Medical imaging in problem-based learning and impact on the students: A cross-sectional study.

OBJECTIVE: To investigate the medical students' performance with and perception towards different multimedia medical imaging tools. METHODS: The cross-sectional study was conducted at the College of Medicine, Qassim University, Saudi Arabia, from 2019 to 2020, and comprised third year undergraduate medical students during the academic year 2019-2020. The students were divided into tow groups. Those receiving multimedia-enhanced problem-based learning sessions were in intervention group A, while those receiving traditional problem-based learning sessions were in control group B. Scores of the students in the formative assessment at the end of the sessions were compared between the groups. Students' satisfaction survey was also conducted online and analysed. Data was analysed using SPSS 21. RESULTS: Of the 130 medical students, 75(57.7%) were males and 55(42.3%) were females. A significant increase in the mean scores was observed for both male and female students in group A compared to those in group B (p<0.05). The perception survey was filled up by 100(77%) students, and open-ended comments were obtained from 88(88%) of them. Overall, 69(74%) subjects expressed satisfaction with the multimedia-enhanced problem-based learning sessions. CONCLUSIONS: Radiological and pathological images enhanced the students' understanding, interaction and critical thinking during problem-based learning sessions.

Renal cell oncocytoma tumor evolutions: a case report.

This report delineates the clinical progression of a renal oncocytoma in a 38-year-old female, initially asymptomatic, over a three-year follow-up period following her treatment for papillary thyroid cancer. The timeline of this case is as follows: In 2016, the patient was treated with total thyroidectomy and I131 for thyroid cancer. During an annual follow-up, an incidental renal mass was detected via FDG PET/CT, initially characterized as a benign, non-FDG-avid renal oncocytoma. Over two years, this lesion demonstrated a remarkable increase in FDG uptake and a slight growth in size, coupled with new I131 uptake in subsequent scans. These findings led to a reassessment of the diagnosis, initially suggesting a potential small renal cell carcinoma (RCC). Histopathological analysis eventually confirmed the diagnosis of oncocytoma. This case is notable for the tumor's unusual metabolic evolution and the challenges it posed in differential diagnosis.

Predicting the Consistency of Pituitary Macroadenomas: The Utility of Diffusion-Weighted Imaging and Apparent Diffusion Coefficient Measurements for Surgical Planning.

Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (p < 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10-3 mm2/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.

Comparative Evaluation of Chest Ultrasonography and Computed Tomography as Predictors of Malignant Pleural Effusion: A Prospective Study.

Malignant pleural effusion (MPE) is a manifestation of advanced cancer that requires a prompt and accurate diagnosis. Ultrasonography (US) and computed tomography (CT) are valuable imaging techniques for evaluating pleural effusions; however, their relative predictive ability for a malignant origin remains debatable. This prospective study aimed to compare chest US with CT findings as predictors of malignancy in patients with undiagnosed exudative pleural effusion. Fifty-four adults with undiagnosed exudative pleural effusions underwent comprehensive clinical evaluation including chest US, CT, and histopathologic biopsy. Blinded radiologists evaluated the US and CT images for features suggestive of malignancy, based on predefined criteria. Diagnostic performance measures were calculated using histopathology as a reference standard. Of the 54 patients, 33 (61.1%) had MPEs confirmed on biopsy. No significant differences between US and CT were found in detecting parietal pleural abnormalities, lung lesions, chest wall invasion, or liver metastasis. US outperformed CT in identifying diaphragmatic pleural thickening ≥10 mm (33.3% vs. 6.1%, p < 0.001) and nodularity (45.5% vs. 3%, p < 0.001), whereas CT was superior for mediastinal thickening (48.5% vs. 15.2%, p = 0.002). For diagnosing MPE, diaphragmatic nodularity detected by US had 45.5% sensitivity and 100% specificity, whereas CT mediastinal thickening had 48.5% sensitivity and 90.5% specificity. Both US and CT demonstrate reasonable diagnostic performance for detecting MPE, with particular imaging findings favoring a malignant origin. US may be advantageous for evaluating diaphragmatic pleural involvement, whereas CT is more sensitive to mediastinal abnormalities.

Diagnostic Accuracy of Integrating Ultrasound and Shear Wave Elastography in Assessing Carpal Tunnel Syndrome Severity: a Prospective Observational Study.

