Development and Validation of an 18-Gene Urine Test for High-Grade Prostate Cancer.

Importance: Benefits of prostate cancer (PCa) screening with prostate-specific antigen (PSA) alone are largely offset by excess negative biopsies and overdetection of indolent cancers resulting from the poor specificity of PSA for high-grade PCa (ie, grade group [GG] 2 or greater). Objective: To develop a multiplex urinary panel for high-grade PCa and validate its external performance relative to current guideline-endorsed biomarkers. Design, Setting, and Participants: RNA sequencing analysis of 58 724 genes identified 54 markers of PCa, including 17 markers uniquely overexpressed by high-grade cancers. Gene expression and clinical factors were modeled in a new urinary test for high-grade PCa (MyProstateScore 2.0 [MPS2]). Optimal models were developed in parallel without prostate volume (MPS2) and with prostate volume (MPS2+). The locked models underwent blinded external validation in a prospective National Cancer Institute trial cohort. Data were collected from January 2008 to December 2020, and data were analyzed from November 2022 to November 2023. Exposure: Protocolized blood and urine collection and transrectal ultrasound-guided systematic prostate biopsy. Main Outcomes and Measures: Multiple biomarker tests were assessed in the validation cohort, including serum PSA alone, the Prostate Cancer Prevention Trial risk calculator, and the Prostate Health Index (PHI) as well as derived multiplex 2-gene and 3-gene models, the original 2-gene MPS test, and the 18-gene MPS2 models. Under a testing approach with 95% sensitivity for PCa of GG 2 or greater, measures of diagnostic accuracy and clinical consequences of testing were calculated. Cancers of GG 3 or greater were assessed secondarily. Results: Of 761 men included in the development cohort, the median (IQR) age was 63 (58-68) years, and the median (IQR) PSA level was 5.6 (4.6-7.2) ng/mL; of 743 men included in the validation cohort, the median (IQR) age was 62 (57-68) years, and the median (IQR) PSA level was 5.6 (4.1-8.0) ng/mL. In the validation cohort, 151 (20.3%) had high-grade PCa on biopsy. Area under the receiver operating characteristic curve values were 0.60 using PSA alone, 0.66 using the risk calculator, 0.77 using PHI, 0.76 using the derived multiplex 2-gene model, 0.72 using the derived multiplex 3-gene model, and 0.74 using the original MPS model compared with 0.81 using the MPS2 model and 0.82 using the MPS2+ model. At 95% sensitivity, the MPS2 model would have reduced unnecessary biopsies performed in the initial biopsy population (range for other tests, 15% to 30%; range for MPS2, 35% to 42%) and repeat biopsy population (range for other tests, 9% to 21%; range for MPS2, 46% to 51%). Across pertinent subgroups, the MPS2 models had negative predictive values of 95% to 99% for cancers of GG 2 or greater and of 99% for cancers of GG 3 or greater. Conclusions and Relevance: In this study, a new 18-gene PCa test had higher diagnostic accuracy for high-grade PCa relative to existing biomarker tests. Clinically, use of this test would have meaningfully reduced unnecessary biopsies performed while maintaining highly sensitive detection of high-grade cancers. These data support use of this new PCa biomarker test in patients with elevated PSA levels to reduce the potential harms of PCa screening while preserving its long-term benefits.

High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker.

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Revisiting the standard blueprint for biomarker development to address emerging cancer early detection technologies.

Novel liquid biopsy technologies are creating a watershed moment in cancer early detection. Evidence supporting population screening is nascent, but a rush to market the new tests is prompting cancer early detection researchers to revisit the standard blueprint that the Early Detection Research Network established to evaluate novel screening biomarkers. In this commentary, we review the Early Detection Research Network's Phases of Biomarker Development (PBD) for rigorous evaluation of novel early detection biomarkers and discuss both hazards and opportunities involved in expedited evaluation. According to the PBD, for a biomarker-based test to be considered for population screening, 1) test sensitivity in a prospective screening setting must be adequate, 2) the shift to early curable stages must be meaningful, and 3) any stage shift must translate into clinically significant mortality benefit. In the past, determining mortality benefit has required lengthy randomized screening trials, but interest is growing in expedited trial designs with shorter-term endpoints. Whether and how best to use such endpoints in a manner that retains the rigor of the PBD remains to be determined. We discuss how computational disease modeling can be harnessed to learn about screening impact and meet the needs of the moment.

