Current Medical Imaging - Volume 20, Issue 1, 2024

We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging.

We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice.

The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs.

In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

This study is to investigate the effectiveness of Acoustic Radiation Force Impulse (ARFI) elastography in differentiating radiologically similar renal cell carcinoma (RCC) and oncocytoma in solid masses of the kidney.

The patients with solid renal mass histopathological diagnosed after excision or tru-cat biopsy who underwent a preoperative ARFI elastography of the lesion during a 4-year period were included in this study. Preoperative shear wave velocity (SWV) values were measured in all the lesions. SWV results of RCCs and oncocytomas were compared by an independent t-test, and cut-off, sensitivity and specificity values were calculated.

Forty-two of the 60 patients included in the study were men (70%) and, 18 were women (30%), and the mean age was 59.7 ± 14 (27-94) years. Among 46 RCCs (76.6%), 23 and 14 oncocytomas, 5 (23.4%) were located in the right kidney (p:0.34722). Mean SWV values were found to be significantly higher in RCCs (2.87± 0.74 (0.96-4.14) m/s) than oncocytomas (1.83 ± 0.78 (0.80-3.76) m/s) (p <0.001). In the ROC analysis, a cut-off value of 2.29 m/s was found to havean 80.4% sensitivity and a 78.6% specificity for the discrimination of RCCs from oncocytomas.

ARFI elastography measurements may be useful in distinguishing RCC and oncocytomas that may have similar solid radiological imaging features.

A contrast agent-free approach would be preferable to the frequently used invasive approaches for evaluating cerebral perfusion in chronic migraineurs (CM). In this work, non-invasive quantitative volumetric perfusion imaging was used to evaluate alterations in cerebral perfusion in CM.

We used conventional brain structural imaging sequences and 3D pseudo-continuous arterial spin labeling (3D PCASL) to examine thirteen CM patients and fifteen normal controls (NCs). The entire brain gray matter underwent voxel-based analysis, and the cerebral blood flow (CBF) values of the altered positive areas were retrieved to look into the clinical variables' significant correlation.

Brain regions with the decreased perfusion were located in the left postcentral gyrus, bilateral middle frontal gyrus, left middle occipital gyrus, left superior parietal lobule, left medial segment of superior frontal gyrus, and right orbital part of the inferior frontal gyrus. White matter fibers with decreased perfusion were located in bilateral superior longitudinal tracts, superior corona radiata, external capsules, anterior and posterior limbs of the internal capsule, anterior corona radiata, inferior longitudinal fasciculus, and right corticospinal tract. However, the correlation analysis showed no significant correlation between the CBF value of the above positive brain regions with clinical variables (p > 0.05).

The current study provided more useful information to comprehend the pathophysiology of CM and revealed a new insight into the neural mechanism of CM from the pattern of cerebral hypoperfusion.

The purpose of this article is to combine the functional information of CT images with the anatomical and soft tissue information of MRI through image fusion technology, providing more detailed information for rehabilitation treatment and thus providing a scientific basis for clinical applications and better training plans.

In this paper, functional brain imaging technology combining CT (computed tomography) and MRI (magnetic resonance imaging) was used for image fusion, and SURF (accelerated robust feature) feature points of images were extracted. In this study, 40 patients with mild and moderate closed traumatic brain injury admitted to the rehabilitation department of a rehabilitation center from 2018 to 2022 were selected as the research objects.

Compared with using only CT images and MRI images for brain injury diagnosis, the fusion image had a higher detection rate of abnormal brain injury diagnosis, with a detection rate of 97.5%. When using fused images for the diagnosis of abnormal brain injury, the patient’s exercise rehabilitation effect was better.

CT and MRI image fusion technology had a high diagnostic accuracy for brain injury, which could timely guide doctors in determining exercise rehabilitation plans and help improve the effectiveness of patient exercise rehabilitation.

In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field.

The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels.

In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation.

Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods.

Our proposed methodology was reported to be a more effective technique than existing techniques.

