Abdominal Computed Tomography Research Articles

Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation-Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model.

The purpose of this study is to assess segmentation reproducibility of artificial intelligence-based algorithm, TotalSegmentator, across 34 anatomical structures using multiphasic abdominal CT scans comparing unenhanced, arterial, and portal venous phases in the same patients. A total of 1252 multiphasic abdominal CT scans acquired at our institution between January 1, 2012, and December 31, 2022, were retrospectively included. TotalSegmentator was used to derive volumetric measurements of 34 abdominal organs and structures from the total of 3756 CT series. Reproducibility was evaluated across three contrast phases per CT and compared to two human readers and an independent nnU-Net trained on the BTCV dataset. Relative deviation in segmented volumes and absolute volume deviations (AVD) were reported. Volume deviation within 5% was considered reproducible. Thus, non-inferiority testing was conducted using a 5% margin. Twenty-nine out of 34 structures had volume deviations within 5% and were considered reproducible. Volume deviations for the adrenal glands, gallbladder, spleen, and duodenum were above 5%. Highest reproducibility was observed for bones (- 0.58% [95% CI: - 0.58, - 0.57]) and muscles (- 0.33% [- 0.35, - 0.32]). Among abdominal organs, volume deviation was 1.67% (1.60, 1.74). TotalSegmentator outperformed the reproducibility of the nnU-Net trained on the BTCV dataset with an AVD of 6.50% (6.41, 6.59) vs. 10.03% (9.86, 10.20; p < 0.0001), most notably in cases with pathologic findings. Similarly, TotalSegmentator's AVD between different contrast phases was superior compared to the interreader AVD for the same contrast phase (p = 0.036). TotalSegmentator demonstrated high intra-individual reproducibility for most abdominal structures in multiphasic abdominal CT scans. Although reproducibility was lower in pathologic cases, it outperforms both human readers and a nnU-Net trained on the BTCV dataset.

Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation.

Anasarca is a condition that results from organ dysfunctions, such as heart, kidney, or liver failure, characterized by the presence of edema throughout the body. The quantification of accumulated edema may have potential clinical benefits. This work focuses on accurately estimating the amount of edema non-invasively using abdominal CT scans, with minimal false positives. However, edema segmentation is challenging due to the complex appearance of edema and the lack of manually annotated volumes. We propose a weakly supervised approach for edema segmentation using initial edema labels from the current state-of-the-art method for edema segmentation (Intensity Prior), along with labels of surrounding tissues as anatomical priors. A multi-class 3D nnU-Net was employed as the segmentation network, and training was performed using an iterative annotation workflow. We evaluated segmentation accuracy on a test set of 25 patients with edema. The average Dice Similarity Coefficient of the proposed method was similar to Intensity Prior (61.5% vs. 61.7%; ). However, the proposed method reduced the average False Positive Rate significantly, from 1.8% to 1.1% ( ). Edema volumes computed using automated segmentation had a strong correlation with manual annotation ( ). Weakly supervised learning using 3D multi-class labels and iterative annotation is an efficient way to perform high-quality edema segmentation with minimal false positives. Automated edema segmentation can produce edema volume estimates that are highly correlated with manual annotation. The proposed approach is promising for clinical applications to monitor anasarca using estimated edema volumes.

Skeleton-guided 3D convolutional neural network for tubular structure segmentation.

Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network that incorporates skeleton information to enhance segmentation accuracy in these tubular structures. Our approach employs a 3D convolutional network to extract 3D tubular structures from medical images such as CT volumetric images. We introduce a skeleton-guided module that operates on extracted features to capture and preserve the skeleton information in the segmentation results. Additionally, to effectively train our deep model in leveraging skeleton information, we propose a sigmoid-adaptive Tversky loss function which is specifically designed for skeleton segmentation. We conducted experiments on two distinct 3D medical image datasets. The first dataset consisted of 90 cases of chest CT volumetric images, while the second dataset comprised 35 cases of abdominal CT volumetric images. Comparative analysis with previous segmentation approaches demonstrated the superior performance of our method. For the airway segmentation task, our method achieved an average tree length rate of 93.0%, a branch detection rate of 91.5%, and a precision rate of 90.0%. In the case of abdominal artery segmentation, our method attained an average precision rate of 97.7%, a recall rate of 91.7%, and an F-measure of 94.6%. We present a skeleton-guided 3D convolutional network to segment tubular structures from 3D medical images. Our skeleton-guided 3D convolutional network could effectively segment small tubular structures, outperforming previous methods.