Incidence and Risk Factors for Hepatocellular Carcinoma in Cirrhosis: The Multicenter Hepatocellular Carcinoma Early Detection Strategy (HEDS) Study.

BACKGROUND & AIMS: Worldwide, hepatocellular carcinoma (HCC) is a common malignancy. We aimed to prospectively determine the incidence and risk factors of HCC in a U.S. METHODS: The multicenter Hepatocellular Carcinoma Early Detection Strategy study of the National Institutes of Health prospectively enrolled patients with cirrhosis who underwent standard surveillance for HCC. Demographics, medical and family history, etiology of liver disease, and clinical features were evaluated for associations with HCC. RESULTS: Between April 10, 2013 and December 31, 2021, 1723 patients were enrolled and confirmed eligible. During median follow-up of 2.2 years (range, 0-8.7 years), there were 109 incident cases of HCC for an incidence rate of 2.4 per 100 person-years: 88 (81%) patients with very early/early Barcelona Clinic Liver Cancer stage (0, A), 20 (18%) intermediate stage (B), and 1 (1%) unknown stage. Risk factor analyses were restricted to 1325 patients, including 95 incident HCC, with at least 6 months of follow-up. The majority were men (53.2%), obese or severely obese (median body mass index, 30.2 kg/m2), and white (86.3%); 42.0% had history of hepatitis C virus infection, 20.7% had alcoholic liver disease, and 24.9% had nonalcoholic fatty liver disease. Fourteen risk factors for HCC were significant (P < .05) in univariate analyses, and a multivariate subset was selected using stepwise logistic regression. The multivariate subset contained gender (P < .001; male; odds ratio [OR], 2.47; 95% confidence interval [CI], 1.54-4.07), years with cirrhosis (P = .004; OR, 1.06; 95% CI, 1.02-1.1), family history of liver cancer (P = .02; yes; OR, 2.69; 95% CI, 1.11-5.86), age (per 5 years; P = .02; OR, 1.17; 95% CI, 1.03-1.33), obesity (P = .02; yes; OR, 1.7; 95% CI, 1.08-2.73), aspartate aminotransferase (log(1+AST); P = .06; OR, 1.54; 95% CI, 0.97-2.42), alpha-fetoprotein (log(1+AFP); P = .07; OR, 1.32; 95% CI, 0.97-1.77), and albumin (P = .10; OR, 0.7; 95% CI, 0.46-1.07). CONCLUSIONS: Thus far, this is the largest prospective and geographically diverse study of a U.S. cohort of patients with cirrhosis that validates known risk factors for HCC (gender, age, obesity, years with cirrhosis, family history of liver cancer, baseline AFP, albumin, and AST). The incidence of HCC was 2.4% per 100 person-years.

Immunoassay Detects Salivary Anti-SSA/Ro-52 Autoantibodies in Seronegative Patients with Primary Sjögren's Syndrome.

