement (Exp(B) = 0.6, P = 0.022). By contrast, other laboratory markers and other imaging findings (such as GB wall thickness) showed lower sensitivities (76–82 %), specificities (16–21 %) and odds ratios (0.2–4.4) for the prediction of infected bile. ConclusionsPericholecystic liver enhancement and gallbladder wall integrity are better visualized on low-DECT images. These findings also potentially predict bile culture positivity in patients with cholecystitis, which may influence clinical management including the need for intervention.

Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images.

Surgery planning for liver tumour is carried out using contrast enhanced computed tomography (CECT) images to determine the optimal resection strategy and to assess the volume of liver and tumour. Current surgery planning tools interpret even the functioning liver cells present within the tumour boundary as tumour. Plain CT images provide inadequate information for treatment planning. This work attempts to address two shortcomings of existing surgery planning tools: (i) to delineate functioning liver cells from the non-functioning tumourous tissues within the tumour boundary and (ii) to provide 3D visualization and actual tumour volume from the plain CT images. All slices of plain CT images containing liver are enhanced by means of fuzzy histogram equalization in Non-Subsampled Contourlet Transform (NSCT) domain prior to 3D reconstruction to clearly delineate liver, non-functioning tumourous tissues and functioning liver cells within the tumour boundary. The 3D analysis from plain and CECT images was carried out on five types of liver lesions viz. HCC, metastasis, hemangioma, cyst, and abscess along with normal liver. The study resulted in clear delineation of functional liver tissues from non-functioning tumourous tissues within the tumour boundary from CECT as well as plain CT images. The volume of liver calculated using the proposed approach is found comparable with that obtained using Myrian-XP, a currently followed surgery planning tool in clinical practice. The obtained results from plain CT images will undoubtedly provide valuable diagnostic assistance and surgery planning even for the subset of patients for whom CECT acquisition is not advisable.

Risk of Cephalic Vein Injury During the Creation of an Anterior Portal in Shoulder Arthroscopy.

There is a risk of cephalic vein injury during shoulder arthroscopy. However, limited data regarding its anatomic course are available. To analyze the positional relationship and factors affecting the distance between the coracoid tip and cephalic veins. Case series; Level of evidence, 4. A total of 80 contrast-enhanced computed tomography images from 80 patients (mean age, 49.6 ± 20.3 years; 61 men) were retrospectively analyzed. The distance between the center of the coracoid tip and the vertical line through the cephalic vein was measured in the axial (D1) and sagittal (D2) planes. The distance between 1 cm lateral to the center of the coracoid tip and the vertical line through the cephalic vein was measured in the sagittal plane (D3). Each distance was compared according to patient sex and laterality. Associations between each distance and the patient's age, height, weight, and body mass index were investigated. The mean D1 was 18.4 ± 7.3 mm in 59 patients. The mean D2 was 23.4 ± 11.6 mm, and it was within 10 mm in 10 patients (12.5%). The mean D3 was 33.7 ± 12.2 mm. There was no significant difference in D1, D2, and D3 according to patient sex or laterality. A positive correlation was observed only between D3 and patient height (r = 0.320; P = .034). The cephalic vein was found to travel a mean of 23.4 mm distal and 33.7 mm distal to 1 cm lateral to the coracoid tip. Therefore, Care should be taken to avoid cephalic vein injury when creating an anterior inferior portal or 5-o'clock portal around these areas.

Predicting response of hepatoblastoma primary lesions to neoadjuvant chemotherapy through contrast-enhanced computed tomography radiomics

ObjectiveTo investigate the clinical value of contrast-enhanced computed tomography (CECT) radiomics for predicting the response of primary lesions to neoadjuvant chemotherapy in hepatoblastoma.MethodsClinical and CECT imaging data were retrospectively collected from 116 children with hepatoblastoma who received neoadjuvant chemotherapy. Tumor response was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST). Subsequently, they were randomly stratified into a training cohort and a test cohort in a 7:3 ratio. The clinical model was constructed using univariate and multivariate logistic regression, while the radiomics model was developed based on selected radiomics features employing the support vector machine algorithm. The combined clinical–radiomics model incorporated both clinical and radiomics features.ResultsThe area under the curve (AUC) for the clinical, radiomics, and combined models was 0.704 (95% CI: 0.563–0.845), 0.830 (95% CI: 0.704–0.959), and 0.874 (95% CI: 0.768–0.981) in the training cohort, respectively. In the validation cohort, the combined model achieved the highest mean AUC of 0.830 (95% CI 0.616–0.999), with a sensitivity, specificity, accuracy, precision, and f1 score of 72.0%, 81.1%, 78.5%, 57.2%, and 63.5%, respectively.ConclusionCECT radiomics has the potential to predict primary lesion response to neoadjuvant chemotherapy in hepatoblastoma.