Association between incidental perirenal fat stranding on CT and metabolic syndrome in otherwise healthy adults.

To investigate the association between metabolic syndrome and perirenal fat stranding (PRFS), which is defined as linear or curvilinear soft tissue densities in the perirenal fat on computed tomography (CT). Adults who had abdominal CT for health screening at a single institution between October 2022 and March 2023 were included retrospectively. Two radiologists assessed the extent of PRFS for each CT and graded it as absent, mild/moderate, and severe. Logistic regression analyses were used to investigate the associations between PRFS and metabolic syndrome-related factors, as well as age and gender. Among 701 participants (mean age, 56.8years ± 9.7; 336 women and 365 men), 87 (12.4%) had mild (n = 80) or moderate (n = 7) PRFS. None had severe PRFS. The presence of PRFS was independently associated with higher body mass index (odds ratio [OR], 2.561 and 9.842 for overweight and obese, respectively; p ≤ 0.001), elevated blood pressure with or without anti-hypertensive medication (OR, 2.232; p = 0.015), anti-diabetic medication (OR, 3.129; p < 0.001), and lipid-lowering medication (OR, 1.919; p = 0.019), older age (OR, 4.545 and 9.109 for 50-59years and ≥ 60years, respectively; p ≤ 0.002), and male gender (OR, 10.065; p < 0.001). Sixty three of 87 (72.4%) participants with PRFS had metabolic syndrome, while 265 of 614 (43.2%) participants without PRFS did (p < 0.001). Incidental mild or moderate PRFS may be associated with the presence of metabolic syndrome or related disorders in otherwise healthy adults.

Liver segmental volumes and their relationship with 5-year prognostication.

This study aimed to analyze the predictive value of caudate to right lobe ratio (CRL-R) and liver segmental volume ratio (LSVR) for chronic liver disease (CLD) on routine abdominal CT scans and their association with 5-year decompensation- and transplant-free survival. This retrospective study included 108 patients without CLD and 98 patients with biopsy-proven CLD. All patients underwent abdominal CT scans between 03/2015 and 08/2017. Patients with CLD were divided into three groups: early CLD (F0-F2; eCLD; n = 40), advanced CLD (F3-F4; aCLD; n = 20), and aCLD with clinically significant portal hypertension (aCLDPH; n = 38). CRL-R and LSVR were compared between groups using Kruskal-Wallis test and ROC analysis to determine cutoff-values. 5-year decompensation- and transplant-free survival were assessed by Kaplan-Meier curve analysis. CRL-R and LSVR were significantly different between all groups (p < 0.001). A CRL-R cutoff-value of > 0.99 predicted aCLD with a sensitivity of 69% and a specificity of 80% (AUC = 0.75, p < 0.001), while LSVR > 0.37 had a sensitivity of 67% and a specificity of 84% (AUC = 0.80, p < 0.001). CLD-patients with both CRL-R > 0.99 and LSVR > 0.37 had a significantly lower probability of 5-year decompensation-free survival (31%) as well as lower probability of 5-year transplant-free survival (41%) than those with a CRL-R < 0.99 and/or LSVR < 0.37 (70%, 62%, p = 0.006, p = 0.038). CRL-R and LSVR showed a high predictive value for CLD on routine abdominal CT scans. In patients with CLD, both CRL-R and LSVR may be combined and are associated with 5-year decompensation-free and transplant-free survival.

Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images

Dual knowledge‐guided two‐stage model for precise small organ segmentation in abdominal CT images

Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT.

To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT). This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm2 or < 100 mm2, respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation. In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT. The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.