The diagnostic work-up for Sjögren's syndrome is challenging and complex, including testing for serum autoantibodies to SSA/Ro and a labial salivary gland biopsy. Furthermore, the diagnosis is often delayed. In this study, we tested the hypothesis that anti-SSA/Ro autoantibodies are detectable in the saliva of patients with primary Sjögren's syndrome (pSS) because the disease affects the salivary glands, and these autoantibodies display greater discriminatory performance in saliva than in serum. SSA/Ro-52 Ags were used to develop what is, to our knowledge, a novel quantitative electrochemical-based immunoassay: the electric field-induced release and measurement (EFIRM) platform. The clinical utility was determined by measuring salivary anti-SSA/Ro-52 autoantibodies in patients with pSS and sicca (n = 34), patients without pSS with sicca (n = 35), and healthy subjects (n = 41). The statistical analysis of discrimination included the area under the receiver operating characteristic curve. Salivary anti-SSA/Ro-52 autoantibodies were measured in 94% (32 of 34) of patients with pSS with 85% (29 of 34) seropositivity. Four of the five seronegative patients with pSS had EFIRM-measurable anti-SSA/Ro-52 autoantibodies in saliva. Additionally, 60% (21 of 35) of the seronegative patients without pSS who had sicca had EFIRM-detectable SSA/Ro-52 autoantibodies in saliva, indicating the onset of autoimmune disease. Two of the 41 healthy control subjects had EFIRM-detectable SSA/Ro-52 autoantibodies in their saliva. Salivary SSA/Ro-52 autoantibodies significantly discriminated patients with pSS or patients with the initial stage of autoimmune disease from healthy subjects with an area under the receiver operating characteristic curve of 0.91. Our findings suggest that the proposed saliva SSA/Ro-52 immunoassay improves early and accurate diagnosis of seronegative patients with pSS and patients with early-onset autoimmune disease.

Mortality Benefit of a Blood-Based Biomarker Panel for Lung Cancer on the Basis of the Prostate, Lung, Colorectal, and Ovarian Cohort.

PURPOSE: To investigate the utility of integrating a panel of circulating protein biomarkers in combination with a risk model on the basis of subject characteristics to identify individuals at high risk of harboring a lethal lung cancer. METHODS: Data from an established logistic regression model that combines four-marker protein panel (4MP) together with the Prostate, Lung, Colorectal, and Ovarian (PLCO) risk model (PLCOm2012) assayed in prediagnostic sera from 552 lung cancer cases and 2,193 noncases from the PLCO cohort were used in this study. Of the 552 lung cancer cases, 387 (70%) died of lung cancer. Cumulative incidence of lung cancer death and subdistributional and cause-specific hazard ratios (HRs) were calculated on the basis of 4MP + PLCOm2012 risk scores at a predefined 1.0% and 1.7% 6-year risk thresholds, which correspond to the current and former US Preventive Services Task Force screening criteria, respectively. RESULTS: When considering cases diagnosed within 1 year of blood draw and all noncases, the area under receiver operation characteristics curve estimate of the 4MP + PLCOm2012 model for risk prediction of lung cancer death was 0.88 (95% CI, 0.86 to 0.90). The cumulative incidence of lung cancer death was statistically significantly higher in individuals with 4MP + PLCOm2012 scores above the 1.0% 6-year risk threshold (modified χ2, 166.27; P < .0001). Corresponding subdistributional and lung cancer death-specific HRs for test-positive cases were 9.88 (95% CI, 6.44 to 15.18) and 10.65 (95% CI, 6.93 to 16.37), respectively. CONCLUSION: The blood-based biomarker panel in combination with PLCOm2012 identifies individuals at high risk of a lethal lung cancer.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

A Flexible Method for Diagnostic Accuracy with Biomarker Measurement Error.

Diagnostic biomarkers are often measured with errors due to imperfect lab conditions or analytic variability of the assay. The ability of a diagnostic biomarker to discriminate between cases and controls is often measured by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, among others. Ignoring measurement error can cause biased estimation of a diagnostic accuracy measure, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Existing assays available are either research grade or clinical grade. Research assays are cost effective, often multiplex, but they may be associated with moderate measurement errors leading to poorer diagnostic performance. In comparison, clinical assays may provide better diagnostic ability, but with higher cost since they are usually developed by industry. Correction for attenuation methods are often valid when biomarkers are from a normal distribution, but may be biased with skewed biomarkers. In this paper, we develop a flexible method based on skew-normal biomarker distributions to correct for bias in estimating diagnostic performance measures including AUC, sensitivity, and specificity. Finite sample performance of the proposed method is examined via extensive simulation studies. The methods are applied to a pancreatic cancer biomarker study.

