Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. METHODS: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering. RESULTS: The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. CONCLUSION: Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.

Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study.

INTRODUCTION: This study investigates how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with clear cell renal cell carcinoma (ccRCC) biomarkers. METHODS: The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive. 2,824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built MATLAB software package. Robust radiomic features with strong inter-scanner reproducibility were selected. Random forest, AdaBoost, and elastic net machine learning (ML) algorithms evaluated the ability of the selected radiomic features to predict the presence of 12 clinically relevant molecular biomarkers identified from the literature. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. RESULTS: Before stratification by tumor grade and stage, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60-0.68). Once stratified, radiomics predicted KDM5C, SETD2, PBRM1, and mTOR mutation status with acceptable to excellent predictive discrimination (AUC ranges from 0.70 to 0.86). CONCLUSIONS: Radiomic texture analysis can potentially identify a variety of clinically relevant biomarkers in patients with ccRCC and may have a prognostic implication.

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.

OBJECTIVES: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.

Non-Invasive Profiling of Advanced Prostate Cancer via Multi-Parametric Liquid Biopsy and Radiomic Analysis.

Integrating liquid biopsies of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) with other minimally invasive measures may yield more comprehensive disease profiles. We evaluated the feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA combined with radiomic analysis of CT scans from patients with metastatic castration-resistant PC (mCRPC). CTCs from 22 patients were enumerated, stained for PC-relevant markers, and clustered based on morphometric and immunofluorescent features using machine learning. DNA from single CTCs, matched cfDNA, and buffy coats was sequenced using a targeted amplicon cancer hotspot panel. Radiomic analysis was performed on bone metastases identified on CT scans from the same patients. CTCs were detected in 77% of patients and clustered reproducibly. cfDNA sequencing had high sensitivity (98.8%) for germline variants compared to WBC. Shared and unique somatic variants in PC-related genes were detected in cfDNA in 45% of patients (MAF > 0.1%) and in CTCs in 92% of patients (MAF > 10%). Radiomic analysis identified a signature that strongly correlated with CTC count and plasma cfDNA level. Integration of cellular, molecular, and radiomic data in a multi-parametric approach is feasible, yielding complementary profiles that may enable more comprehensive non-invasive disease modeling and prediction.

Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors.

OBJECTIVES: Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. METHODS: Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. RESULTS: Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. CONCLUSION: Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. KEY POINTS: • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

OBJECTIVES: Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. MATERIALS AND METHODS: Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. RESULTS: Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively. CONCLUSION: Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. KEY POINTS: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

OBJECTIVE: The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS: A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS: A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION: The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

Thalamic atrophy in multiple sclerosis: A magnetic resonance imaging marker of neurodegeneration throughout disease.

OBJECTIVE: Thalamic volume is a candidate magnetic resonance imaging (MRI)-based marker associated with neurodegeneration to hasten development of neuroprotective treatments. Our objective is to describe the longitudinal evolution of thalamic atrophy in MS and normal aging, and to estimate sample sizes for study design. METHODS: Six hundred one subjects (2,632 MRI scans) were analyzed. Five hundred twenty subjects with relapse-onset MS (clinically isolated syndrome, n = 90; relapsing-remitting MS, n = 392; secondary progressive MS, n = 38) underwent annual standardized 3T MRI scans for an average of 4.1 years, including a 1mm3 3-dimensional T1-weighted sequence (3DT1; 2,485 MRI scans). Eighty-one healthy controls (HC) were scanned longitudinally on the same scanner using the same protocol (147 MRI scans). 3DT1s were processed using FreeSurfer's longitudinal pipeline after lesion inpainting. Rates of normalized thalamic volume loss in MS and HC were compared in linear mixed effects models. Simulation-based sample size calculations were performed incorporating the rate of atrophy in HC. RESULTS: Thalamic volume declined significantly faster in MS subjects compared to HC, with an estimated decline of -0.71% per year (95% confidence interval [CI] = -0.77% to -0.64%) in MS subjects and -0.28% per year (95% CI = -0.58% to 0.02%) in HC (p for difference = 0.007). The rate of decline was consistent throughout the MS disease duration and across MS clinical subtypes. Eighty or 100 subjects per arm (α = 0.1 or 0.05, respectively) would be needed to detect the maximal effect size with 80% power in a 24-month study. INTERPRETATION: Thalamic atrophy occurs early and consistently throughout MS. Preliminary sample size calculations appear feasible, adding to its appeal as an MRI marker associated with neurodegeneration. Ann Neurol 2018;83:223-234.

Texture Analysis of Imaging: What Radiologists Need to Know.

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.

Peripheral Thyroid Nodule Calcifications on Sonography: Evaluation of Malignant Potential.

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.

Assessment of Fibrosis in Liver Transplant Recipients: Diagnostic Performance of Shear Wave Elastography (SWE) and Correlation of SWE Findings With Biopsy Results.