ThP5.15 - "Unusual Impaction: A Rare Case of Gallstone Ileus''

Abstract Introduction Gallstone-related mechanical gastrointestinal tract obstruction is infrequent, manifesting in only 0.3%-0.5% of cholelithiasis cases. Typically, stones exceeding 2 cm find themselves lodged in the terminal ileum. However, in Bouveret's syndrome which is observed in 1 to 3% of instances the impaction occurs in the duodenum causing gastric outlet obstruction. The epidemiology and prevalence of gallstone impaction in the proximal jejunum remain inadequately documented, making this a rare case. Case Report An 83-year-old patient with known cholelithiasis and choledocholithiasis presented with acute abdominal pain and vomiting a week after undergoing ERCP, sphincterotomy and partial duct clearance. The presence of repeated bilious vomiting prompted investigation with abdominal CT scan. This revealed an approximately 4.5 cm gallstone impacted in the proximal jejunum, prompting emergency laparotomy which confirmed that the stone was impacted 10-15 cm from the dudeno-jejunal junction. Jejunotomy, stone extraction with primary closure was performed. Post-operative recovery was uneventful. Conclusion This case demonstrates gallstone impaction in the proximal jejunum causing bowel obstruction and necessitating emergency intervention. Timely diagnosis and prompt surgical intervention led to a successful outcome. The case highlights the need for awareness and consideration of the entire spectrum of possible gallstone-related complications in the acutely presenting patient with gall stones.

Report of incomplete intestinal obstruction in an adult patient with Down Syndrome: A literature review

We report a case of incomplete intestinal obstruction in a 29-year-old adult male with Down syndrome in The First Affiliated Hospital, Hebei Medical University from January 2023 to July 2023 the patient had been experiencing intermittent abdominal distension and reduced bowel movements and gas for a period of two years. Physical examination revealed tenderness in the left lower abdomen. Abdominal CT and gastrointestinal imaging showed intestinal and colonic distension, while colonoscopy did not show any abnormalities. During laparoscopic exploration, adhesions were observed between the greater omentum and the left lower abdominal wall, resulting in dilation of the proximal colon. After the adhesions were released, intestinal contents were able to pass through the distal segment, relieving the obstruction. The patient had no history of trauma or surgery. This case highlights a potential association between Down syndrome and congenital abdominal adhesions that can lead to intestinal obstruction. Due to the communication difficulties in patients with Down syndrome, there is a potential for misdiagnosis, emphasizing the importance of awareness. Laparoscopic examination not only aids in the diagnosis but also offers therapeutic benefits in such cases. doi: https://doi.org/10.12669/pjms.40.9.8777 How to cite this: Yang N. Report of incomplete intestinal obstruction in an adult patient with Down Syndrome: A literature review. Pak J Med Sci. 2024;40(9):---------. doi: https://doi.org/10.12669/pjms.40.9.8777 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

WP4.11 - Oral Gastrograffin Usage in Adhesive Small Bowel Obstruction: a re-audit

Abstract Aim to establish the successful usage of oral gastrograffin in patients presenting with adhesive small bowel obstruction (SBO). Methods This is a retrospective follow-up study, using a combined data collected from two NHS Trusts, from July 2022 to January 2024. Patients presented with adhesive SBO were included in this study, who were given 100 ml of oral gastrograffin after diagnosis for SBO. Patients were arranged to have plain abdominal x-ray approximately 8-hours post-gastrograffin drinks/or infusion via nasogastric (NG) tube to ascertain if oral contrast had passed the transition point, identified on initial abdominal CT scan. Patients were then reviewed for their symptomatic relief including bowels movement. Results 132 patients were identified but 108 patients were included, who had surgery. 24 patients were excluded because they did not have post-gastrograffin x-ray. Average age of patients was 63+/-14 years. It was observed that 82 patients (75.9%) opened their bowels after administration of first dose of gastrograffin, while 26 patients (24%) did not. 16 out of these 26 patients required surgery; and the remaining 10 (38.4%) had 2nd dose of gastrograffin, of which 4 patients (40%) moved bowels but 6 (60%) still did not and required surgery. Post-contrast x-rays, showed that contrast passed through into large bowel in 76 patients (70.3%) after 1st dose of gastrograffin and in further 40% after 2nd dose. Conclusion clinically significant number of patients responded to such conservative measures. Therefore, routine oral gastrograffin usage is suggested as an effective adjunct to non-operative measures in adhesive SBO management.

