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Background: Ultrasound-guided first sacral transforaminal epidural steroid injection (S1 TFESI) is a useful and easily applicable alternative to fluoroscopy or computed tomography (CT) in lumbosacral radiculopathy. When a needle approach is used, poor visualization of the needle tip reduces the accuracy of the procedure, increasing its difficulty. This study aimed to improve ultrasound-guided S1 TFESI by evaluating radiological S1 posterior foramen data obtained using three-dimensional CT (3D-CT). Methods: Axial 3D-CT images of the pelvis were retrospectively analyzed. The radiological measurements obtained from the images included 1st posterior sacral foramen depth (S1D, mm), 1st posterior sacral foramen width (S1W, mm), the angle of the 1st posterior sacral foramen (S1A, °), and 1st posterior sacral foramen distance (S1ds, mm). The relationship between the demographic factors and measured values were then analyzed. Results: A total of 632 patients (287 male and 345 female) were examined. The mean S1D values for males and females were 11.9 ± 1.9 mm and 10.6 ± 1.8 mm, respectively (P < 0.001); the mean S1A 28.2 ± 4.8° and 30.1 ± 4.9°, respectively (P < 0.001); and the mean S1ds, 24.1 ± 2.9 mm and 22.9 ± 2.6 mm, respectively (P < 0.001); however, the mean S1W values were not significantly different. Height was the only significant predictor of S1D (β = 0.318, P = 0.004). Conclusions: Ultrasound-guided S1 TFESI performance and safety may be improved with adjustment of needle insertion depth congruent with the patient’s height. [ABSTRACT FROM AUTHOR]

Objectives: Coronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC. Methods: Fully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1–10, 11–100, 101–400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers). Results: Of 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89–0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98–0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland–Altman analysis demonstrated little difference (1.69, 95% limits of agreement: −41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88–0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan. Conclusions: This artificial intelligence–based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation. Key Points: • Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)–based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis. [ABSTRACT FROM AUTHOR]

Objective: To describe the prevalence and consequences of incidental findings when implementing routine noncontrast CT prior to cardiac surgery. Methods: In the multicenter randomized controlled CRICKET study, 862 adult patients scheduled for cardiac surgery were randomized 1:1 to undergo standard of care (SoC), which included a chest-radiograph, or an additional preoperative noncontrast chest CT-scan (SoC+CT). In this subanalysis, all incidental findings detected on the chest radiograph and CT-scan were analyzed. The influence of smoking status on incidental findings was also evaluated, adjusting for sex, age, and group allocation. Results: Incidental findings were observed in 11.4% (n = 49) of patients in the SoC+CT group and in 3.7% (n = 16) of patients in the SoC-group (p < 0.001). The largest difference was observed in findings requiring follow-up (SoC+CT 7.7% (n = 33) vs SoC 2.3% (n = 10), p < 0.001). Clinically relevant findings changing the surgical approach or requiring specific treatment were observed in 10 patients (1.2%, SoC+CT: 1.6% SoC: 0.7%), including lung cancer in 0.5% of patients (n = 4) and aortic dilatation requiring replacement in 0.2% of patients (n = 2). Incidental findings were more frequent in patients who stopped smoking (OR 1.91, 1.03–3.63) or who actively smoked (OR 3.91, 1.85–8.23). Conclusions: Routine CT-screening increases the rate of incidental findings, mainly by identifying more pulmonary findings requiring follow-up. Incidental findings are more prevalent in patients with a history of smoking, and preoperative CT might increase the yield of identifying lung cancer in these patients. Incidental findings, but not specifically the use of routine CT, are associated with delay of surgery. Key Points: • Clinically relevant incidental findings are identified more often after a routine preoperative CT-scan, when compared to a standard of care workup, with some findings changing patient management. • Patients with a history of smoking have a higher rate of incidental findings and a lung cancer rate comparable to that of lung cancer screening trials. • We observed no clear delay in the time to surgery when adding routine CT screening. [ABSTRACT FROM AUTHOR]

Objective: To investigate the performance of quantitative CT analysis in predicting the prognosis of patients with locally advanced oesophageal squamous cell carcinoma (ESCC) after two cycles of induction chemotherapy before definitive chemoradiotherapy/radiotherapy. Methods: A total of 110 patients with locally advanced ESCC were retrospectively analysed. Baseline chest CT and CT after two cycles of induction chemotherapy were analysed. A multivariate Cox proportional-hazard regression model was used to identify independent prognostic markers for survival analysis. Then, a CT scoring system was established. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were employed for analysing the prognostic value of the CT scoring system. Results: Body mass index, treatment strategy, change ratios of thickness (ΔTHmax), CT value of the primary tumour (ΔCTVaxial) and the short diameter (ΔSD-LN), and the presence of an enlarged small lymph node (ESLN) after two cycles of chemotherapy were noted as independent factors for predicting overall survival (OS). The specificity of the presence of ESLN for death after 12 months was up to 100%. Areas under the curve value of the CT scoring system for predicting OS and progression-free survival (PFS) were higher than that of the RECIST (p < 0.05). Responders had significantly longer OS and PFS than non-responders. Conclusion: Quantitative CT analysis after two cycles of induction chemotherapy could predict the outcome of locally advanced ESCC patients treated with definitive chemoradiotherapy/radiotherapy. The CT scoring system could contribute to the development of an appropriate strategy for patients with locally advanced ESCC. Key Points: • Quantitative CT evaluation after two cycles of induction chemotherapy can predict the long-term outcome of locally advanced oesophageal cancer treated with definitive chemoradiotherapy/radiotherapy. • A CT scoring system provides valuable imaging support for indicating the prognosis at the early stage of therapy. • Quantitative CT evaluation can assist clinicians in personalising treatment plans. [ABSTRACT FROM AUTHOR]

Objectives: Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD). Materials and methods: A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses). Results: Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available. Conclusion: The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice. Key Points: • A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083). [ABSTRACT FROM AUTHOR]

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Objective: To measure the prevalence of adrenal nodules detected on staging CT in patients with resectable colorectal cancer, and the proportion of patients with malignant nodules among them. Methods: This retrospective study included 6474 patients (median age, 65; interquartile range, 56–73; 3902 men) who underwent staging CT for colorectal cancer between May 2003 and December 2018. The patients had potentially resectable colorectal cancer, including resectable hepatic or pulmonary metastases. Through retrospective CT image review, patients with adrenal nodules were identified for the prevalence of adrenal nodule. Among patients with adrenal nodules, per-patient proportions of malignant nodules, adrenal metastasis from colorectal cancer, and additional adrenal examinations (biopsy or imaging tests) were measured. A secondary analysis was performed using data from the official CT reports. Results: The prevalence of adrenal nodules was 5.6% (363 of 6474; 95% CI: 5.1, 6.2). The proportions of malignant nodules and adrenal metastasis from colorectal cancer were 0.8% (3 of 363; 0.2, 2.4) and 0.3% (1 of 363; 0.0, 1.5), respectively. 6.1% (22 of 363; 3.8, 9.0) of the patients underwent additional adrenal examination. According to official CT reports, the prevalence of adrenal nodules and proportions of malignant nodules, adrenal metastasis from colorectal cancer, and additional adrenal examination were 1.9% (125 of 6474; 1.6, 2.3), 1.6% (2 of 125; 0.2, 5.7), 0% (0 of 125; 0.0, 2.9), and 10.4% (1 of 125; 5.7, 17.1), respectively. Conclusion: Adrenal nodules detected in staging CTs in patients with otherwise resectable colorectal cancers are rarely malignant. Key Points: • Among 6474 patients who underwent staging CT and had potentially resectable colorectal cancer, 363 had adrenal nodules (≥ 10 mm) detected in retrospective CT image review. • Three out of the 363 patients with adrenal nodules detected on staging CT had malignant adrenal nodules, one of whom had metastasis from colorectal cancer. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the clinical performance of an artificial intelligence (AI)–based motion correction (MC) reconstruction algorithm for cerebral CT. Methods: A total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment. Results: Compared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group. Conclusions: The AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT. Key Points: • An artificial intelligence–based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department. [ABSTRACT FROM AUTHOR]

