Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks.

OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. METHODS: This retrospective single-center study included 292 patients (237 M/55F, mean age 61 years) with pathologically confirmed HCC between 08/2015 and 06/2019 and who underwent MRI before surgery. The dataset was randomly divided into training (n = 195), validation (n = 66), and test sets (n = 31). Volumes of interest (VOIs) were manually placed on index lesions by 3 independent radiologists on different sequences (T2-weighted imaging [WI], T1WI pre-and post-contrast on arterial [AP], portal venous [PVP], delayed [DP, 3 min post-contrast] and hepatobiliary phases [HBP, when using gadoxetate], and diffusion-weighted imaging [DWI]). Manual segmentation was used as ground truth to train and validate a CNN-based pipeline. For semiautomated segmentation of tumors, we selected a random pixel inside the VOI, and the CNN provided two outputs: single slice and volumetric outputs. Segmentation performance and inter-observer agreement were analyzed using the 3D Dice similarity coefficient (DSC). RESULTS: A total of 261 HCCs were segmented on the training/validation sets, and 31 on the test set. The median lesion size was 3.0 cm (IQR 2.0-5.2 cm). Mean DSC (test set) varied depending on the MRI sequence with a range between 0.442 (ADC) and 0.778 (high b-value DWI) for single-slice segmentation; and between 0.305 (ADC) and 0.667 (T1WI pre) for volumetric-segmentation. Comparison between the two models showed better performance in single-slice segmentation, with statistical significance on T2WI, T1WI-PVP, DWI, and ADC. Inter-observer reproducibility of segmentation analysis showed a mean DSC of 0.71 in lesions between 1 and 2 cm, 0.85 in lesions between 2 and 5 cm, and 0.82 in lesions > 5 cm. CONCLUSION: CNN models have fair to good performance for semiautomated HCC segmentation, depending on the sequence and tumor size, with better performance for the single-slice approach. Refinement of volumetric approaches is needed in future studies. KEY POINTS: • Semiautomated single-slice and volumetric segmentation using convolutional neural networks (CNNs) models provided fair to good performance for hepatocellular carcinoma segmentation on MRI. • CNN models' performance for HCC segmentation accuracy depends on the MRI sequence and tumor size, with the best results on diffusion-weighted imaging and T1-weighted imaging pre-contrast, and for larger lesions.

Precision of MRI radiomics features in the liver and hepatocellular carcinoma.

OBJECTIVES: To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. METHODS: The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month to evaluate: (1) test-retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV). RESULTS: There was moderate to excellent test-retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53-0.99; CV: 3-29%), and moderate to good test-retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53-0.73; CV: 12-19%). There was poor inter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58-0.99; CV: 3-15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80-0.99; CV: 4-15%) and moderate to good for liver (CCC: 0.45-0.86; CV: 6-25%). CONCLUSIONS: MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies. KEY POINTS: • MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms. • MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC. • Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.

MR elastography outperforms shear wave elastography for the diagnosis of clinically significant portal hypertension.

OBJECTIVES: Portal hypertension (PH) is associated with complications such as ascites and esophageal varices and is typically diagnosed through invasive hepatic venous pressure gradient (HVPG) measurement, which is not widely available. In this study, we aim to assess the diagnostic performance of 2D/3D MR elastography (MRE) and shear wave elastography (SWE) measures of liver and spleen stiffness (LS and SS) and spleen volume, to noninvasively diagnose clinically significant portal hypertension (CSPH) using HVPG measurement as the reference. METHODS: In this prospective study, patients with liver disease underwent 2D/3D MRE and SWE of the liver and spleen, as well as HVPG measurement. The correlation between MRE/SWE measures of LS/SS and spleen volume with HVPG was assessed. ROC analysis was used to determine the utility of MRE, SWE, and spleen volume for diagnosing CSPH. RESULTS: Thirty-six patients (M/F 22/14, mean age 55 ± 14 years) were included. Of the evaluated parameters, 3D MRE SS had the strongest correlation with HVPG (r = 0.686, p < 0.001), followed by 2D MRE SS (r = 0.476, p = 0.004). 3D MRE SS displayed the best performance for diagnosis of CSPH (AUC = 0.911) followed by 2D MRE SS (AUC = 0.845) and 3D MRE LS (AUC = 0.804). SWE SS showed poor performance for diagnosis of CSPH (AUC = 0.583) while spleen volume was a fair predictor (AUC = 0.738). 3D MRE SS was significantly superior to SWE LS/SS (p ≤ 0.021) for the diagnosis of CSPH. CONCLUSION: SS measured with 3D MRE outperforms SWE for the diagnosis of CSPH. SS appears to be a useful biomarker for assessing PH severity. These results need further validation. KEY POINTS: • Spleen stiffness measured with 2D and 3D MR elastography correlates significantly with hepatic venous pressure gradient measurement. • Spleen stiffness measured with 3D MR elastography demonstrates excellent performance for the diagnosis of clinically significant portal hypertension (AUC 0.911). • Spleen stiffness measured with 3D MR elastography outperforms liver and spleen stiffness measured with shear wave elastography for diagnosis of clinically significant portal hypertension.

Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI.

OBJECTIVES: To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis. METHODS: This single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; test, n = 54) who underwent gadoxetic acid-enhanced abdominal MRI, including HBP and MRE, and pathological evaluation of the liver within 1 year of MRI. Cropped liver HBP images from a custom-written fully automated liver segmentation were used as input for DL. A transfer learning approach based on the ImageNet VGG16 model was used. Different DL models were built for the prediction of fibrosis stages F1-4, F2-4, F3-4, and F4. ROC analysis was performed to evaluate the performance of DL in training, validation, and test sets and of MRE liver stiffness in the test set. RESULTS: AUC values of DL were 0.99/0.70/0.77 (F1-4), 0.92/0.71/0.91 (F2-4), 0.91/0.78/0.90 (F3-4), and 0.98/0.83/0.85 (F4) for training/validation/test sets, respectively. The AUCs of MRE liver stiffness in the test set were 0.86 (F1-4), 0.87 (F2-4), 0.92 (F3-4), and 0.86 (F4). AUCs of MRE and DL were not significantly different for any of the fibrosis stages (p > 0.134). CONCLUSIONS: The fully automated DL models based on HBP gadoxetic acid MRI showed good-to-excellent diagnostic performance for staging of liver fibrosis, with similar diagnostic performance to MRE. After validation in independent sets, the DL algorithm may allow for noninvasive liver fibrosis assessment without the need for additional MRI hardware. KEY POINTS: • The developed deep learning algorithm, based on routine standard-of-care gadoxetic acid-enhanced MRI data, showed good-to-excellent diagnostic performance for noninvasive staging of liver fibrosis. • The diagnostic performance of the deep learning algorithm was equivalent to that of MR elastography in a separate test set.

Splenic T1ρ as a noninvasive biomarker for portal hypertension.

BACKGROUND: There is a need for noninvasive methods for the diagnosis and monitoring of portal hypertension (PH). PURPOSE: To 1) assess the correlation of liver and spleen T1 and T1ρ measurements with portal pressures in patients with chronic liver disease, and 2) to compare the diagnostic performance of the relaxation parameters with radiological assessment of PH. STUDY TYPE: Prospective. SUBJECTS: Twenty-five patients (M/F 16/9, mean age 56 years, range 21-78 years) undergoing portal pressure (hepatic venous pressure gradient [HVPG]) measurements. FIELD STRENGTH/SEQUENCE: 1.5T abdominal MRI scan, including T1ρ and T1 mapping. ASSESSMENT: Liver and spleen T1ρ and T1 , radiological PH score, and (normalized) spleen length were evaluated. STATISTICAL TESTS: Spearman correlation of all MRI parameters with HVPG was assessed. The diagnostic performance of the assessed parameters for prediction of PH (HVPG ≥5 mmHg) and clinically significant PH (CSPH, HVPG ≥10 mmHg) was determined by receiver operating characteristic (ROC) analysis. RESULTS: The mean HVPG measurement was 7.8 ± 5.3 mmHg (PH, n = 18 [72%] including CSPH, n = 9 [36%]). PH score, (normalized) spleen length and spleen T1ρ significantly correlated with HVPG, with the strongest correlation found for spleen T1ρ (r = 0.613, P = 0.001). Spleen T1ρ was the only parameter that showed significant diagnostic performance for assessment of PH (area under the curve [AUC] 0.817, P = 0.015) and CSPH (AUC = 0.778, P = 0.024). Normalized spleen length also showed significant diagnostic performance for prediction of CSPH, with a slightly lower AUC (= 0.764, P = 0.031). The radiological PH score, T1ρ and T1 of the liver and T1 of the spleen, did not show significant diagnostic performance for assessment of CSPH (P > 0.075). DATA CONCLUSION: Spleen T1ρ showed a significant correlation with portal pressure and showed improved diagnostic performance for prediction of CSPH compared to radiological assessment. These initial results need confirmation in a larger cohort. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:787-794.