Fallopian tube leiomyoma is an uncommon, benign gynecologic tumor that originates from the smooth muscle of the fallopian tube or vascular cells supplying the fallopian tube.

In this study, we report a case of a patient with fallopian tube leiomyoma. What makes this instance even more unique is the association of the leiomyoma with cystic degeneration, manifesting as a large abdominopelvic cystic mass. CT scan suspected that the mass might be an ovarian cystadenoma. However, ultrasonography, a widely used diagnostic tool, effectively assisted the clinicians in confidently ruling out the possibility that the tumor was originating from the ovaries. Ultimately, the patient underwent exploratory laparoscopy and the pathologic diagnosis was fallopian tube leiomyoma with cystic degeneration. To our knowledge, no instance of a fallopian tube leiomyoma of this size with cystic degeneration has been reported. Thus, it is worth mentioning.

In summary, fallopian tube leiomyomas are classified as uncommon benign gynecologic tumors, which pose challenges in clinical diagnosis. The combined use of multiple imaging modalities may be more helpful in the proper diagnosis of this disease entity.

The performance of automatic liver segmentation and manual sampling MRI strategies needs be compared to determine interchangeability.

To compare automatic liver segmentation and manual sampling strategies (manual whole liver segmentation and standardized manual region of interest) for performance in quantifying liver volume and MRI-proton density fat fraction (MRI-PDFF), identifying steatosis grade, and time burden.

Fifty patients with obesity who underwent liver biopsy and MRI between December 2017 and November 2018 were included. Sampling strategies included automatic and manual whole liver segmentation and 4 and 9 large regions of interest. Intraclass correlation coefficient (ICC), Bland–Altman, linear regression, receiver operating characteristic curve, and Pearson correlation analyses were performed.

Automatic whole liver segmentation liver volume and manual whole liver segmentation liver volume showed excellent agreement (ICC=0.97), high correlation (R²=0.96), and low bias (3.7%, 95% limits of agreement, -4.8%, 12.2%) in liver volume. There was the best agreement (ICC=0.99), highest correlation (R²=1.00), and minimum bias (0.84%, 95% limits of agreement, -0.20%, 1.89%) between automated whole liver segmentation MRI-PDFF and manual whole liver segmentation MRI-PDFF. There was no difference of each paired comparison of receiver operating characteristic curves for detecting steatosis (P=0.07–1.00). The minimum time burden for automatic whole liver segmentation was 0.32 s (0.32–0.33 s).

Automatic measurement has similar effects to manual measurement in quantifying liver volume, MRI-PDFF, and detecting steatosis. Time burden of automatic whole liver segmentation is minimal among all sampling strategies. Manual measurement can be replaced by automatic measurement to improve quantitative efficiency.

To compare the integrity, clarity, conciseness, etc., of the structured report (SR) versus free-text report (FTR) for computed tomography enterography of Crohn’s disease (CD).

FTRs and SRs were generated for 30 patients with CD. The integrity, clarity, conciseness etc., of SRs versus FTRs, were compared. In this study, an evidence-based medicine practice model was utilized on 92 CD patients based on SR in order to evaluate its clinical value. Then, the life quality of the patients in two groups was evaluated before and after three months of intervention using an Inflammatory Bowel Disease Questionnaire (IBDQ).

SRs received higher ratings for satisfaction with integrity (median rating 4.27 vs. 3.75, P=0.008), clarity (median rating 4.20 vs. 3.43, P=0.003), conciseness (median rating 4.23 vs. 3.20, P=0.003), the possibility of contacting a radiologist to interpret (median rating 4.17 vs. 3.20, P<0.001), and overall clinical impact (median rating 4.23 vs. 3.27, P<0.001) than FTRs. Besides, research group had higher score of IBDQ intestinal symptom dimension (median score 61.13 vs. 58.02, P=0.003), IBDQ systemic symptom dimension (median score 24.48 vs. 20.67, P<0.001), IBDQ emotional capacity dimension (median score 65.65 vs. 61.74, P<0.001), IBDQ social ability dimension (median score 26.80 vs. 22.37, P<0.001), and total IBDQ score (median score 178.07 vs. 162.80, P<0.001) than control group.