Objectives: To describe injury patterns in children with multiple trauma (MT), evaluate the yield of dual-phase whole-body CT (WBCT), and quantify missed injuries detected on second reading. Methods: Remotely analyzed WBCT performed between 2011 and 2020 in 63 emergency departments on children admitted for MT were included. Second reading occurred within 24 h. Collected data included age, sex, mechanism, Injury Severity Score (ISS), radiologists' experience, time and duration of first reading, conclusion of both readings, and dosimetry. Melvin score assessed the clinical impact of missed injuries. Results: Overall, 1114 patients were included, 1982 injuries were described in 662 patients (59.4%), 452/1114 (40.6%) WBCT were negative, and 314 (28.2%) patients had MT (≥ 2 body parts injured). The most frequent injuries were pulmonary contusions (8.3%), costal fractures (6.2%), and Magerl A1 vertebral fractures (4.9%). Overall, 151 injuries were missed in 92 (8.3%) patients. Independent predictors for missed injuries were age ≤ 4 years (p = 0.03), number of injured body parts ≥ 2 (p = 0.01), and number of injuries ≥ 3 (p < 0.001). Melvin score grade 3 lesions were found in 16/92 (17.4%) patients with missed injuries (1.4% of all WBCT), where only prolonged follow-up was necessary. Thirteen active bleeding or pseudoaneurysms were detected (0.7% of injuries). Conclusion: Injuries were diagnosed in 59.4% of patients. Double-reading depicted additional injuries in 8.3% of patients, significantly more in children ≤ 4 years, with ≥ 3 injuries or ≥ 2 injured body parts. As 28 % of patients had MT and 1.1% had active extravasation or pseudoaneurysm, indication for WBCT should be carefully weighted. Key Points: • When performed as a first-line imaging evaluation, approximately 41% of WBCT for MT children were considered normal. • The three most common injuries were pulmonary contusions, costal fractures, and Magerl A1 vertebral fractures, but the patterns of traumatic injuries on WBCT depended on the children's age and the trauma mechanism. • The independent predictors of missed injuries were age ≤ 4 years, number of body parts involved ≥ 2, and total number of injuries ≥ 3. [ABSTRACT FROM AUTHOR]

Objectives: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently.Methods: We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance.Results: DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001).Conclusion: DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM.Key Points: • Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures. [ABSTRACT FROM AUTHOR]

The characteristics of bone fragments are the main influencing factors for the choice of treatment in intertrochanteric fractures. This study aimed to develop a deep learning algorithm for recognizing and segmenting individual fragments in CT images of complex intertrochanteric fractures for orthopedic surgeons. This study was based on 160 hip CT scans (43,510 images) of complex fractures of three types based on the Evans-Jensen classification (40 cases of type 3 (IIA) fractures, 80 cases of type 4 (IIB)fractures, and 40 cases of type 5 (III)fractures) retrospectively. The images were randomly split into two groups to construct a training set of 120 CT scans (32,045 images) and a testing set of 40 CT scans (11,465 images). A deep learning model was built into a cascaded architecture composed by a convolutional neural network (CNN) for location of the fracture ROI and another CNN for recognition and segmentation of individual fragments within the ROI. The accuracy of object detection and dice coefficient of segmentation of individual fragments were used to evaluate model performance. The model yielded an average accuracy of 89.4% for individual fragment recognition and an average dice coefficient of 90.5% for segmentation in CT images. The results demonstrated the feasibility of recognition and segmentation of individual fragments in complex intertrochanteric fractures with a deep learning approach. Altogether, these promising results suggest the potential of our model to be applied to many clinical scenarios. [ABSTRACT FROM AUTHOR]

BackgroundTakayasu's arteritis (TAK) presenting as fever of unknown origin (FUO) is rare in clinic. Diagnosis is difficult in the early stageas the non-specific clinical symptoms and signs, the lack of imaging findings of great artery stenosis and occlusionof patients. With the wide application of positron emission computed tomography/computed tomography (PET-CT) in the diagnosis and differential diagnosis of FUO, it may contribute to the early diagnosis of TAK.ObjectiveTo investigate the application value of PET-CT in the diagnosis and condition assessment of early TAK presenting as FUO.MethodsThe clinical presentations, laboratory examinations, PET-CT findings, and treatment outcomes of 7 cases of TAK presenting as FUO that eventually diagnosed by PET-CT from January 2013 to January 2021were retrospectively analyzed, and the medical literature from 1991 to 2021 was reviewed.ResultsAll the 7 patients were female, the age of onsetranging from 27 to 58 years old, with an average age of (37.9±10.6) years. The course of disease was from 1 to 6 months, with an average of (3.9±2.4) months. FUO was the first symptom of all patients (100.0%) , which may be accompanied by palpitation (14.3%) , dizziness (28.6%) , headache (14.3%) , fatigue (28.6%) and other non-specific clinical symptoms. Laboratory tests showed elevated levels of leukocytes (71.4%) , platelets (57.1%) , erythrocyte sedimentation rate (ESR) (100.0%) , C-reactive protein (CRP) (85.7%) , tumor necrosis factor-α (TNF-α) (42.9%) and interleukin-6 (IL-6) (85.7%) . Color Doppler ultrasound, computed tomography angiography (CTA) and magnetic resonance angiography (MRA) showed no stenosis and occlusion of large arteries, while PET-CT showed inflammatory changes in arterial walls at multiple locations, including thecending aorta (28.6%) , aortic arch (14.3%) , common carotid artery (28.6%) , subclavian artery (28.6%) , thoracic aorta (14.3%) , abdominal aorta (14.3%) and renal artery (14.3%) .ConclusionPET-CT is helpful in the diagnosis and condition assessment of pre-pulseless Takayasu's arteritis presenting as FUO.

Introduction: The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist’s expertise, which may result in subjective evaluations. Objective: To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples. Materials and methods: Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic’s dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers. Results: The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively. Conclusion: Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings.

