Detection and size measurements of kidney stones on virtual non-contrast reconstructions derived from dual-layer computed tomography in an ex vivo phantom setup.

OBJECTIVES: To systematically investigate the usability of virtual non-contrast reconstructions (VNC) derived from dual-layer CT (DLCT) for detection and size measurements of kidney stones with regards to different degrees of surrounding iodine-induced attenuation and radiation dose. METHODS: Ninety-two kidney stones of varying size (3-14 mm) and composition were placed in a phantom filled with different contrast media/water mixtures exhibiting specific iodine-induced attenuation (0-1500 HU). DLCT-scans were acquired using CTDIvol of 2 mGy and 10 mGy. Conventional images (CI) and VNC0H-1500HU were reconstructed. Reference stone size was determined using a digital caliper (Man-M). Visibility and stone size were assessed. Statistical analysis was performed using the McNemar test, Wilcoxon test, and the coefficient of determination. RESULTS: All stones were visible on CI0HU and VNC200HU. Starting at VNC400 HU, the detection rate decreased with increasing HU and was significantly lower as compared to CI0HU on VNC≥ 600HU (100.0 vs. 94.0%, p < 0.05). The overall detection rate was higher using 10 mGy as compared to 2 mGy protocol (87.9 vs. 81.8%; p < 0.001). Stone size was significantly overestimated on all VNC compared to Man-M (7.0 ± 3.5 vs. 6.6 ± 2.8 mm, p < 0.001). Again, the 10 mGy protocol tended to show a better correlation with Man-M as compared to 2 mGy protocol (R2 = 0.39-0.68 vs. R2 = 0.31-0.57). CONCLUSIONS: Detection and size measurements of kidney stones surrounded by contrast media on VNC are feasible. The detection rate of kidney stones decreases with increasing iodine-induced attenuation and with decreasing radiation dose as well as stone size, while remaining comparable to CI0HU on VNC ≤ 400 HU. KEY POINTS: • The detection rate of kidney stones on VNC depends on the surrounding iodine-induced attenuation, the used radiation dose, and the stone size. • The detection rate of kidney stones on VNC decreases with greater iodine-induced attenuation and with lower radiation dose, particularly in small stones. • The visibility of kidney stones on VNC ≤ 400 HU remains comparable to true-non-contrast scans even when using a low-dose technique.

Post-mortem CT radiomics for the prediction of time since death.

OBJECTIVES: Post-mortem interval (PMI) estimation has long been relying on sequential post-mortem changes on the body as a function of extrinsic, intrinsic, and environmental factors. Such factors are difficult to account for in complicated death scenes; thus, PMI estimation can be compromised. Herein, we aimed to evaluate the use of post-mortem CT (PMCT) radiomics for the differentiation between early and late PMI. METHODS: Consecutive whole-body PMCT examinations performed between 2016 and 2021 were retrospectively included (n = 120), excluding corpses without an accurately reported PMI (n = 23). Radiomics data were extracted from liver and pancreas tissue and randomly split into training and validation sets (70:30%). Following data preprocessing, significant features were selected (Boruta selection) and three XGBoost classifiers were built (liver, pancreas, combined) to differentiate between early (< 12 h) and late (> 12 h) PMI. Classifier performance was assessed with receiver operating characteristics (ROC) curves and areas under the curves (AUC), which were compared by bootstrapping. RESULTS: A total of 97 PMCTs were included, representing individuals (23 females and 74 males) with a mean age of 47.1 ± 23.38 years. The combined model achieved the highest AUC reaching 75% (95%CI 58.4-91.6%) (p = 0.03 compared to liver and p = 0.18 compared to pancreas). The liver-based and pancreas-based XGBoost models achieved AUCs of 53.6% (95%CI 34.8-72.3%) and 64.3% (95%CI 46.7-81.9%) respectively (p > 0.05 for the comparison between liver- and pancreas-based models). CONCLUSION: The use of radiomics analysis on PMCT examinations differentiated early from late PMI, unveiling a novel image-based method with important repercussions in forensic casework. CLINICAL RELEVANCE STATEMENT: This paper introduces the employment of radiomics in forensic diagnosis by presenting an effective automated alternative method of estimating post-mortem interval from targeted tissues, thus paving the way for improvement in speed and quality of forensic investigations. KEY POINTS: • A combined liver-pancreas radiomics model differentiated early from late post-mortem intervals (using a 12-h threshold) with an area under the curve of 75% (95%CI 58.4-91.6%). • XGBoost models based on liver-only or pancreas-only radiomics demonstrated inferior performance to the combined model in predicting the post-mortem interval.