Associations of Race and Ethnicity with Hepatocellular Carcinoma, Decompensation, and Mortality in US Veterans with Cirrhosis.

BACKGROUND: Among patients with cirrhosis, it remains unclear whether there are racial/ethnic differences in cirrhosis complications and mortality. We examined the associations between race/ethnicity and risk for hepatocellular carcinoma (HCC), cirrhosis decompensation, and all-cause mortality overall and by cirrhosis etiology. METHODS: US Veterans diagnosed with cirrhosis from 2001 to 2014 (n = 120,992), due to hepatitis C virus (HCV; n = 55,814), alcohol-associated liver disease (ALD; n = 36,323), hepatitis B virus (HBV; n = 1,972), nonalcoholic fatty liver disease (NAFLD; n = 17,789), or other (n = 9,094), were followed through 2020 for incident HCC (n = 10,242), cirrhosis decompensation (n = 27,887), and mortality (n = 81,441). Multivariable Cox proportional hazards regression was used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). RESULTS: Compared with non-Hispanic White patients, Hispanic patients had higher risk for HCC overall (aHR, 1.32; 95% CI, 1.24-1.41) and by cirrhosis etiology, particularly for ALD- (aHR, 1.63; 95% CI, 1.42-1.87) and NAFLD-cirrhosis (aHR, 1.76; 95% CI, 1.41-2.20), whereas non-Hispanic Black patients had lower HCC risk in ALD- (aHR, 0.79; 95% CI, 0.63-0.98) and NAFLD-cirrhosis (aHR, 0.54; 95% CI, 0.33-0.89). Asian patients had higher HCC risk (aHR, 1.70; 95% CI, 1.29-2.23), driven by HCV- and HBV-cirrhosis. Non-Hispanic Black patients had lower risk for cirrhosis decompensation overall (aHR, 0.71; 95% CI, 0.68-0.74) and by cirrhosis etiology. There was lower risk for mortality among all other racial/ethnic groups compared with non-Hispanic White patients. CONCLUSIONS: Race/ethnicity is an important predictor for risk of developing HCC, decompensation, and mortality. IMPACT: Future research should examine factors underlying these racial/ethnic differences to inform prevention, screening, and treatment for patients with cirrhosis.

Conditional concordance-assisted learning under matched case-control design for combining biomarkers for population screening.

Incorporating promising biomarkers into cancer screening practices for early-detection is increasingly appealing because of the unsatisfactory performance of current cancer screening strategies. The matched case-control design is commonly adopted in biomarker development studies to evaluate the discriminative power of biomarker candidates, with an intention to eliminate confounding effects. Data from matched case-control studies have been routinely analyzed by the conditional logistic regression, although the assumed logit link between biomarker combinations and disease risk may not always hold. We propose a conditional concordance-assisted learning method, which is distribution-free, for identifying an optimal combination of biomarkers to discriminate cases and controls. We are particularly interested in combinations with a clinically and practically meaningful specificity to prevent disease-free subjects from unnecessary and possibly intrusive diagnostic procedures, which is a top priority for cancer population screening. We establish asymptotic properties for the derived combination and confirm its favorable finite sample performance in simulations. We apply the proposed method to the prostate cancer data from the carotene and retinol efficacy trial (CARET).

Multicenter Validation of Abbreviated MRI for Detecting Early-Stage Hepatocellular Carcinoma.