The SR of CTE in CD patients was conducive to improving the quality and readability of the report, and CD patients’ life quality could significantly improve after the intervention of an evidence-based medicine model based on SR.

Zinner’s syndrome is a rare congenital malformation of the seminal vesicle and ipsilateral upper urinary tract caused by mesonephric duct developmental anomaly during early embryogenesis. This study aimed to demonstrate the significance of magnetic resonance imaging (MRI) in distinguishing pelvic cysts in males, given that MRI is the gold standard exam for confirming the diagnosis and managing therapy.

A 21-year-old male patient with a solitary kidney who had been diagnosed since birth presented with abdominal pain. Transabdominal and transrectal ultrasonography (US), computed tomography (CT), and MRI were performed. The contrast-enhanced MRI of the pelvis showed a tubular fluid-filled, macrolobulated lesion measuring 6 x 6 x 4 cm, mildly high signal intensity in the T2-weighted images, and slightly high signal intensity in the T1-weighted images, without contrast enhancement. The left kidney was hypoplasic. Imaging findings led to the diagnosis of Zinner’s syndrome, and conservative treatment was planned.

Zinner’s syndrome is characterized by a triad consisting of unilateral renal agenesis or hypoplasia, ipsilateral seminal vesicle cyst, and ipsilateral ejaculatory duct obstruction. MRI is the modality of choice for an impeccable depiction of the anatomy of the male genital tract, for demonstrating the seminal vesicles and evaluating anomalies of the mesonephric duct. It is also useful in distinguishing seminal vesicle cysts from other cystic pelvic masses.

Zinner’s syndrome should be considered when diagnosing cystic pelvic masses in males with renal agenesis or hypoplasia. Because of its high soft tissue contrast resolution, MRI is the gold standard modality for confirming the diagnosis and assessing the cyst’s origin and contents.

Patients with atherosclerosis have a rather high risk of showing complications, if not diagnosed quickly and efficiently.

In this paper we aim to test and compare different pre-trained deep learning models, to find the best model for atherosclerosis detection in coronary CT angiography.

We experimented with different pre-trained deep learning models and fine-tuned each model to achieve the best classification accuracy. We then used the Haar wavelet decomposition to improve the model’s sensitivity.

We found that the Resnet101 architecture had the best performance with an accuracy of 95.2%, 60.8% sensitivity, and 90.48% PPV. Compared to the state of the art which uses a 3D CNN and achieved 90.9% accuracy, 68.9% Sensitivity and 58.8% PPV, sensitivity was quite low. To improve the sensitivity, we chose to use the Haar wavelet decomposition and trained the CNN model with the module of the three details: Low_High, High_Low, and High_High. The best sensitivity reached 80% with the CNN_KNN classifier.

It is possible to perform atherosclerosis detection straight from CCTA images using a pretrained Resnet101, which has good accuracy and PPV. The low sensitivity can be improved using Haar wavelet decomposition and CNN-KNN classifier.

Medical imaging plays a key role in neurosurgery; thereby, imaging and analysis of the soft and hard tissues during bone grinding is of paramount importance for neurosurgeons. Bone grinding, a minimally invasive operation in the field of neurosurgery amid osteotomy, has been used during brain cancer surgery.

With increasing attention to neural tissue damage in machining operations, imaging of these neural tissues becomes vital and reducing temperature is imperative.

In the present study, a novel attempt has been made to perform the imaging of bone tissues during the bone grinding procedure and further investigate the relationship between rotational speed, feed rate, depth of cut with cutting forces, and temperature. The role of cutting forces and temperature has been addressed as per the requirements of neurosurgeons. Firstly, a three-factor, three-level design was constructed with a full factorial design. Regression models were employed to construct the models between input parameters and response characteristics. Medical imaging techniques were used to perform a thorough analysis of thermal necrosis and damage to the bone. Subsequently, the non-dominated sorting genetic algorithm (NSGA-III) was used to optimize the parameters for reduction in the cutting forces and temperature during bone grinding while reducing neural tissue damage.