Optimal radiological gallbladder lesion characterization by combining visual assessment with CT-based radiomics.

OBJECTIVES: Differentiating benign gallbladder diseases from gallbladder cancer (GBC) remains a radiological challenge because they can appear very similar on imaging. This study aimed at investigating whether CT-based radiomic features of suspicious gallbladder lesions analyzed by machine learning algorithms could adequately discriminate benign gallbladder disease from GBC. In addition, the added value of machine learning models to radiological visual CT-scan interpretation was assessed. METHODS: Patients were retrospectively selected based on confirmed histopathological diagnosis and available contrast-enhanced portal venous phase CT-scan. The radiomic features were extracted from the entire gallbladder, then further analyzed by machine learning classifiers based on Lasso regression, Ridge regression, and XG Boosting. The results of the best-performing classifier were combined with radiological visual CT diagnosis and then compared with radiological visual CT assessment alone. RESULTS: In total, 127 patients were included: 83 patients with benign gallbladder lesions and 44 patients with GBC. Among all machine learning classifiers, XG boosting achieved the best AUC of 0.81 (95% CI 0.72-0.91) and the highest accuracy rate of 73% (95% CI 65-80%). When combining radiological visual interpretation and predictions of the XG boosting classifier, the highest diagnostic performance was achieved with an AUC of 0.98 (95% CI 0.96-1.00), a sensitivity of 91% (95% CI 86-100%), a specificity of 93% (95% CI 90-100%), and an accuracy of 92% (95% CI 90-100%). CONCLUSIONS: Machine learning analysis of CT-based radiomic features shows promising results in discriminating benign from malignant gallbladder disease. Combining CT-based radiomic analysis and radiological visual interpretation provided the most optimal strategy for GBC and benign gallbladder disease differentiation. KEY POINTS: Radiomic-based machine learning algorithms are able to differentiate benign gallbladder disease from gallbladder cancer. Combining machine learning algorithms with a radiological visual interpretation of gallbladder lesions at CT increases the specificity, compared to visual interpretation alone, from 73 to 93% and the accuracy from 85 to 92%. Combined use of machine learning algorithms and radiological visual assessment seems the most optimal strategy for GBC and benign gallbladder disease differentiation.

Are preoperative CT variables associated with the success or failure of subsequent ventral hernia repair: nested case-control study.

OBJECTIVES: Systematic review of CT measurements to predict the success or failure of subsequent ventral hernia repair has found limited data available in the indexed literature. To rectify this, we investigated multiple preoperative CT metrics to identify if any were associated with postoperative reherniation. METHODS: Following ethical permission, we identified patients who had undergone ventral hernia repair and had preoperative CT scanning available. Two radiologists made multiple measurements of the hernia and abdominal musculature from these scans, including loss of domain. Patients were divided subsequently into two groups, defined by hernia recurrence at 1-year subsequent to surgery. Hypothesis testing investigated any differences between CT measurements from each group. RESULTS: One hundred eighty-eight patients (95 male) were identified, 34 (18%) whose hernia had recurred by 1-year. Only three of 34 CT measurements were significantly different when patients whose hernia had recurred were compared to those who had not; these significant findings were assumed contingent on multiple testing. In particular, preoperative hernia volume (recurrence 155.3 cc [IQR 355.65] vs. no recurrence 78.2 [IQR 303.52], p = 0.26) nor loss of domain, whether calculated using the Tanaka (recurrence 0.02 [0.04] vs. no recurrence 0.009 [0.04], p = 0.33) or Sabbagh (recurrence 0.019 [0.05] vs. no recurrence 0.009 [0.04], p = 0.25) methods, differed between significantly between groups. CONCLUSIONS: Preoperative CT measurements of ventral hernia morphology, including loss of domain, appear unrelated to postoperative recurrence. It is likely that the importance of such measurements to predict recurrence is outweighed by other patient factors and surgical reconstruction technique. KEY POINTS: • Preoperative CT scanning is often performed for ventral hernia but systematic review revealed little data regarding whether CT variables predict postoperative reherniation. • We found that the large majority of CT measurements, including loss of domain, did not differ significantly between patients whose hernia did and did not recur. • It is likely that the importance of CT measurements to predict recurrence is outweighed by other patient factors and surgical reconstruction technique.

COVID-19 CT Scan Lung Segmentation: How We Do It.