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists. We retrospectively included abdominopelvic CT images with the following inclusion criteria: a) CT images from patients with solid organ malignancies between March 1 and March 31, 2019, in a single institution; and b) abdominal CT images interpreted as negative for basal lung metastases. Reference standards for diagnosis of lung metastases were confirmed by reviewing medical records and subsequent CT images. An AI system that could automatically detect lung nodules on CT images was applied retrospectively. A radiologist reviewed the AI detection results to classify them as lesions with the possibility of metastasis or clearly benign. The performance of the initial AI results and the radiologist's review of the AI results were evaluated using patient-level and lesion-level sensitivities, false-positive rates, and the number of false-positive lesions per patient. A total of 878 patients (580 men; mean age, 63 years) were included, with overlooked basal lung metastases confirmed in 13 patients (1.5%). The AI exhibited an area under the receiver operating characteristic curve value of 0.911 for the identification of overlooked basal lung metastases. Patient- and lesion-level sensitivities of the AI system ranged from 69.2% to 92.3% and 46.2% to 92.3%, respectively. After a radiologist reviewed the AI results, the sensitivity remained unchanged. The false-positive rate and number of false-positive lesions per patient ranged from 5.8% to 27.6% and 0.1% to 0.5%, respectively. Radiologist reviews significantly reduced the false-positive rate (2.4%-12.6%; all P values < 0.001) and the number of false-positive lesions detected per patient (0.03-0.20, respectively). The AI system could accurately identify basal lung metastases detected in abdominopelvic CT images that were overlooked by radiologists, suggesting its potential as a tool for radiologist interpretation. The AI system can identify missed basal lung lesions in abdominopelvic CT scans in patients with malignancy, providing feedback to radiologists, which can reduce the risk of missing basal lung metastasis.

TUMOR IN HORSESHOE KIDNEYS: A CASE REPORT AND LITERATURE REVIEW

Objective: This study aims to report a renal tumor in a horseshoe kidney successfully treated with an open left nephrectomy. Case(s) Presentation: A 3 years old girl with a lump in the left abdomen and intermittent hematuria was referred to our hospital. Whole abdominal contrast CT scan showed the presence of a left renal mass in a horseshoe kidney that infiltrates the left renal artery and is attached to the spleen. The patient underwent preoperative chemotherapy followed by surgical resection. Discussion: In a horseshoe kidney, renal tumors are uncommon. The limited kidney mobilization and access to the renal hilum and the abnormal and highly variable vasculature make surgical treatment of a tumor in a horseshoe kidney extremely difficult. Conclusion: Open surgery continues to be the treatment of choice for horseshoe kidney tumors due to their anatomical complexity, particularly in cases where the tumor is difficult to eradicate. Keywords: Renal tumor, horseshoe kidney, surgical management.

Computer tomography-based assessment of perivascular adipose tissue in patients with abdominal aortic aneurysms

This retrospective study investigates perivascular adipose tissue (PVAT) alterations in CT as a marker of inflammation in patients with abdominal aortic aneurysms (AAA). 100 abdominal CT scans of patients with abdominal aortic aneurysms and 100 age and sex matched controls without underlying aortic disease were included. Artificial Intelligence (AI) assisted segmentation of the aorta and the surrounding adipose tissue was performed. Adipose tissue density was measured in Hounsfield units (HU) close (2-5mm, HUclose) and distant (10-12mm, HUdistant) to the aortic wall. To investigate alterations in adipose tissue density close to the aorta (HUclose) as a potential marker of inflammation, we calculated the difference HUΔ = HUclose-HUdistant and the fat attenuation ratio HUratio = HUclose/HUdistant as normalized attenuation measures. These two markers were compared i) inter-individually between AAA patients and controls and ii) intra-individually between the aneurysmal and non-aneurysmal segments in AAA patients. Since most AAAs are generally observed infrarenal, the aneurysmal section of the AAA patients was compared with the infrarenal section of the aorta of the control patients. In inter-individual comparisons, higher HUΔ and a lower HUratio were observed (aneurysmal: 8.9 ± 5.1 HU vs. control: 6.9 ± 4.8 HU, p-value = 0.006; aneurysmal: 89.8 ± 5.7% vs. control: 92.1 ± 5.5% p-value = 0.004). In intra-individual comparisons, higher HUΔ and lower HUratio were observed (aneurysmal: 8.9 ± 5.1 HU vs. non-aneurysmal: 5.5 ± 4.1 HU, p-value < 0.001; aneurysmal: 89.8 ± 5.7% vs. non-aneurysmal 93.3 ± 4.9%, p-value < 0.001). The results indicate PVAT density alterations in AAA patients. This motivates further research to establish non-invasive imaging markers for vascular and perivascular inflammation in AAA.