Influence of Acromial Morphologic Characteristics and Acromioclavicular Arthrosis on the Effect of Platelet-Rich Plasma on Partial Tears of the Supraspinatus Tendon.

OBJECTIVE. The purposes of this study were to determine the medium-term effect of ultrasound-guided infiltration of platelet-rich plasma (PRP) on partial tears of the supraspinatus tendon (SST) and to identify prognostic indicators of an unfavorable outcome. SUBJECTS AND METHODS. Over a period of 4 years, patients with a partial SST tear smaller than 1.5 cm referred for ultrasound-guided PRP infiltration (1 mL) for shoulder pain lasting more than 3 months were recruited consecutively. MRI was used to analyze the type of acromion and presence of acromioclavicular (AC) arthrosis. Primary (size of the tear and associated bursitis) and secondary (mobility and pain) results were collected at 3 months. RESULTS. The study included 128 patients (66 men, 62 women; mean age, 48.3 years; range, 20-59 years). At 3 months, favorable evolution of the tear repair was recorded in 71.1% (91/128) of patients and resolution of bursitis in 66.7% (42/63). Changes in tear size had large effect sizes (Cohen d ≥ 1.16), as did pain and shoulder mobility (Cohen d ≥ 0.95). The strongest predictors of unfavorable evolution of tear and bursitis were type 3 acromion and types 1 and 2 acromion with AC arthrosis (p < 0.001; ß = 20.412). CONCLUSION. Ultrasound-guided PRP infiltration of partial tears of the SST relieves pain and improves shoulder mobility, but its effect on the size of the tear is influenced by the morphologic characteristics of the acromion and the presence of AC arthrosis. The effect of PRP is insufficient in patients with a type 3 acromion or severe AC arthrosis.

The Use of Recommendations on Physician Rating Websites: The Number of Raters Makes the Difference When Adjusting Decisions.

Physician rating websites allow users to check physicians' profiles, write reviews, or rate their performance. The opinion of other users regarding a physician can affect our decision to visit her/him. To investigate the specific role of the number of users rating a physician when choosing a physician with support of these platforms, we used a Judge-Advisor System in which participants answered their likelihood to visit a physician before and after seeing the recommendations of others. Within the experiment, three conditions were presented: high and low number of reviewers recommending a physician, and no recommendations. We found that the participants' likelihood to visit a physician varied with respect to the displayed physician characteristics on the platform. Importantly, after the recommendation of others was presented, participants' likelihood to visit the physician changed significantly. The participants' adjusted response was significantly closer to the recommendation coming from a higher number of users, which indicate that this online, social media cue influences our decision to visit physicians. Comments and ratings on physician ratings are generally positive, but we show that negative ratings have a direct negative influence in the decision to visit a physician. We suggest administrators of these platforms to pay special attention to the content that users upload.

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy. METHODS: This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6 months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6 months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6 months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests. RESULTS: A total 168 HCCs (mean size 2.9 ± 1.7 cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001). CONCLUSION: The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.

Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.

PURPOSE: To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). METHODS: This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. RESULTS: Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. CONCLUSION: Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

Performance of native and gadoxetate-enhanced liver and spleen T1 mapping for noninvasive diagnosis of clinically significant portal hypertension: preliminary results.