The National Health Systems have been severely stressed out by the COVID-19 pandemic because 14% of patients require hospitalization and oxygen support, and 5% require admission to an Intensive Care Unit (ICU). Relationship between COVID-19 prognosis and the extent of alterations on chest CT obtained by both visual and software-based quantification that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one has been proven. While commercial applications for automatic medical image computing and visualization are expensive and limited in their spread, the open-source systems are characterized by not enough standardization and time-consuming troubles. We analyzed chest CT exams on 246 patients suspected of COVID-19 performed in the Emergency Department CT room. The lung parenchyma segmentation was obtained by a threshold-based method using the open-source 3D Slicer software and software tools called "Segment Editor" and "Segment Quantification." For the three main characteristics analyzed on lungs affected by COVID-19 pneumonia, a specifical densitometry value range was defined: from - 950 to - 700 HU for well-aerated parenchyma; from - 700 to - 250 HU for interstitial lung disease; from - 250 to 250 HU for parenchymal consolidation. For the well-aerated parenchyma and the interstitial alterations, the procedure was semi-automatic with low time consumption, whereas consolidations' analysis needed manual interventions by the operator. After the chest CT, 13% of the sample was admitted to intensive care, while 34% of them to the sub-intensive care. In patients moved to intensive care, the parenchyma analysis reported a higher crazy paving presentation. The quantitative analysis of the alterations affecting the lung parenchyma of patients with COVID-19 pneumonia can be performed by threshold method segmentation on 3D Slicer. The segmentation could have an important role in the quantification in different COVID-19 pneumonia presentations, allowing to help the clinician in the correct management of patients.

Skeletal muscle fat quantification by dual-energy computed tomography in comparison with 3T MR imaging.

OBJECTIVES: To quantify the proportion of fat within the skeletal muscle as a measure of muscle quality using dual-energy CT (DECT) and to validate this methodology with MRI. METHODS: Twenty-one patients with abdominal contrast-enhanced DECT scans (100 kV/Sn 150 kV) underwent abdominal 3-T MRI. The fat fraction (DECT-FF), determined by material decomposition, and HU values on virtual non-contrast-enhanced (VNC) DECT images were measured in 126 regions of interest (≥ 6 cm2) within the posterior paraspinal muscle. For validation, the MR-based fat fraction (MR-FF) was assessed by chemical shift relaxometry. Patients were categorized into groups of high or low skeletal muscle mean radiation attenuation (SMRA) and classified as either sarcopenic or non-sarcopenic, according to the skeletal muscle index (SMI) and cut-off values from non-contrast-enhanced single-energy CT. Spearman's and intraclass correlation, Bland-Altman analysis, and mixed linear models were employed. RESULTS: The correlation was excellent between DECT-FF and MR-FF (r = 0.91), DECT VNC HU and MR-FF (r = - 0.90), and DECT-FF and DECT VNC HU (r = - 0.98). Intraclass correlation between DECT-FF and MR-FF was good (r = 0.83 [95% CI 0.71-0.90]), with a mean difference of - 0.15% (SD 3.32 [95% CI 6.35 to - 6.66]). Categorization using the SMRA yielded an eightfold difference in DECT VNC HU values between both groups (5 HU [95% CI 23-11], 42 HU [95% CI 33-56], p = 0.05). No significant relationship between DECT-FF and SMI-based classifications was observed. CONCLUSIONS: Fat quantification within the skeletal muscle using DECT is both feasible and reliable. DECT muscle analysis offers a new approach to determine muscle quality, which is important for the diagnosis and therapeutic monitoring of sarcopenia, as a comorbidity associated with poor clinical outcome. KEY POINTS: • Dual-energy CT (DECT) material decomposition and virtual non-contrast-enhanced DECT HU values assess muscle fat reliably. • Virtual non-contrast-enhanced dual-energy CT HU values allow to differentiate between high and low native skeletal muscle mean radiation attenuation in contrast-enhanced DECT scans. • Measuring muscle fat by dual-energy computed tomography is a new approach for the determination of muscle quality, an important parameter for the diagnostic confirmation of sarcopenia as a comorbidity associated with poor clinical outcome.

Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network.

OBJECTIVES: To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. METHODS: Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (-50% and -80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. RESULTS: The -80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the -50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. CONCLUSIONS: The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. KEY POINTS: • The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks. • Not only the image quality but especially the pathological consistency must be evaluated to assess safety. • A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%.

Contrast-enhanced ultrasound LI-RADS 2017: comparison with CT/MRI LI-RADS.

