Conventional and spectral- CT for renal cell carcinoma thermal ablation: A case report.

Dual-energy or spectral computed tomography (CT) information may be obtained by either sending X-ray beams of different energy spectra through the patient or by discriminating the energy of the X-rays that reach the detector. The spectral signal is then used to generate multiple results: conventional, virtual monoenergetic (MonoE), effective atomic number, electron density, and other material specific (e.g., iodine, calcium, or uric acid). This report demonstrates the potential benefits of spectral CT imaging during percutaneous tumor ablation procedures, specifically regarding visualization of inconspicuous tumors, accurate probe placement, and assessment of treatment efficacy.

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

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

Acute Coronary Syndrome and Acute Abdomen Suspected for Type B Aortic Dissection in an Elderly Woman.

Being an uncommon and challenging disorder, acute aortic dissection (AAD) can have fatal outcomes in the event of missed diagnosis or treatment delay. AAD could easily be misdiagnosed, as symptoms usually mimic other common clinical syndromes showing up in Accident and Emergency (A&E), including acute coronary syndrome (ACS), pericarditis, pulmonary embolism, acute abdomen, musculoskeletal pain, as well as presenting as heart failure, stroke, syncope, and absent peripheral pulses. We present a case of a 77-year-old female who presented to the medical decision unit with acute-onset chest, back, and abdominal pain that occurred on standing for six hours She was thought initially to have acute coronary syndrome based on electrocardiography (ECG) changes, troponin, a normal chest X-ray, and no blood pressure discrepancies in upper extremities. Due to worsening abdominal pain and a previous history of a perforated diverticulum, contrast computed tomography (CT) of the abdomen was arranged and this showed acute type B aortic dissection. By the time the CT was performed, the patient had been in hospital for 16 hours, almost 22 hours from the onset of pain.

Enhancement Patterns in Differentiated and Undifferentiated Gastric Carcinoma: Multiphasic Contrast-Enhanced Computed Tomography Versus Histopathology.

Background Gastric adenocarcinoma (GCA) poses a significant global health burden due to its prevalence and high morbidity and mortality rates. GCA is classified into three main histological types: well-differentiated (intestinal type), poorly differentiated (diffuse type), and mixed or indeterminate forms. These types vary in causes, epidemiology, and genetics, with the diffuse type often associated with the worst prognosis. Endoscopic biopsy is the primary method for characterization, but it has its limitations. There is potential in using contrast-enhanced computed tomography (CT) to differentiate between histological subtypes of gastric adenocarcinoma, which could aid subtype differentiation. Building on this, our study aims to assess CT's effectiveness in distinguishing between broad histological groups of gastric adenocarcinoma based on enhancement patterns, contributing to improved diagnostic accuracy Objective Our research focuses on evaluating the effectiveness of multiphasic contrast-enhanced computed tomography (CECT) in distinguishing between the three broad histopathological subtypes of gastrointestinal cancers. Methods This study was a prospective, analytical observational study that was approved and carried out in our institutional tertiary care hospital. Consecutive individuals who had undergone endoscopic-guided biopsy and demonstrated histological evidence of GCA were taken into consideration for participation in the study. In order to complete the clinical staging process, further multiphasic CT scans were carried out on each of the fifty patients and were categorised accordingly based on the findings of histopathology. Results In the differentiated type, segmental distribution was: 5.5% upper segment, 16.7% middle segment, 66.7% lower segment, and 11.1% diffuse type. Esophageal involvement was 5.6%, duodenal involvement was similar, and lymph node involvement was approximately 38.8%. TNM staging: 38.8% IIIB, 22.2% III, 27.8% IVA, and 11.1% IVB. In the undifferentiated type, segmental distribution: 6.2% upper segment, 31.2% middle segment, 50.0% lower segment, and 12.5% diffuse type. Esophageal involvement was around 6.25%, duodenal involvement was 18.75%, and lymph node involvement was about 71.8%. TNM staging: 34.4% IIIB, 21.8% III, 28.1% IVA, and 15.6% IVB. Conclusion Multiphasic CT evaluations provide valuable insights into the prognostic aspects of gastric carcinomas by assessing peak enhancement. Differentiated tumors typically exhibit arterial phase enhancement, while undifferentiated tumors show venous phase enhancement, reflecting their microvascular architecture. Recent studies emphasize the importance of understanding gastric carcinoma characteristics for diagnosis and prognosis. Our research aligns with this, revealing distinct contrast enhancement patterns between differentiated and undifferentiated types. However, discrepancies in histological classifications and contrast enhancement patterns across studies warrant further investigation. Integrating histopathological and radiological insights is essential for accurate diagnosis and treatment planning.

Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Diagnostic performance and clinical impact of 18F-AlF-NOTA-octreotide in a large cohort of patients with neuroendocrine neoplasms: A prospective single-center study.

Purpose: Somatostatin receptor imaging with 18F-AlF-NOTA-octreotide (18F-AlF-OC) has shown promising performance in neuroendocrine neoplasms (NENs). In this study, we aim to investigate the diagnostic performance and clinical impact of 18F-AlF-OC in a large prospective cohort of patients with NEN. Methods: Between January 2023 and November 2023, a total of 219 patients with confirmed or suspected NEN were enrolled prospectively and underwent 18F-AlF-OC PET/CT at 2 h post-injection. The primary endpoint was the diagnostic performance, including sensitivity, specificity, and accuracy. An additional primary endpoint was the impact of 18F-AlF-OC on clinical management. The reference standard was based on the results of histopathology or radiological follow-up. Results: 205 patients were included in the final analysis. The patient-level sensitivity, specificity, and accuracy of 18F-AlF-OC PET/CT compared with contrast-enhanced CT/MRI were 90.5% vs. 81.8%, 93.1% vs. 71.1%, and 91.2% vs. 79.4%, respectively. 26 patients had tiny gastrointestinal NENs (smaller than 1 cm in diameter). The patient-based sensitivity of 18F-AlF-OC PET/CT and contrast-enhanced CT/MRI were 61.5% (16/26) and 37.5% (9/24), respectively. The smallest diameter of gastrointestinal NEN detected by 18F-AlF-OC PET/CT was 0.6 cm in the rectum, 0.3 cm in the stomach, and 0.5 cm in the duodenum. 18F-AlF-OC PET/CT results led to changes in clinical management in 19.5% of patients (40/205), owing mainly to new or unexpected findings compared to contrast-enhanced CT/MRI. Conclusion: 18F-AlF-OC PET/CT demonstrated great diagnostic performance in patients with NEN, particularly for detecting tiny gastrointestinal NEN. Furthermore, 18F-AlF-OC PET/CT impacted the therapeutic management in 19.5% of patients. Our results further validate the role of 18F-AlF-OC as a somatostatin receptor imaging tracer in clinical practice.

Superiority of 18F-FAPI-42 PET/CT in the detection of primary tumor and management of appendiceal neoplasm to 18F-FDG PET/CT and CE-CT.

BACKGROUND: In the present study, we investigated the value of 18F-fibroblast-activation protein inhibitor (FAPI) positron emission tomography/computed tomography (18F-FAPI-42 PET/CT) to preoperative evaluations of appendiceal neoplasms and management for patients. METHODS: This single-center retrospective clinical study, including 16 untreated and 6 treated patients, was performed from January 2022 to May 2023 at Southern Medical University Nanfang Hospital. Histopathologic examination and imaging follow-up served as the reference standard. 18F-FAPI-42 PET/CT was compared to 18F-fluorodeoxyglucose (18F-FDG) PET/CT and contrast-enhanced CT (CE-CT) in terms of maximal standardized uptake value (SUVmax), diagnostic efficacy and impact on treatment decisions. RESULTS: The accurate detection of primary tumors and peritoneal metastases were improved from 28.6% (4/14) and 50% (8/16) for CE-CT, and 43.8% (7/16) and 85.0% (17/20) for 18F-FDG PET/CT, to 87.5% (14/16) and 100% (20/20) for 18F-FAPI-42 PET/CT. Compared to 18F-FDG PET/CT, 18F-FAPI-42 PET/CT detected more regions infiltrated by peritoneal metastases (108 vs. 43), thus produced a higher peritoneal cancer index (PCI) score (median PCI: 12 vs. 5, P < 0.01). 18F-FAPI-42 PET/CT changed the intended treatment plans in 35.7% (5/14) of patients compared to CE-CT and 25% (4/16) of patients compared to 18F-FDG PET/CT but did not improve the management of patients with recurrent tumors. CONCLUSIONS: The present study revealed that 18F-FAPI-42 PET/CT can supplement CE-CT and 18F-FDG PET/CT to provide a more accurate detection of appendiceal neoplasms and improved treatment decision making for patients.

