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BackgroundPatients with prostate cancer undergoing radiation therapy (RT) need comfortably full bladders to reduce toxicities during treatment. Poor compliance is common with standard of care written or verbal instructions, leading to wasted patient value (PV) and clinic resources via poor throughput efficiency (TE). ObjectiveHerein, we assessed the feasibility and acceptability of a smartphone-based behavioral intervention (SBI) to improve bladder-filling compliance and methods for quantifying PV and TE. MethodsIn total, 36 patients with prostate cancer were enrolled in a single-institution, closed-access, nonrandomized feasibility trial. The SBI consists of a fully automated smart water bottle and smartphone app. Both pieces alert the patient to empty his bladder and drink a personalized volume goal, based on simulation bladder volume, 1.25 hours before his scheduled RT. Patients were trained to adjust their volume goal and notification times to achieve comfortably full bladders. The primary end point was met if qualitative (QLC) and quantitative compliance (QNC) were >80%. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily cone-beam tomography were >75% of the simulation’s volume. The Service User Technology Acceptability Questionnaire (SUTAQ) was given in person pre- and post-SBI. Additional acceptability and engagement end points were met if >3 out of 5 across 4 domains on the SUTAQ and >80% (15/18) of patients used the device >50% of the time, respectively. Finally, the impact of SBI on PV and TE was measured by time spent in a clinic and on the linear accelerator (linac), respectively, and contrasted with matched controls. ResultsQLC was 100% in 375 out of 398 (94.2%) total treatments, while QNC was 88.9% in 341 out of 398 (85.7%) total treatments. Of a total score of 5, patients scored 4.33 on privacy concerns, 4 on belief in benefits, 4.56 on satisfaction, and 4.24 on usability via SUTAQ. Further, 83% (15/18) of patients used the SBI on >50% of treatments. Patients in the intervention arm spent less time in a clinic (53.24, SEM 1.71 minutes) compared to the control (75.01, SEM 2.26 minutes) group (P

Abstract Background The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. Methods We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. Results Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. Conclusions We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Abstract Objective The study aims to identify the optimal 4Kscore thresholds to determine the need for a prostate biopsy when multiparametric magnetic resonance imaging (MRI) (mpMRI) is negative or indeterminate. Materials and methods We analysed retrospective data from men in eight different institutions who underwent an mpMRI, 4Kscore and prostate biopsy for evaluation of prostate cancer. We selected men with a negative (PIRADS ≤2) or indeterminate (PIRADS 3) mpMRI. 4Kscore values were categorized into ranges of 1–7, 8–19, 20–32 and greater than 32. We evaluated the proportion of men with grade group 2 or higher (GG2+) cancer in groups defined by PIRADS and 4Kscore. We also evaluated the number of biopsies avoided and GG2+ cancer missed in each group reported depend on 4Kscore cutoff points. Results Among 1111 men who had an mpMRI, 4Kscore and biopsy, 625 of them had PIRADS ≤3 on mpMRI: 374 negative (PIRADS ≤2) and 251 indeterminate (PIRADS 3). In men with a negative mpMRI, we found a 4Kscore cut‐point of 33 resulted in an increased risk of GG2+ cancer on biopsy. In patients with an equivocal lesion on mpMRI, men with a 4Kscore cutoff ≥8 had a greater risk of GG2+ cancer on biopsy. Decision curve analysis supported the proposed cut‐points in each mpMRI group. Conclusions In men with negative and indeterminate mpMRI, we found the best 4Kscore threshold to determine the need for biopsy to be 33 and 8 respectively. Future prospective studies in independent populations are needed to confirm these findings.

BackgroundImmune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients.MethodsFifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab.ResultsModels incorporating baseline NLR and identical radiomics (surface-to-mass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank).DiscussionThis study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response.

Abstract Hispanic patients have been reported to have an increased incidence of AML and possibly inferior outcomes compared to non‐Hispanics. We conducted a retrospective study of 225 AML patients (58 Hispanic and 167 non‐Hispanic) at two academic medical centers in Florida. Disease characteristics, cytogenetics, mutation profiles, and clinical outcomes were assessed. Hispanic patients were younger at presentation than non‐Hispanics (p = 0.0013). We found associations between single gene mutations and ethnicity, with IDH1 mutations being more common in non‐Hispanics (95.2% vs. 4.8%, p = 0.0182) and WT1 mutations more common in Hispanics (62.5% vs. 37.5%, p = 0.0455). We also found an emerging trend towards adverse risk cytogenetics in Hispanic patients (p = 0.1796), as well as high risk fusions such as MLL‐r (70% vs. 30%, p = 0.004). There was no difference in overall survival (OS) between Hispanic and non‐Hispanics patients. When examining only newly diagnosed patients (n = 105), there was improved OS in Hispanics (median 44.7 months vs. 14 months, p = 0.026) by univariate analysis and equivalent OS by multivariate analysis (hazard ratio = 1.52 [95% CI = 0.74–3.15]). Hispanics with a driver mutation not class‐defining had improved survival compared to non‐Hispanics. Our study demonstrates significant genetic differences between Floridian Hispanics and non‐Hispanics, but no difference in OS in patients treated at an academic medical center.