In nature, basically 2 types of myocardial vascular patterns exist: the sinusoidal and the coronary type. In the sinusoidal type, the sinusoid is completely fed by blood coming directly from the ventricle through a spongy sinusoidal network. This pattern is found in cold-blooded animals and in the early embryologic development of human (warm-blooded) hearts. A 61-year-old man with atrial fibrillation developed severe tachymyopathy with a severely reduced left-ventricular ejection fraction (LVEF) of 20%. The patient had no history of prior heart surgery or other cardiac interventions. He was referred for a computed tomography (CT) scan for assessment of pulmonary vein anatomy prior to their isolation. Incidentally, a focal myocardial defect of the midventricular infero-septal wall with tail-like extension into the right ventricular cavity was detected. In a cardiac magnetic resonance (CMR) scan there was no evidence of a myocardial infarction or fibrosis. In the absence of a ventricular septal defect by CT, CMR and echocardiography the diagnosis of a persistent myocardial sinusoid was evident. In this case, we used state-of-the art methods for pathology visualization, illustrating the effectiveness of CT and CMR in the precise detection and differential diagnosis of myocardial anomalies including a multi-coloured 3D-printed model that may further enhance visuospatial appreciation of those anomalies.

Abstract Objective To compare magnetic resonance imaging (MRI) using a body coil with computed tomography (CT) in measuring the tibial tubercle-trochlear groove distance (TT-TG) and the patellar tendon-cartilaginous trochlear groove (PT-CTG) distances, and evaluate interrater reliability. Methods The study group consisted of 34 knees from 17 asymptomatic subjects with no history of knee pathology, trauma or surgery. A low-dose CT scan and an axial T1-weighted MRI sequence of the knees were performed with rigorous standardization of the positioning with full extension of the knees and parallel feet. Two musculoskeletal radiologists performed the measurements independently. The reliability of the TT-TG and PT-CTG distances on CT (17.1 ± 4.2 mm and 17.3 ± 4.2 mm) and of MRI (16.2 ± 3.7 mm and 16.5 ± 4.1 mm) was assessed by intraclass correlation coefficient (ICC [2,1]) and Bland-Altman graphs, as well as the interrater reliability for both methods. Results Good reliability and agreement was observed between CT and MRI measurements for TT-TG and PT-CTG, with an ICC of 0.774 (p< 0.001) and 0.743 (p< 0.001), respectively, and no systematic bias was observed. The interrater reliability was excellent for all measurements on both imaging methods. Conclusion This was the first study that compared MRI using a body coil with CT in measuring the TT-TG distance, with the potential clinical implication that the CT in this clinical setting could be avoided.

Abstract Objective The approachability of the cervicothoracic region anteriorly based on age and gender, and the possibility of anatomic variances in different geographic populations have not been previously investigated. The aim of the present work was to perform a radiographic analysis of Brazilian patients to assess anterior approachability of the cervicothoracic junction based on age and gender. Methods Retrospective radiographic analysis of 300 computed tomography scans. Patients were separated based on age and gender. The radiographic parameters studied were: horizontal level above the sternum (HLS), vertebral body angle (VBA), intervertebral disc line (IDL), and intervertebral disc line angulation (IDLA). Results The most frequent HLS and IDL were T2 (34.3%) and C7-T1 (46%) respectively. Vertebral body angleand IDLA had average values of 18 ± 8.94 and 19 ± 7.9 degrees, respectively. Males had higher values in both IDLA (p= 0.003) and VBA (p= 0.02). Older groups had higher values in both IDLA (p= 0.01) and VBA (p= 0.001). No differences were observed in HLS between gender (p= 0.3) or age groups (p= 0.79). No differences were seen in IDL between gender groups (p= 0.3); however, the older group had a more caudal level than the younger groups (p= 0.12). ConclusionsCompared to other populations, our sample had a more cephalad IDL and HLS. Vertebral body angle and IDLA were higher in males and higher angles for VBA and IDLA were shown for older groups. Intervertebral disc line was more caudal with aging.

Abstract Background In wrist arthrograms, aberrant contrast material is frequently seen extending into the soft tissue adjacent to the ulnar styloid process. Since the prestyloid recess can mimic contrast leakage in CT arthrography, this study aims to provide a detailed analysis of its morphologic variability, while investigating whether actual ulnar-sided leakage is associated with injuries of the triangular fibrocartilage complex (TFCC). Methods Eighty-six patients with positive wrist trauma history underwent multi-compartment CT arthrography (40 women, median age 44.5 years). Studies were reviewed by two board-certified radiologists, who documented the morphology of the prestyloid recess regarding size, opening type, shape and position, as well as the presence or absence of ulnar-sided contrast leakage. Correlations between leakage and the presence of TFCC injuries were assessed using the mean square contingency coefficient (rɸ). Results The most common configuration of the prestyloid recess included a narrow opening (73.26%; width 2.26 ± 1.43 mm), saccular shape (66.28%), and palmar position compared to the styloid process (55.81%). Its mean length and anterior–posterior diameter were 6.89 ± 2.36 and 5.05 ± 1.97 mm, respectively. Ulnar-sided contrast leakage was reported in 29 patients (33.72%) with a mean extent of 12.30 ± 5.31 mm. Leakage occurred more often in patients with ulnar-sided TFCC injuries (rɸ = 0.480; p

Objective: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA.This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA.Radiologists’ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67–0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003).Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA.QuestionHow good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection?FindingsRadiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56).Clinical relevanceRadiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.Methods: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA.This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA.Radiologists’ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67–0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003).Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA.QuestionHow good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection?FindingsRadiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56).Clinical relevanceRadiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.Results: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA.This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA.Radiologists’ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67–0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003).Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA.QuestionHow good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection?FindingsRadiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56).Clinical relevanceRadiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.Conclusion: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA.This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA.Radiologists’ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67–0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003).Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA.QuestionHow good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection?FindingsRadiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56).Clinical relevanceRadiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.Key Points: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA.This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA.Radiologists’ diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67–0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003).Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA.QuestionHow good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection?FindingsRadiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56).Clinical relevanceRadiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA. [ABSTRACT FROM AUTHOR]

Background and Objectives: We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Materials and Methods: Three 5 × 5 × 5 mm3 uric acid stones were placed in a physical human phantom in various locations. Three tube voltages (120, 100, and 80 kV) and four current–time products (100, 70, 30, and 15 mAs) were implemented in 12 scans. Each scan was reconstructed with filtered back projection (FBP), statistical iterative reconstruction (IR, iDose), and knowledge-based iterative model reconstruction (IMR). By applying deep learning to each image, we took 12 more scans. Objective image assessments were calculated using the standard deviation of the Hounsfield unit (HU). Subjective image assessments were performed by one radiologist and one urologist. Two radiologists assessed the subjective assessment and found the stone under the absence of information. We used this data to calculate the diagnostic accuracy. Results: Objective image noise was decreased after applying a deep learning tool in all images of FBP, iDose, and IMR. There was no statistical difference between iDose and deep learning-applied FBP images (10.1 ± 11.9, 9.5 ± 18.5 HU, p = 0.583, respectively). At a 100 kV–30 mAs setting, deep learning-applied FBP obtained a similar objective noise in approximately one third of the radiation doses compared to FBP. In radiation doses with settings lower than 100 kV–30 mAs, the subject image assessment (image quality, confidence level, and noise) showed deteriorated scores. Diagnostic accuracy was increased when the deep learning setting was lower than 100 kV–30 mAs, except for at 80 kV–15 mAs. Conclusions: At the setting of 100 kV–30 mAs or higher, deep learning-applied FBP did not differ in image quality compared to IR. At the setting of 100 kV–30 mAs, the radiation dose can decrease by about one third while maintaining objective noise.

