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OBJECTIVE. Liver transplant patients are monitored for rejection and hepatic fibrosis and often undergo liver biopsies. The purpose of the present study is to determine whether noninvasive shear wave elastography (SWE) can quantify fibrosis in liver transplant recipients, with the aim of decreasing and possibly eliminating unnecessary biopsies for patients with suspected or progressive hepatic fibrosis. MATERIALS AND METHODS. Between May 1, 2015, and December 31, 2017, our prospective study evaluated 111 adult liver transplant patients (age range, 23-79 years) who underwent 147 ultrasound (US) SWE examinations of the right hepatic lobe followed by biopsies. SWE values were compared with the histologic fibrosis (Metavir) scores of the biopsy samples. SWE threshold values were determined using classification and regression tree analysis by anchoring to the degree of fibrosis. The sensitivity, specificity, positive predictive value, and negative predictive value (with 95% CIs) were calculated on the basis of the threshold value. Overall prediction accuracy was estimated using the AUC value from the ROC curve. RESULTS. From the 147 US SWE examinations and liver biopsies, consistent threshold values were identified for patients with no or minimal fibrosis (Metavir scores of F0 and F1, respectively) compared with significant fibrosis (Metavir scores of F2, F3, or F4). A median SWE value of 1.76 m/s or less denoted no or minimal fibrosis, whereas a value greater than 1.76 m/s denoted significant fibrosis. The sensitivity of US SWE examinations in classifying fibrosis was 0.77 (95% CI, 0.5-0.93). The specificity, positive predictive value, and negative predictive value were 0.79 (95% CI, 0.71-0.86), 0.33 (95% CI, 0.19-0.49), and 0.96 (95% CI, 0.91-0.99), respectively. CONCLUSION. Liver transplant patients may avoid liver biopsy if US SWE examination shows a median shear wave velocity of 1.76 or less, which corresponds to a Metavir score of F0 or F1, denoting no or minimal fibrosis.

OBJECTIVE. The purpose of this study is to assess the association of thyroid cancer with sonographic features of peripheral calcifications. MATERIALS AND METHODS. We retrospectively reviewed patients who had a total of 97 thyroid nodules with peripheral calcifications who underwent ultrasound-guided fine-needle aspiration from 2008 to 2018. Three board-certified radiologists evaluated the nodules for features of peripheral calcifications: the percentage of the nodule involved by peripheral calcifications, whether the calcifications were continuous or discontinuous, the visibility of internal components of the nodule, and the presence of extrusion of soft tissue beyond the calcifications. The correlation of peripheral calcification parameters with the rate of thyroid nodule malignancy was evaluated. In addition, the interobserver agreement between readers was assessed with Cohen kappa coefficient. RESULTS. Of the 97 nodules with peripheral calcifications, 27% ( n = 26) were found to be malignant on biopsy. The continuity of peripheral calcifications, visibility of internal components, and extrusion of soft tissue beyond the calcification rim showed no significant association with benign or malignant nodules. Readers had good agreement on peripheral calcification continuity (κ = 0.63; 95% CI, 0.53-0.73) and moderate agreement on internal component visibility (κ = 0.43; 95% CI, 0.35-0.51) and percentage of the nodule involved by rim calcifications (κ = 0.52; 95% CI, 0.44-0.59). There was fair agreement for extranodular soft-tissue extrusion (κ = 0.32, 95% CI, 0.24-0.39). CONCLUSION. Peripheral rim calcifications are highly associated with malignancy. However, specific peripheral rim calcification features do not aid in distinguishing benign from malignant nodules, which may in part be caused by high interobserver variability.

Objective: Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies., Conclusion: A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.

Objective: The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses., Materials and Methods: In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC., Results: Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses., Conclusion: The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.

Objective: The objective of this study is to compare forward-projected model-based iterative reconstruction solution (FIRST), a newer fully iterative CT reconstruction method, with adaptive iterative dose reduction 3D (AIDR 3D) in low-dose screening CT for lung cancer. Differences in image noise, image quality, and pulmonary nodule detection, size, and characterization were specifically evaluated., Materials and Methods: Low-dose chest CT images obtained for 50 consecutive patients between December 2015 and January 2016 were retrospectively reviewed. Images were reconstructed using FIRST and AIDR 3D for both lung and soft-tissue reconstruction. Images were independently reviewed to assess image noise, subjective image quality (with use of a 5-point Likert scale, with 1 denoting far superior image quality; 2, superior quality; 3, equivalent quality; 4, inferior quality; and 5, far inferior quality), pulmonary nodule count, size of the largest pulmonary nodule, and characterization of the largest pulmonary nodule (i.e., solid, part solid, or ground glass)., Results: Across all 50 cases, measured image noise was lower with FIRST than with AIDR 3D (lung window, 44% reduction, 41 ± 7 vs 74 ± 8 HU, respectively; soft-tissue window, 32% reduction, 11 ± 2 vs 16 ± 2 HU, respectively). Readers subjectively rated images obtained with FIRST as comparable to images obtained with AIDR 3D (mean [± SD] Likert score for FIRST vs AIDR 3D, 3.2 ± 0.3 for soft-tissue reconstructions and 3.0 ± 0.3 for lung reconstructions). For each reader, very good agreement regarding nodule count was noted between FIRST and AIDR 3D (interclass correlation coefficient [ICC], 0.83 for reader 1 and 0.78 for reader 2). Excellent agreement regarding nodule size (ICC, 0.99 for reader 1 and 0.99 for reader 2) and characterization of the largest nodule (kappa value, 0.92 for reader 1 and 0.82 for reader 2) also existed., Conclusion: Images reconstructed with FIRST are superior to those reconstructed AIDR 3D with regard to image noise and are equivalent with regard to subjective image quality, pulmonary nodule count, and nodule characterization.

