Factors Contributing to Computed Tomography Trauma Scan Times at a Tertiary Center: Improving Emergency Department Trauma Imaging Workflow Through Targeted Interventions.

PURPOSES: The aims of the study are to identify factors contributing to computed tomography (CT) trauma scan turnaround time variation and to evaluate the effects of an automated intervention on time metrics. METHODS: Throughput metrics were captured via picture archiving and communication system from January 1, 2018, to December 16, 2019, and included 17,709 CT trauma scans from our institution. Initial data showed that imaging technologist variation played a significant role in trauma imaging turnaround time. In December 2019, we implemented a 2-pronged intervention: (1) educational intervention to techs and (2) modified trauma CT abdomen/pelvis to autogenerate and autosend reformats to picture archiving and communication system. A total of 13,169 trauma CT scans were evaluated from the postintervention period taking place from January 2020 to March 2021. Throughput metrics such as last image to first report interval and emergency department length of stay were captured and compared with performing technologist, time of day, and weekday versus weekend scans. RESULTS: Substantial variability among trauma CT scans was observed. For CT trauma abdomen/pelvis, the interval from last image to initial report decreased from 26.4 to 24.0 minutes ( P = 0.001) while the interval between first and last image time decreased from 11.4 to 4.2 minutes ( P < 0.001). Emergency department length of stay also decreased from 3.9 to 3.7 hours ( P < 0.0001) in the postintervention period. Variation among imaging technologist was statistically significant and became less significant after intervention ( P = 0.09, P = 0.54). CONCLUSIONS: Factors such as imaging technologist variability, time of day, and day of the week of trauma scans played a significant role in CT trauma turnaround time variability. Automation interventions can help with efficiency in image turnaround time.

Comparison of CT Texture Analysis Software Platforms in Renal Cell Carcinoma: Reproducibility of Numerical Values and Association With Histologic Subtype Across Platforms.

OBJECTIVE. The purpose of this article is to evaluate interobserver, intraobserver, and interplatform variability and compare the previously established association between texture metrics and tumor histologic subtype using three commercially available CT texture analysis (CTTA) software platforms on the same dataset of large (> 7 cm) renal cell carcinomas (RCCs). MATERIALS AND METHODS. CT-based texture analysis was performed on contrast-enhanced MDCT images of large (> 7 cm) untreated RCCs in 124 patients (median age, 62 years; 82 men and 42 women) using three different software platforms. Using this previously studied cohort, texture features were compared across platforms. Features were correlated with histologic subtype, and strength of association was compared between platforms. Single-slice and volumetric measures from one platform were compared. Values for interobserver and intraobserver variability on a tumor subset (n = 30) were assessed across platforms. RESULTS. Metrics including mean gray-level intensity, SD, and volume correlated fairly well across platforms (concordance correlation coefficient [CCC], 0.66-0.99; mean relative difference [MRD], 0.17-5.97%). Entropy showed high variability (CCC, 0.04; MRD, 44.5%). Mean, SD, mean of positive pixels (MPP), and entropy were associated with clear cell histologic subtype on almost all platforms (p < .05). Mean, SD, entropy, and MPP were highly reproducible on most platforms on both interobserver and intraobserver analysis. CONCLUSION. Select texture metrics were reproducible across platforms and readers, but other metrics were widely variable. If clinical models are developed that use CTTA for medical decision making, these differences in reproducibility of some features across platforms need to be considered, and standardization is critical for more widespread adaptation and implementation.

Volumetric Versus Unidimensional Measures of Metastatic Colorectal Cancer in Assessing Disease Response.

INTRODUCTION: The purpose of this study was to compare unidimensional (1D/linear) and volumetric (3D) measures of metastatic colorectal cancer (mCRC) at computed tomography (CT) for predicting clinical outcome. PATIENTS AND METHODS: Analysis of CT images in 105 patients (mean age, 59 years; range, 25-81 years; 45 women, 60 men) receiving treatment for mCRC was performed. Both unidimensional and volumetric measures were obtained on index lesions at 3 time points (baseline/midpoint/post-therapy; mean interval, 4.1 months; median, 3.7 months) by 3 readers using a semi-automated technique. Measurements were summed and compared using best overall response across the 3 time points. Patient response was categorized based on Response Evaluation Criteria In Solid Tumors (RECIST) 1.1 thresholds for unidimensional and volume measures (CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease). Survival data was correlated (mean follow-up, 19.9 ± 17.1 months; median, 14.7 months). Intra/interobserver variability and reproducibility of 1D and 3D measures was assessed. Cox survival and Kaplan-Meier models were constructed and compared. RESULTS: Cox models and Kaplan-Meier curves for unidimensional versus volumetric assessment were very similar in appearance. Both 1D and 3D measurements effectively separated PD from the SD/PR groups, but neither separated SD from PR well. Volumetric measures showed comparable intra/interobserver variability on Bland-Altman analysis to unidimensional measures across readers using a semi-automated measurement technique. Metastatic site (lung, liver, node, other) did not seem to impact measurement reproducibility. CONCLUSIONS: Although CT volumetric assessment of metastatic colorectal cancer is fairly reproducible by reader and site using a semi-automated technique, the ability to stratify progressive disease from other disease response categories in terms of survival was similar to unidimensional measurement.

CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes.