The results revealed that the maximum value of tangential force was 21.32 N, thrust force was 9.25 N, grinding force ratio was 0.453, torque was 4.55 N-mm, and temperature was 59.3°C. It has been observed that maximum temperature was generated at a rotational speed of 55000 rpm, feed rate of 60 mm/min, and depth of cut of 1.0 mm. Histopathological imaging analysis revealed the presence of viable lacunas, empty lacunas, haversian canals, and osteocytes in the bone samples. Furthermore, the elemental composition of the bone highlights the presence of carbon (c) 59.49%, oxygen (O) 35.82%, sodium (Na) 0.11%, phosphorous 1.50%, sulphur 0.33%, chlorine 0.98%, and calcium 1.77%.

The study revealed that compared to the initial scenario, NSGA-III can produce better results without compromising the trial results. According to a statistical study, the rise in temperature during bone grinding was significantly influenced by rotating speed. The density of osteocytes in the lacunas was higher at lower temperatures. Furthermore, the results of surface electron microscopy and energy dispersive spectroscopy revealed the presence of bone over the surface of the grinding burr, which resulted in the loading of the grinding burr. The results of the present investigation will be beneficial for researchers and clinical practitioners worldwide.

The primary objective of this comparative investigation was to examine the qualitative attributes of image reconstructions utilizing two distinct algorithms, namely OSEM and HYPER Iterative, in total-body 18F- FDG PET/CT under various acquisition durations and injection activities.

An initial assessment was executed using a NEMA phantom to compare image quality engendered by OSEM and HYPER Iterative algorithms. Parameters such as BV, COV, and CRC were meticulously evaluated. Subsequently, a prospective cohort study was conducted on 50 patients, employing both reconstruction algorithms. The study was compartmentalized into distinct acquisition time and dosage groups. Lesions were further categorized into three size-based groups. Quantifiable metrics including SD of noise, SUVmax, SNR, and TBR were computed. Additionally, the differences in values, namely ΔSUVmax, ΔTBR, %ΔSUVmax, %ΔSD, and %ΔSNR, between OSEM and HYPER Iterative algorithms were also calculated.

The HYPER Iterative algorithm showed reduced BV and COV compared to OSEM in the phantom study, with constant acquisition time. In the clinical study, lesion SUVmax, TBR, and SNR were significantly elevated in images reconstructed using the HYPER Iterative algorithm in comparison to those generated by OSEM (p < 0.001). Furthermore, an amplified increase in SUVmax was predominantly discernible in lesions with dimensions less than 10 mm. Metrics such as %ΔSNR and %ΔSD in HYPER Iterative exhibited improvements correlating with reduced acquisition times and dosages, wherein a more pronounced degree of enhancement was observable in both ΔSUVmax and ΔTBR.

The HYPER Iterative algorithm significantly improves SUVmax and reduces noise level, with particular efficacy in lesions measuring ≤ 10 mm and under conditions of abbreviated acquisition times and lower dosages.

In the realm of cancer studies,the differences among the biological behavior of oligometastatic prostate cancer (OPCa), localized prostate cancer (LPCa), and widely prostate cancer (WPCa) are still unclear.

The purpose of our study was to assess the clinical and intravoxel incoherent motion (IVIM) parameters of tumor burden in OPCa. In addition, the correlation between clinical and IVIM parameters and the prostate-specific antigen nadir (PSAN) and time to nadir (TTN) during initial androgen deprivation therapy (ADT) in OPCa was explored. It was found that the IVIM parameters could effectively differentiate LPCa and WPCa, as well as LPCa and OPC. Moreover, Gleason score (GS) was positively correlated with PSAN, while prostate volume was positively correlated with TTN.