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. PURPOSE: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. METHODS: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics. RESULTS: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent. CONCLUSIONS: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.

A Case of Pheochromocytoma With Coagulation Necrosis Due to Hypertensive Crisis Aggravated by Contrast-Enhanced CT Scan and Negative 123I-Metaiodobenzylguanidine (MIBG) Scintigraphy.

123I-metaiodobenzylguanidine (123I-MIBG) scintigraphy is a highly sensitive and specific imaging test for the diagnosis of pheochromocytoma. Typical pheochromocytomas are positive on 123I-MIBG scintigraphy; however, cases of paragangliomas eliciting negative results have been reported. We encountered a case of hypertensive crisis resulting in extensive coagulative necrosis of a pheochromocytoma and negative findings on 123I-MIBG scintigraphy. A 50-year-old Japanese female presented with an acute onset of vomiting, epigastralgia, and abdominal pain. Immediately after contrast-enhanced CT, the patient developed respiratory failure and was intubated. The CT scan revealed a 5-cm left adrenal mass, and a pheochromocytoma crisis was suspected. The patient's condition stabilized following phentolamine administration. Regarding the assessment for pheochromocytoma, plasma metanephrine levels were not markedly increased, and 123I-MIBG scintigraphy was negative. However, a histological examination of the left adrenal mass revealed extensive coagulative necrosis of the entire adrenal mass, comprising trabecular and alveolar growth of large polygonal cells that were immunopositive for chromogranin A/synaptophysin, thereby suggesting a diagnosis of pheochromocytoma. There have been three reported cases of 123I-MIBG scintigraphy-negative pheochromocytomas because of pure avascular necrosis without hemorrhage or rupture. To the best of our knowledge, this is the first reported case of massive tumor necrosis due to hypertensive crisis exacerbated after contrast-enhanced CT imaging. In conclusion, pheochromocytoma cannot be ruled out even with negative findings on 123I-MIBG scintigraphy. Accordingly, clinical judgment must be made based on a comprehensive assessment of the clinical course and pathological diagnosis, especially for cases involving a hypertensive crisis.

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.

Rim Enhancement on Contrast-Enhanced CT as a Predictor of Prognosis in Patients with Pancreatic Ductal Adenocarcinoma.

This study investigated the utility of imaging features, such as rim enhancement on contrast-enhanced CT (CECT), in predicting the prognosis of pancreatic ductal adenocarcinoma (PDAC). This retrospective study included 158 patients (84 men; mean age, 68 years) with pathologically confirmed PDAC. The following imaging features were evaluated on CECT by two radiologists: tumor size, tumor attenuation, and the presence of rim enhancement. Cox proportional hazards analysis was performed to identify the imaging and clinicopathological features for predicting disease-free survival (DFS) and overall survival (OS). Pathological features were compared with the presence of rim enhancement. Among the 158 patients, 106 (67%) underwent curative surgery (surgery group) and 52 (33%) received conservative treatment (non-surgery group). Rim enhancement was observed more frequently in the non-surgery group than in the surgery group (44% vs. 20%; p < 0.001). Rim enhancement showed significant associations with shorter DFS and OS in the surgery group (hazard ratios (HRs), 3.03 and 2.99; p < 0.001 and p = 0.003, respectively), whereas tumor size showed significant associations with shorter OS (HR per 1 mm increase, 1.08; p < 0.001). PDACs with rim enhancement showed significant associations with higher histological tumor grades (p < 0.001). PDAC with rim enhancement on CECT could predict poorer prognosis and more aggressive tumor grades.