Purpose: Carpal tunnel syndrome (CTS) is a common condition characterized by compression of the median nerve (MN) within the carpal tunnel. Accurate diagnosis and assessment of CTS severity are crucial for appropriate management decisions. This study aimed to investigate the combined diagnostic utility of B-mode ultrasound (US) and shear wave elastography (SWE) for assessing the severity of CTS in comparison to electrodiagnostic tests (EDT). Materials and Methods: This prospective observational study was conducted over 9-month periods at a tertiary care hospital. A total of 48 patients (36 females, 12 males; mean age 44 ± 10.9 years; age range 28-57 years) with clinically suspected CTS were enrolled. All patients underwent EDT, US, and SWE. Based on the EDT results, CTS cases were categorized into four groups: mild, moderate, severe, and negative. The cross-sectional area (CSA) and elasticity (E) of the MN were measured at the tunnel inlet (CSAu and Eu) and pronator quadratus region (CSAo and Eo). The differences (CSAu-CSAo and Eu-Eo) were calculated. The primary outcomes were the diagnostic performance of CSAu, CSAu-CSAo, Eu, and Eu-Eo in differentiating moderate/severe from mild/negative CTS compared to EDT findings. Secondary outcomes included a correlation of US/SWE parameters with EDT grades and between each other. ANOVA, correlation, regression, and receiver operating characteristic (ROC) curve analyses were performed. Results: CSAu and CSAu-CSAo increased progressively with worsening CTS severity. E measurements were significantly higher in moderate-to-severe CTS compared to mild or negative cases. The combined metric of CSAu-CSAo at a 5 mm threshold exhibited enhanced performance, with a higher sensitivity (83.3%), specificity (100%), and area under the curve (AUC) (0.98), surpassing the results of CSAu when used independently. Similarly, the SWE measurements indicated that Eu-Eo at a 56.1kPa cutoff achieved an AUC of 0.95, with a sensitivity of 93.3% and specificity of 94.4%, outperforming the metrics for Eu when used alone, which had an AUC of 0.93, with identical sensitivity and specificity values (93.3% and 94.4%, respectively). Conclusion: The integration of ultrasound, shear wave elastography, and electrodiagnostic tests provides a comprehensive approach to evaluate anatomical and neurological changes and guide management decisions for CTS.

PET/CT in treatment response assessment in lung cancer. When should it be recommended?

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Different treatment options are now possible both for surgical candidates and for those NSCLC patients deemed not suitable for surgery. Despite the treatments available, only a limited number of less advanced stages are potentially curable, with many patients suffering local recurrence or distant metastases. FDG-PET/CT is commonly used in many centers for post-treatment evaluation, follow-up, or surveillance; Nonetheless, there is no clear consensus regarding the indications in these cases. Based upon the results of a literature review and local expertise from a large lung cancer unit, we built clinical evidence-based recommendations for the use of FDG-PET/CT in response assessment. We found that in general this is not recommended earlier than 3 months from treatment; however, as described in detail the correct timing will also depend upon the type of treatment used. We also present a structured approach to assessing treatment changes when reporting FDG-PET/CT, using visual or quantitative approaches.

FDG avid solitary pulmonary nodule mimicking lung cancer.

A healthy 49-year-old nonsmoker lady, who was found to have an incidental finding of a lung lesion on a chest X-ray. A Chest CT scan was performed and revealed left upper lobe, 1.5 cm solitary nodule with ground glass borders that highly suspicious for Bronchioloalveolar carcinoma and warranted further investigation to rule out malignancy. The FDG PET and/or CT scan was performed for staging and further evaluation and it displayed avidity of the nodule with a standardized uptake value (SUV) of 6.2, no abnormal uptake elsewhere in the body. CT guided biopsy was arranged and the histopathology result revealed eosinophilic pneumonia.

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis.

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient's life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.

Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier.

In today's world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.

Diagnostic Validity and Reliability of Low-Dose Prospective ECG-Triggering Cardiac CT in Preoperative Assessment of Complex Congenital Heart Diseases (CHDs).

For the precise preoperative evaluation of complex congenital heart diseases (CHDs) with reduced radiation dose exposure, we assessed the diagnostic validity and reliability of low-dose prospective ECG-gated cardiac CT (CCT). Forty-two individuals with complex CHDs who underwent preoperative CCT as part of a prospective study were included. Each CCT image was examined independently by two radiologists. The primary reference for assessing the diagnostic validity of the CCT was the post-operative data. Infants and neonates were the most common age group suffering from complex CHDs. The mean volume of the CT dose index was 1.44 ± 0.47 mGy, the mean value of the dose-length product was 14.13 ± 5.4 mGy*cm, and the mean value of the effective radiation dose was 0.58 ± 0.13 mSv. The sensitivity, specificity, PPV, NPV, and accuracy of the low-dose prospective ECG-gated CCT for identifying complex CHDs were 95.6%, 98%, 97%, 97%, and 97% for reader 1 and 92.6%, 97%, 95.5%, 95.1%, and 95.2% for reader 2, respectively. The overall inter-reader agreement for interpreting the cardiac CCTs was good (κ = 0.74). According to the results of our investigation, low-dose prospective ECG-gated CCT is a useful and trustworthy method for assessing coronary arteries and making a precise preoperative diagnosis of complex CHDs.