Introduction. Metastatic head and neck carcinoma (HNC) from an unknown primary tumor is defined as a metastatic disease in the neck’s lymph nodes without evidence of a primary tumor after appropriate investigation. Multiple national guidelines recommend taking essential steps in diagnostic protocols which involve a detailed clinical exam with radiological imaging, fine-needle aspiration (FNA) biopsy of the cervical tumor, esophagogastroduodenoscopy (EGD) with palatine and lingual tonsillectomy, immunohistochemical staining, and human papillomavirus (HPV) detection. Treatment of HNCs of unknown primary origin involves surgery (neck dissection) with radiotherapy (RT), while some authors recommend chemo-radiotherapy in cases of advanced regional disease. Case report. A 44-year-old male was referred to the tertiary medical center because of a large ulcero-infiltrative cervical mass on the right side. Examination of the head and neck and flexible nasopharyngolaryngeal endoscopy was conducted, followed by computed tomography (CT) of the head, neck, and thorax with intravenous contrast. The primary localization of the tumor was not confirmed by these diagnostic methods. An open biopsy of the neck mass established a histopathology diagnosis of metastatic squamous cell carcinoma (SCC). Results of EGD with biopsies and bilateral tonsillectomy were negative for malignancy. Treatment included extended radical neck dissection with reconstruction and postoperative ipsilateral RT. The patient presented with an extensive pharyngolaryngeal tumor five years after the first surgery. Biopsy with histopathology examination confirmed the diagnosis of SCC. Conclusion. A structured step-by-step diagnostic approach to identifying the primary site of the metastatic HNC is mandatory. Substantial advances in diagnostics and operative techniques have increased the likelihood of primary tumor identification and detection of the disease's regional and systemic spread. The purpose of adherence to guidelines results in higher overall survival and longer regional disease-free survival in these patients.

Background/Aim. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global threat and a huge problem for our community. There are so many open questions. The aim of this study was to establish the frequency of gastrointestinal (GI) symptoms in hospitalized patients with infection caused by this virus (coronavirus disease-19 –COVID-19), but also to compare if patients with GI symptoms have a higher computed tomography (CT) scan severity score of interstitial pneumonia (IP) compared to patients with COVID-19 without GI symptoms. Methods. Our database comprised 322 patients with COVID-19 who were divided into two groups, patients with and without GI symptoms. All information was taken from anamnestic data and patients’ history, followed by statistical analysis. Results. Thorax CT scans of 206 patients (63.9%) were described as bilateral IP, of which 76 CT scans (36.9%) were described by radiologists as the peak of infection. Moreover, 130 patients (40.4%) had GI symptoms, and even 58 out of 130 patients (44.6%) reported GI symptoms as the first manifestation of COVID-19 infection. The most commonly reported one was the lack of appetite (73 patients or 56.15%). Furthermore, 65 (50%) patients reported diarrhea, 25 (19.2%) patients reported nausea and vomiting, and 9 (6.9%) patients reported abdominal pain. In addition, among patients with bilateral IP and GI tract symptoms, 31 (40.79%) of them did not have a higher CT scan severity score at the peak of the disease compared to the patients without GI symptoms (45 of them or 59.2%), (p = 0.704). Conclusion. GI symptoms often are the first manifestation of COVID-19. Therefore, every patient with newly formed digestive tract symptoms should be tested for COVID-19. On the other hand, GI symptoms do not indicate COVID-19 patients will have a severe form of IP.

Introduction. Late metastases of malignant tumors in the temporal bone are very rare lesions. They can be asymptomatic for a long time and usually manifest them-selves in the form of hearing loss, dizziness, tinnitus, and paralysis of the facial nerve. Modern radiological diagnostics and explorative surgery with biopsy are essential for diagnosis. Case report. We present a rare and unusual case of a 66-year-old female patient with facial nerve paralysis that appeared as the first sign of metastatic breast cancer in the temporal bone 10 years after treatment. A sudden hearing loss and dizziness occurred six months later, and the value of CA 15-3 was elevated. Scintigraphy pointed to susceptible metastatic deposits of the axial skeleton without lesions in the temporal bone. Finally, repeated computed tomography revealed osteolytic changes in the temporal bone six months after that. Immunohistochemical analysis of mastoid tissue samples confirmed that it was a breast cancer metastasis. One year after palliative radiotherapy and oral hormone therapy, a patient had a good general condition with a better function of the facial nerve. Conclusion. A high degree of clinical suspicion sometimes requires repeated radio-logical diagnostics in order to detect osteolytic metastatic changes in the temporal bone but also in other bone structures within the hematogenous dissemination of the malignant disease.

ABSTRACT Purpose: Contrast-enhanced CT scan is the standard staging modality for patients with bladder cancer undergoing radical cystectomy (RC). Involvement of lymph nodes (LN) determines prognosis of patients with bladder cancer. The detection of LN metastasis by CT scan is still insufficient. Therefore, we investigated various CT scan characteristics to predict lymph node ratio (LNR) and its impact on survival. Also, pre-operative CT scan characteristics might hold potential to risk stratify cN+ patients. Materials and Methods: We analyzed preoperative CT scans of patients undergoing RC in a tertiary high volume center. Retrospectively, local tumor stage and LN characteristics such as size, morphology (MLN) and number of loco-regional LN (NLN) were investigated and correlation to LNR and survival was analyzed. CT scan characteristics were used to develop a risk stratification using Kaplan-Maier and multivariate analysis. Results: 764 cN0 and 166 cN+ patients with complete follow-up and imaging data were included in the study. Accuracy to detect LN metastasis and locally advanced tumor stage in CT scan was 72% and 62%. LN larger than 15mm in diameter were significantly associated with higher LNR (p=0.002). Increased NLN correlated with decreased CSS and OS (p=0.001: p=0.002). Furthermore, CT scan based scoring system precisely differentiates low-risk and high-risk profiles to predict oncological outcome (p 15mm significantly correlated with higher LNR. Identification of increased loco-regional LN was associated with worse survival. For the first time, precise risk stratification based on computed-tomography findings was developed to predict oncological outcome for clinical lymph node-positive patients undergoing RC.

Introduction. Aggressive fibromatosis, also known as desmoid type fibromatosis (DF) is a locally aggressive fibroblastic neoplasm that can arise anywhere in the body with no potential for metastasis and a high recurrence rate after surgical resection. Mesenteric fibromatosis are locally aggressive DF of the mesentery with a high propensity for bowel involvement. The real etiology of these tumors remains unknown, occurring sporadically or in association with familial adenomatous polyposis (FAP), as Gardnerʼs syndrome. Case report. A 34-year-old female patient presented with a palpable solid tumefactive mass in the left hemiabdomen. Contrast enhanced computed tomography (CT) and magnetic resonance imaging (MRI) revealed multiple massive solid tumefactions in the mesentery and in between the small bowel loops. Colonoscopy confirmed the presence of multiple sessile polyps characteristic of FAP. Tissue samples of the mesenteric mass were acquired via ultrasound guided biopsy with histopathologic confirmation of desmoid fibromatosis with imunohistochemical analysis. The risk of surgery was deemed too high at the time due to the size of the mass and proximity to mesenteric vascular structures, therefore the patient was planned for chemotherapy with a potential for further surgical reevaluation. Conclusion. Mesenteric fibromatosis is a rare neoplasm that presents with a wide range of histologic and imaging features. CT and MRI play a crucial role in evaluation and planning an optimal treatment model for patients with mesenteric fibromatosis.