Findings of Abdominal Computed Tomography In COVID-19 Patients with Abnormal Abdominal Symptoms In Siriraj Hospital

Background: The coronavirus (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest imaging findings of COVID-19 disease have been widely published. Only a few studies of abdominal imaging findings have been documented. The majority of these studies demonstrate thrombotic events associated with COVID-19. Previous studies were mainly conducted in the USA, Europe and China which limited the applicability in Southeast Asia (SEA) including Thailand. Objective: To provide a summary of various abdominal imaging findings of COVID-19 patients admitted to xxx Hospital with findings associated with clinical outcomes. Materials and Methods: All CT abdominal imaging of adult patients who tested positive for COVID-19 performed from January 1st, 2020 to August 31st, 2022 were retrospectively reviewed. We collected clinical data, abdominal signs and symptoms, laboratory data and various CT findings, for example; bowel-wall thickening, bowel ischemia, fluid-filled colon and bleeding manifestations. The clinical outcomes were gathered as death confirmation, invasive mechanical ventilation, days of invasive mechanical ventilation and days of hospitalization. Results: A large number of patients who had stage 3-5 chronic kidney disease (CKD), abdominal distension, abnormal bowel findings and longer days of hospitalization were significantly observed in a group with worse clinical outcomes. Abnormal intestinal imaging findings were related to a higher risk of worse outcomes, invasive mechanical ventilation, death and days of hospitalization without statistical significance. Conclusion: Abdominal CT scans performed on COVID-19 patients frequently revealed abnormal bowel findings, which were strongly associated with poor clinical outcomes. Radiologists need to be concerned about abnormal bowel findings and point them out to clinicians and surgeons.

A Reference Interval for CT-Based Liver Volume in Dogs without Hepatic Disease

In both human and veterinary medicine, computed tomography (CT) volumetry provides a quantitative and accurate measure of liver volume. While CT volumetry is recognized as a useful method for assessing liver volume in dogs, a statistically significant reference interval for liver volume in dogs with no history of hepatic disease has not been reported. The purpose of the present study was to define a reference interval for liver volume with no history of hepatic disease using CT volumetry. Medical records from 2 June 2020 to 25 July 2022 were retrospectively reviewed, including 121 dogs that underwent abdominal CT scans and had no history of hepatic disease. Liver volumes were measured using CT volumetry and normalized by body weight. The median of normalized CT-based liver volume in 121 dogs was 22.2 cm3/kg. Based on these data, a weight-based reference interval lower limit of 11.1–15.5 (90% confidence interval [CI]) to an upper limit of 31.9–42.6 (90% CI) cm3/kg for CT-based liver volume was defined in dogs without hepatic disease. This study provides an accurate assessment of liver volume changes in dogs with various hepatic diseases.

Investigating the causes and pattern of peritoneal involvement in CT scan and comparison with ultrasound findings in peritoneal conditions: A two-center, cross-sectional study.

Ultrasound and computed tomography (CT) scans can be used as methods to help make a more accurate diagnosis in diseases involving the omentum and peritoneum. The present study was conducted to determine etiology and CT scan pattern of peritoneal involvement and compare it with ultrasound findings. Patients referred to Ghaem and Imam Reza hospitals who had undergone CT scans and had involvement of peritoneum in abdominal CT scans were included in the study. The findings identified in the CT scan were recorded. According to the underlying cause determined by laparotomy or laparoscopy sample, each disease's most common pattern of involvement and types of patterns were examined. An ultrasound was conducted for every patient and the obtained information was analyzed. A total of 101 patients were included in the study, of which 61 patients (59.8%) were female and the rest were male. The most common involvement patterns in CT scans included nodular (37.6%), mixed (21.8%), and omental cake (17.8%), respectively. In 80 patients (79.2%), CT scan findings were consistent with ultrasound, but in 21 patients (20.8%), CT scan findings were not visible in ultrasound. The most common diagnoses of the patients were colorectal adenocarcinoma and gastric adenocarcinoma (20 cases, 19.8% each), followed by ovarian and uterine adenocarcinoma (19 cases, 18.8%). The most common patterns of involvement of peritoneum in CT scans include nodular pattern, mix, and omental cake. The most common diseases that lead to the involvement of peritoneum are gastrointestinal cancers, uterine and ovarian cancers, and peritonitis.