Additional Non-contrast CT to Portal Venous Phase is not Relevant for Patients referred for Colonic Diverticulitis or Sigmoiditis Suspicion.

OBJECTIVE: To evaluate the usefulness of unenhanced CT added to the portal venous phase in the diagnostic accuracy of acute colonic diverticulitis/sigmoiditis. METHODS: Between January 1st and December 31st, 2020, all consecutive adult patients referred to the radiology department for clinical suspicion of acute colonic diverticulitis/sigmoiditis were retrospectively screened. To be included, patients must have undergone a CT with both unenhanced (UCT) and contrast-enhanced portal venous phase CT (CECT). CT examinations were assessed for features of diverticulitis, complications, differential diagnosis and incidental findings using UCT + CECT association, medical management, and follow-up as the reference. Radiation doses were recorded on our image archiving system and assessed. RESULTS: Of the 114 patients included (mean age was 67±18 years; 60% were female), 46 had acute colonic diverticulitis/sigmoiditis. No diagnosis of sigmoiditis/diverticulitis, complication or differential diagnosis was missed with the CECT alone. Apart from diverticulitis, only one 2 mm meatal urinary microlithiasis was missed with no impact on patient management. The confidence level in diagnosis was not increased by UCT. The average DLP of CECT was 450 mGy.cm, and 382 mGy.cm for UCT. The use of a single-phase CECT acquisition allowed a reduction of 45.9% of the irradiation. CONCLUSION: Unenhanced CT is not necessary for patients addressed with clinical suspicion of acute colonic diverticulitis/sigmoiditis, and CECT alone protocol must be used.

Prospective validation of the role of PET/CT in detecting disease after neoadjuvant chemotherapy in advanced ovarian cancer.

OBJECTIVES: The study aimed to compare the diagnostic accuracies of 2-[18F]FDG PET/CT and contrast-enhanced CT (ceCT) after neoadjuvant chemotherapy (NACT) in advanced ovarian cancer (OC). MATERIALS AND METHODS: This study consisted historical observational cohort and prospective validation cohort. Patients with newly diagnosed stage III-IV OC scheduled for NACT were recruited, with imaging performed after three to six cycles of NACT before interval debulking surgery. Nineteen regions in the abdominopelvic cavity were scored for the presence and absence of disease, referenced to the intra-operative findings or histological specimens. Diagnostic metrics were compared using McNemar's test. RESULTS: In the historical cohort (23 patients, age 58 ± 13), 2-[18F]FDG PET had an overall accuracy (Acc) 82%, sensitivity (Sen) 38%, specificity (Spe) 97%, positive predictive value (PPV) 79% and negative predictive value (NPV) 82%; ceCT had an overall Acc 86%, Sen 64%, Spe 93%, PPV 75% and NPV 89%. In the prospective cohort (46 patients, age 59 ± 9), 2-[18F] FDG PET had an overall Acc 87%, Sen 48%, Spe 98%, PPV 84% and NPV 88%; ceCT had an overall Acc 89%, Sen 66%, Spe 95%, PPV 77% and NPV 91%. No significant difference was demonstrated between the two imaging modalities (p > 0.05). High false-negative rates were observed in the right subdiaphragmatic space, omentum, bowel mesentery and serosa. High omental metabolic uptake after NACT was associated with histological non-responders (p < 0.05). CONCLUSION: 2-[18F]FDG PET/CT had no additional value over ceCT with comparable diagnostic accuracy in detecting disease after NACT in advanced OC. CLINICAL RELEVANCE STATEMENT: 2-[18F]FDG PET/CT is not superior to contrast-enhanced CT in determining disease after neoadjuvant chemotherapy in advanced ovarian cancer; contrast-enhanced CT should be suffice for surgical planning before interval debulking surgery. KEY POINTS: • Additional value of 2-[18F]FDG PET/CT over contrast-enhanced CT is undefined in detecting disease after neoadjuvant chemotherapy. • 2-[18F]FDG PET/CT has comparable diagnostic accuracy compared to contrast-enhanced CT. • Contrast-enhanced CT will be suffice for surgical planning after neoadjuvant chemotherapy.

