Faculty Bibliography

Patients with renal cell carcinoma may develop metastases after radical nephrectomy, and therefore monitoring with imaging for recurrent or metastatic disease is critical. Imaging varies with specific suspected site of disease. Computed tomography/MRI of the abdomen and pelvis are mainstay modalities. Osseous and central nervous system imaging is reserved for symptomatic patients. Radiologic reporting is evolving to reflect effects of systemic therapy on lesion morphology. Nuclear medicine studies compliment routine imaging as newer agents are evaluated for more accurate tumor staging. Imaging research aims to fill gaps in treatment selection and monitoring of treatment response in metastatic renal cell carcinoma.

OBJECTIVES/OBJECTIVE:To perform a systematic review on apparent diffusion coefficient (ADC) values of renal tumor subtypes and meta-analysis on the diagnostic performance of ADC for differentiation of localized clear cell renal cell carcinoma (ccRCC) from other renal tumor types. METHODS:Medline, Embase, and the Cochrane Library databases were searched for studies published until May 1, 2019, that reported ADC values of renal tumors. Methodological quality was evaluated. For the meta-analysis on diagnostic test accuracy of ADC for differentiation of ccRCC from other renal lesions, we applied a bivariate random-effects model and compared two subgroups of ADC measurement with vs. without cystic and necrotic areas. RESULTS:We included 48 studies (2588 lesions) in the systematic review and 13 studies (1126 lesions) in the meta-analysis. There was no significant difference in ADC of renal parenchyma using b values of 0-800 vs. 0-1000 (p = 0.08). ADC measured on selected portions (sADC) excluding cystic and necrotic areas differed significantly from whole-lesion ADC (wADC) (p = 0.002). Compared to ccRCC, minimal-fat angiomyolipoma, papillary RCC, and chromophobe RCC showed significantly lower sADC while oncocytoma exhibited higher sADC. Summary estimates of sensitivity and specificity to differentiate ccRCC from other tumors were 80% (95% CI, 0.76-0.88) and 78% (95% CI, 0.64-0.89), respectively, for sADC and 77% (95% CI, 0.59-0.90) and 77% (95% CI, 0.69-0.86) for wADC. sADC offered a higher area under the receiver operating characteristic curve than wADC (0.852 vs. 0.785, p = 0.02). CONCLUSIONS:ADC values of kidney tumors that exclude cystic or necrotic areas more accurately differentiate ccRCC from other renal tumor types than whole-lesion ADC values. KEY POINTS/CONCLUSIONS:• Selective ADC of renal tumors, excluding cystic and necrotic areas, provides better discriminatory ability than whole-lesion ADC to differentiate clear cell RCC from other renal lesions, with area under the receiver operating characteristic curve (AUC) of 0.852 vs. 0.785, respectively (p = 0.02). • Selective ADC of renal masses provides moderate sensitivity and specificity of 80% and 78%, respectively, for differentiation of clear cell renal cell carcinoma (RCC) from papillary RCC, chromophobe RCC, oncocytoma, and minimal-fat angiomyolipoma. • Selective ADC excluding cystic and necrotic areas are preferable to whole-lesion ADC as an additional tool to multiphasic MRI to differentiate clear cell RCC from other renal lesions whether the highest b value is 800 or 1000.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

BACKGROUND:The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation. METHODS:Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting. RESULTS:Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy). CONCLUSIONS:These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.

OBJECTIVE:Guideline-concordant follow-up of incidental lung nodules (ILNs) is suboptimal. We aimed to improve communication and tracking for follow-up of these common incidental findings detected on imaging examinations. METHODS:We implemented a process improvement program for reporting and tracking ILNs at a large urban academic health care system. A multidisciplinary committee designed, tested, and implemented a multipart tracking system in the electronic health record (EHR) that included Fleischner Society management recommendations for each patient. Plan-do-study-act cycles addressed gaps in the follow-up of ILNs, broken into phases of developing and testing components of the conceived EHR toolkit. RESULTS:The program resulted in standardized text macros with discrete categories and recommendations for ILNs, with ability to track each case in a work list within the EHR. The macros incorporated evidence-based guidelines and also input of collaborating clinical referrers in the respective specialty. The ILN macro was used 3,964 times over the first 2 years, increasing from 104 to over 300 uses per month. Usage spread across all subspecialty divisions, with nonthoracic radiologists currently accounting for 80% (56 of 70) of the radiologists using the system and 31% (1,230 of 3,964) of all captured ILNs. When radiologists indicated ILNs as warranting telephone communication to provider offices, completion was documented in 100% of the cases captured in the EHR-embedded tracking report. CONCLUSION/CONCLUSIONS:An EHR-based system for managing incidental nodules enables case tracking with exact recommendations, provider communication, and completion of follow-up testing. Future efforts will target consistent radiologist use of the system and follow-up completion.