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

Unusual presentation of PD-1 inhibitors in people living with HIV with advanced gastric cancer: Case report.

This paper seeks to determine the effect of combination anti-PD-1 and antiretroviral therapy (ART) on people living with HIV (PLWH) with advanced gastric cancer. In our case, a PLWH with recurrent locally advanced gastric cancer was treated with anti-PD-1 inhibitor and ART. A significant reduction in tumor lesions (as demonstrated by contrast-enhanced CT imaging) and a better quality of life were achieved following treatment. There have been limited studies on the treatment of PLWH with advanced gastric cancer. Chemotherapy is most often used, however, with unsatisfactory outcomes. to date, there have been no published reports on the use of PD-1 inhibitors in PLWH with advanced gastric cancer. Our report provides a valuable reference for future management of such patients.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85). CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study

BackgroundCombining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.MethodsA total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.ResultsThe comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.ConclusionsThe novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.Trial registration: Not applicable.

Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

A cycle generative adversarial network for generating synthetic contrast-enhanced computed tomographic images from non-contrast images in the internal jugular lymph node-bearing area.

The objectives of this study were to create a mutual conversion system between contrast-enhanced computed tomography (CECT) and non-CECT images using a cycle generative adversarial network (cycleGAN) for the internal jugular region. Image patches were cropped from CT images in 25 patients who underwent both CECT and non-CECT imaging. Using a cycleGAN, synthetic CECT and non-CECT images were generated from original non-CECT and CECT images, respectively. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were calculated. Visual Turing tests were used to determine whether oral and maxillofacial radiologists could tell the difference between synthetic versus original images, and receiver operating characteristic (ROC) analyses were used to assess the radiologists' performances in discriminating lymph nodes from blood vessels. The PSNR of non-CECT images was higher than that of CECT images, while the SSIM was higher in CECT images. The Visual Turing test showed a higher perceptual quality in CECT images. The area under the ROC curve showed almost perfect performances in synthetic as well as original CECT images. In conclusion, synthetic CECT images created by cycleGAN appeared to have the potential to provide effective information in patients who could not receive contrast enhancement.

A histological and diceCT-derived 3D reconstruction of the avian visual thalamofugal pathway

Amniotes feature two principal visual processing systems: the tectofugal and thalamofugal pathways. In most mammals, the thalamofugal pathway predominates, routing retinal afferents through the dorsolateral geniculate complex to the visual cortex. In most birds, the thalamofugal pathway often plays the lesser role with retinal afferents projecting to the principal optic thalami, a complex of several nuclei that resides in the dorsal thalamus. This thalamic complex sends projections to a forebrain structure called the Wulst, the terminus of the thalamofugal visual system. The thalamofugal pathway in birds serves many functions such as pattern discrimination, spatial memory, and navigation/migration. A comprehensive analysis of avian species has unveiled diverse subdivisions within the thalamic and forebrain structures, contingent on species, age, and techniques utilized. In this study, we documented the thalamofugal system in three dimensions by integrating histological and contrast-enhanced computed tomography imaging of the avian brain. Sections of two-week-old chick brains were cut in either coronal, sagittal, or horizontal planes and stained with Nissl and either Gallyas silver or Luxol Fast Blue. The thalamic principal optic complex and pallial Wulst were subdivided on the basis of cell and fiber density. Additionally, we utilized the technique of diffusible iodine-based contrast-enhanced computed tomography (diceCT) on a 5-week-old chick brain, and right eyeball. By merging diceCT data, stained histological sections, and information from the existing literature, a comprehensive three-dimensional model of the avian thalamofugal pathway was constructed. The use of a 3D model provides a clearer understanding of the structural and spatial organization of the thalamofugal system. The ability to integrate histochemical sections with diceCT 3D modeling is critical to better understanding the anatomical and physiologic organization of complex pathways such as the thalamofugal visual system.

Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images

Background and objectiveLiver tumor segmentation (LiTS) accuracy on contrast-enhanced computed tomography (CECT) images is higher than that on non-contrast computed tomography (NCCT) images. However, CECT requires contrast medium and repeated scans to obtain multiphase enhanced CT images, which is time-consuming and cost-increasing. Therefore, despite the lower accuracy of LiTS on NCCT images, which still plays an irreplaceable role in some clinical settings, such as guided brachytherapy, ablation, or evaluation of patients with renal function damage. In this study, we intend to generate enhanced high-contrast pseudo-color CT (PCCT) images to improve the accuracy of LiTS and RECIST diameter measurement on NCCT images. MethodsTo generate high-contrast CT liver tumor region images, an intensity-based tumor conspicuity enhancement (ITCE) model was first developed. In the ITCE model, a pseudo color conversion function from an intensity distribution of the tumor was established, and it was applied in NCCT to generate enhanced PCCT images. Additionally, we design a tumor conspicuity enhancement-based liver tumor segmentation (TCELiTS) model, which was applied to improve the segmentation of liver tumors on NCCT images. The TCELiTS model consists of three components: an image enhancement module based on the ITCE model, a segmentation module based on a deep convolutional neural network, and an attention loss module based on restricted activation. Segmentation performance was analyzed using the Dice similarity coefficient (DSC), sensitivity, specificity, and RECIST diameter error. ResultsTo develop the deep learning model, 100 patients with histopathologically confirmed liver tumors (hepatocellular carcinoma, 64 patients; hepatic hemangioma, 36 patients) were randomly divided into a training set (75 patients) and an independent test set (25 patients). Compared with existing tumor automatic segmentation networks trained on CECT images (U-Net, nnU-Net, DeepLab-V3, Modified U-Net), the DSCs achieved on the enhanced PCCT images are both improved compared with those on NCCT images. We observe improvements of 0.696–0.713, 0.715 to 0.776, 0.748 to 0.788, and 0.733 to 0.799 in U-Net, nnU-Net, DeepLab-V3, and Modified U-Net, respectively, in terms of DSC values. In addition, an observer study including 5 doctors was conducted to compare the segmentation performance of enhanced PCCT images with that of NCCT images and showed that enhanced PCCT images are more advantageous for doctors to segment tumor regions. The results showed an accuracy improvement of approximately 3%–6%, but the time required to segment a single CT image was reduced by approximately 50 %. ConclusionsExperimental results show that the ITCE model can generate high-contrast enhanced PCCT images, especially in liver regions, and the TCELiTS model can improve LiTS accuracy in NCCT images.

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging.

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

Anatomical variations in the superficial venous system of the neck: an image-based study using contrast-enhanced computed tomography.

The superficial venous system (SVS) of the neck receives blood from the face and oral cavity. The SVS comprises the anterior jugular vein (AJV), external jugular vein (EJV), and facial vein (FV). Comprehensive knowledge of the normal anatomy and potential variations in the venous system is valuable in surgical and radiological procedures. This study aimed to update the anatomic knowledge of the SVS using a radiographic approach, which is a beneficial data source in clinical practice. Contrast-enhanced computed tomography images of the neck of patients with head and neck cancer treated between 2017 and 2020 were retrospectively evaluated. Each side of the neck was counted separately. A total of 302 necks of 151 patients were enrolled in this study. The medial AJV was absent in 49.7% (75/151) of the patients on the left side, which was significantly greater than the 19.2% (29/151) on the right (p < 0.001). The left AJV drained into the right venous system in 6.6% (10/151) of the necks. In 48.3% (146/302) of the necks, the FV did not flow into the internal jugular vein but rather into the EJV or AJV; these findings were significantly more frequent than those reported in previous studies. The diameters of the veins were significantly larger when they received blood from the FV than when they were not connected to the FV. These findings indicate that the AJV has a rightward preference during its course. The course of the FV is diverse and affects the diameter of connected veins.

Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features

PurposeTo evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models. MethodsThis study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on “real-world” radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with “real-world” radiomic training data to evaluate their impact on the predictive model's performance. ResultsA prediction model was generated using only “real-world” radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to “real-world” data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so. ConclusionSynthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.