Background Abbreviated MRI is a proposed paradigm shift for hepatocellular carcinoma (HCC) surveillance, but data on its performance are lacking for histopathologically confirmed early-stage HCC. Purpose To evaluate the sensitivity and specificity of dynamic contrast-enhanced abbreviated MRI for early-stage HCC detection, using surgical pathologic findings as the reference standard. Materials and Methods This retrospective study was conducted at three U.S. liver transplant centers in patients with cirrhosis who underwent liver resection or transplant between January 2009 and December 2019 and standard "full" liver MRI with and without contrast enhancement within 3 months before surgery. Patients who had HCC-directed treatment before surgery were excluded. Dynamic abbreviated MRI examinations were simulated from the presurgical full MRI by selecting the coronal T2-weighted and axial three-dimensional fat-suppressed T1-weighted dynamic contrast-enhanced sequences at precontrast, late arterial, portal venous, and delayed phases. Two abdominal radiologists at each center independently interpreted the simulated abbreviated examinations with use of the Liver Imaging Reporting and Data System version 2018. Patients with any high-risk liver observations (>LR-3) were classified as positive; otherwise, they were classified as negative. With liver pathologic findings as the reference standard for the presence versus absence of early-stage HCC, the sensitivity, specificity, and their 95% CIs were calculated. Logistic regression was used to identify factors associated with correct classification. Results A total of 161 patients with early-stage HCC (median age, 62 years [IQR, 58-67 years]; 123 men) and 138 patients without HCC (median age, 55 years [IQR, 47-63 years]; 85 men) were confirmed with surgical pathologic findings. The sensitivity and specificity of abbreviated MRI were 88.2% (142 of 161 patients) (95% CI: 83.5, 92.5) and 89.1% (123 of 138 patients) (95% CI: 84.4, 93.8), respectively. Sensitivity was lower for Child-Pugh class B or C versus Child-Pugh class A cirrhosis (64.1% vs 94.2%; P < .001). Conclusion With surgical pathologic findings as the reference standard, dynamic abbreviated MRI had high sensitivity and specificity for early-stage hepatocellular carcinoma detection in patients with compensated cirrhosis but lower sensitivity in those with decompensated cirrhosis. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kim in this issue.

Contributions of Circulating microRNAs for Early Detection of Lung Cancer.

There is unmet need to develop circulating biomarkers that would enable earlier interception of lung cancer when more effective treatment options are available. Here, a set of 30 miRNAs, selected from a review of the published literature were assessed for their predictive performance in identifying lung cancer cases in the pre-diagnostic setting. The 30 miRNAs were assayed using sera collected from 102 individuals diagnosed with lung cancer within one year following blood draw and 212 controls matched for age, sex, and smoking status. The additive performance of top-performing miRNA candidates in combination with a previously validated four-protein marker panel (4MP) consisting of the precursor form of surfactant protein B (Pro-SFTPB), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA) and cytokeratin-19 fragment (CYFRA21-1) was additionally assessed. Of the 30 miRNAs evaluated, five (miR-320a-3p, miR-210-3p, miR-92a-3p, miR-21-5p, and miR-140-3p) were statistically significantly (Wilcoxon rank sum test p < 0.05) elevated in case sera compared to controls, with individual AUCs ranging from 0.57−0.62. Compared to the 4MP alone, the combination of 3-miRNAs + 4MP improved sensitivity at 95% specificity by 19.1% ((95% CI of difference 0.0−28.6); two-sided p: 0.006). Our findings demonstrate utility for miRNAs for early detection of lung cancer in combination with a four-protein marker panel.

The COMPASS study: A prospective, randomized, multi-center trial testing the impact of a clinic-based intervention informing patients of colorectal cancer screening options on screening completion.

