Omar Darwish Al Matroushi, Srinivasa Rao Sirasanagandla, Ammar Al Shabibi, Athari Al Obaidani, Humoud Al Dhuhli, Sanjay Jaju, Mohamed Al Mushaiqri. Anat Cell Biol -0001;0:. https://doi.org/10.5115/acb.20.194

Ghaliya Al Hinai, Mai Al Shandoodi, Srinivasa Rao Sirasanagandla, Salwa Al Sarhani, Humoud Al Dhuhli, Sanjay Jaju, Mohamed Al Mushaiqri. Anat Cell Biol 2021;54:436-40. https://doi.org/10.5115/acb.21.101

Background: The radiological evaluation of the carotid sinus (CS) anatomy and its morphometry is essentially important for various surgical procedures involving the carotid bifurcation and the CS itself. Despite its tremendous clinical significance, studies dealing with the CS anatomy are seldom reported. Hence, the present study aimed to evaluate the frequencies of the CS positional variants and their morphometry and correlate them with age and body mass index (BMI). Methods: In this retrospective cross-sectional study, a total of 754 disease-free carotid arteries were examined using computed tomography angiography scans to determine the CS positional variations (such as types I to III) and its morphometry, including the CS diameter and length. Additionally, the association between these parameters and factors such as sex, age, and body mass index were explored using appropriate statistical tests. The inter-rater agreement of the collected dataset was evaluated using Cohen’s Kappa. Results: The CS type I was observed in 87.67% of the cases, and type II and type III were observed at lower frequencies with 9.02% and 3.32%, respectively. There were statistically significant (p < 0.001) differences observed in the mean diameter and length of the sinus between the sex and the type I CS variations. However, there was no significant and strong correlation between the age and BMI factors with sinus length and sinus diameter. The kappa values for inter-rater agreement ranged from 0.77 to 0.99 for all parameters. Conclusions: In type I, the CS length and carotid vessel’s diameter is significantly different between the sexes. However, age and BMI do not affect the CS anatomy in radiologically disease-free carotid arteries. Knowledge of the CS variant anatomy is clinically significant as it influences the patients’ surgical and physiological outcomes.

Background: The distance and angle between the superior mesenteric artery (SMA) and abdominal aorta play a role in the etiology of SMA syndrome. Retroperitoneal fat has been reported to affect both the distance and angle. Very few studies have reported the normal range of these measurements. The present study aimed to evaluate the reference values of the aortomesenteric angle (AMA) and aortomesenteric distance (AMD) in asymptomatic patients, as well as to determine the influence of age, sex, and anthropometric variables on these values. Methods: A retrospective cross-sectional study was conducted at Sultan Qaboos University Hospital. Computed tomography angiography scans of the abdomen from 141 Omani adult patients (aged ≥18 years) were included in the study. The morphometric data of the AMA and AMD were measured at the third part of the duodenum using sagittal and axial multiplanar reconstruction of CTA images. Patient data, including age, sex, height, weight, and BMI, were collected from the medical records. Statistical analyses included the Mann–Whitney U, Kruskal–Wallis H and Jonckheere–Terpstra J-T tests with significance set at p < 0.05. Results: The mean AMA was 57.16 ± 22.06°, and the mean AMD was 21.35 ± 10.25 mm. The AMD varied significantly across age groups (H = 17.29, p < 0.001) and showed a positive trend with increasing age (p = 0.001). Both AMA (p = 0.001) and AMD (p < 0.001) differed significantly across BMI categories, with significant increasing trends (p = 0.033 and p ≤ 0.001, respectively). No statistically significant differences were observed between sexes and study parameters (p > 0.05). Conclusions: The present study demonstrates that the reference values of the AMA and AMD are within the range of those reported in other populations. The variations in these values with BMI and age underscore the importance of individualized imaging interpretation and preoperative planning. The reported baseline data may enhance diagnostic accuracy and assist with surgical planning and radiological evaluation of suspected SMA syndrome.

Objective: We aimed to develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. Methods: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. For severity prediction, two radiologists scored involvement of lungs in COVID-19 and pneumonia scans based on percentage of involvement in all 5 lobes. Datasets were classified into mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation method. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. For both tasks, datasets were randomly divided into 90% training sets (70% and 30% for training and validation, respectively) and 10% test sets. Pearson correlation coefficient (PCC≥90%) was performed to exclude highly correlated features. Subsequently, different feature selection algorithms (Correlation attribute evaluation, Information gain attribute, Wrapper Subset selection algorithm, Relief method, and Correlation-based feature selection) were assessed. The most pertinent features were finally selected using voting method based on the evaluation of all algorithms. Several ML-based supervised algorithms were utilized, namely Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting. The synthetic minority oversampling technique (SMOTE) was used to balance the three classes in training sets. The optimal model was first selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation sets 20 times, followed by testing using the test set. To ensure the repeatability of the results, the entire process was repeated 50 times. Results: Nine pertinent features (2 shape, 1 first-order, and 6 second-order features) were obtained after feature selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. Conclusion: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.