A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19's impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students' health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student's remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

Mucormycosis co-infection in COVID-19 patients: An update.

Mucormycosis (MCM) is a rare fungal disorder that has recently been increased in parallel with novel COVID-19 infection. MCM with COVID-19 is extremely lethal, particularly in immunocompromised individuals. The collection of available scientific information helps in the management of this co-infection, but still, the main question on COVID-19, whether it is occasional, participatory, concurrent, or coincidental needs to be addressed. Several case reports of these co-infections have been explained as causal associations, but the direct contribution in immunocompromised individuals remains to be explored completely. This review aims to provide an update that serves as a guide for the diagnosis and treatment of MCM patients' co-infection with COVID-19. The initial report has suggested that COVID-19 patients might be susceptible to developing invasive fungal infections by different species, including MCM as a co-infection. In spite of this, co-infection has been explored only in severe cases with common triangles: diabetes, diabetes ketoacidosis, and corticosteroids. Pathogenic mechanisms in the aggressiveness of MCM infection involves the reduction of phagocytic activity, attainable quantities of ferritin attributed with transferrin in diabetic ketoacidosis, and fungal heme oxygenase, which enhances iron absorption for its metabolism. Therefore, severe COVID-19 cases are associated with increased risk factors of invasive fungal co-infections. In addition, COVID-19 infection leads to reduction in cluster of differentiation, especially CD4+ and CD8+ T cell counts, which may be highly implicated in fungal co-infections. Thus, the progress in MCM management is dependent on a different strategy, including reduction or stopping of implicit predisposing factors, early intake of active antifungal drugs at appropriate doses, and complete elimination via surgical debridement of infected tissues.

False-Positive Gallium-68 DOTATATE PET/CT in Vertebral Hemangioma.

A healthy 52-year-old man started to have few minutes spells of palpitation, tachycardia, hot flashes, and chest tightness. He had a lab work-up for carcinoid, including 5-hydroxyindoleacetic acid (5-HIAA), which was negative. Months later, his symptoms became worse which warranted further investigation to exclude carcinoid disease. Gallium-68 DOTATATE positron emission tomography combined with computed tomography (PET/CT) scanning was performed to assess the patient for carcinoid tumor. It showed foci of radiotracer avidity in the thoracic and lumber spine. Magnetic resonance imaging (MRI) for the vertebral lesions showed atypical hemangioma. Subsequently, follow-up serum chromogranin A testing was negative.

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

Ischemic reperfusion injury of renal transplant mimics acute rejection on renal scintigraphy in early post-operative period.

Renal scintigraphy of a renal graft is a non-invasive imaging used to evaluate renal graft dysfunction in relay postoperative period. We presented the case of a 42-year-old man when underwent renal transplantation and developed anuria with severe renal impairment. Renal scintigraphy yielded no visualization of the renal graft. Subsequently, the patient underwent an exploratory laparotomy. The graft was found to have normal perfusion, and a surgical biopsy suggestive of an acute perfusion injury and mild tubular necrosis. The patient recovered with conservative therapy. This case highlights a common limitation of renal scintigraphy in a post-renal-transplantation patient with severe renal impairment.

Digitalization plan in medical education during COVID-19 lockdown.

BACKGROUND: The COVID-19 pandemic has enhanced the adoption of virtual learning after the urgent suspension of traditional teaching. Different online learning strategies were established to face this learning crisis. The present descriptive cross-sectional study was conducted to reveal the different digital procedures implemented by the College of Medicine at Qassim University for better student performance and achievement. METHODS: The switch into distance-based learning was managed by the digitalization committee. Multiple online workshops were conducted to the staff and students about the value and procedures of such a shift. New procedures for online problem-based learning (PBL) sessions were designed. Students' satisfaction was recorded regarding the efficiency of live streaming educational activities and online assessment. RESULTS: The students were satisfied with the overall shift into this collaborative e-learning environment and the new successful procedures of virtual PBL sessions. The digital learning tools facilitated the performance of the students and their peer sharing of knowledge. The role of informatics computer technologies was evident in promoting the students, research skills, and technical competencies. CONCLUSIONS: The present work elaborated on the procedures and privileges of the transformation into digitalized learning, particularly the PBL sessions, which were appreciated by the students and staff. It recommended the adoption of future online theoretical courses as well as the development of informatics computer technologies.

Normal ventilation/perfusion lung scan in patients with extensive chronic thromboembolism pulmonary hypertension: A case report.