Introduction. Annular pancreas is a rare congenital anomaly in which a band of the pancreatic tissue, in continuity with the pancreatic head, completely or incompletely surrounds the descending part of the duodenum. An abnormal pancreatic development can cause complete annular pancreas, partial annular pancreas, and pancreas divisum. Complete annular pancreas is diagnosed in newborns, while the diagnosis of the partial annular pancreas is more frequently established in adults. The most reliable diagnostic methods are computed tomography and magnetic resonance cholangiopancreatography. The anomaly is treated surgically, using bypass procedures. Case report. A 12-year-old girl presented malnourished, with occasional feeding problems, vomiting, heartburn, and pain from infancy. The upper gastrointestinal series showed an extremely dilated stomach, the first and the second part of the duodenum. An endoscopic exam revealed the dilated stomach, pylorus, and the first and the second part of the duodenum with retained contrast, while the entrance of the endoscope into the third part of the duodenum was not possible. Computed tomography showed pancreatic tissue encircling the second part of the duodenum and the characteristic "crocodile jaw" sign. Roux-en-Y duodenojejunostomy was performed as a bypass procedure. Conclusion. The complete annular pancreas is a well-known and easily diagnosed anomaly in newborns. The partial annular pancreas is often poorly recognized, especially in patients who do not present with marked duodenal obstruction. Unrevealed, it causes chronic problems in food intake, with possible serious complications. Although a very rare condition in the pediatric population, partial annular pancreas should be taken into consideration in unclear cases of chronic poor oral food intake and vomiting.

OBJECTIVE: To explore the prevalence of osteopenia and osteoporosis within a cohort of Pakistani postmenopausal women with respect to the lumbar spine and hip. METHODS: This cross-sectional study was conducted at Shifa International Hospital Islamabad, Pakistan from September 2019 to Feb 2020. Study comprised of 237 postmenopausal females who visited the outpatient department of the hospital. The T-scores of Bone Mineral density (BMD) data was collected and arranged in three groups: normal, osteopenia and osteoporosis. Data was analyzed to explore the distribution of the data and correlation analyses using R software version 3.6.3. RESULTS: Out of 237 females, majority were ranging in age from 61-70 years (n=110; 46.4%), followed by 51-60 years age group (n=60; 25.3%). Osteopenia was noted in 98 (41.4%) cases in lumber spine and hip area. Osteoporosis was found in 79 (33.3%) and 59 (24.9%) cases in lumber spine and hip region respectively. Mean T score was -1.775±-2.000 and median T score was -1.469 for lumber spine and hip. T-scores distribution of lumbar spine and hip indicated the highest proportion having a sore of -2 SD (n=60; 25.3% each), followed by -3 SD in 52 (21.9%) cases. Bone mass density was negatively correlated with age (p≤0.01). However, no significant difference was found among the BMD values of lumbar spine and hip region. CONCLUSION: Decreased bone density was a common occurrence affecting postmenopausal females and there is increase in degenerative bone loss with increasing age. Hip and lumbar spine region are equally affected by degenerative bone loss.

Purpose This study aimed to evaluate the predictive value of preoperative chest computed tomography (CT) for axillary lymph node (ALN) metastasis in patients with breast cancer. Methods CT features of ALNs were retrospectively reviewed in 212 patients with breast cancer who underwent preoperative chest CT examination and ALN dissection. Primary tumor size and CT characteristics of ALNs (cortical thickness, cortical shape, the presence or absence of contrast enhancement of ALNs, and the presence or absence of perinodal infiltration) were recorded and analyzed. A nomogram was developed to visualize the associations between the predictors and each ALN status endpoint. Results Of 212 patients, 95 (44.8%) had ALN metastasis. Primary tumor size and CT characteristics of ALNs were identified as predictors of ALN metastasis. The nomogram comprising primary tumor size and cortical shape was a good validation model for predicting ALN metastasis. The sensitivity, specificity, and accuracy of the nomogram for predicting ALN metastasis were 88.4%, 79.5%, and 83.5%, respectively. Conclusion Using preoperative chest CT scans, a nomogram combining the cortical shape of ALNs with the primary tumor size showed good performance in predicting ALN metastasis.

Objective: The objective of the study was to assess the efficacy of the LapiCotton procedure on patients with hallux valgus (HV) combined with medial longitudinal arch collapse. Methods: Preoperative and postoperative weight-bearing computed tomography (WBCT) scans were obtained from patients with HV submitted to the LapiCotton procedure. Semi-automatic measurements were applied to 22 WBCT images across 11 patients enrolled in the study using a software package (Bonelogic, Disior™, Helsinki, Finland). Significance level was set at 0.05. Results: The hallux valgus angle (HVA) was significantly larger (p=0.026) in the preoperative group (Mdn = 27.52) than in the postoperative group (Mdn = 20). In addition, the Meary sagittal measurement was found to be significantly increased (p=0.033) in the preoperative group (Mdn = -14.28) when compared to the postoperative group (Mdn = -11.15). It was also observed that the intermetatarsal angle was significantly larger (p=0.003) in the preoperative group (Mdn = 15.68) compared to the postoperative group (Mdn = 11.26). Conclusion: The LapiCotton procedure effectively corrected radiographic parameters in patients with HV combined with the medial longitudinal arch collapse. Level of Evidence III; Therapeutic Studies; Comparative Retrospective Study.

Objective: The goal of this paper was to evaluate the validity of foot and ankle offset (FAO) measurements in the setting of severe foot and ankle deformities. Methods: This study included 57 feet (36 patients) that had a history of severe cavovarus deformity. Each participant received a weight-bearing computed tomography (WBCT) scan that was then used to measure FAO. This measurement was performed once using the traditional measurement technique and two additional times using a modified technique that allows for rotational correction of the images to align the talus. Results: Traditional FAO (TFAO) and modified FAO (MFAO) were found to have a significant correlation with one another (r (54)=0.92, p

Objective: This cadaveric pilot study was to develop a weight bearing computed tomography (WBCT) three-dimensional (3D) distance mapping algorithm that would allow for detection of syndesmotic instability. Methods: Pilot study, two cadaveric specimens. Syndesmotic instability was induced by release of all syndesmotic ligaments through a conventional lateral ankle approach. WBCT imaging under simulated weight bearing was acquired before and after syndesmotic destabilization. Syndesmotic incisura and ankle gutter distances were assessed using a 3D distance mapping WBCT algorithm. Results: We found increases in the overall mean syndesmotic distances in the injured syndesmosis when compared to pre-injury state, and color coded distance maps allowed easy interpretation of the syndesmotic widening following ligament sectioning and destabilization of the syndesmotic joint. Conclusion: The WBCT 3D distance mapping algorithm has the potential to allow detection of mild syndesmotic instability with a relatively ease of interpretation by using color-coded distance maps. Level of Evidence V; Cadaveric Study.