About 54 patients were included in this retrospective study (mean age=74±7.4 years). ADC, D, D*, and f were acquired according to the biexponential Diffusion Weighted Imaging (DWI) model. The Kruskal-Wallis test was used to test the differences in clinical and IVIM parameters among the three groups. The Receiver Operating Characteristic (ROC) curve was used to evaluate the discrimination abilities. The Area Under the Curve (AUC) was compared using the DeLong test. Furthermore, Spearman correlation analysis was performed to assess the correlation between clinical and IVIM parameters of PSAN and TTN during initial ADT with OPCa.

There were significant differences among the three groups observed for age, PSA, GS, ADC, D and D* values (P<0.05). Multi-parameter pairwise comparison results showed that significant differences between LPCa and WPCa were observed for the age, PSA, GS, ADC, D and D* values (P<0.05). However, D* was different between the LPCa and OPCa groups (P=0.032). GS showed a significant positive correlation with PSAN (Rho=0.594, P=0.042), and prostate volume showed a significant positive correlation with TTN (Rho=0.777, P=0.003).

The IVIM parameters can effectively differentiate LPCa and WPCa, as well as LPCa and OPCa. Moreover, there was a certain trend in their distribution, which could reflect the tumor burden of PCa.

Plunging ranula is a variant of ranula, which present as a painless subcutaneous anterolateral neck mass and is located beyond the mylohyoid muscle. Plunging ranula is a diagnostic challenge and can present with intraoral component.

An elderly male presented with painless neck mass in the cervical region for three months. The mass was excised, and the patient is doing well on follow-up. We report a case of recurrent plunging ranula without any intraoral component.

Whenever the intraoral component is missing in ranula, chances of misdiagnosis and mismanagement are high. Awareness of this entity and high index of suspicion is needed for accurate diagnosis and effective management.

Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method.

The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research.

This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work.

Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.

Tetralogy of Fallot (ToF) is the most common form of cyanotic congenital heart disease, where right ventricular (RV) function is an important determinant of subsequent intervention.

In this study, we evaluate the feasibility of fast strain-encoding (fastSENC; a one-heartbeat sequence) magnetic resonance imaging (MRI) for assessing regional cardiac function in ToF.

FastSENC was implemented to characterize regional circumferential (Ecc) and longitudinal (Ell) strains in the left ventricle (LV) and RV in post-repair ToF. Data analysis was conducted to compare strain measurements in the RV to those in the LV, as well as to those generated by the MRI Tissue-Tracking (MRI-TT) technique, and to assess the relationship between strain and ejection fraction (EF).

Despite normal LVEF (55±8.5%), RVEF was borderline (46±6.4%), but significantly lower than LVEF. RV strains (RV-Ell=-20.2±2.9%, RV-Ecc=-15.7±6.4%) were less than LV strains (LV-Ell=-21.7±3.7%, LV-Ecc=-18.3±4.7%), and Ell was the dominant strain component. Strain differences between fastSENC and MRI-TT were less significant in RV than in LV. There existed moderate and weak correlations for RV-Ecc and RV-Ell, respectively, against RVEF. Compared to LV strain, RV strain showed regional heterogeneity with a trend for reduced strain from the inferior to anterior regions. Inter-ventricular strain delay was larger for Ell (64±47ms) compared to Ecc (36±40ms), reflecting a trend for contraction dyssynchrony.

FastSENC allows for characterizing subclinical regional RV dysfunction in ToF. Due to its sensitivity for evaluating regional myocardial contractility patterns and real-time imaging capability without the need for breath-holding, fastSENC makes it more suitable for evaluating RV function in ToF.

Accurate prediction of recurrence risk after resction in patients with Hepatocellular Carcinoma (HCC) may help to individualize therapy strategies. This study aimed to develop machine learning models based on preoperative clinical factors and multiparameter Magnetic Resonance Imaging (MRI) characteristics to predict the 1-year recurrence after HCC resection.

Eighty-two patients with single HCC who underwent surgery were retrospectively analyzed. All patients underwent preoperative gadoxetic acid-enhanced MRI examination. Preoperative clinical factors and MRI characteristics were collected for feature selection. Least Absolute Shrinkage and Selection Operator (LASSO) was applied to select the optimal features for predicting postoperative 1-year recurrence of HCC. Four machine learning algorithms, Multilayer Perception (MLP), random forest, support vector machine, and k-nearest neighbor, were used to construct the predictive models based on the selected features. A Receiver Operating Characteristic (ROC) curve was used to assess the performance of each model.