BACKGROUND: Colorectal cancer (CRC) screening is underutilized despite evidence that screening improves survival. Since healthcare provider recommendation is a strong predictor of CRC screening completion, providers are encouraged to engage eligible patients in collaborative decision-making that attends to patients' values, needs, and preferences for guideline-concordant screening modalities. METHODS: This three-arm randomized controlled trial is testing the effectiveness of an evidence-based video intervention informing patients of screening choices delivered in a clinic prior to a healthcare appointment. We hypothesize that participants randomized to watch a basic video describing CRC and screening in addition to an informed choice video showing the advantages and disadvantages of fecal immunochemical test (FIT), stool DNA FIT (s-DNA FIT), and colonoscopy (Arm 3) will exhibit a greater proportion of time adherent to CRC screening guidelines after 1, 3 and 6 years than those who only watch the basic video (Arm 2) or no video at all (Arm 1). Primary care and Obstetrician/Gynecology clinics across the United States are recruiting 5280 patients, half who have never been screened and half who previously screened but are currently not guideline adherent. Participants complete surveys prior to and following an index appointment to self-report personal, cognitive, and environmental factors potentially associated with screening. Proportion of time adherent to screening guidelines will be assessed using medical record data and supplemented with annual surveys self-reporting screening. CONCLUSION: Results will provide evidence on the effectiveness of informational and motivational videos to encourage CRC screening that can be easily integrated into clinical practice. CLINICALTRIALS: gov #NCT05246839.

Combining FIB-4 and Liver Stiffness Into the FIB-5, a Single Model that Accurately Predicts Complications of Portal Hypertension.

INTRODUCTION: We aimed to combine the fibrosis (FIB)-4 score and fibroscan-derived liver stiffness (LS) into a single score (FIB-5) that predicts incident complications of portal hypertension (PH) in persons with compensated liver disease. METHODS: In this retrospective cohort study, we identified 5849 US veterans who underwent LS measurement from May 01, 2014 to June 30, 2019, and laboratory tests enabling FIB-4 calculation within 6 months of LS measurement. Patients were followed up from the LS measurement date until February 05, 2020, for incident complications of PH. We combined LS values and the individual components of the FIB-4 score (i.e. age, aspartate aminotransferase, alanine aminotransferase, and platelet count) using multivariable Cox proportional hazards modeling and the machine learning algorithm eXtreme gradient boosting to develop the C-FIB-5 and X-FIB-5 models, respectively. Models were internally validated using optimism-corrected measures. RESULTS: Among 5,849 patients, the mean age was 62.8 years, 95.9% were men, and the mean follow-up time was 2.14 ± 1.21 years. Within 3 years after LS measurement date, 116 (2.0%) patients developed complications of PH. The X-FIB-5 (area under the receiver operating characteristic [AUROC] 0.845) and C-FIB-5 scores (AUROC 0.868) demonstrated superior discrimination over LS (AUROC 0.688) and FIB-4 (AUROC 0.672) for predicting incident complications of PH. Both the X-FIB-5 and C-FIB-5 models demonstrated higher classification accuracy across all sensitivity cutoffs when compared with LS or FIB-4 alone. DISCUSSION: We combined LS and the individual components of the FIB-4 into a single scoring system (FIB-5, www.fib5.net ), which can help identify patients with compensated liver disease at risk of developing complications of PH.

Imaging of the Pancreas in New-Onset Diabetes: A Prospective Pilot Study.

INTRODUCTION: The aim of this study was to assess the feasibility of cross-sectional imaging for detection of pancreatic cancer (PDAC) in patients with new-onset hyperglycemia and diabetes (NOD). METHODS: We conducted a prospective pilot study from November 2018 to March 2020 within an integrated health system. Patients aged 50-85 years with newly elevated glycemic parameters without a history of diabetes were invited to complete a 3-phase contrast-enhanced computed tomography pancreas protocol scan while participating in the Prospective Study to Establish a NOD Cohort. Abnormal pancreatic findings, incidental extrapancreatic findings, and subsequent clinical evaluation were identified. Variability in clinical reporting between medical centers based on descriptors of pancreatic duct and parenchyma was assessed. RESULTS: A total of 130 of 147 participants (88.4%) consented to imaging; 93 scans were completed (before COVID-19 stay-at-home order). The median age was 62.4 years (interquartile range 56.3-68.8), 37.6% women; Hispanic (39.8%), White (29.0%), Black (14.0%), and Asian (13.3%). One (1.1%) case of PDAC (stage IV) was diagnosed, 12 of 93 participants (12.9%) had additional pancreatic findings: 5 fatty infiltration, 3 cysts, 2 atrophy, 1 divisum, and 1 calcification. There were 57 extrapancreatic findings among 52 of 93 (56%) unique patients; 12 of 57 (21.1%) prompted clinical evaluation with 2 additional malignancies diagnosed (nonsmall cell lung and renal oncocytoma). Reports from 1 participating medical center more frequently provided description of pancreatic parenchyma and ducts (92.9% vs 18.4%), P < 0.0001. DISCUSSION: High proportion of incidental findings and variability in clinical reports are challenges to be addressed for a successful NOD-based early detection strategy for PDAC.