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.

Reliability of CT-based texture features: Phantom study.

OBJECTIVE: To determine the intra-, inter- and test-retest variability of CT-based texture analysis (CTTA) metrics. MATERIALS AND METHODS: In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust. RESULTS: As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used. CONCLUSION: Following large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

Brain Irradiation and Gadobutrol Administration in Pediatric Patients with Brain Tumors: Effect on MRI Brain Signal Intensity.

Purpose To determine whether treatment affects MRI signal intensity in pediatric patients with primary brain tumors independent of the administration of macrocyclic gadolinium-based contrast agents (GBCAs). Materials and Methods This retrospective, single-center study included 78 patients (mean age, 7.7 years ± 5.4) with primary brain tumors who underwent macrocyclic GBCA-enhanced MRI from 2015 to 2018. Three groups were compared: (a) patients who had undergone radiation therapy (37 patients, 26 of whom had undergone concurrent chemotherapy), (b) patients who had undergone chemotherapy only (17 patients), and (c) patients who had received no treatment ("no-treatment group," 24 patients). The signal intensity in the globus pallidus (GP), thalamus, dentate nucleus (DN), and pons was measured on unenhanced T1-weighted images. GP-to-thalamus and DN-to-pons signal intensity ratios were compared among groups with analysis of variance by using the Kruskal-Wallis test, followed by post hoc pairwise tests with Tukey adjustment, and were analyzed relative to group, total cumulative doses of GBCA, age, and sex with multivariable linear models. Results The mean number of GBCA-enhanced MRI examinations in the radiation therapy, chemotherapy-only, and no-treatment groups was 7.11, 7.29, and 4.96, respectively (P < .01 for the radiation therapy and chemotherapy groups compared with the no-treatment group). The DN-to-pons ratio in the radiation therapy group was higher than that in both the no-treatment group and the chemotherapy-only group (P < .01 for both). There was no significant difference in the DN-to-pons ratios between the chemotherapy-only group and the no-treatment group (P = .99). The GP-to-thalamus ratios did not differ among all three groups (P = .09). There was no dose-dependent effect of GBCA on the DN-to-pons and GP-to-thalamus ratios when adjusting for the effects of treatment (P = .21 and P = .38, respectively). Conclusion Brain irradiation contributes to a higher dentate nucleus signal intensity in pediatric patients with brain tumor independent of the administration of macrocyclic gadolinium-based contrast agents. © RSNA, 2018.

Predictors of 30- and 90-day readmission following craniotomy for malignant brain tumors: analysis of nationwide data.

Hospital readmissions are a major contributor to increased health care costs and are associated with worse patient outcomes after neurosurgery. We used the newly released Nationwide Readmissions Database (NRD) to describe the association between patient, hospital and payer factors with 30- and 90-day readmission following craniotomy for malignant brain tumor. All adult inpatients undergoing craniotomy for primary and secondary malignant brain tumors in the NRD from 2013 to 2014 were included. We identified all cause readmissions within 30- and 90-days following craniotomy for tumor, excluding scheduled chemotherapeutic procedures. We used univariate and multivariate models to identify patient, hospital and administrative factors associated with readmission. We identified 27,717 admissions for brain tumor craniotomy in 2013-2014, with 3343 (13.2%) 30-day and 5271 (25.7%) 90-day readmissions. In multivariate analysis, patients with Medicaid and Medicare were more likely to be readmitted at 30- and 90-days compared to privately insured patients. Patients with two or more comorbidities were more likely to be readmitted at 30- and 90-days, and patients discharged to skilled nursing facilities or home health care were associated with increased 90-day readmission rates. Finally, hospital procedural volume above the 75th percentile was associated with decreased 90-day readmission rates. Patients treated at high volume hospitals are less likely to be readmitted at 90-days. Insurance type, non-routine discharge and patient comorbidities are predictors of postoperative non-scheduled readmission. Further studies may elucidate potentially modifiable risk factors when attempting to improve outcomes and reduce cost associated with brain tumor surgery.

Forward-Projected Model-Based Iterative Reconstruction in Screening Low-Dose Chest CT: Comparison With Adaptive Iterative Dose Reduction 3D.

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.

Preliminary Results of Lung Cancer Screening in a Socioeconomically Disadvantaged Population.

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.

Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.

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.

Infection rate correlated with time to repair of open neural tube defects (myelomeningoceles): an institutional and national study.