OBJECTIVE: To compare the classification based on contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) with that of contrast-enhanced CT and MRI (CECT/MRI) LI-RADS for liver nodules in patients at high risk of hepatocellular carcinoma. METHODS: Two hundred thirty-nine patients with 273 nodules were enrolled in this retrospective study. Each nodule was categorized according to the CEUS LI-RADS version 2017 and CECT/MRI LI-RADS version 2017. The diagnostic performance of CEUS and CECT/MRI was compared. The reference standard was histopathology diagnosis. Inter-modality agreement was assessed with Cohen's kappa. RESULTS: The inter-modality agreement for CEUS LI-RADS and CECT/MRI LI-RADS was fair with a kappa value of 0.319 (p < 0.001). The positive predictive values (PPVs) of hepatocellular carcinoma (HCC) in LR-5, LR-4, and LR-3 were 98.3%, 60.0%, and 25.0% in CEUS, and 95.9%, 65.7%, and 48.1% in CECT/MRI, respectively. The sensitivities and specificities of LR-5 for diagnosing HCC were 75.6% and 93.8% in CEUS, and 83.6% and 83.3% in CECT/MRI, respectively. The positive predictive values of non-HCC malignancy in CEUS LR-M and CECT/MRI LR-M were 33.9% and 93.3%, respectively. The sensitivity, specificity, and accuracy for diagnosing non-HCC malignancy were 90.9%, 84.5%, and 85.0% in CEUS LR-M and 63.6%, 99.6%, and 96.7% in CECT/MRI LR-M, respectively. CONCLUSIONS: The inter-modality agreement of the LI-RADS category between CEUS and CECT/MRI is fair. The positive predictive values of HCCs in LR-5 of the CEUS and CECT/MRI LI-RADS are comparable. CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M. KEY POINTS: • The inter-modality agreement for the final LI-RADS category between CEUS and CECT/MRI is fair. • The LR-5 of CEUS and CECT/MRI LI-RADS corresponds to comparable positive predictive values (PPVs) of HCC. For LR-3 and LR-4 nodules categorized by CECT/MRI, CEUS examination should be performed, at least if they can be detected on plain ultrasound. • CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M. For LR-M nodules categorized by CEUS, re-evaluation by CECT/MRI is necessary.

Automated patient positioning in CT using a 3D camera for body contour detection: accuracy in pediatric patients.

OBJECTIVE: To assess the accuracy of a 3D camera for body contour detection in pediatric patient positioning in CT compared with routine manual positioning by radiographers. METHODS AND MATERIALS: One hundred and ninety-one patients, with and without fixation aid, which underwent CT of the head, thorax, and/or abdomen on a scanner with manual table height selection and with table height suggestion by a 3D camera were retrospectively included. The ideal table height was defined as the position at which the scanner isocenter coincides with the patient's isocenter. Table heights suggested by the camera and selected by the radiographer were compared with the ideal height. RESULTS: For pediatric patients without fixation aid like a baby cradle or vacuum cushion and positioned by radiographers, the median (interquartile range) absolute table height deviation in mm was 10.2 (16.8) for abdomen, 16.4 (16.6) for head, 4.1 (5.1) for thorax-abdomen, and 9.7 (9.7) for thorax CT scans. The deviation was less for the 3D camera: 3.1 (4.7) for abdomen, 3.9 (6.3) for head, 2.2 (4.3) for thorax-abdomen, and 4.8 (6.7) for thorax CT scans (p < 0.05 for all body parts combined). CONCLUSION: A 3D camera for body contour detection allows for automated and more accurate pediatric patient positioning than manual positioning done by radiographers, resulting in overall significantly smaller deviations from the ideal table height. The 3D camera may be also useful in the positioning of patients with fixation aid; however, evaluation of possible improvements in positioning accuracy was limited by the small sample size. KEY POINTS: • A 3D camera for body contour detection allows for automated and accurate pediatric patient positioning in CT. • A 3D camera outperformed radiographers in positioning pediatric patients without a fixation aid in CT. • Positioning of pediatric patients with fixation aid was feasible using the 3D camera, but no definite conclusions were drawn regarding the positioning accuracy due to the small sample size.

Radiation dose reduction with the wide-volume scan mode for interstitial lung diseases.

