Iranian Journal of Radiology
Volume:21 Issue: 3, Jul 2024

Mild traumatic brain injury (mTBI) is a common neurological condition characterized by subtle brain dysfunction. Although generally considered less severe than moderate and severe TBI, it can have significant consequences on cognitive and motor function. Iron deposition in the basal ganglia nuclei, particularly in individuals with mTBI, has been reported as a potential biomarker for neurodegenerative and neurocognitive changes following injury. While previous studies have explored the relationship between iron deposition and mTBI, few human studies have examined these changes using quantitative susceptibility mapping (QSM). The QSM is a powerful technique that enables the non-invasive measurement of iron content in deep brain structures, providing valuable insights into the pathophysiology of mTBI.

This study aimed to compare magnetic susceptibility values (iron content) in the deep gray matter (DGM) nuclei regions of patients with mTBI and healthy controls (HCs). Patients and

In this case-control study, we enrolled 10 acute mTBI patients and 10 age-matched HCs who underwent imaging with a 3 Tesla Prisma scanner magnetic resonance imaging (MRI), including T1 weighted magnetization-prepared rapid gradient echo (T1-MPRAGE) and multi-echo 3D gradient-recalled echo (GRE) sequences for QSM reconstruction. Inclusion criteria included patients aged 16 - 50 years, a Glasgow Coma Scale (GCS) score of 13 - 15 upon emergency department arrival, and a history of loss of consciousness for less than 30 minutes. Exclusion criteria included prior brain injuries, neurological disorders, psychiatric conditions, or substance abuse. The magnetic susceptibility of the DGM nuclei was calculated using the streaking artifact reduction for QSM (STAR-QSM) technique. Group comparisons were performed using independent t -tests.

Significantly higher magnetic susceptibility values were observed in the DGM nuclei regions, specifically in the right caudate (P < 0.001), left caudate (P = 0.002), right thalamus (P = 0.004), and right hippocampus (P = 0.002), in mTBI patients compared to HCs.

These findings suggest that mTBI may be associated with alterations in iron content or magnetic susceptibility within the DGM nuclei regions compared to HCs, potentially reflecting microstructural changes. This study provides preliminary evidence for the potential of QSM as a tool for investigating tissue susceptibility in acute mTBI. Future research should focus on longitudinal studies with larger sample sizes to examine the temporal evolution of iron accumulation and its potential relationship with functional outcomes in mTBI patients.

Endometrial cancer is the most common gynecological cancer. Cervical stromal invasion in patients with endometrial carcinoma is associated with local recurrence and overall survival, making accurate preoperative evaluation essential. Currently, the delayed phase of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is recommended for diagnosing cervical stromal invasion. However, this approach is time-consuming, and diagnostic interpretations can vary across observers and institutions.

To compare the diagnostic accuracy of arterial-phase and delayed-phase DCE-MRI for detecting cervical stromal invasion in endometrial carcinoma. Patients and

This cross-sectional study retrospectively collected data from 445 patients with endometrial cancer. Two radiologists jointly evaluated cervical stromal invasion using histopathology as the gold standard reference. The McNemar test was used to compare the sensitivity and specificity of cervical stromal invasion detection between the arterial and delayed phases of DCE-MRI. Logistic regression analysis was conducted to assess the impact of tumor location and cervical lesions on diagnostic accuracy for cervical stromal invasion using DCE-MRI.

The mean age of the study population was 53.5 years [standard deviation (SD) = 3.1]. Dynamic contrast-enhanced magnetic resonance imaging images of the cervix demonstrated distinct enhancement characteristics. For detecting cervical stromal invasion, arterial-phase DCE-MRI showed a sensitivity of 66.4% [95% confidence interval (CI): 60.0 - 74.6%] and a specificity of 87.9% (95% CI: 83.9 - 91.0%). In the delayed phase, sensitivity was 69.1% (95% CI: 60.0 - 77.1%) and specificity was 88.2% (95% CI: 84.3 - 91.2%). There was no statistically significant difference between the arterial and delayed phases (P = 1.00). Factors influencing the assessment of cervical stromal invasion included the cluster distribution of Nabothian cysts and lesions located in the lower uterine segment or the internal os (P = 0.01, P < 0.01).

The arterial phase of DCE-MRI is a feasible, time-saving, and effective approach for detecting cervical stromal invasion in endometrial carcinoma.

Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies.

In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Patients and

This retrospective study employed a cross-sectional design, with a total of 140 cases, including 21 with osteoporosis. The study's inclusion criteria consisted of concurrent lumbar MRI and DEXA within a year, while exclusion criteria included infectious or neoplastic lumbar lesions, fractures, instrumentation, significant osteodegenerative changes, cases where the first four lumbar vertebrae were not included in the imaging field, and absence of diffusion-weighted imaging. Manual segmentation of lumbar vertebrae from ADC maps was performed to create a comprehensive dataset, comprising 5,580 radiomics features per case. Subsequently, the top five features selected by fast correlation-based filter (FCBF) were used to test the performance of seven Machine Learning algorithms (k-Nearest neighbors, decision tree, random forest, logistic regression, support vector machine, naive bayes, and neural network). Statistical tests and ROC curve analysis were conducted to determine the significance and thresholds of these features.

The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t -tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P < 0.05). ROC analysis revealed 58 features with AUC values above 0.70.

This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.

Uterine artery embolization (UAE) is a well-established treatment option for symptomatic uterine leiomyomas. In cases where the internal iliac artery, from which the uterine artery normally originates, is ligated, blood flow to the uterus can be preserved through various collateral vessels. Here, we report a case of a 44-year-old patient who underwent UAE via collateral branches of the right lumbar artery due to a history of kidney transplantation.