Radiomic Analysis of Contrast-Enhanced CT Predicts Glypican 3-Positive Hepatocellular Carcinoma.

BACKGROUND: The Glypican 3 (GPC3)-positive expression in Hepatocellular Carcinoma (HCC) is associated with a worse prognosis. Moreover, GPC3 has emerged as an immunotherapeutic target in advanced unresectable HCC systemic therapy. It is significant to diagnose GPC3-positive HCCs before therapy. Regarding imaging diagnosis of HCC, dynamic contrast-enhanced CT is more common than MRI in many regions. OBJECTIVE: The aim of this study was to construct and validate a radiomics model based on contrast-enhanced CT to predict the GPC3 expression in hepatocellular carcinoma. METHODS: This retrospective study included 141 (training cohort: n = 100; validation cohort: n = 41) pathologically confirmed HCC patients. Radiomics features were extracted from the Artery Phase (AP) images of contrast-enhanced CT. Logistic regression with the Least Absolute Shrinkage and Selection Operator (LASSO) regularization was used to select features to construct radiomics score (Rad-score). A final combined model, including the Rad-score of the selected features and clinical risk factors, was established. Receiver Operating Characteristic (ROC) curve analysis, Delong test, and Decision Curve Analysis (DCA) were used to assess the predictive performance of the clinical and radiomics models. RESULTS: 5 features were selected to construct the AP radiomics model of contrast-enhanced CT. The radiomics model of AP from contrast-enhanced CT was superior to the clinical model of AFP in training cohorts (P < 0.001), but not superior to the clinical model in validation cohorts (P = 0.151). The combined model (AUC = 0.867 vs. 0.895), including AP Rad-score and serum Alpha-Fetoprotein (AFP) levels, improved the predictive performance more than the AFP model (AUC = 0.651 vs. 0.718) in the training and validation cohorts. The combined model, with a higher decision curve indicating more net benefit, exhibited a better predictive performance than the AP radiomics model. DCA revealed that at a range threshold probability approximately above 60%, the combined model added more net benefit compared to the AP radiomics model of contrastenhanced CT. CONCLUSION: A combined model including AP Rad-score and serum AFP levels based on contrast-enhanced CT could preoperatively predict GPC3-positive expression in HCC.

Ruptured Mycotic Aneurysm of the Superior Mesenteric Artery: A Case Report.

An infected (mycotic) aneurysm of the visceral arteries is an uncommon entity, which may arise from a secondary infection of a preexisting aneurysm or be due to degeneration from a primary infection. Mycotic aneurysms require prompt recognition and definitive treatment; otherwise, there can be devastating morbidity and mortality. We present the case of a 51-year-old female with HIV and Crohn's disease who presented with subacute abdominal pain, nausea, and vomiting and was found to have an ultimately fatal mycotic aneurysm of the superior mesenteric artery. In addition, we discuss the characteristic imaging features of mycotic aneurysms on computed tomography and magnetic resonance imaging.

Improving the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm using DenseNet.

INTRODUCTION: Thinner slices are more susceptible in detecting small lesions but suffer from higher statistical fluctuation. This work aimed to reduce image noise in multiphase contrast-enhanced CT reconstructed with slice thickness thinner than the clinical setting (i.e., 5 mm) using convolutional neural network (CNN) for enabling better detection of hypo-vascular liver metastasis. METHODS: A DenseNet model was used to generate noise map for multiphase CT reconstructed with slice thickness of 2.5 mm and 1.25 mm. Image denoising was conducted by subtracting the CNN-generated noise map from CT images with reduced photon flux due to thinner slice thickness. The performance of DenseNet was evaluated on CT scans of electron density phantoms and patients with hypovascular liver metastases less than 1.5 cm in terms of Hounsfield Unit (HU) variation, statistical fluctuation, and contrast-to-noise ratio (CNR). RESULTS: The phantom study demonstrated that the CNN-based denoising method was able to reduce statistical fluctuation in CT images reconstructed with slice thickness of 2.5 mm and 1.25 mm without causing significant edge blurring or variation in HU values. With regards to patient study, it was found that the denoised 2.5-mm and 1.25-mm slices had higher CNR than the conventional 5-mm slices for hypo-vascular liver metastases in all 4 phases of multiphase CT. CONCLUSION: Our results demonstrated that the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm could be improved by using the CNN-based denoising method. IMPLICATIONS FOR PRACTICE: Reconstruction slice thickness has a strong influence on the image quality of CT imaging. A CNN-based denoising method was used in this work to reduce the image noise in multiphase contrast-enhanced CT reconstructed with slice thickness less than 5 mm.