OBJECTIVES/OBJECTIVE:Oral potentially malignant disorders (OPMDs) encompass histologically benign, dysplastic, and cancerous lesions that are often indistinguishable by appearance and inconsistently managed. We assessed the potential impact of test-and-treat pathways enabled by a point-of-care test for OPMD characterization. MATERIALS AND METHODS/METHODS:We constructed a decision-analytic model to compare life expectancy of test-treat strategies for 60-year-old patients with OPMDs in the primary dental setting, based on a trial for a point-of-care cytopathology tool (POCOCT). Eight strategies of OPMD detection and evaluation were compared, involving deferred evaluation (no further characterization), prompt OPMD characterization using POCOCT measurements, or the commonly recommended usual care strategy of routine referral for scalpel biopsy. POCOCT pathways differed in threshold for additional intervention, including surgery for any dysplasia or malignancy, or for only moderate or severe dysplasia or cancer. Strategies with initial referral for biopsy also reflected varied treatment thresholds in current practice between surgery and surveillance of mild dysplasia. Sensitivity analysis was performed to assess the impact of variation in parameter values on model results. RESULTS:Requisite referral for scalpel biopsy offered the highest life expectancy of 20.92 life-years compared with deferred evaluation (+0.30 life-years), though this outcome was driven by baseline assumptions of limited patient adherence to surveillance using POCOCT. POCOCT characterization and surveillance offered only 0.02 life-years less than the most biopsy-intensive strategy, while resulting in 27% fewer biopsies. When the probability of adherence to surveillance and confirmatory biopsy was ≥ 0.88, or when metastasis rates were lower than reported, POCOCT characterization extended life-years (+0.04 life-years) than prompt specialist referral. CONCLUSION/CONCLUSIONS:Risk-based OPMD management through point-of-care cytology may offer a reasonable alternative to routine referral for specialist evaluation and scalpel biopsy, with far fewer biopsies. In patients who adhere to surveillance protocols, POCOCT surveillance may extend life expectancy beyond biopsy and follow up visual-tactile inspection.

Introduction/UNASSIGNED:To evaluate overall survival (OS) of T1a kidney cancers stratified by histologic subtype and curative treatment including partial nephrectomy (PN), percutaneous ablation (PA), and radical nephrectomy (RN). Materials and Methods/UNASSIGNED:We queried the National Cancer Data Base (2004-2015) for patients with T1a kidney cancers who were treated surgically. OS was estimated by Kaplan-Meier curves based on histologic subtype and management. Cox proportional regression models were used to determine whether histologic subtypes and management procedure predicted OS. Results/UNASSIGNED:= 0.392) were observed. Adjusted Cox regression showed worse OS for PA than PN among patients with clear cell (HR 1.58, 95%CI [1.44-1.73], papillary RCC (1.53 [1.34-1.75]), and chromophobe RCC (2.19 [1.64-2.91]). OS was worse for RN than PN for clear cell (HR 1.38 [1.28-1.50]) papillary (1.34 [1.16-1.56]) and chromophobe RCC (1.92 [1.43-2.58]). Predictive models using Cox proportional hazards incorporating histology and surgical procedure alone were limited (c-index 0.63) while adding demographics demonstrated fair predictive power for OS (c-index 0.73). Conclusions/UNASSIGNED:In patients with pathologic T1a RCC, patterns of OS differed by surgery and histologic subtype. Patients receiving PN appears to have better prognosis than both PA and RN. However, the incorporation of histologic subtype and treatment modality into a risk stratification model to predict OS had limited utility compared with variables representing competing risks.

INTRODUCTION/BACKGROUND:Communication and physician burn out are major issues within Radiology. This study is designed to determine the utilization and cost benefit of a hybrid computer/human communication tool to aid in relay of clinically important imaging findings. MATERIAL AND METHODS/METHODS:Analysis of the total number of tickets, (requests for assistance) placed, the type of ticket and the turn-around time was performed. Cost analysis of a hybrid computer/human communication tool over a one-year period was based on human costs as a multiple of the time to close the ticket. Additionally, we surveyed a cohort of radiologists to determine their use of and satisfaction with this system. RESULTS:14,911 tickets were placed in the 6-month period, of which 11,401 (76.4%) were requests to "Get the Referring clinician on the phone." The mean time to resolution (TTR) of these tickets was 35.3 (±17.4) minutes. Ninety percent (72/80) of radiologists reported being able to interpret a new imaging study instead of waiting to communicate results for the earlier study, compared to 50% previously. 87.5% of radiologists reported being able to read more cases after this system was introduced. The cost analysis showed a cost savings of up to $101.12 per ticket based on the length of time that the ticket took to close and the total number of placed tickets. CONCLUSIONS:A computer/human communication tool can be translated to significant time savings and potentially increasing productivity of radiologists. Additionally, the system may have a cost savings by freeing the radiologist from tracking down referring clinicians prior to communicating findings.