OBJECTIVES: The wide-volume mode, available on wide-area detector row CTs, has the advantage of reducing exposure time and radiation dose. It is infrequently used for lung diseases. The purpose of this study is to compare image quality and radiation dose of wide-volume chest CT to those of standard helical CT in the setting of interstitial lung diseases. METHODS: Retrospective monocentric study including 50 consecutive patients referred for follow-up or screening of interstitial lung diseases, requiring prone scan, acquired with the wide-volume mode, in addition to the routine supine scan, acquired with the helical mode. The optimal collimation in wide-volume mode (320 × 0.5mm or 240 × 0.5mm) was chosen according to the length of the thorax. Wide-volume acquisitions were compared to helical acquisitions for radiation dose (CTDIvol, DLP) and image quality, including analysis of normal structures, lesions, overall image quality, and artifacts (Wilcoxon signed-rank test). RESULTS: Median CTDIvol and DLP with wide volumes (3.1 mGy and 94.6 mGy·cm) were significantly reduced (p < 0.0001) as compared to helical mode (3.7mGy and 122.1 mGy·cm), leading to a median 21% and 32% relative reduction of CTDIvol and DLP, respectively. Image noise and quality were not significantly different between the two modes. Misalignment artifact at the junction of two volumes was occasionally seen in the wide-volume scans and, when present, did not impair the diagnostic quality in the majority of cases. CONCLUSIONS: Wide-volume mode allows 32% radiation dose reduction compared to the standard helical mode and could be used routinely for diagnosis and follow-up of interstitial lung diseases. KEY POINTS: • Retrospective monocentric study showed that wide-volume scan mode reduces radiation dose by 32% in comparison to helical mode for chest CT in the setting of interstitial lung diseases. • Mild misalignment may be observed at the junction between volumes with the wide-volume mode, without decrease of image quality in the majority of cases and without impairing diagnostic quality. • Wide-volume mode could be used routinely for the diagnosis and follow-up of interstitial lung diseases.

Detection of distant metastases in rectal cancer: contrast-enhanced CT vs whole body MRI.

OBJECTIVES: To evaluate the diagnostic performance of whole-body MRI (WB-MRI) including contrast-enhanced T1-weighted imaging (T1WI) and WB-DWI in rectal cancer initial staging. METHODS: This retrospective study was approved by the IRB and the requirement of informed consent was waived. From September 2013 to Feb 2015, patients who underwent rectal MRI including WB-MRI, as well as chest and abdominopelvic CT for initial staging, were included. WB-MRI consisted of contrast-enhanced T1-weighted imaging and DWI covering neck to the pelvis. Three radiologists reviewed WB-MRI and CECT independently for the M-classification. The diagnostic performance of CECT and WB-MRI was compared using a reference standard incorporating histology, FDG-PET results, and clinical follow-up. RESULTS: A total of 139 patients (male:female = 89:50, mean age 63.2 ± 12.4 years) were included and metastasis was observed in 15.2% (21/139). WB-MRI showed significantly higher specificity (96.7% [114/118] vs. 85.6% [101/118], p = 0.001) and positive predictive value (PPV) (80% [16/20] vs. 48.5% [16/33], p < 0.001) than CECT. However, there were no significant differences in sensitivity (76.2% [16/21] for both, p > 0.99) and negative predictive value (95.3% [101/106] at CECT vs. 95.8% [114/119] at WB-MRI, p = 0.77) between CECT and WB-MRI. CONCLUSIONS: WB-MRI showed higher specificity and PPV than CECT in newly diagnosed rectal cancer. Adding WB-MRI to standard rectal MRI is a feasible option for initial staging workup of rectal cancer. KEY POINTS: • WB-MRI showed a higher specificity and PPV than those of CECT for identifying metastasis at initial staging workup of rectal cancer. • WB-MRI and CECT did not show a significant difference in sensitivity and NPV for the M-classification. • WB-MRI can be used as an alternative to CECT for the initial M-classification modality in newly diagnosed rectal cancer.

Clinical and chest CT features as a predictive tool for COVID-19 clinical progress: introducing a novel semi-quantitative scoring system.

OBJECTIVE: Proposing a scoring tool to predict COVID-19 patients' outcomes based on initially assessed clinical and CT features. METHODS: All patients, who were referred to a tertiary-university hospital respiratory triage (March 27-April 26, 2020), were highly clinically suggestive for COVID-19 and had undergone a chest CT scan were included. Those with positive rRT-PCR or highly clinically suspicious patients with typical chest CT scan pulmonary manifestations were considered confirmed COVID-19 for additional analyses. Patients, based on outcome, were categorized into outpatient, ordinary-ward admitted, intensive care unit (ICU) admitted, and deceased; their demographic, clinical, and chest CT scan parameters were compared. The pulmonary chest CT scan features were scaled with a novel semi-quantitative scoring system to assess pulmonary involvement (PI). RESULTS: Chest CT scans of 739 patients (mean age = 49.2 ± 17.2 years old, 56.7% male) were reviewed; 491 (66.4%), 176 (23.8%), and 72 (9.7%) cases were managed outpatient, in an ordinary ward, and ICU, respectively. A total of 439 (59.6%) patients were confirmed COVID-19 cases; their most prevalent chest CT scan features were ground-glass opacity (GGO) (93.3%), pleural-based peripheral distribution (60.3%), and multi-lobar (79.7%), bilateral (76.6%), and lower lobes (RLL and/or LLL) (89.1%) involvement. Patients with lower SpO2, advanced age, RR, total PI score or PI density score, and diffuse distribution or involvement of multi-lobar, bilateral, or lower lobes were more likely to be ICU admitted/expired. After adjusting for confounders, predictive models found cutoffs of age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 (15) for ICU admission (death). A combination of all three factors showed 89.1% and 95% specificity and 81.9% and 91.4% accuracy for ICU admission and death outcomes, respectively. Solely evaluated high PI score had high sensitivity, specificity, and NPV in predicting the outcome as well. CONCLUSION: We strongly recommend patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score to be considered as high-risk patients for further managements and care plans. KEY POINTS: • Chest CT scan is a valuable tool in prioritizing the patients in hospital triage. • A more accurate and novel 35-scale semi-quantitative scoring system was designed to predict the COVID-19 patients' outcome. • Patients with age ≥ 53, SpO2 ≤ 91, and PI score ≥ 8 or even only high PI score should be considered high-risk patients.