Inferior Pancreaticoduodenal Artery Pseudoaneurysm Causing Biliary Obstruction: A Case Report

Introduction: Visceral arterial aneurysms and pseudoaneurysms are rare but dangerous pathologies, with reported incidence of 0.01–0.2% of the worldwide population, as found on autopsy. Pancreaticoduodenal artery pathology accounts for approximately 2% of all visceral aneurysms; it is commonly caused by chronic inflammatory processes, such as pancreatitis or adjacent pseudocysts. Morbidity and mortality commonly result from rupture of the aneurysm itself, leading to life-threatening hemorrhage into the peritoneum or gastrointestinal tract.Case Report: Here we present the case of a 64-year-old male patient with previous history of alcohol use disorder leading to chronic pancreatitis and prior embolization of an inferior pancreaticoduodenal pseudoaneurysm, who presented to the emergency department (ED) with abdominal pain, nausea, and vomiting, and was found to have a large recurrent inferior pancreaticoduodenal pseudoaneurysm with associated obstructive cholangitis and pancreatitis via contrast-enhanced computed tomography (CT) of the abdomen and pelvis. The patient was managed emergently by interventional radiology angiography with embolic coiling and percutaneous biliary catheter placement, and he subsequently underwent biliary duct stenting with gastroenterology. The patient was successfully discharged after a brief hospitalization after resolution of his pancreatitis and associated hyperbilirubinemia.Conclusion: Pancreaticoduodenal artery aneurysms and pseudoaneurysms are rare and dangerous visceral pathologies. Patients can be diagnosed rapidly in the ED with CT imaging and needurgent endovascular management to prevent morbidity and mortality.

Abstract 3392: Generation and characterization of transgenic oncopig model for lung cancer

Abstract Lung cancer (LC) is the leading cause of cancer-associated death in the United States. Preclinical findings in small animal models do not translate into human clinics. There is an urgent need to develop relevant preclinical large animal models for LC. We describe a novel method to induce LC in transgenic pigs carrying Cre-inducible KrasG12D and TrP53R167H mutations (Oncopigs). The Oncopigs (n=12; 9 females, and 3 males) at 9 weeks of age were anesthetized and adenovirus (1 × 1011 PFU) carrying Cre recombinase gene (Ad-Cre) ± polybrene (1:100) ± IL-8 (5 ng/ml) was injected via flexible bronchoscopy with two techniques: (1) Bronchial lavage (n=6) and (2) Transbronchial needle injection (n=6) into different lung lobes. As controls, a combination of polybrene ± IL-8 was injected without AdCre into the contralateral lobes in the same pigs (n=8). Oncopigs were monitored via clinical monitoring, blood counts, basic metabolic panel, and contrast-enhanced computed tomography (CT) imaging for LC progression and metastasis at 3-, 4-, 7-, 10-, 16-, and 26 weeks post-intervention. All animals tolerated the injections with expected weight gains. Blood counts and metabolic panels remained normal during LC progression. At the site of injection/lavage, lung masses were detected on CT imaging at 3 weeks post-injections. However, a decrease in masses was observed on CT at 7 weeks post-intervention. The decrease in size of the lung masses coincided with increasing attenuation. No distant metastases were observed on CT imaging. Lavage-based lesions appeared to be more heterogeneous with surrounding inflammatory changes. Transbronchial-injection-based lesions appeared more consolidated and coin-shaped without surrounding inflammatory changes. Control injection sites did not show any mass growth on CT imaging. Immunohistochemistry analysis on lung tumors revealed increased pancytokeratin, trichrome, E-cad, Ki-67, IBA, and CD3 expression. Further, immunofluorescence revealed decreased E-Cad, CK-19, and Zo-1 expression, whereas increased expression of N-Cad, FN1, snail, CD44, α-SMA, and γ-catenin, was observed in the LC and metastatic tissue compared to normal. We successfully generated and characterized a novel transgenic Oncopig LC model. There is robust tumor production status post AdCre delivery. One of four Oncopigs had carcinomatous metastatic skin growth. The remaining 7 Oncopigs are monitored longitudinally for tumor growth and development of metastasis. Citation Format: Kirtan Joshi, Nagabhishek Sirpu, Amanda Schmelzle, Ravikanth Poonooru, Benjamin Nelson, Pradeep Subramanyam, Colleen Garrett, Mariana Sponchiado, Sarah Schlink, Samantha Gerb, Jesse Porter, Dae Kim, Jeffrey Kunin, Jeffrey Bryan, Timothy Hoffman, Bhanu Telugu, Jussuf Kaifi, Satyanarayana Rachagani. Generation and characterization of transgenic oncopig model for lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3392.