Objective In the present study, we aimed at determining the correlation between tomographic findings of intracranial hypertension and ultrasound measurement of the optic nerve sheath diameter (ONSD).

Background: Thymus has a pivotal role in combating infectious diseases. Although some reviews have been published about its critical role in COVID-19, there is not enough research. In this study, the size and density of thymus related to computed tomography pulmonary severity score (CT-SS) were researched. Results: A total of 196 patients were analyzed with a mean age of 52.54 ± 18.78 years; 97 (49.5%) of them were RT-PCR (−) and 99 (50.5%) were RT-PCR (+). Within RT-PCR (+) group 62 (62.6%) of them had pneumonia with a mean CT-SS of 9.37 ± 8.83; within RT-PCR (−) group 20 (20.6%) of them had pneumonia with the mean CT-SS of 12.00 ± 10.18. CT-SS had moderate negative correlation with thymus volume and thymus maximum diameter in patients having nodular-type thymus (R = −0.591, P = 0.02; R = −0.515, P = 0.049, respectively). Homogenous fat infiltration was more commonly seen in RT-PCR (−) group while reticular and nodular types were commonly seen in RT-PCR (+) group (p = 0.015). The mean volume and maximum diameter of thymus were statistically significantly higher in RT-PCR (+) group (p = 0.027 and p = 0.048, respectively). Conclusion: This study showed the higher thymic volume and maximum diameter and more involution in COVID-19 patients. CT-SS had a moderate negative correlation with thymus volume and thymus maximum diameter. Pneumonia was more frequent in COVID patients, but mean CT-SS of the non-COVID cases was higher. [ABSTRACT FROM AUTHOR]

Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted. In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose. [Display omitted] • Higher tumour burden and the presence of liver metastasis indicate worse survival. • presence of symptomatic brain metastases predicts worse survival in patients with melanoma. • More subcutaneous fat and skeletal muscle may be predictive of better survival. • Radioactive drug labelling techniques show promising results in pilot studies. • Radiomics appear promising in published literature but require further validation. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to analyze the clinicopathological and computed tomography (CT) findings of papillary gastric adenocarcinoma and to evaluate the feasibility of the multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas. Methods: This retrospective study included 22 patients with papillary gastric adenocarcinoma and 88 patients with tubular adenocarcinoma. The demographic data, tumor markers, histopathological information, CT morphological characteristics, and CT value-related parameters of all patients were collected and analyzed. The multivariate model based on regression analysis was performed to improve the diagnostic efficacy for discriminating papillary gastric adenocarcinomas preoperatively. The diagnostic performance of the established nomogram was evaluated by receiver operating characteristic curve analysis. Results: The distribution of age, carcinoembryonic antigen, differentiation degree, neural invasion, human epidermal growth factor receptor 2 overexpression, P53 mutation status, 4 CT morphological characteristics, and 10 CT valued-related parameters differed significantly between papillary gastric adenocarcinoma and tubular adenocarcinoma groups (all p < 0.05). The established multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas preoperatively achieved the area under the curve of 0.920. Conclusion: There existed differences in clinicopathological features and CT findings between papillary gastric adenocarcinomas and tubular adenocarcinomas. The combination of demographic data, tumor markers, CT morphological characteristics, and CT value-related parameters could discriminate papillary gastric adenocarcinomas preoperatively with satisfactory diagnostic efficiency. [ABSTRACT FROM AUTHOR]

Introduction. Evaluation of the prognostic factors and the survival rate in oral squamous cell carcinoma is extremely important, because patients in the same tumor-node-metastasisstage may have a different survival rate. Numerous studies have been conducted on various clinical and pathological prognostic factors in order to develop a prognostic model for the survival rate of patients with oral cancer. Material and Methods. The study was designed as a prospective study including 65 consecutive patients (n = 65) of both sexes who underwent surgical treatment of oral cancer. The diagnosis of oral cancer was based on the medical history, physical examination, and biopsy. The clinical tumor-node-metastasis staging was determined based on clinical examination. The radiological tumor-node-metastasis staging was done by computed tomography of the head, neck, and chest. The tumor thickness was determined by computed tomography and histopathological analysis of surgical specimens. Results. The histopathological analysis showed a mean tumor thickness of 13.446 mm, while the mean computed tomography tumor thickness was 15.2707 mm. The correlation between computed tomography tumor thickness and histopathological tumor thickness was moderately significant (Spearman’s rho=.581, p =0.000). Conclusion. This study supports the use of computed tomography in the determination of tumor thickness in patients with oral squamous cell carcinoma. We want to emphasize the importance of preoperative, detailed imaging evaluation of patients in order to avoid multiple surgical procedures, significant morbidity, and unnecessary costs. [ABSTRACT FROM AUTHOR]

Objectives: We explored the prevalence of health complaints subjectively associated with static magnetic field (SMF) and acoustic noise exposure among MR radiographers in Sweden, using CT radiographers as a control group. Additionally, we explored radiographers' use of strategies to mitigate adverse health effects. Methods: A cross-sectional survey was sent to all hospitals with MR units in Sweden. MR and/or CT personnel reported prevalence and attribution of symptoms (vertigo/dizziness, nausea, metallic taste, illusion of movement, ringing sensations/tinnitus, headache, unusual drowsiness/tiredness, forgetfulness, difficulties concentrating, and difficulties sleeping) within the last year. We used logistic regression to test associations between sex, age, stress, SMF strength, working hours, and symptom prevalence. Data regarding hearing function, work-environmental noise, and strategies to mitigate adverse symptoms were also analysed. Results: In total, 529 out of 546 respondents from 86 hospitals were eligible for participation. A ≥ 20 working hours/week/modality cut-off rendered 342 participants grouped into CT (n = 75), MR (n = 121), or mixed personnel (n = 146). No significant differences in symptom prevalence were seen between groups. Working at ≥ 3T increased SMF-associated symptoms as compared with working at ≤ 1.5T (OR: 2.03, CI95: 1.05–3.93). Stress was a significant confounder. Work-related noise was rated as more troublesome by CT than MR personnel (p < 0.01). MR personnel tended to use more strategies to mitigate adverse symptoms. Conclusion: No significant differences in symptom prevalence were seen between MR and CT radiographers. However, working at 3T increased the risk of SMF symptoms, and stress increased adverse health effects. Noise nuisance was considered more problematic by CT than MR personnel. Key Points: • No significant differences in symptom prevalence were seen between MR and CT radiographers. • Working at ≥ 3 T doubled the odds of experiencing SMF symptoms (vertigo/dizziness, nausea, metallic taste, and/or illusion of movement) as compared to working exclusively at ≤ 1.5 T. • Work-related acoustic noise was less well mitigated and was rated as more troublesome by CT personnel than by MR personnel. [ABSTRACT FROM AUTHOR]