PURPOSE: In this preliminary study, our aim was to assess the utility of quantitative native-T1 (T1-pre), iron-corrected T1 (cT1) of the liver/spleen and T1 mapping of the liver obtained during hepatobiliary phase (T1-HBP) post-gadoxetate disodium, compared to spleen size/volume and APRI (aspartate aminotransferase-to-platelet ratio index) for noninvasive diagnosis of clinically significant portal hypertension [CSPH, defined as hepatic venous pressure gradient (HVPG) ≥ 10 mm Hg]. METHODS: Forty-nine patients (M/F: 27/22, mean age 53y) with chronic liver disease, HVPG measurement and MRI were included. Breath-held T1 and cT1 measurements were obtained using an inversion recovery Look-Locker sequence and a T2* corrected modified Look-Locker sequence, respectively. Liver T1-pre (n = 49), spleen T1 (obtained pre-contrast, n = 47), liver and spleen cT1 (both obtained pre-contrast, n = 30), liver T1-HBP (obtained 20 min post gadoxetate disodium injection, n = 36) and liver T1 uptake (ΔT1, n = 36) were measured. Spleen size/volume and APRI were also obtained. Spearman correlation coefficients were used to assess the correlation between each of liver/spleen T1/cT1 parameters, spleen size/volume and APRI with HVPG. ROC analysis was performed to determine the performance of measured parameters for diagnosis of CSPH. RESULTS: There were 12/49 (24%) patients with CSPH. Liver T1-pre (r = 0.287, p = 0.045), liver T1-HBP (r = 0.543, p = 0.001), liver ΔT1 (r = - 0.437, p = 0.008), spleen T1 (r = 0.311, p = 0.033) and APRI (r = 0.394, p = 0.005) were all significantly correlated with HVPG, while liver cT1, spleen cT1 and spleen size/volume were not. The highest AUCs for the diagnosis of CSPH were achieved with liver T1-HBP, liver ΔT1 and spleen T1: 0.881 (95%CI 0.76-1.0, p = 0.001), 0.852 (0.72-0.98, p = 0.002) and 0.781 (0.60-0.95, p = 0.004), respectively. CONCLUSION: Our preliminary results demonstrate the potential of liver T1 mapping obtained during HBP post gadoxetate disodium for the diagnosis of CSPH. These results require further validation.

Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Abbreviated MR Protocols for Chronic Liver Disease and Liver Cancer.

MR imaging has become a powerful tool for assessing liver disease and liver cancer; however, it entails complex, time-consuming, and costly protocols. Abbreviated MR imaging (AMRI) is emerging as a simpler, faster, and low-cost alternative to full-abdominal MR imaging protocols. Different AMRI approaches have been tested successfully in hepatocellular carcinoma detection and for assessment of diffuse liver disease. The most accurate, time-effective, and cost-effective protocol as well as the target population need to be defined. Prospective and multicentric studies, exploring different AMRI protocols versus the current standard of reference, should be performed.

Assessment of hepatic function, perfusion and parenchyma attenuation with indocyanine green, ultrasound and computed tomography in a healthy rat model: Preliminary determination of baseline parameters in a healthy liver.

BACKGROUND: Defining reference intervals in experimental animal models plays a crucial role in pre-clinical studies. The hepatic parameters in healthy animals provide useful information about type and extension of hepatic damage. However, in the majority of the cases, to obtain them require an invasive techniques. Our study combines these determinations with dynamic functional test and imaging techniques to implement a non-invasive protocol for liver evaluation. The aim of the study was to determine reference intervals for hepatic function, perfusion and parenchyma attenuation with analytical and biochemical blood parameters, indocyanine green, ultrasound and computed tomography in six healthy SD rats. METHODS: Six males healthy SD rats were followed for 4 weeks. To determine hepatic function, perfusion and parenchyma attenuation analytical and biochemical blood parameters, indocyanine green, ultrasound and computed tomography were studied. Results were expressed as Means ± standard error of mean (SEM). The significance of differences was calculated by using student t-test, p < 0.05 was considered statistically significant. RESULTS: Indocyanine green clearance 5 and 10 minutes after its injection was 80.12% and 96.59%, respectively. Approximate rate of decay during the first 5 minutes after injection was 38% per minute. Hepatic perfusion evaluation with the high-frequency ultrasound was related to cardiovascular hemodynamic and renal perfusion. Portal area, hepatic artery resistance index, hepatic artery and portal peak systolic velocity and average between hepatic artery and porta was 3.41 ± 0.62 mm2, 0.57 ± 0.04 mm2/s, 693.24±102.53 mm2/s, 150.72 ± 17.80 mm2/s and 4.82 ± 0.96 mm2/s, respectively. Heart rate, cardiac output, left renal artery diammetre and renal blood flow were 331.01 ± 22.22 bpm, 75.58 ± 8.72 mL/min, 0.88 ± 0.04 mm2 and 13.65 ± 1.95 mm2/s. CT-scan hepatic average volume for each rat were 21.08±3.32, 17.57±2.76, 14.87±2.83 and 13.67±2.45 cm3 with an average attenuation coefficient of 113.51±18.08, 129,19±7.18, 141,47±1.95 y 151,67±1.2 HU. CONCLUSION: Indocyanine green and high-frequency ultrasound could be used in rats as a suitable marker of liver function. Computed tomography, through the study of raw data, help to characterize liver parenchyma, and could be a potential tool for early detection of liver parenchymal alterations and linear follow-up of patients. Further studies in rats with liver disease are necessary to verify the usefulness of these parameters.