Personalized statistical learning algorithms to improve the early detection of cancer using longitudinal biomarkers.

BACKGROUND: Patients undergoing screening for early detection of cancer have serial biomarker measurements that are not traditionally being incorporated into decision making when evaluating biomarkers. OBJECTIVE: We discuss statistical learning algorithms that have the ability to learn from patient history to make personalized decision rules to improve the early detection of cancer. These artificial intelligence algorithms are able to learn in real time from data collected on the patient to identify changes in the patient that could signal asymptomatic cancer. METHODS: We discuss the parametric empirical Bayes (PEB) algorithm for a single biomarker and a Bayesian screening algorithm for multiple biomarkers. RESULTS: We provide tools to implement these algorithms and discuss their clinical utility for the early detection of hepatocellular carcinoma (HCC). The PEB algorithm is a robust, easily implemented algorithm for defining patient specific thresholds that can improve the patient-level sensitivity of a biomarker in many settings, including HCC. The fully Bayesian algorithm, while more complex, can accommodate multiple biomarkers and further improve the clinical utility of the algorithms. CONCLUSIONS: These algorithms could be used in many clinical settings and we aim to guide the reader on how these algorithms may improve the detection performance of their biomarkers.

Analysis of separate training and validation radical prostatectomy cohorts identifies 0.25 mm diameter as an optimal definition for "large" cribriform prostatic adenocarcinoma.

Cribriform growth pattern is well-established as an adverse pathologic feature in prostate cancer. The literature suggests "large" cribriform glands associate with aggressive behavior; however, published studies use varying definitions for "large". We aimed to identify an outcome-based quantitative cut-off for "large" vs "small" cribriform glands. We conducted an initial training phase using the tissue microarray based Canary retrospective radical prostatectomy cohort. Of 1287 patients analyzed, cribriform growth was observed in 307 (24%). Using Kaplan-Meier estimates of recurrence-free survival curves (RFS) that were stratified by cribriform gland size, we identified 0.25 mm as the optimal cutoff to identify more aggressive disease. In univariable and multivariable Cox proportional hazard analyses, size >0.25 mm was a significant predictor of worse RFS compared to patients with cribriform glands ≤0.25 mm, independent of pre-operative PSA, grade, stage and margin status (p < 0.001). In addition, two different subset analyses of low-intermediate risk cases (cases with Gleason score ≤ 3 + 4 = 7; and cases with Gleason score = 3 + 4 = 7/4 + 3 = 7) likewise demonstrated patients with largest cribriform diameter >0.25 mm had a significantly lower RFS relative to patients with cribriform glands ≤0.25 mm (each subset p = 0.004). Furthermore, there was no significant difference in outcomes between patients with cribriform glands ≤ 0.25 mm and patients without cribriform glands. The >0.25 mm cut-off was validated as statistically significant in a separate 419 patient, completely embedded whole-section radical prostatectomy cohort by biochemical recurrence, metastasis-free survival, and disease specific death, even when cases with admixed Gleason pattern 5 carcinoma were excluded. In summary, our findings support reporting cribriform gland size and identify 0.25 mm as an optimal outcome-based quantitative measure for defining "large" cribriform glands. Moreover, cribriform glands >0.25 mm are associated with potential for metastatic disease independent of Gleason pattern 5 adenocarcinoma.