Objective To evaluate dual-energy CT (DECT) in determining the degree of head and neck squamous cell carcinoma (SCC) differentiation. Methods Clinical data including age, gender, smoking history, and tumor location were retrospectively collected on 148 patients with pathologically confirmed head and neck SCC. All patients were divided into a poorly (46) and moderately-highly differentiated (102) groups and underwent preoperative DECT. The normalized iodine concentration (NIC), slope of the energy spectrum curve (λHU), and the normalized effective atomic number (nZeff) in the arterial and venous phases of the lesions were measured. The clinical characteristics and DECT parameters were compared between the two groups. Multivariate logistic regression analysis was used to screen the independent predictors of oral SCC differentiation. The clinical characteristics model, DECT parameter model, and combined model were constructed. The diagnostic performance of each model was assessed using receiver operating characteristic (ROC) curve analysis. Results The 46 poorly differentiated SCC [age: (49.28±11.67) years] was in the oral cavity and oropharynx (10), hypopharynx (28), and larynx (8). The 102 well-differentiated SCC [age: (55.03±9.07) years] was in the oral cavity and oropharynx (12), hypopharynx (18), larynx (72). There were significant differences in age and tumor locations between the two groups (all P<0.05). The NIC, λHU and nZeff of the poorly differentiated group in the arterial (0.26, 2.03, 0.80) and venous (0.48, 2.75, 0.87) phases were significantly (all P<0.05) higher than those of the moderately-highly differentiated group (arterial 0.20, 1.56, 0.77; venous 0.42, 2.26, 0.85). The combined model had significantly better diagnostic performance (Z=3.589, P<0.001) with AUC of 0.919, sensitivity of 86.27%, and specificity of 86.96 compared to that of clinical model (0.781, 76.47%, 71.74%) and DECT parameter model (Z=2.596, P<0.001; 0.863, 83.33%, 76.09%). Conclusion DECT combined with clinical features can significantly improve the diagnostic efficiency of head and neck SCC differentiation. [ABSTRACT FROM AUTHOR]

Objective To analyze the MRI features of adult medulloblastoma (MB). Methods 12 patients (8 men, 4 women; age range: 18-40 years, median age: 23 years) with pathologically confirmed MB from May 2014 to August 2021 underwent MRI including diffusion-weighted imaging (DWI). The tumor location and MRI features were analyzed respectively. Results The tumor was located in the cerebellar vermis (7, median age of 21 years) and cerebellar hemisphere (5, median age of 35 years). MRI showed slightly low T1 and slightly high T2 signal intensities with reduced apparent diffusion coefficients (ADC) and variable degree of contrast enhancement in the solid components of tumors. Cystic changes were observed in 5 patients (41.7%). The tumor was surrounded by cerebrospinal fluid anteriorly in 5 patients. Hydrocephalus was found in 5 patients. CT density of the solid components was higher than the normal brain parenchyma in 8 patients who also underwent CT. Conclusion The solid components of adult MB are hyperdense on nonenhanced CT and with reduced diffusion on DWI. The lesions of older adults are mostly located in the cerebellar hemisphere whereas the imaging features of MB in young adults are similar to that of children. [ABSTRACT FROM AUTHOR]

In recent years, cirrhosis has been increasing due to the increase of alcoholic liver disease, non-alcoholic fatty liver disease and the high infection rate of the hepatitis virus. Timely and accurate monitoring and evaluation are helpful for disease reversal and treatment guidance, in which the dual-energy CT with abundant additional functions has good prospects. The applications of dualenergy CT in technical principles, liver parenchymal lesions, liver functional reserve and cirrhosis complications in recent years were reviewed. [ABSTRACT FROM AUTHOR]

ObjectiveTo investigate the imaging features of adult abdominal neuroblastoma on multi-slice spiral CT (MSCT). Methods Nine patients (4 men, 5 women; age range: 18-67 years, mean age: 28 years) with pathologically confirmed abdominal neuroblastoma underwent nonenhanced (9) and enhanced (8) MSCT and PET-CT (4) examination. The MSCT features were analyzed and summarized. Results All tumors were located in the sympathetic nerve chain distribution area with 8 involving the retroperitoneum including the adrenal glands (6), between the retroperitoneum and the pelvic wall (1), and para-aortic retroperitoneum (1) and 1 tumor in the pelvic cavity. All tumors were irregular in shape with lobulation (8) and the maximum diameter ranging from 41 to 140 mm. All lesions were iso- or hypodense on nonenhanced CT with patchy calcification (5), heterogeneous marked (3) or mild to moderate (5) contrast enhancement. Multiple lymph node metastases were found in 4 including 1 patient with distant metastasis. Conclusion Adult neuroblastoma should be considered if CT shows irregular low-density mass with calcification in the area of sympathetic nerve chain distribution in young adults. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the artificial intelligence (AI)-based coronary artery calcium (CAC) quantification and regional distribution of CAC on non-gated chest CT, using standard electrocardiograph (ECG)-gated CAC scoring as the reference. Methods: In this retrospective study, a total of 405 patients underwent non-gated chest CT and standard ECG-gated cardiac CT. An AI-based algorithm was used for automated CAC scoring on chest CT, and Agatston score on cardiac CT was manually quantified. Bland-Altman plots were used to evaluate the agreement of absolute Agatston score between the two scans at the patient and vessel levels. Linearly weighted kappa (κ) was calculated to assess the reliability of AI-based CAC risk categorization and the number of involved vessels on chest CT. Results: The AI-based algorithm showed moderate reliability for the number of involved vessels in comparison to measures on cardiac CT (κ = 0.75, 95% CI 0.70–0.79, P < 0.001) and an assignment agreement of 76%. Considerable coronary arteries with CAC were not identified with a per-vessel false-negative rate of 59.3%, 17.8%, 34.9%, and 34.7% for LM, LAD, CX, and RCA on chest CT. The leading causes for false negatives of LM were motion artifact (56.3%, 18/32) and segmentation error (43.8%, 14/32). The motion artifact was almost the only cause for false negatives of LAD (96.6%, 28/29), CX (96.7%, 29/30), and RCA (100%, 34/34). Absolute Agatston scores on chest CT were underestimated either for the patient and individual vessels except for LAD (median difference: − 12.5, − 11.3, − 5.6, − 18.6 for total, LM, CX, and RCA, all P < 0.01; − 2.5 for LAD, P = 0.18). AI-based total Agatston score yielded good reliability for risk categorization (weighted κ 0.86, P < 0.001) and an assignment agreement of 86.7% on chest CT, with a per-patient false-negative rate of 15.2% (28/184) and false-positive rate of 0.5% (1/221) respectively. Conclusions: AI-based per-patient CAC quantification on non-gated chest CT achieved a good agreement with dedicated ECG-gated CAC scoring overall and highly reliable CVD risk categorization, despite a slight but significant underestimation. However, it is challenging to evaluate the regional distribution of CAC without ECG-synchronization. [ABSTRACT FROM AUTHOR]