Luminal Narrowing Alone Allows an Accurate Diagnosis of Crohn's Disease Small Bowel Strictures at Cross-Sectional Imaging.

BACKGROUND AND AIMS: Current consensus recommendations define small bowel strictures [SBS] in Crohn's disease [CD] on imaging as luminal narrowing with unequivocal upstream bowel dilation. The aim of this study was to [1] evaluate the performance of cross-sectional imaging for SBS diagnosis in CD using luminal narrowing with upstream SB dilation and luminal narrowing with or without upstream dilation, and [2] compare the diagnostic performance of computed tomography [CT] and magnetic resonance enterography [MRE] for SBS diagnosis. METHODS: In total, 111 CD patients [81 with pathologically confirmed SBS, 30 controls] who underwent CT and/or MRE were assessed. Two radiologists [R1, R2] blinded to pathology findings independently assessed the presence of luminal narrowing and upstream SB dilation. Statistical analysis was performed for [1] luminal narrowing with or without SB upstream dilation ['possible SBS'], and [2] luminal narrowing with upstream SB dilation ≥3 cm ['definite SBS']. RESULTS: Sensitivity for detecting SBS was significantly higher using 'possible SBS' [R1, 82.1%; R2, 77.9%] compared to 'definite SBS' [R1, 62.1%; R2, 65.3%; p < 0.0001] with equivalent specificity [R1, 96.7%; R2, 93.3%; p > 0.9]. Using the criterion 'possible SBS', sensitivity/specificity were equivalent between CT [R1, 87.3%/93.3%; R2, 83.6%/86.7%] and MRE [R1, 75.0%/100%; R2: 70.0%/100%]. Using the criterion 'definite SBS', CT showed significantly higher sensitivity [78.2%] compared to MRE [40.0%] for R1 but not R2 with similar specificities [CT, 86.7-93.3%; MRE, 100%]. CONCLUSION: SBS can be diagnosed using luminal narrowing alone without the need for upstream dilation. CT and MRE show similar diagnostic performance for SBS diagnosis using luminal narrowing with or without upstream dilation.

Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest CT in combination with plasma cytokines using a machine learning approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n=152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within 5 days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α) were collected from the electronic medical record. We found that chest CT combined with plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82), whereas CT quantitative was better at predicting severity (AUC 0.81 vs 0.70) while cytokine measurements better predicted death (AUC 0.70 vs 0.66). Finally, we provide a simple scoring system using plasma IL-6, IL-8, TNF-α, GGO to aerated lung ratio and age as novel metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Evaluation of rat liver with ARFI elastography: In vivo and ex vivo study.

OBJECTIVE: The aim of this study was to compare in vivo vs ex vivo liver stiffness in rats with acoustic radiation force impulse (ARFI) elastography using the histological findings as the gold standard. METHODS: Eighteen male Wistar rats aged 16-18 months were divided into a control group (n = 6) and obese group (n = 12). Liver stiffness was measured with shear wave velocity (SWV) using the ARFI technique both in vivo and ex vivo. The degree of fibrosis, steatosis and liver inflammation was evaluated in the histological findings. Pearson's correlation coefficient was applied to relate the SWV values to the histological parameters. RESULTS: The SWV values acquired in the ex vivo study were significantly lower than those obtained in vivo (P < 0.004). A significantly higher correlation value between the degree of liver fibrosis and the ARFI elastography assessment was observed in the ex vivo study (r = 0.706, P < 0.002), than the in vivo study (r = 0.623, P < 0.05). CONCLUSION: Assessment of liver stiffness using ARFI elastography yielded a significant correlation between SWV and liver fibrosis in both the in vivo and ex vivo experiments. We consider that by minimising the influence of possible sources of artefact we could improve the accuracy of the measurements acquired with ARFI.