Development and validation of a quantitative reactive stroma biomarker (qRS) for prostate cancer prognosis.

To develop and validate a new tissue-based biomarker that improves prediction of outcomes in localized prostate cancer by quantifying the host response to tumor. We use digital image analysis and machine learning to develop a biomarker of the prostate stroma called quantitative reactive stroma (qRS). qRS is a measure of percentage tumor area with a distinct, reactive stromal architecture. Kaplan Meier analysis was used to determine survival in a large retrospective cohort of radical prostatectomy samples. qRS was validated in two additional, distinct cohorts that include international cases and tissue from both radical prostatectomy and biopsy specimens. In the developmental cohort (Baylor College of Medicine, n = 482), patients whose tumor had qRS > 34% had increased risk of prostate cancer-specific death (HR 2.94; p = 0.039). This result was replicated in two validation cohorts, where patients with qRS > 34% had increased risk of prostate cancer-specific death (MEDVAMC; n = 332; HR 2.64; p = 0.02) and also biochemical recurrence (Canary; n = 988; HR 1.51; p = 0.001). By multivariate analysis, these associations were shown to hold independent predictive value when compared to currently used clinicopathologic factors including Gleason score and PSA. qRS is a new, validated biomarker that predicts prostate cancer death and biochemical recurrence across three distinct cohorts. It measures host-response rather than tumor-based characteristics, and provides information not represented by standard prognostic measurements.

Counting Advanced Precancerous Lesions as True Positives When Determining Colorectal Cancer Screening Test Specificity.

The landmark Centers for Medicare & Medicaid Services (CMS) decision memo on blood-based biomarkers to screen for colorectal cancer (CRC) sets thresholds of 74% or higher for sensitivity and 90% or higher for specificity for CRC. This approach does not consider detection of advanced precancerous lesions as true positives. We contrasted the impact of counting advanced precancerous lesions as true vs false positives and projected CRC outcomes under contrasting tests in a validated model. A test with the threshold performance set by CMS decreased CRC incidence by 30% and CRC mortality by 48% in individuals aged 45 years. If this test also detected advanced precancerous lesions with 30% sensitivity, CRC incidence decreased by 45% and mortality by 58%, but the CRC specificity of the test of only 88% would not satisfy the CMS threshold. CMS should reconsider its definition of threshold specificity for CRC screening biomarkers. Future coverage determinations on biomarkers to screen for cancer should consider detection of relevant precursor lesions and projected outcomes.

A multivariate parametric empirical Bayes screening approach for early detection of hepatocellular carcinoma using multiple longitudinal biomarkers.

The early detection of hepatocellular carcinoma (HCC) is critical to improving outcomes since advanced HCC has limited treatment options. Current guidelines recommend HCC ultrasound surveillance every 6 months in high-risk patients however the sensitivity for detecting early stage HCC in clinical practice is poor. Blood-based biomarkers are a promising direction since they are more easily standardized and less resource intensive. Combining of multiple biomarkers is more likely to achieve the sensitivity required for a clinically useful screening algorithm and the longitudinal trajectory of biomarkers contains valuable information that should be utilized. We propose a multivariate parametric empirical Bayes (mPEB) screening approach that defines personalized thresholds for each patient at each screening visit to identify significant deviations that trigger additional testing with more sensitive imaging. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) trial provides a valuable source of data to study HCC screening algorithms. We study the performance of the mPEB algorithm applied to serum α -fetoprotein, a widely used HCC surveillance biomarker, and des- γ carboxy prothrombin, an HCC risk biomarker that is FDA approved but not used in practice in the United States. Using cross-validation, we found that the mPEB algorithm demonstrated moderate but improved sensitivity compared to alternative screening approaches. Future research will validate the clinical utility of the approach in larger cohort studies with additional biomarkers.