Objectives: Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score).Methods: We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint.Results: The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively.Conclusions: In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor).Key Points: • The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images.Methods: We analyzed 30 venous phase acute abdominal DECT (80/140 kVp) scans. Data were reconstructed to ASIR-V and DLIR-High at four different keV levels (40, 50, 74, and 100) with 1- and 3-mm slice thickness. Quantitative Hounsfield unit (HU) and noise assessment were measured within the liver, aorta, fat, and muscle. Subjective assessment of image noise, sharpness, texture, and overall quality was performed by two board-certified radiologists.Results: DLIR reduced image noise by 19.9-35.5% (p < 0.001) compared to ASIR-V in all reconstructions at identical keV levels. Contrast-to-noise ratio (CNR) increased by 49.2-53.2% (p < 0.001) in DLIR 40-keV images compared to ASIR-V 50 keV, while no significant difference in noise was identified except for 1 and 3 mm in aorta and for 1-mm liver measurements, where ASIR-V 50 keV showed 5.5-6.8% (p < 0.002) lower noise levels. Qualitative assessment demonstrated significant improvement particularly in 1-mm reconstructions (p < 0.001). Lastly, DLIR 40 keV demonstrated comparable or improved image quality ratings when compared to ASIR-V 50 keV (p < 0.001 to 0.22).Conclusion: DLIR significantly reduced image noise compared to ASIR-V. Qualitative assessment showed that DLIR significantly improved image quality particularly in thin sliced images. DLIR may facilitate 40 keV as a new standard for routine low-keV VM reconstruction in contrast-enhanced abdominal DECT.Key Points: • DLIR enables 40 keV as the routine low-keV VM reconstruction. • DLIR significantly reduced image noise compared to ASIR-V, across a wide range of keV levels in VM DECT images. • In low-keV VM reconstructions, improvements in image quality using DLIR were most evident and consistent in 1-mm sliced images. [ABSTRACT FROM AUTHOR]

Objectives: Mucosal healing (MH) is currently the gold standard in Crohn's disease (CD) management. Noninvasive assessment of MH in CD patients is increasingly a concern of clinicians.Methods: This retrospective study included 106 patients with confirmed CD who were divided into a training cohort (n = 73) and a testing cohort (n = 33). Patient demographics were evaluated to establish a clinical model. Radiomics features were extracted from computed tomography enterography (CTE) images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical radiomics nomogram was then built by incorporating the Rad-score and significant clinical features. The diagnostic performance of the three models was evaluated using receiver operating characteristic (ROC) curve analysis.Results: Of the 106 patients with CD, 37 exhibited MH after 26 weeks of infliximab (IFX) treatment. The area under the ROC curve (AUC) of the clinical radiomics nomogram for distinguishing MH from non-MH, which was based on the disease duration and Rad-score, was 0.880 (95% confidence interval [CI]: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort. Decision curve analysis (DCA) confirmed the clinical utility of the clinical radiomics nomogram.Conclusions: This is a preliminary study suggesting that this CTE-based radiomics model has potential value for predicting MH in CD patients. A nomogram constructed by combining radiomics signatures and clinical features can help clinicians select appropriate therapeutic strategies for CD patients.Key Points: • The disease duration (odds ratio (OR) = 0.969, 95% confidence interval (CI) = 0.943-0.995, p = 0.021) was an independent predictor of MH in the clinical model. • The AUC of the radiomics model constructed by the five radiomics features was 0.846 (95% CI: 0.759-0.921) in the training cohort and 0.817 (95% CI: 0.665-0.945) in the testing cohort for differentiating MH from non-MH. • The AUC of the clinical radiomics nomogram was 0.880 (95% CI: 0.809-0.943) in the training cohort and 0.877 (95% CI: 0.745-0.983) in the testing cohort for distinguishing MH from non-MH. [ABSTRACT FROM AUTHOR]

Objective: To compare the performance of Liver Imaging Reporting and Data System category 5 (LR-5) for diagnosing HCC between CT and MRI using comparative studies.Methods: The MEDLINE and EMBASE databases were searched from inception to April 21, 2021, to identify studies that directly compare the diagnostic performance of LR-5 for HCC between CT and MRI. A bivariate random-effects model was fitted to calculate the pooled per-observation sensitivity and specificity of LR-5 of each modality, and compare the pooled estimates of paired data. Subgroup analysis was performed according to the MRI contrast agent.Results: Seven studies with 1145 observations (725 HCCs) were included in the final analysis. The pooled per-observation sensitivity of LR-5 for diagnosing HCC was higher using MRI (61%; 95% confidence interval [CI], 43-76%; I2 = 95%) than CT (48%; 95% CI, 31-65%; I2 = 97%) (p < 0.001). The pooled per-observation specificities of LR-5 did not show statistically significant difference between CT (96%; 95% CI, 92-98%; I2 = 0%) and MRI (93%; 95% CI, 88-96%; I2 = 16%) (p = 0.054). In the subgroup analysis, extracellular contrast agent-enhanced MRI showed significantly higher pooled per-observation sensitivity than gadoxetic acid-enhanced MRI for diagnosing HCC (73% [95% CI, 55-85%] vs. 55% [95% CI, 39-70%]; p = 0.007), without a significant difference in specificity (93% [95% CI, 80-98%] vs. 94% [95% CI, 87-97%]; p = 0.884).Conclusions: The LR-5 of MRI showed significantly higher pooled per-observation sensitivity than CT for diagnosing HCC. The pooled per-observation specificities of LR-5 were comparable between the two modalities.Key Points: • The pooled sensitivity of LR-5 using MRI was higher than that using CT (61% versus 48%), but the pooled specificities of LR-5 were not significantly different between CT and MRI (96% versus 93%). • Subgroup analysis according to the MRI contrast media showed a significantly higher pooled per-observation sensitivity using ECA-enhanced MRI than with EOB-enhanced MRI (73% versus 55%), and comparable specificities (93% versus 94%). • Although LI-RADS provides a common diagnostic algorithm for CT or MRI, the per-observation performance of LR-5 can be affected by the imaging modality as well as the MRI contrast agent. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the targeting accuracy of stereotactic punctures based on a hybrid robotic device in combination with optical tracking—a phantom study. Methods: CT data sets of a gelatin-filled plexiglass phantom with 1-, 3-, and 5-mm slice thickness were acquired. An optical navigation device served for planning of a total of 150 needle trajectories. All punctures were carried out semi-automatically with help of the trackable iSYS-1 robotic device. Conically shaped targets inside the phantom were punctured using Kirschner wires. Up to 8 K-wires were positioned sequentially based on the same planning CT and placement accuracy was assessed by taking control CTs and measuring the Euclidean (ED) and normal distances (NDs) between the wire and the entry and target point. Results: Using the StealthStation S7, the accomplished mean ND at the target for the 1-mm, 3-mm, and 5-mm slice thickness was 0.89 mm (SD ± 0.42), 0.93 mm (SD ± 0.45), and 0.73 mm (SD ± 0.50), respectively. The corresponding mean ED was 1.61 mm (SD ± 0.36), 2.04 mm (SD ± 0.59), and 1.76 mm (SD ± 0.45). The mean duration of the total procedure was 27.9 min, including image acquisition, trajectory planning, registration, placement of 8 wires, and the control-CT. Conclusions: The optically tracked iSYS-1 robot allows for precise punctures in a phantom. The StealthStation S7 provided acceptable results and may be helpful for interventions in difficult anatomical regions and for those requiring complex multi-angle trajectories. In combination with our optical navigation tool, the trackable robot unit allows to cover a large treatment field and the compact design facilitates placement of needle-like instruments. Key Points: • The use of a robotic targeting device in combination with optical tracking (hybrid system) allows for accurate placement of needle-like instruments without repeated control imaging. • The compact robotic positioning unit in combination with a camera for optical tracking facilitates sequential placement of multiple K-wires in a large treatment volume. [ABSTRACT FROM AUTHOR]