Objective: The objective of our study was to describe the preliminary results of our clinical low-dose CT (LDCT) lung cancer screening program targeting a minority, socioeconomically disadvantaged, high-risk population different from that studied in the National Lung Screening Trial (NLST)., Materials and Methods: Community partner clinics in an underserved region of south Los Angeles County referred interested candidates to our program. All patients met National Comprehensive Cancer Network eligibility criteria for lung cancer screening., Results: From July 21, 2015, through April 3, 2017, 889 individuals were referred to the program. Of the 329 eligible participants, 275 (mean age, 59 years; 52% men) underwent baseline screening LDCT: 84% of patients were black, and 66% had a high school education or less. The median pack-years was 40, and 81% of patients were current smokers. Thirty-one percent of participants reported occupational exposure to one or more known lung carcinogens. Lung CT Screening Reporting and Data System (Lung-RADS) categories were assigned using baseline LDCT examinations: Lung-RADS category 1 or 2 were assigned in 86% of patients, category 3 in 7%, category 4A in 4%, and category 4B or 4X in 3%. Lung cancer has been diagnosed in two of these patients (0.7%) to date: stage IIIB small cell lung carcinoma in one patient and stage IV lung cancer of unknown type in the other patient. Among the 275 patients, 29% had potentially clinically significant incidental findings., Conclusion: Lung cancer screening with LDCT in a minority, socioeconomically disadvantaged, high-risk population is feasible but may yield a different lung cancer profile than screening populations in more privileged communities. More follow-up time is required to determine whether the reduction in lung cancer mortality shown in the NLST applies to this underserved population.

Objective: This study was undertaken to define and evaluate echogenic foci and their posterior acoustic artifacts in thyroid nodules. Whether these findings were indicative of benignity or malignancy was assessed., Materials and Methods: Echogenic foci were classified into five types: no posterior artifact, large comet-tail artifact, small comet-tail artifact (≤1.0 mm), and posterior shadowing (subdivided into internal versus peripheral). Nodules were also classified into four parenchymal patterns: hypoechoic, hyperechoic, >50% solid, and cystic. Results were compared with the cytologic or surgical findings., Results: A total of 704 nodules had echogenic foci; 246 did not. The prevalence of malignancy ranged between 15.4% and 19.5% for all types of foci except large comet-tail artifacts (3.9%). Foci without posterior artifacts had a 21.9% rate of cancer in hypoechoic lesions and 15.8% in hyperechoic lesions. Foci with small comet-tail artifacts had a 27.6% rate of malignancy in hypoechoic nodules. For internal calcifications, no malignant nodules were found in either partially cystic group. Nodules with peripheral calcifications had prevalence rates of 20.0% in cystic nodules and 11.1% in predominately solid nodules. Identification of one additional type of high-risk focus increased the chance of malignancy 1.48 times., Conclusion: All categories of echogenic foci except those with large comet-tail artifacts are associated with high cancer risk. Identification of large comet-tail artifacts suggests benignity. Nodules with small comet-tail artifacts have a high incidence of malignancy in hypoechoic nodules. With the exception of nodules that have peripheral calcifications, the risk of malignancy is low when echogenic foci are present in partially cystic lesions.

Objective: Superolateral Hoffa fat-pad edema is a frequent finding with patellar maltracking and may precede clinically significant chondrosis. The purpose of this study was to clarify which patellofemoral measurements are most highly associated and to develop a prediction rule to guide clinical decision making., Materials and Methods: Twenty-three patellofemoral measurements were performed on 71 knees retrospectively identified as having superolateral Hoffa fat-pad edema at MRI (Hoffa group) and on 45 normal knees (normal group). Univariate analysis was performed to examine the association between these measurements and Hoffa fat-pad edema. Classification and regression tree analysis with 10-fold cross validation was used to generate a prediction model., Results: For 16 of the 23 patellofemoral measurements, there was a statistically significant difference (p < 0.05) between the Hoffa and normal groups. Classification and regression tree analysis identified a prediction model in which a patient is placed into the Hoffa group if one of three conditions is met: lateral patellar displacement greater than -3.6 mm and Insall-Salvati ratio greater than 0.99; lateral patellar displacement of -3.6 mm or less and Insall-Salvati ratio greater than 1.23; or lateral patellar displacement of -3.6 mm or less, Insall-Salvati ratio of 1.23 or less, and lateral trochlear inclination of 16.5° or less. In fitting of the original sample, this model had 91.6% sensitivity and 88.9% specificity for identifying the Hoffa group. When 10-fold cross validation was applied, the estimated generalizable sensitivity and specificity were 85.9% and 75.6%., Conclusion: Superolateral Hoffa fat-pad is strongly associated with a number of measures of patellar maltracking. A prediction model based on these measurements is accurate for differentiating knees with superolateral Hoffa fat-pad edema from normal knees.