OBJECTIVE: The purpose of the present study is to determine whether CT texture features of newly diagnosed primary renal cell carcinomas (RCCs) correlate with pathologic features and oncologic outcomes. MATERIALS AND METHODS: CT texture analysis was performed on large (> 7 cm; mean size, 9.9 cm) untreated RCCs in 157 patients (52 women and 105 men; mean age, 60.3 years). Measures of tumor heterogeneity, including entropy, kurtosis, skewness, mean, mean of positive pixels, and SD of pixel distribution histogram were derived from multiphasic CT using various filter settings: unfiltered (spatial scaling factor, 0), fine (spatial scaling factor, 2), medium (spatial scaling factor, 3-4), or coarse (spatial scaling factor, 5-6). Texture values were correlated with histologic subtype, nuclear grade, pathologic stage, and clinical outcome. RESULTS: When a coarse filter setting (spatial scaling factor, 6) was used, entropy on portal venous phase CT images was positively associated with clear cell histologic findings (odds ratio [OR], 134; 95% CI, 16-1110; p < 0.001) and was negatively associated with non-clear cell subtype findings (papillary spatial scale factor, 6; OR, 0.016; 95% CI, 0.002-0.132; p < 0.001). ROC curve analysis for entropy (on portal venous phase images obtained with a spatial scaling factor of 6) revealed an AUC of 0.943 (95% CI, 0.892-0.993) for clear cell histologic findings, with similar values noted for non-clear cell histologic findings. The mean of positive pixels and the SD of the pixel distribution histogram were statistically significantly associated with histologic cell type in a similar fashion. Entropy, the SD of the pixel distribution histogram, and the mean of positive pixels were associated with nuclear grade, most prominently when fine or medium texture filters were used (p < 0.05). There was a statistically significant association of texture features noted on unenhanced CT, including the SD of the pixel distribution histogram, the mean of positive pixels, and entropy, with the time to disease recurrence and death due to disease (e.g., for entropy noted on unenhanced CT images obtained with a spatial scaling factor of 6, the hazard ratio was 3.49 [95% CI, 1.55-7.84]; p = 0.002). CONCLUSION: CT texture features (in particular, entropy, the mean of positive pixels, and the SD of the pixel distribution histogram) are associated with tumor histologic findings, nuclear grade, and outcome measures. The contrast phase does seem to affect heterogeneity measures.

CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes.

PURPOSE: The purpose of the study was to determine if CT texture features of untreated hepatic metastatic colorectal cancer (CRC) relate to pathologic features and clinical outcomes. METHODS: Tumor texture analysis was performed on single hepatic metastatic lesions on pre-treatment contrast-enhanced CT scans in 77 pts (mean age 58, 34F/43M) using a novel tool. Measures of heterogeneity, including entropy, kurtosis, skewness, mean, mean positive pixels (MPP), and standard deviation (SD) of pixel distribution histogram were derived with filter values corresponding to fine (spatial scaling factor (ssf) 2), medium (ssf 3, 4), and coarse textures (ssf 5, 6). Texture parameters were correlated with tumor grade, baseline serum CEA, and KRAS mutation status. Overall survival was also correlated using Cox proportional hazards models. Single-slice 2D vs. whole-tumor volumetric 3D texture analysis was compared in a subcohort of 20 patients. RESULTS: Entropy, MPP, and SD at medium filtration levels were significantly associated with tumor grade (MPP ssf 3 P = 0.002, SD ssf 3 P = 0.004, entropy ssf 4 P = 0.007). Skewness was negatively associated KRAS mutation (P = 0.02). Entropy at coarse filtration levels was associated with survival (Hazard ratio (HR) for death 0.65, 95% CI 0.44-0.95, P = 0.03). Texture results for 2D and 3D analysis were similar. CONCLUSIONS: CT texture features, particularly entropy, MPP, and SD, are significantly associated with tumor grade in untreated CRC liver metastases. Tumor entropy at coarse filters correlates with overall survival. Single-slice 2D texture analysis appears to be adequate.

Characterization of Escherichia coli D-cycloserine transport and resistant mutants.

d-Cycloserine (DCS) is a broad-spectrum antibiotic that inhibits d-alanine ligase and alanine racemase activity. When Escherichia coli K-12 or CFT073 is grown in minimal glucose or glycerol medium, CycA transports DCS into the cell. E. coli K-12 cycA and CFT073 cycA mutant strains display increased DCS resistance when grown in minimal medium. However, the cycA mutants exhibit no change in DCS sensitivity compared to their parental strains when grown in LB (CFT073 and K-12) or human urine (CFT073 only). These data suggest that cycA does not participate in DCS sensitivity when strains are grown in a non-minimal medium. The small RNA GvcB acts as a negative regulator of E. coli K-12 cycA expression when grown in LB. Three E. coli K-12 gcvB mutant strains failed to demonstrate a change in DCS sensitivity when grown in LB. This further suggests a limited role for cycA in DCS sensitivity. To aid in the identification of E. coli genes involved in DCS sensitivity when grown on complex media, the Keio K-12 mutant collection was screened for DCS-resistant strains. dadA, pnp, ubiE, ubiF, ubiG, ubiH, and ubiX mutant strains showed elevated DCS resistance. The phenotypes associated with these mutants were used to further define three previously characterized E. coli DCS-resistant strains (χ316, χ444, and χ453) isolated by Curtiss and colleagues (R. Curtiss, III, L. J. Charamella, C. M. Berg, and P. E. Harris, J. Bacteriol. 90:1238-1250, 1965). A dadA mutation was identified in both χ444 and χ453. In addition, results are presented that indicate for the first time that DCS can antagonize d-amino acid dehydrogenase (DadA) activity.