BACKGROUND: The optimal time to closure of a newborn with an open neural tube defect (NTD-myelomeningocele) has been the subject of a number of investigations. One aspect of timing that has received attention is its relationship to repair site and central nervous system (CNS) infection that can lead to irreversible deficits and prolonged hospital stays. No studies have evaluated infection as a function of surgical timing at a national level. We hypothesized an increase in wound infection in those patients with delays in myelomeningocele repair when evaluated in both a single-center and national database. METHODS: Treatment outcomes following documented times to transfer and closure were evaluated at Children's Hospital of Los Angeles (CHLA) for the years 2004 to 2014. Data of newborns with a myelomeningocele with varying time to repair were also obtained from non-overlapping abstracts of the 2000-2010 Kids' Inpatient Database (KID) and Nationwide Inpatient Sample (NIS). Poisson multivariable regression analyses were used to assess the effect of time to repair on infection and time to discharge. RESULTS: At CHLA, 95 neonates who underwent myelomeningocele repair were identified, with a median time from birth to treatment of 1 day. Six (6 %) patients were noted to have postrepair complications. CHLA data was not sufficiently powered to detect a difference in infection following delay in closure. In the NIS, we identified 3775 neonates with repaired myelomeningocele of whom infection was reported in 681 (18 %) patients. There was no significant difference in rates of infection between same-day and 1-day wait times (p = 0.22). Wait times of two (RR = 1.65 [1.23, 2.22], p < 0.01) or more days (RR = 1.88 [1.39, 2.54], p < 0.01), respectively, experienced a 65 % and 88 increase in rates of infection compared to same-day procedures. Prolonged wait time was 32 % less likely at facilities with increased myelomeningocele repair volume (RR = 0.68 [0.56 0.83], p < 0.01). The presence of infection was associated with a 54 % (RR = 1.54 [1.36, 1.74], p < 0.01) increase in the length of stay when compared to neonates without infection. CONCLUSION: Myelomeningocele closure, when delayed more than 1 day after birth, is associated with an increased rate of infection and length of stay in the national cohort. High-volume centers are associated with fewer delays to repair. Though constrained by limitations of a national coded database, these results suggest that early myelomeningocele repair decreases the rate of infection.

Impact of emergency medical services stroke routing protocols on Primary Stroke Center certification in California.

BACKGROUND AND PURPOSE: Organized stroke systems of care include Primary Stroke Center (PSC) certification and preferential emergency medical services (EMS) routing of suspected patients with stroke to designated PSCs. Stroke EMS routing is not nationally governed; in California, routing is determined by county. EMS routing policies might provide an incentive for PSC accreditation. We evaluated the relationship between independent adoption of EMS routing protocols and PSC designation acquisition in California. METHODS: Dates of PSC certification were obtained through The Joint Commissions Website and confirmatory calls to stroke coordinators. Starting date of county EMS PSC routing policies was obtained from county EMS agencies. We provide descriptive analysis of number of hospitals achieving PSC designation relative to implementation of EMS routing policies for all counties with PSCs. RESULTS: By June 2012, there were 131 California PSCs in 27 counties, and 22 of 58 counties had implemented EMS routing policies. The greatest number of PSCs was in Los Angeles (30) followed by San Diego (11), Orange (9), and Santa Clara (9) counties. Achievement of PSC designation occurred more frequently immediately before and after EMS routing: 51 PSCs (39%) within 1 year; 85 PSCs (65%) within 2 years. The yearly rate of eligible hospital conversion to PSC designation accelerated concurrent with EMS diversion policy adoption from 3.8% before to 16.2% during and decelerated afterward to 7.6%. CONCLUSIONS: Implementation of EMS routing policies may be an important factor driving PSC certification. National adoption of stroke routing policies may lead to more PSCs, positively impacting patient care.

Does exercise tolerance testing at 60 days poststroke predict rehabilitation performance?

OBJECTIVE: To assess the relationship between exercise tolerance test (ETT) performance at 6 weeks poststroke and subsequent performance in a treadmill and overground locomotor training program (LTP). DESIGN: Prospective cohort study. SETTING: Exercise testing laboratory in either a primary care hospital or outpatient clinic. PARTICIPANTS: Community-dwelling individuals (N=469), 54.9±19.0 days poststroke, enrolled in the Locomotor Experience Applied Post-Stroke randomized controlled trial. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: For participants randomly assigned to LTP, the number of sessions needed to attain the training goal of 20 minutes of treadmill stepping was determined. Regression analyses determined the contribution of ETT performance (cycling duration), age, and 6-minute walk test (6MWT) distance to attainment of the stepping duration goal. RESULTS: Age, 6MWT, and ETT performance individually accounted for 10.74%, 10.82%, and 10.76%, respectively, of the variance in the number of sessions needed to attain 20 minutes of stepping. When age and 6MWT were included in the model, the additional contribution of ETT performance was rendered nonsignificant (P=.150). CONCLUSIONS: To the extent that ETT performance can be viewed as a measure of cardiovascular fitness rather than neurologic impairment, cardiovascular fitness at the time of the ETT did not make a significant unique contribution to the number of sessions needed to achieve 20 minutes of stepping. The 6MWT, which involves less intensive exercise than the ETT and therefore is likely to be predominantly affected by neurologic impairment and muscular condition, appeared to account for as much variance as the ETT.