).q RSNA, 2014 Purpose:To evaluate the accuracy of shear-wave elastography (SWE) for staging liver fibrosis in patients with diffuse liver disease (including patients with hepatitis C virus [HCV]) and to determine the relative accuracy of SWE measurements obtained from different hepatic acquisition sites for staging liver fibrosis. Materials and Methods:The institutional review board approved this single-institution prospective study, which was performed between January 2010 and March 2013 in 136 consecutive patients who underwent SWE before their scheduled liver biopsy (age range, 18-76 years; mean age, 49 years; 70 men, 66 women). Informed consent was obtained from all patients. SWE measurements were obtained at four sites in the liver. Biopsy specimens were reviewed in a blinded manner by a pathologist using METAVIR criteria. SWE measurements and biopsy results were compared by using the Spearman correlation and receiver operating characteristic (ROC) curve analysis. Results:SWE values obtained at the upper right lobe showed the highest correlation with estimation of fibrosis (r = 0.41, P , .001). Inflammation and steatosis did not show any correlation with SWE values except for values from the left lobe, which showed correlation with steatosis (r = 0.24, P = .004). Abbreviations: AUC = area under the ROC curve CI = confidence interval CLD = chronic liver disease DANA = difference between the mean fibrosis stage of advanced fibrosis and the mean fibrosis stage of nonadvanced fibrosis HCV = hepatitis C virus ROC = receiver operating characteristic SWE = shear-wave elastography Author contributions:Guarantors of integrity of entire study, A.E.S., M.D.; study concepts/study design or data acquisition or data analysis/ interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, A.E.S., M.D., A.V., J.M.N., K.E.C.; clinical studies, A.E.S., A.V., A.K.B., J.M.N., K.E.C., R.T.C.; statistical analysis, M.D., E.F.H.; and manuscript editing, A.E.S., M.D., A.V., R.T.C. Funding:R.T.C. supported by the National Institutes of Health (grant DK078772).Conflicts of interest are listed at the end of this article. (1), with as many as 150 000 new cases diagnosed each year (2)-20% of which had cirrhosis at presentation. The multiple causes of CLD follow a common pathway of progressive liver fibrosis, ultimately culminating in cirrhosis. These include hepatitis C virus (HCV), hepatitis B virus, nonalcoholic fatty liver disease, and alcoholic liver disease (3). Although the prevalence of major causes of CLD remains stable, data from the National Health and Nutrition Examination Surveys show that nonalcoholic fatty liver disease will be a substantial burden on the prevalence of CLD in the United States (1). Advanced fibrosis, cirrhosis, and hepatocellular carcinoma develop in about 17%-55% of patients with HCV ...

Background: Five percent to 20% of thyroid nodule fine-needle aspiration (FNA) samples are nondiagnostic. The objective of this study was to determine whether a combination of FNA and core biopsy (CFNACB) would yield a higher proportion of diagnostic readings compared with FNA alone in patients with a history of one or more prior nondiagnostic FNA readings. Methods: We conducted a retrospective study of 90 core biopsies (CBs) performed in 82 subjects (55 women and 27 men) between 2006 and 2008 in an outpatient clinic. Results: CFNACB yielded a diagnostic reading in 87%. The diagnostic reading yield of the CB component of CFNACB was significantly superior to the concurrent FNA component, with CB yielding a diagnosis in 77% of cases and FNA yielding a diagnosis in 47% ( p < 0.0001). The combination of CB and FNA had a higher diagnostic reading yield than either alone. In 69 nodules that had only one prior nondiagnostic FNA, CB was diagnostic in 74%, FNA was diagnostic in 52%, CFNACB was diagnostic in 87%, and CB performed significantly better than FNA ( p = 0.0135). In 21 nodules with two or more prior nondiagnostic FNAs, CFNACB and CB were diagnostic in 86%, FNA was diagnostic in 29%, and CB was significantly better than FNA ( p = 0.0005). Clinical, ultrasound, or histopathologic follow-up was available for 81% (73/90) of the CFNACB procedures. No subject with a benign CFNACB reading was diagnosed with thyroid malignancy in the follow-up period (range 4-37 months, mean 18 months), although one subject had minimal increase in nodule size and was awaiting repeat sonography at study conclusion. Conclusion: Thyroid nodule CFNACB is safe and clinically useful in selected patients when a prior FNA reading is nondiagnostic. CFNACB is superior to either CB or FNA alone. CFNACB should be strongly considered as an alternative to surgery in individuals with two prior nondiagnostic FNAs.

Computed tomographic colonography was accurate in detecting adenomas 10 mm or larger but less so for smaller lesions. Patient experience was better with laxative-free CTC. These results suggest a possible role for laxative-free CTC as an alternate screening method.