A 3D U-NET BASED ON EARLY FUSION MODEL: IMPROVEMENT, COMPARATIVE ANALYSIS WITH STATE-OF-THE-ART MODELS AND FINE-TUNING

Multi-organ segmentation is the process of identifying and separating multiple organs in medical images. This segmentation allows for the detection of structural abnormalities by examining the morphological structure of organs. Carrying out the process quickly and precisely has become an important issue in today's conditions. In recent years, researchers have used various technologies for the automatic segmentation of multiple organs. In this study, improvements were made to increase the multi-organ segmentation performance of the 3D U-Net based fusion model combining HSV and grayscale color spaces and compared with state-of-the-art models. Training and testing were performed on the MICCAI 2015 dataset published at Vanderbilt University, which contains 3D abdominal CT images in NIfTI format. The model's performance was evaluated using the Dice similarity coefficient. In the tests, the liver organ showed the highest Dice score. Considering the average Dice score of all organs, and comparing it with other models, it has been observed that the fusion approach model yields promising results.

Diagnostic Approach of Mesenteric Cyst in Children: A Case Report

Background: Mesenteric cysts are intra-abdominal masses that arise from obstructed lymphatic drainage within the mesentery. Early diagnosis can be challenging, as it is often difficult to recognize based solely on clinical findings. The diagnosis is frequently made following imaging studies or during surgery. Despite their generally benign nature, delayed diagnosis can lead to serious complications. This case report aims to share our experience on the diagnostic approach for mesenteric cysts in children. Case: A 2-year-old boy presented with a 14-month history of slowly progressive abdominal distension and signs of bowel obstruction, including weight loss. Abdominal ultrasound revealed thin-walled, well-demarcated, septated fluid sacs consistent with a cyst. Abdominal CT-Scan showed massive ascites filling the intraperitoneal cavity and pushing the intestine upward along the thickened mesentery, causing bowel obstruction. Histopathological examination confirmed the diagnosis of cyst lymphangioma derived from mesentery. The cyst was successfully excised via laparotomy, and histological examination confirmed it as a cystic lymphangioma. Discussion: Our diagnostic approach focused on exclusion of differential diagnosis for abdominal distention in children, including ascites, abdominal tuberculosis, mesenteric cysts, and intra-abdominal masses. The absence of constitutional symptoms and lack of response to anti-tuberculosis therapy made it an unlikely diagnosis. While the CT-scan suggested ascites, the ultrasound results suggested a mesenteric cyst. Despite its large size, a complete excision of the cyst was successfully performed via laparotomy. Conclusion: The nonspecific nature of mesenteric cysts often delays diagnosis, making abdominal ultrasound an essential initial imaging modality for patients suspected of having this condition.

Obesity is a predictor of abdominal computed tomography imaging in pediatric trauma patients.