Optimizing image quality and minimizing radiation dose in pediatric abdominal multiphase contrast-enhanced computed tomography: a study on CARE kV and CARE Dose 4D.

Background: Multiphase contrast-enhanced computed tomography (CECT) is a commonly used modality in pediatric computed tomography (CT) scans. However, the purposes and focus of each phase, such as CT angiography (CTA), and parenchymal phase, are different. In routine practice, the same scanning parameters are used for all phases, resulting in unnecessary radiation exposure for children. Accurately and rapidly adjusting the scanning parameters for each phase of CECT is challenging in clinical settings. This retrospective cross-sectional study was designed to investigate the feasibility of using both CARE kV and CARE Dose 4D to reduce the radiation dose while maintaining diagnostic quality in multiphase CECT scans of children. Methods: Overall, 57 children (33 males and 24 females) who underwent multiphase abdominal CECT in Xinjiang Hospital of Beijing Children's Hospital with an average age of 6.52±4.30 years (range, 0.1-15 years), were enrolled. The tube voltage was automatically modulated using CARE kV. The tube current was automatically modulated using CARE Dose 4D. Different dose saving optimization indices (DI) were used for the three phases: a DI value of 3 was used for the unenhanced CT phase, a DI value of 12 was used for the CTA phase, and a DI value of 7 was used for the parenchymal phase. The tube voltage and volume CT dose index (CTDIvol) were recorded for each phase. Two reviewers subjectively evaluated the overall image quality and noise level of the three phases using a 5-point Likert scale (1-2 points: unqualified, 3 points: qualified, 4 points: better, 5 points: best). The CT and noise values of the descending aorta, liver, and back muscle were measured objectively. The voltage distribution and the image quality and CTDIvol in each phase were compared. Results: The most selected tube voltage in the unenhanced CT, CTA, and parenchymal phases was 100 kV (49/57, 85.96%), 70 kV (36/57, 63.16%), and 80 kV (32/57, 56.14%), respectively. The differences between the three phases were statistically significant (P<0.001). The CTDIvol values of the three phases were 3.99±1.99, 2.02±1.71, and 3.18±2.10 mGy, respectively, with a significant difference between the three phases (P<0.001). The CTDIvol decreased linearly as the DI value increased. All images met the diagnostic requirements. The overall quality scores for the three phases were 4.24±0.42, 4.41±0.49, and 4.50±0.45, respectively, with no significant linear relationship with the change in the DI. Conclusions: The combined use of CARE Dose 4D and CARE kV could effectively reduce the radiation dose in children during multiphase abdominal CECT without compromising the diagnostic image quality.

Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer.

Objectives: To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC). Methods: A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness. Results: In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction. Conclusion: CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.

Delayed presentation of congenital sigmoid colon stenosis: a rare entity.

Congenital sigmoid colon stenosis is a rare entity that can mimic Hirschsprung disease. Presentation of congenital colon stenosis is usually within first few weeks of life. Our case presented with features of distal bowel obstruction at 2 years of age with the history of chronic constipation and progressive abdominal distention from first week of life and bilious vomiting for the last 1 week. Clinical diagnosis of Hirschsprung disease was made. Contrast enhanced CT abdomen showed bowel obstruction with transition point at the level of proximal sigmoid colon. There was a short segment stenosis at the level of proximal sigmoid colon. Contrast enema showed stenosis at proximal sigmoid colon. The bowel distal to stenosis was normal in calibre. Similar findings were seen during surgery. Mesocolon was present in stenosed segment of the bowel. The resected stenotic segment showed adequate ganglion cells in histopathology.