A 44-year-old patient was referred for UAE for leiomyomas. She had a history of kidney transplantation with right internal iliac artery to graft renal artery anastomosis performed using the end-to-end method. The right uterine artery was successfully embolized through collaterals from the right lumbar artery, resulting in a positive outcome.

We present a case in which collateral circulation was recruited during UAE in a patient with absent unilateral internal iliac artery flow due to prior kidney transplantation.

The spatial pattern of monosodium urate (MSU) crystal deposition is a hallmark of gout progression and contributes to the pathogenesis of associated comorbidities. However, the correlations between MSU crystal volume in the feet/ankle, knee, and hand/wrist joint sites and clinical features in gout patients remain unclear.

This study aims to explore the spatial pattern of MSU crystal deposition and its potential relationship with associated comorbidities. Additionally, it evaluates the correlation between MSU crystal volume detected by dual-energy computed tomography (DECT) in different joints and clinical characteristics, including serum uric acid (sUA), glomerular filtration rate (GFR), and disease duration. Patients and

In this single-center study conducted from October 2017 to August 2023, 527 patients (mean age 49.0 ± 23.0 years) diagnosed with gout and confirmed MSU deposition via DECT were included. Spearman's rank correlation coefficient was used to assess relationships between sUA, gout duration, GFR, and MSU volumes in the foot/ankle, knee, and hand/wrist.

Among the 527 patients, the median gout duration was 6.0 years. MSU crystals were most commonly found in the feet/ankles (84.8%), followed by knees (63.6%) and hands/wrists (28.8%). Gout duration positively correlated with MSU crystal volumes (r = 0.32, P < 0.01). MSU volumes in the feet/ankle and knee showed negative correlations with GFR (r = -0.18, P < 0.01; r = -0.16, P = 0.03, respectively), while no significant correlation was observed in hand/wrist volume (r = -0.06, P = 0.55). No significant associations were found between MSU volumes and sUA levels across all groups.

The MSU crystal burden negatively correlates with GFR but not with sUA levels. The volumes of MSU crystals in the ankle/foot and knee joints, along with the total volume of MSU crystals in the ankle/foot, hands/wrists, and knee joints, show a negative correlation with GFR and a positive correlation with disease duration. This indicates a need for further research on the relationship between MSU deposition and renal dysfunction in gout patients.

Advancements in technology have significantly improved the diagnosis of solitary pulmonary nodules in the lungs. Various computed tomography (CT) imaging techniques, including modern dual-energy computed tomography (DECT), have enhanced the ability to accurately classify pulmonary nodules as benign or malignant. In this study, three different dual-energy parameters — iodine load, contrast load, and visual assessment — were evaluated for their potential in characterizing pulmonary nodules.

The aim of this study was to assess the reliability and effectiveness of DECT in distinguishing benign from malignant pulmonary nodules using different parameters, including visual assessment, iodine concentration, and contrast load. Patients and

This prospective study included patients who underwent contrast-enhanced thoracic DECT for solitary pulmonary nodules, had histopathological examination results, or had at least a two-year follow-up CT scan. Patients with nodules smaller than 6 mm or completely calcified nodules were excluded. Patients diagnosed with a suspicious solitary pulmonary nodule on chest radiography and subsequently underwent contrast-enhanced DECT, or those diagnosed with a lung nodule on routine non-contrast CT scans and later evaluated using DECT, were included in the study.Benign and malignant nodules were compared based on gender, age, contrast load, iodine load, and color map assessment. Nodule images were obtained 40 seconds after intravenous contrast administration using single-source DECT (120 kV split filter) with twin-beam technology. The visual enhancement and color map evaluation, including contrast and iodine load measurements, were separately calculated and recorded for each lung nodule.

A total of 59 patients [30 males (50.8%) and 29 females (49.2%)] with a solitary pulmonary nodule met the inclusion criteria. Among the 59 pulmonary nodules, 16 (27.1%) were malignant, and 43 (72.9%) were benign. Of the benign lesions, 23 (53.5%) were found in males and 20 (46.5%) in females.The mean age of patients with benign nodules was 53.5 ± 12 years (range: 25 - 73 years), while for those with malignant nodules, it was 69.2 ± 5.59 years (range: 57 - 75 years). There was no statistically significant difference in age between the two groups (P = 0.506).The median contrast load was 0.0 Hounsfield units (HU) [interquartile range (IQR: 64)] in benign nodules and 63 HU (IQR: 154) in malignant nodules. Malignant nodules had a significantly higher contrast load than benign nodules (P = 0.003). Using a cut-off value of 22 HU for contrast load in malignancy diagnosis, the sensitivity was 100%, specificity was 58.14%, positive predictive value (PPV) was 47.06%, and negative predictive value (NPV) was 100%. The area under the curve (AUC) was 0.746.The median iodine load was 0.0 mg/dL (IQR: 4.5) in benign nodules and 4.5 mg/dL (IQR: 11.8) in malignant nodules. Malignant nodules had a significantly higher iodine load than benign nodules (P < 0.001). Using a cut-off value of 1 mg/mL for malignancy diagnosis, the sensitivity was 100%, specificity was 62.79%, PPV was 50%, and NPV was 100% (AUC: 0.768).

Dual-energy computed tomography provides valuable contributions in differentiating benign and malignant pulmonary nodules. In this study, the diagnostic value of three different approaches — visual iodine coverage color map, iodine concentration, and contrast load — was demonstrated in distinguishing these lesions.