Immune infiltration is typically quantified using cellular density, not accounting for cellular clustering. Tumor-associated macrophages (TAM) activate oncogenic signaling through paracrine ...interactions with tumor cells, which may be better reflected by local cellular clustering than global density metrics. Using multiplex immunohistochemistry and digital pathologic analysis we quantified cellular density and cellular clustering for myeloid cell markers in 129 regions of interest from 55 samples from 35 patients with metastatic ccRCC. CD68+ cells were found to be clustered with tumor cells and dispersed from stromal cells, while CD163+ and CD206+ cells were found to be clustered with stromal cells and dispersed from tumor cells. CD68+ density was not associated with OS, while high tumor/CD68+ cell clustering was associated with significantly worse OS. These novel findings would not have been identified if immune infiltrate was assessed using cellular density alone, highlighting the importance of including spatial analysis in studies of immune cell infiltration of tumors. Significance: Increased clustering of CD68+ TAMs and tumor cells was associated with worse overall survival for patients with metastatic ccRCC. This effect would not have been identified if immune infiltrate was assessed using cell density alone, highlighting the importance of including spatial analysis in studies of immune cell infiltration of tumors.
Physician work relative value units are determined based on operative time, technical skill, mental effort and stress. In theory, work relative value units should account for the operative time ...involved in a procedure, resulting in similar work relative value units per unit time for short and long procedures. We assessed whether operative time is adequately accounted for by the current work relative value units assignments.
The American College of Surgeons National Surgical Quality Improvement Program database was reviewed from 2015 to 2017. The 50 most frequently coded urology CPT codes were included in the study. The primary variable was work relative value units per hour of operative time (work relative value units per hour). Linear regression analysis was used to assess the associations between work relative value units, operative time and the work relative value units per hour variable.
A total of 105,931 cases were included in the study. Among the included urology CPTs the median work relative value units was 15.26, median operative time was 48 minutes and median work relative value units per hour was 11.2. CPTs with operative time less than 90 minutes had higher work relative value units per hour compared with longer procedures (12.2 vs 8.7, p <0.001). Univariable analysis revealed that each additional hour of operative time was associated with a decrease in work relative value units per hour by 1.32 (-0.022 per minute, 95% CI -0.037 - -0.001, p <0.001) and that work relative value units were not statistically associated with work relative value units per hour (-0.093, 95% CI -0.193 - 0.007, p=0.07).
This analysis of large population, national level data suggests that the current work relative value units assignments do not proportionally compensate for longer operative times.
Immune-modulating systemic therapies are often used to treat advanced cancer such as metastatic clear cell renal cell carcinoma (ccRCC). Used alone, sequence-based biomarkers neither accurately ...capture patient dynamics nor the tumor immune microenvironment. To better understand the tumor ecology of this immune microenvironment, we quantified tumor infiltration across three distinct ccRCC patient tumor cohorts using complementarity determining region-3 (CDR3) sequence recovery counts in tumor-infiltrating lymphocytes and a generalized diversity index (GDI) for CDR3 sequence distributions. GDI can be understood as a curve over a continuum of diversity scales that allows sensitive characterization of distributions to capture sample richness, evenness, and subsampling uncertainty, along with other important metrics that characterize tumor heterogeneity. For example, richness quantified the total unique sequence count, while evenness quantified similarities across sequence frequencies. Significant differences in receptor sequence diversity across gender and race revealed that patients with larger and more clinically aggressive tumors had increased richness of recovered tumoral CDR3 sequences, specifically in those from T-cell receptor alpha and B-cell immunoglobulin lambda light chain. The GDI inflection point (IP) allowed for a novel and robust measure of distribution evenness. High IP values were associated with improved overall survival, suggesting that normal-like sequence distributions lead to better outcomes. These results propose a new quantitative tool that can be used to better characterize patient-specific differences related to immune cell infiltration, and to identify unique characteristics of tumor-infiltrating lymphocyte heterogeneity in ccRCC and other malignancies.
Assessment of tumor-infiltrating T-cell and B-cell diversity in renal cell carcinoma advances the understanding of tumor-immune system interactions, linking tumor immune ecology with tumor burden, aggressiveness, and patient survival. See related commentary by Krishna and Hakimi, p. 764.
To assess whether inaccurate operative time estimates utilized by the Relative Value Update Committee (RUC) contribute to the undervaluation of longer urologic procedures.
The National Surgical ...Quality Improvement Program (NSQIP) and Centers for Medicare and Medicaid Services (CMS) data sets were reviewed from 2015 to 2017. NSQIP operative time is directly measured, contrasting with CMS times which are determined by the RUC via survey-generated estimates. The 50 most frequently coded urology current procedural terminologies were included. Operative time difference was compared between the 2 data sets, and Spearman's correlation coefficient was utilized to assess differences in wRVU/h.
A total of 105,931 cases were included. Overall, RUC operative time estimates were longer than NSQIP (124.4 vs 103.5 minutes, P < .001). RUC data overestimated operative time by 42.9% for procedures ≤90 minutes and 16.4% for longer procedures (P < .001). Using NSQIP, procedures ≤90 minutes had higher wRVU/h than longer procedures (12.2 vs 8.7, P < .001), but this was not statistically different using RUC estimates (8.4 vs 7.7, P = .13). Spearman's correlation coefficient confirmed a statistically significant negative relationship between wRVU/h and operative time using NSQIP data (r = −0.57, 95% confidence interval: −7.4 to −0.36), and no statistically significant relationship using RUC data (r = −0.24, 95% confidence interval: −0.49 to 0.04).
The RUC-intended wRVU/h is more equitable than the NSQIP real-world wRVU/h with regard to operative time. Inaccurate RUC operative time estimates contribute to the undervaluation of longer urologic procedures.
To evaluate the patterns of financial transaction between industry and urologists in the first 5 years of reporting in the Open Payments Program (OPP) by comparing transactions over time, between ...academic and nonacademic urologists, and by provider characteristics among academic urologists.
The Center for Medicare & Medicaid Services OPP database was queried for General Payments to urologists from 2014-2018. Faculty at ACGME-accredited urology training programs were identified and characterized via publicly available websites. Industry transfers were analyzed by year, practice setting (academic vs nonacademic), provider characteristics, and AUA section. Payment nature and individual corporate contributions were also summarized.
A total of 12,521 urologists – representing 75% of the urology workforce in any given year – received $168 million from industry over the study period. There was no significant trend in payments by year (P = .162). Urologists received a median of $1602 over the study period, though 14% received >$10,000. Payment varied significantly by practice setting (P <.001), with nonacademic urologists receiving more but smaller payments than academic urologists. Among academic urologists, gender (P <.001), department chair status (P <.001), fellowship training (P <.001), and subspecialty (P <.001) were significantly associated with amount of payment from industry. Annual payments from industry varied significantly by AUA section.
Reporting of physician-industry transactions has not led to a sustained decline in transactions with urologists. Significant differences in industry interaction exist between academic and nonacademic urologists, and values transferred to academic urologists varied by gender, chair status, subspecialty, and AUA section.
Abstract
Summary
Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the ...tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g. Ripley’s K, Besag’s L, Macron’s M and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time.
Availability and implementation
spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME).
Supplementary information
Supplementary data are available at Bioinformatics online.
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