COVID-19 pneumonia-ultrasound, radiographic, and computed tomography findings: a comprehensive pictorial essay.

Ultrasound, chest X-ray, and computed tomography (CT) have been used with excellent results in diagnosis, first assessment, and follow-up of COVID-19 confirmed and suspected patients. Ultrasound and chest X-ray have the advantages of the wide availability and acquisition at the patient's bed; CT showed high sensitivity in COVID-19 diagnosis. Ground-glass opacities and consolidation are the main CT and X-ray features; the distribution of lung abnormalities is typically bilateral and peripheral. Less typical findings, including pleural effusion, mediastinal lymphadenopathies, the bubble air sign, and cavitation, can also be visible on chest CT. Radiologists should be aware of the advantages and limitations of the available imaging techniques and of the different pulmonary aspects of COVID-19 infection.

Radiologic evaluation of the mastoid segment of the facial nerve tract in the intact temporal bone.

PURPOSE: To localize the facial nerve course in the mastoid segment and to measure its distances relative to the tympanic membrane. METHODS: This is a cross-sectional descriptive study. During 2019 in a tertiary hospital, 129 non-contrast and non-pathologic temporal CT images were studied in a tertiary hospital. Facial nerve distances were measured from the planes passing through the annulus in the axial cross-sections at superior, umbo, and inferior levels of the tympanic membrane. It was done in two different dimensions which are anteroposterior (toward the plane of the ear canal wall) and mediolateral (toward the plane of the tympanic membrane). RESULTS: The least mean anteroposterior distance between the facial nerve and the posterior ear canal wall was at the level of umbo (3.66 ± 0.76 mm). The nearest point of the nerve toward the tympanic membrane was the inferior level (- 0.03 ± 0.81 mm). Overall external ear canal lengths were statistically significantly lower in women rather than men. There was a reverse correlation between the age and the ear canal length. CONCLUSION: Posterior canalplasty seems to be safe unless dissection does not cross the plane of annulus. In this study, the safe margin was 1.4 mm in posterior canal wall drilling. It also should be performed carefully if it extends to the inferior side of the canal. Measuring the mediolateral dimension of the nerve toward the annulus in the axial CT images seems to be practically beneficial, especially in the inferior where the ear canal wall turns and might not act as a good landmark. Paying attention to this plane may reduce the risks of nerve injury in any procedures with transcanal approaches, particularly in inferior canaloplasty.

[Three-dimentional radiographic features of 67 maxillary radicular cysts].