Among the enrolled patients, 32 patients experienced recurrences within one year, while 50 did not. Tumor size, peritumoral hypointensity, decreasing ratio of liver parenchyma T1 value (ΔT1), and α-fetoprotein (AFP) levels were selected by using LASSO to develop the machine learning models. The area under the curve (AUC) of each model exceeded 0.72. Among the models, the MLP model showed the best performance with an AUC, accuracy, sensitivity, and specificity of 0.813, 0.742, 0.570, and 0.853, respectively.

Machine learning models can accurately predict postoperative 1-year recurrence in patients with HCC, which may help to provide individualized treatment.

While cerebral cavernous malformations (CCMs) have been extensively described, few reports have described the imaging appearance of giant CCMs (GCCMs).

To describe the imaging characteristics of GCCMs and study the reasons for preoperative misdiagnosis.

We retrospectively analyzed the data of 12 patients (5 men, 7 women; mean age, 35.23 ± 12.64 years) with histopathologically confirmed GCCMs. Two radiologists analyzed the CT (n = 12) and MRI (n = 10) features: location, number, size, shape, boundary, signal intensity, and enhancement.

The sellar region, cerebral hemisphere, skull bone, and ventricle were involved in 5, 4, 2, and 1 patients, respectively. Three tumors were irregularly shaped, while nine were oval. Eleven lesions showed slightly high- and/or high-density on CT; 1 lesion appeared as a low-density cyst. Calcifications were found in 11 lesions. Four tumors showed uniform hypointensity on T1-weighted imaging (T1WI) and hyperintense signals on T2-weighted imaging (T2WI). Six tumors showed mixed low-, equal-, and high-intensity signals on T1WI and T2WI. Noticeable contrast enhancement and gradual strengthening were noted on T1WI. Ten lesions showed hemorrhage and hemosiderin deposition. The GCCMs were wrongly diagnosed as cartilage-derived tumors/ meningioma (3 patients); tumor and hematoma (2 patients each); and pituitary tumor/ meningioma, chondroma, chordoma, ependymoma, and macroadenoma (1 patient each).

GCCMs present as an oval mass with slightly high- and/or high-density calcifications on CT and show hemorrhage and hemosiderin accumulation on MRI. Therefore, slightly high- and/or high-density calcification and hemosiderin accumulation are critical clinical characteristics of GCCMs.

Acute inflammation induced by COVID-19 may lead to atherosclerotic plaque development or complicate existing plaque. In this study, we aimed to determine the atherogenic effect of COVID-19 pneumonia, confirmed by thoracic computed tomography, on coronary and carotid arteries in patients who recovered from the disease.

Our study included patients who were diagnosed with COVID-19 in our hospital at least 1 year ago, recovered, and then underwent coronary CT angiography with suspected coronary artery disease. The aim was to evaluate the burden of atherosclerotic plaque in the coronary arteries of these patients who underwent coronary CT angiography.

Patients were assigned to 3 groups according to the results of the CT scan. Group 1 included patients in the control group with no history of COVID-19 (n=36), group 2 included those with mild to moderate pneumonia symptoms (n=43), and group 3 included those with severe pneumonia symptoms (n=29). The calcium scores were 23.25±36.8 in group 1, 27.65±33.4 in group 2, and 53.58±55.1 in group 3. The calcium score was found to be significantly higher in group 3 patients with severe pneumonia (group 1-2 p=0.885, group 1-3 p<0.05, group 2-3 p<0.05).

Although there is no conclusive evidence of a relationship between COVID-19 and atherosclerosis, our study suggests a possible relationship between them. Since this relationship was found especially in cases with severe disease in our study, we believe that the treatment should focus on preventing excessive inflammatory response, and such patients should be under control in terms of coronary artery disease.