Computed tomography (CT) has helped to reduce the morbidity due to missed injuries. However, CT imaging is associated with radiation exposure and thus has limited indications in pediatric patients. In this study, we aimed to identify the association between obesity and abdominal CT imaging in pediatric trauma patients. We performed a 4-year retrospective analysis of the American College of Surgeons Trauma Quality Improvement 2017-2020. We identified all pediatric trauma patients aged between 7 and 17 years presenting with isolated abdominal trauma (nonabdominal Abbreviated Injury Scale score, 0). We excluded patients undergoing hemorrhage control surgeries and those with missing information in height and weight. Patients were stratified by body mass index into four groups (underweight, normal, overweight, and obese [body mass index, ≥30 kg/m2]). Outcomes were predictors of undergoing CT imaging of the abdomen. Descriptive statistics and multivariable logistic regression analyses were performed. We identified a total of 10,204 pediatric trauma patients. The mean age was 13 years, 68% were male, and 77% were White. The median abdominal Abbreviated Injury Scale score in all the four groups was 2. On univariate analysis, underweight patients had lowest rates (25%), whereas obese patients had highest rates of CT imaging (38%) (p < 0.001). On multivariable regression analysis, increasing age (adjusted odds ratio [aOR], 1.08; 95% confidence interval [CI], 1.06-1.10; p < 0.001), male sex (aOR, 1.14; 95% CI, 1.03-1.26; p = 0.009), White race (aOR, 0.84; 95% CI, 0.76-0.92; p < 0.011), penetrating injury (aOR, 1.16; 95% CI, 1.03-1.32; p = 0.017), obesity (aOR, 1.30; 95% CI, 1.07-1.57; p = 0.008), and management at American College of Surgeons level II (aOR, 1.63; 95% CI, 1.44-1.85; p < 0.001) and level III or lower centers (aOR, 1.17; 95% CI, 1.06-1.26; p = 0.002) were identified as independent predictors of receiving CT imaging. Obesity is associated with increased odds of undergoing CT imaging in pediatric trauma patients independent of injury characteristics. Future efforts to define the appropriate indications for CT imaging in pediatric trauma patients are warranted to reduce the adverse effects of CT radiation. Prognostic and Epidemiological; Level III.

One Haemolytic Anaemia May Hide Another: Paroxysmal Nocturnal Haemoglobinuria Masquerading As Plasmodium Falciparum Infection.

Paroxysmal nocturnal haemoglobinuria (PNH) is a rare, genetic and acquired haematologic disease that causes complement-mediated intravascular haemolytic anaemia, thrombosis and bone marrow failure. A 27-year-old migrant patient attended the emergency department in a context of fever and chills over the previous few days as well as chronic fatigue, dyspnoea and chest pain. His medical history included chronic anaemia and erectile dysfunction. Initial biology showed a haemoglobin of 6.3 g/dl, platelets of 25,000/μl, total leucocytes of 3,500/μl with 1,500 neutrophils. B12 vitamin, folic acid, ferritin and thyroid stimulating hormone were normal. Lactate dehydrogenase levels were high and haptoglobin was non-measurable. C-reactive protein was 46.1 mg/l. A thick blood smear revealed Plasmodium falciparum infection with 0.1% parasitaemia. The patient was treated with an oral combination of artemether and lumefantrine. Three weeks later, the patient consulted the infectious disease department given the lack of clinical improvement. The cytopenias worsened, and lactate dehydrogenase (LDH) and reticulocytes increased. Tests for schistocytes, a thick blood smear for malaria and a direct Coombs test were negative; a myelogram was reassuring. An abdominal, pelvic and thoracic CT scan showed a mild hepatomegaly with no focal lesion and no splenomegaly or adenomegaly. A 12-colour flow cytometry unveiled a PNH clone on 90.9545% of neutrophils and 80.7371% of monocytes. PNH patients can be vulnerable to parasites infection (such as P. falciparum) as it may trigger breakthrough haemolysis through uncontrolled resurgence of activity of the complement system. In our patient, P. falciparum infection was a confounding factor, as it commonly causes haemolytic anaemia and thrombocytopenia, and patients living in malaria-endemic regions can carry low parasitaemia while being slightly symptomatic or asymptomatic. Plasmodium falciparum infection can cause breakthrough haemolysis in patients with paroxysmal nocturnal haemoglobinuria.Low P. falciparum parasitemia in patients living in malaria-endemic regions is not always significant as these patients often carry acquired immunity.Patients from malaria-endemic regions presenting with severe sickness and low P. falciparum parasitemia must be assessed for other diseases, as it cannot explain heavy illness.Patients presenting with haemolytic anaemia, no schistocytes, a negative direct Coombs test and other unexplained cytopenia such as thrombocytopenia/neutropenia and other unexplained clinical manifestations such as dyspnoea, chest pain or erectile dysfunction should be assessed for paroxysmal nocturnal haemoglobinuria.