This study aimed to determine whether active ultrasound surveillance may obviate the need for surgical resection in selected patients with small testicular lesions (STLs). A retrospective 11-year review was conducted of adults who were diagnosed with an STL on scrotal ultrasonography and who either had orchiectomy or sonographic follow-up during a period of at least 3 months. A total of 101 subjects were enrolled. Ultrasound findings, clinical features, histopathology/follow-up imaging were recorded. Logistic regression analysis was performed to select independent risk factors for the diagnosis of malignancy. Seventeen (16.8%) subjects underwent immediate surgery, 8 (7.9%) of 101 underwent surgery after ultrasound follow-up, and 76 (75.3%) of 101 were followed with ultrasound only. The follow-up period ranged from 1 to 7 months in the 8 patients who ultimately underwent surgery after ultrasound follow-up and from 6 to 84 months in the 76 patients followed up with ultrasound only. All 15 malignant cases underwent immediate surgery without follow-up sonography. The frequency of lesions, either benign at surgery or stable on ultrasound, was 85.1% (86 of 101; 95% confidence interval, 77%-91%). Logistic regression analysis showed that lesion size was the only independent risk factor for malignancy in hypoechoic STLs (P < 0.05). Most of the STLs were stable on serial sonograms and likely benign. Active ultrasound surveillance may be an appropriate management strategy in patients with STLs.

OBJECTIVE-Our purpose in this study was to develop an automated computer-aided volumetry (CAV) scheme for quantifying pneumothorax in MDCT images for pediatric patients and to investigate the imaging parameters that may affect its accuracy.MATERIALS AND METHODS-Fifty-eight consecutive pediatric patients (mean age 12±6 years) with pneumothorax who underwent MDCT for evaluation were collected retrospectively for this study. All cases were imaged by a 16-or 64-MDCT scanner with weight-based kilovoltage, low-dose tube current, 1.0 ~ 1.5 pitch, 0.6 ~ 5.0 mm slice thickness, and a B70f (sharp) or B31f (soft) reconstruction kernel. Sixty-three pneumothoraces ≥1 cc were visually identified in the left (n = 30) or/and right (n = 33) lungs. Each identified pneumothorax was contoured manually on an Amira workstation V4.1.1 (Mercury Computer Systems, Chelmsford, Massachusetts) by two radiologists in consensus. The computerized volumes of the pneumothoraces were determined by application of our CAV scheme. The accuracy of our automated CAV scheme was evaluated by comparison between computerized volumetry and manual volumetry, for the total volume of pneumothoraces in the left and right lungs. RESULTS-The mean difference between the computerized volumetry and the manual volumetry for all 63 pneumothoraces ≥1 cc was 8.2%. For pneumothoraces ≥10 cc, ≥50 cc, and ≥200 cc, the mean differences were 7.7% (n=57), 7.3% (n=33), and 6.4% (n=13), respectively. The correlation coefficient was 0.99 between the computerized volume and the manual volume of pneumothoraces. Bland-Altman analysis showed that computerized volumetry has a mean difference of −5.1% compared to manual volumetry. For all pneumothoraces ≥10 cc, the mean differences for slice thickness ≤1.25 mm, =1.5 mm, and =5.0 mm were 6.1% (n=28), 3.5% (n=10), and 12.2% (n=19), respectively. For the two reconstruction kernels, B70f and B31f, the mean differences were 6.3% (n=42, B70f) and 11.7% (n=15, B31f), respectively. CONCLUSION-Our automated CAV scheme provides an accurate measurement of pneumothorax volume in MDCT images of pediatric patients. For accurate volumetric quantification of pneumothorax in children in MDCT images by use of the automated CAV scheme, we recommended reconstruction parameters based on a slice thickness ≤1.5 mm and the reconstruction kernel B70f.

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BackgroundDemand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2‐weighted imaging (T2WI).PurposeTo compare conventional bpMRIs (CL‐bpMRI) with bpMRIs including a deep learning‐accelerated T2WI (DL‐bpMRI) in diagnosing prostate cancer.Study TypeRetrospective.PopulationEighty consecutive men, mean age 66 years (47–84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow‐up included prostate biopsy or stability of prostate‐specific antigen (PSA) for 1 year.Field Strength and SequencesA 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL‐T2), 3‐fold deep learning‐accelerated axial and coronal T2‐weighted sequence (DL‐T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2, 1000 sec/mm2, calculated b = 1500 sec/mm2.AssessmentCL‐bpMRI and DL‐bpMRI including the same conventional diffusion‐weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer‐assisted detection algorithm (DL‐CAD). The readers evaluated image quality using a 4‐point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI‐RADS) v2.1. DL‐CAD identified and assigned lesions of PI‐RADS 3 or greater.Statistical TestsQuality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. Significance: P = 0.05.ResultsEighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL‐T2, DL‐T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient‐based analysis, the reader results of AUC are (CL‐bpMRI, DL‐bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL‐CAD (CL‐bpMRI, DL‐bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48).ConclusionDeep learning‐accelerated T2‐weighted imaging may potentially be used to decrease acquisition time for bpMRI.Evidence Level3.Technical EfficacyStage 2.