Chronic thromboembolism pulmonary hypertension (CTEPH) is a common cause of severe pulmonary hypertension, resulting in significant morbidity and mortality. In patients with unexplained pulmonary hypertension, a ventilation-perfusion (VQ) scan should be considered the initial diagnostic test of choice. VQ scans are widely available with excellent sensitivity, specificity, and diagnostic accuracy. However, the occurrence of a normal VQ scan in the presence of CTEPH is believed to be rare. In fact, the rate of actual false negatives in VQ scans is unknown because pulmonary digital subtraction angiography and computed tomography pulmonary angiography are rarely performed in patients with a normal VQ scan. This study reports a patient with a high clinical likelihood of CTEPH due to a hypercoagulable state, recurrent deep vein thrombosis, and prior history of acute pulmonary embolism with negative VQ scans. He subsequently underwent pulmonary digital subtraction angiography, which revealed bilateral extensive emboli with partial recanalization of the organized thrombus. The patient underwent successful pulmonary endarterectomy, with marked improvement of his symptoms postoperatively.

FDG PET/CT of Spinal Brucellosis.

A 17-year-old adolescent boy's known case of classical seminoma stage IB for which he underwent right radical orchiectomy. At the first-year follow-up, CT abdomen and pelvis was performed and showed lytic vertebral lesion with left paravertebral necrotic mass that warranted further investigation to exclude disease recurrence. FDG PET/CT scan was performed to evaluate the extent of disease and showed high uptake in the same areas. A biopsy was obtained from the hypermetabolic soft tissue lesion and did not reveal malignant cells. Subsequently, serologic tests were suggestive of brucellosis.

Successful Localization of the Source of Hemorrhage in Patient with Post-Whipple Surgery by 99mTc-Labelled Red Blood Cell Scintigraphy.

Gastrointestinal Bleeding Scintigraphy (GIBS) of 99mTc-labelled red blood cells is a relatively simple examination to perform, with high diagnostic accuracy and a relatively lower radiation dose. A positive scan can either suggest surgery without further investigation or can indicate angiography, a more targeted procedure. Whipple pancreatoduodenectomy is most often performed for tumors of the head of the pancreas. Pancreatoduodenectomy has 30%-40% morbidity and mortality, and while post-pancreatoduodenectomy hemorrhage is seen in less than 10% of patients, it accounts for 11%-38% mortality. The role of imaging in patients to detect relative hemodynamic stability is essential. Computed tomography angiography (CTA) shows the cause, site, and nature of bleeding, while digital subtraction angiography (DSA) has a diagnostic as well as a therapeutic role. We present a patient who presented with active gastrointestinal bleeding (GI) bleeding after undergoing a Whipple procedure, to highlight the role of GIBS in the successful localization of a bleeding site and the guidance of digital DSA in the embolization and control of the active bleeding.

The Relationship Between Absence Coronary Artery Calcification and Myocardial Perfusion Single Photon Emission Computed Tomography.

BACKGROUND: Coronary artery calcification score (CACS) is well validated prognostic tool in coronary artery disease (CAD). The data on the prevalence of myocardial ischemia on myocardial perfusion single photon emission computed tomography (MPS) in symptomatic patients with zero CACS and low to intermediate risk probability is lacking and controversial. The aim of our study was to evaluate the capability of zero CACS to exclude myocardial ischemia on MPS. METHODS: A total of 157 patients ((mean age 53 ± 10 years), 88 (56%) female patients, 69 (44%) male patients) who were suspected to have CAD and having low to intermediate pretest likelihood for CAD underwent CACS on dedicated computed tomography (CT) scanners. CACS was reported as zero in all patients, subsequently all patients underwent MPS. Patients with abnormal MPS underwent additional imaging with coronary computed tomography angiography (CCTA). RESULTS: All patients had zero CACS, of which 122 (78%) had normal MPS, and 35 (22%) had abnormal MPS. Abnormal MPS included fixed defect in 22 (13%), equivocal in 10 (6%), and reversible defect in four (3%) patients. All patients with abnormal MPS had further imaging with CTCA. CTCA was normal in 30 (85%) patients, one patient had coronary artery stenosis more than 50%, one patient had coronary artery stenosis less than 50%, one patient had anomalous origin coronary artery, and two patients had myocardial bridging. Patients with abnormal MPS and normal coronary artery had dilated cardiomyopathy in 14 (40%), asymmetrical septal hypotrophy in one (3%), and mitral valve disease in three (9%). CONCLUSIONS: Zero CACS in stable patients with low or intermediate risk indicated very low likelihood of obstructive CAD, less than 1%. Patients with zero CACS and normal MPS most likely will not benefit from further testing; however, patients with abnormal MPS will need further imaging with CCTA. CCTA is helpful in this group of patients for evaluation of coronary artery and cardiac morphology.