OBJECTIVE: To analyze the three-dimensional radiographic characteristics of maxillary radi-cular cysts using cone-beam computed tomography (CBCT) and spiral CT. METHODS: Clinical records, histopathological reports, and CBCT or non-enhanced spiral CT images of 67 consecutive patients with maxillary radicular cysts were retrospectively acquired, and radiographic features, including size, shape, expansion, internal structure and relationship with the surrounding tissues, were analyzed. The lesions were divided into three types according to the involved tooth number, as follows: type Ⅰ (single tooth), the epicenter of the cyst was located at the apex of a nonvital tooth, without involvement of the neighbo-ring tooth; type Ⅱ (adjacent tooth involvement), the cyst was located at the apex of a nonvital tooth with involvement of the mesial and/or distal tooth root; and type Ⅲ (multi-teeth), the cyst involved the apexes of ≥4 teeth. Besides, these cysts were classified as another three types on sagittal views, as follows: centripetal, the root apex was oriented centripetally to the center of the cyst; palatal, the cyst was located mainly at the palatal side of the apex; and labial/buccal, the cyst was located mainly at the labial/buccal side of the apex. RESULTS: Totally, 67 patients with maxillary radicular cysts were acquired, including 38 males and 29 females, and their ages ranged from 13 to 77 years. Among them, 46 lesions (68.7%) were located in the anterior maxilla and 65 (97.0%) were round or oval. Labial/buccal cortex expansion was present in 43 cases (64.2%) and palatal cortex expansion in 37 cases (55.2%). The nasal floor was invaded in 27 cases (40.3%), the maxillary sinus was invaginated in 26 cases (38.8%), and root resorption was present in 9 cases (13.4%). The average diameter of lesions was (20.89±8.11) mm mesio-distally and (16.70±5.88) mm bucco-palatally. In spite of the 4 residual cysts, the remaining 63 lesions included 14 type Ⅰ, 26 type Ⅱ and 23 type Ⅲ cysts according to the involved tooth number. Besides, the 63 lesions included 46 centripetal, 15 palatal and 2 buccal cysts on sagittal views. CONCLUSION: The maxillary radicular cysts were frequently well-circumscribed round or oval radiolucency, with significantly different sizes. According to the involved tooth number, it can be divided into single tooth, adjacent tooth involvement and multi-teeth types. On sagittal views, the root-cyst relationship was centripetal in most cases, while a minority of cysts expanded palatally or buccally.

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning.

BACKGROUND AND PURPOSE: This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke. METHODS: A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18-36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90. RESULTS: Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733-0.747) and an accuracy of 0.711 (95% CI, 0.705-0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740-0.755]; accuracy, 0.720 [95% CI, 0.714-0.727]; P=0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850-0.861]; accuracy, 0.804 [95% CI, 0.799-0.810]; P<0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%). CONCLUSIONS: Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.

Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network.

INTRODUCTION: The pathological grading of pancreatic neuroendocrine neoplasms (pNENs) is an independent predictor of survival and indicator for treatment. Deep learning (DL) with a convolutional neural network (CNN) may improve the preoperative prediction of pNEN grading. METHODS: Ninety-three pNEN patients with preoperative contrast-enhanced computed tomography (CECT) from Hospital I were retrospectively enrolled. A CNN-based DL algorithm was applied to the CECT images to obtain 3 models (arterial, venous, and arterial/venous models), the performances of which were evaluated via an eightfold cross-validation technique. The CECT images of the optimal phase were used for comparing the DL and traditional machine learning (TML) models in predicting the pathological grading of pNENs. The performance of radiologists by using qualitative and quantitative computed tomography findings was also evaluated. The best DL model from the eightfold cross-validation was evaluated on an independent testing set of 19 patients from Hospital II who were scanned on a different scanner. The Kaplan-Meier (KM) analysis was employed for survival analysis. RESULTS: The area under the curve (AUC; 0.81) of arterial phase in validation set was significantly higher than those of venous (AUC 0.57, p = 0.03) and arterial/venous phase (AUC 0.70, p = 0.03) in predicting the pathological grading of pNENs. Compared with the TML models, the DL model gave a higher (although insignificantly) AUC. The highest OR was achieved for the p ratio <0.9, the AUC and accuracy for diagnosing G3 pNENs were 0.80 and 79.1% respectively. The DL algorithm achieved an AUC of 0.82 and an accuracy of 88.1% for the independent testing set. The KM analysis showed a statistical significant difference between the predicted G1/2 and G3 groups in the progression-free survival (p = 0.001) and overall survival (p < 0.001). CONCLUSION: The CNN-based DL method showed a relatively robust performance in predicting pathological grading of pNENs from CECT images.

Chest computed tomography findings of coronavirus disease 2019 (COVID-19) pneumonia.

OBJECTIVE: To retrospectively analyze the chest computed tomography (CT) features in patients with coronavirus disease 2019 (COVID-19) pneumonia. METHODS: From January 9, 2020, to February 26, 2020, totally 56 laboratory-confirmed patients with COVID-19 underwent chest CT. For 40 patients, follow-up CT scans were obtained. The CT images were evaluated for the number, type and distribution of the opacity, and the affected lung lobes. Furthermore, the initial CT scan and the follow-up CT scans were compared. RESULTS: Forty patients (83.6%) had two or more opacities in the lung. Eighteen (32.7%) patients had only ground-glass opacities; twenty-nine patients (52.7%) had ground-glass and consolidative opacities; and eight patients (14.5%) had only consolidation. A total of 43 patients (78.2%) showed two or more lobes involved. The opacities tended to be both in peripheral and central (30/55, 54.5%) or purely peripheral distribution (25/55, 45.5%). Fifty patients (90.9%) had the lower lobe involved. The first follow-up CT scans showed that twelve patients (30%) had improvement, 26 (65%) patients had mild-moderate progression, and two patients (5%) had severe progression with "white lungs." The second follow-up CT showed that 22 patients (71%) showed improvement compared with the first follow-up CT, four patients (12.9%) had aggravated progression, and five patients (16.1%) showed unchanged radiographic appearance. CONCLUSIONS: The common CT features of COVID-19 pneumonia are multiple lung opacities, multiple types of the opacity (ground-glass, ground-glass and consolidation, and consolidation alone), and multiple lobes especially the lower lobe involved. Follow-up CT could demonstrate the rapid progression of COVID-19 pneumonia (either in aggravation or absorption). KEY POINTS: • The predominant CT features of COVID-19 pneumonia are multiple ground-glass opacities with or without consolidation and, with both lungs, multiple lobes and especially the lower lobe affected. • CT plays a crucial role in early diagnosis and assessment of COVID-19 pneumonia progression. • CT findings of COVID-19 pneumonia may not be consistent with the clinical symptoms or the initial RT-PCR test results.

CT features of novel coronavirus pneumonia (COVID-19) in children.

A serious epidemic of COVID-19 broke out in Wuhan, Hubei Province, China, and spread to other Chinese cities and several countries now. As the majority of patients infected with COVID-19 had chest CT abnormality, chest CT has become an important tool for early diagnosis of COVID-19 and monitoring disease progression. There is growing evidence that children are also susceptible to COVID-19 and have atypical presentations compared with adults. This review is mainly about the differences in clinical symptom spectrum, diagnosis of COVID-19, and CT imaging findings between adults and children, while highlighting the value of radiology in prevention and control of COVID-19 in pediatric patients. KEY POINTS: • Compared with adults, pediatric patients with COVID-19 have the characteristics of lower incidence, slighter clinical symptoms, shorter course of disease, and fewer severe cases. • The chest CT characteristics of COVID-19 in pediatric patients were atypical, with more localized GGO extent, lower GGO attenuation, and relatively rare interlobular septal thickening. • Chest CT should be used with more caution in pediatric patients with COVID-19 to protect this vulnerable population from risking radiation.

Protocol analysis of dual-energy CT for optimization of kidney stone detection in virtual non-contrast reconstructions.

OBJECTIVES: Previous studies have shown that split-bolus protocols in virtual non-contrast (VNC) reconstructions of dual-energy computed tomography (DE-CT) significantly decrease radiation dose in patients with urinary stone disease. To evaluate the impact on kidney stone detection rate of stone composition, size, tube voltage, and iodine concentration for VNC reconstructions of DE-CT. METHODS: In this prospective study, 16 kidney stones of different sizes (1.2-4.5 mm) and compositions (struvite, cystine, whewellite, brushite) were placed within a kidney phantom. Seventy-two scans with nine different iodine contrast agents/saline solutions with increasing attenuation (0-1400 HU) and different kilovoltage settings (70 kV/150 kV; 80 kV/150 kV; 90 kV/150 kV; 100 kV/150 kV) were performed. Two experienced radiologists independently rated the images for the presence and absence of stones. Multivariate classification tree analysis and descriptive statistics were used to evaluate the diagnostic performance. RESULTS: Classification tree analysis revealed a higher detection rate of renal calculi > 2 mm in size compared with that of renal calculi < 2 mm (84.7%; 12.7%; p < 0.001). For stones with a diameter > 2 mm, the best results were found at 70 kV/Sn 150 kV and 80 kV/Sn 150 kV in scans with contrast media attenuation of 600 HU or less, with sensitivity of 99.6% and 96.0%, respectively. A higher luminal attenuation (> 600 HU) resulted in a significantly decreased detection rate (91.8%, 0-600 HU; 70.7%, 900-1400 HU; p < 0.001). In our study setup, the detection rates were best for cystine stones. CONCLUSION: Scan protocols in DE-CT with lower tube current and lower contrast medium attenuation show excellent results in VNC for stones larger than 2 mm but have limitations for small stones. KEY POINTS: • The detection rate of virtual non-contrast reconstructions is highly dependent on the surrounding contrast medium attenuation at the renal pelvis and should be kept as low as possible, as at an attenuation higher than 600 HU the VNC reconstructions are susceptible to masking ureteral stones. • Protocols with lower tube voltages (70 kV/Sn 150 kV and 80 kV/Sn 150 kV) improve the detection rate of kidney stones in VNC reconstructions. • The visibility of renal stones in virtual non-contrast of dual-energy CT is highly associated with the size, and results in a significantly lower detection rate in stones below 2 mm.