Abstract For prostate cancer (PCa) patients treated with definitive radiotherapy (RT), acute and late RT-related genitourinary (GU) toxicities adversely impact disease-specific quality of life. Early warning of potential RT toxicities can prompt interventions that may prevent or mitigate future adverse events. During intensity modulated RT (IMRT) of PCa, daily cone-beam computed tomography (CBCT) images are used to improve treatment accuracy through image guidance. This work investigated the performance of CBCT-based delta-radiomic features (DRF) models to predict acute and sub-acute International Prostate Symptom Scores (IPSS) and Common Terminology Criteria for Adverse Events (CTCAE) version 5 GU toxicity grades for 50 PCa patients treated with definitive RT. Delta-radiomics models were built using logistic regression, random forest for feature selection, and a 1000 iteration bootstrapping leave one analysis for cross validation. To our knowledge, no prior studies of PCa have used DRF models based on daily CBCT images. AUC of 0.83 for IPSS and greater than 0.7 for CTCAE grades were achieved as early as week 1 of treatment. DRF extracted from CBCT images showed promise for the development of models predictive of RT outcomes. Future studies will include using artificial intelligence and machine learning to expand CBCT sample sizes available for radiomics analysis.

Current first-line standard therapy for metastatic non-small cell lung cancer without driver mutations involves chemotherapy and immunotherapy combination. Prior to the advent of immune checkpoint inhibition, REVEL, a randomized phase III trial demonstrated improved progression-free and overall survival with ramucirumab and docetaxel (ram+doc) in patients who failed platinum-based first-line therapy. Long-term outcomes related to second-line ramucirumab and docetaxel after first-line immunotherapy exposure remain unknown. We analyzed outcomes for 35 patients from our center whom received ramucirumab and docetaxel following disease progression on chemotherapy and immunotherapy combination. Median progression-free survival among patients who received ram+doc after exposure to immunotherapy was 6.6 months (95% CI = 5.5 to 14.9 months; p

ObjectivesRadiation therapy (RT) is an integral part of treatment of head/neck cancer (HNC) but is associated with many toxicities. We sought to evaluate sociodemographic, pathologic, and clinical factors associated with emergency department (ED) visits, hospital admissions (HA), and RT breaks in HNC patients undergoing curative-intent RT.MethodsWe completed a Level 3 (Oxford criteria for evidence-based medicine) analysis of a cohort of HNC patients who underwent curative-intent RT at our institution from 2013 to 2017. We collected demographic characteristics and retrospectively assessed for heavy opioid use, ED visits or HA during RT as well as RT breaks. Treatment breaks were defined as total days to RT fractions ratio ≥1.6. Multivariable stepwise logistic regression analyses were done to determine the association of various sociodemographic, pathologic, and clinical characteristics with ED visits, HA and RT treatment breaks.ResultsThe cohort included 376 HNC patients (294 male, 82 female, median age 61). On multivariable analysis, significant factors associated with ED visits during RT were heavy opioid use and black race. Receipt of concomitant chemotherapy was the only factor associated with hospital admissions during RT. Advanced age, lower socioeconomic class, glandular site, and receipt of chemotherapy were all independently associated with RT breaks. Lower cancer stage and lack of substance abuse history were independently associated with lack of treatment breaks.ConclusionHNC patients with factors such as heavy opioid use, Black race, receipt of concomitant chemotherapy, and lower socioeconomic class may require closer monitoring during RT.

In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is particularly problematic when exposures must be reconstructed from physical measurements, for example, for environmental or occupational radiation doses that were received by a study population for which radiation doses cannot be measured directly. To incorporate complex uncertainties in reconstructed exposures, the two-dimensional Monte Carlo (2DMC) dose estimation method has been proposed and used in various dose reconstruction efforts. The 2DMC method generates multiple exposure realizations from dosimetry models that incorporate various sources of errors to reflect the uncertainty of the dose distribution as well as the uncertainties in individual doses in the exposed population. Traditional measurement-error model approaches, typically based on using mean doses in the dose-exposure analysis, do not fully account exposure uncertainties. A recently developed statistical approach that overcomes many of these limitations by analyzing multiple exposure realizations in relation to disease risk is Bayesian model averaging (BMA). The analytic advantage of the BMA is its ability to better accommodate complex exposure uncertainty in the risk estimation, but a practical. Drawback is its significant computational complexity. In this present paper, we propose a novel frequentist model averaging (FMA) approach which has all the analytical advantages of the BMA method but is much simpler to implement and computationally faster. We show in simulations that, like BMA, FMA yields 95% confidence intervals for association parameters that close to 95% coverage rate. In simulations, the FMA has shorter length of CIs than those of another frequentist approach, the corrected information matrix (CIM) method. We illustrate the similarities in performance of BMA and FMA from a study of exposures from radioactive fallout in Kazakhstan.

Background: Partial/complete pathologic response following neoadjuvant chemotherapy (NAC) in pancreatic cancer (PDAC) patients undergoing pancreatectomy is associated with improved survival. We sought to determine whether neutrophil-to-lymphocyte ratio (NLR) dynamics predict pathologic response following chemotherapy in PDAC, and if manipulating NLR impacts chemosensitivity in preclinical models and uncovers potential mechanistic underpinnings underlying these effects. Methods: Pathologic response in PDAC patients (n=94) undergoing NAC and pancreatectomy (7/2015-12/2019) was dichotomized as partial/complete or poor/absent. Bootstrap-validated multivariable models assessed associations between pre-chemotherapy NLR (%neutrophils÷%lymphocytes) or NLR dynamics during chemotherapy (ΔNLR = pre-surgery—pre-chemotherapy NLR) and pathologic response, disease-free survival (DFS), and overall survival (OS). To preclinically model effects of NLR attenuation on chemosensitivity, Ptf1aCre/+; KrasLSL-G12D/+;Tgfbr2flox/flox (PKT) mice and C57BL/6 mice orthotopically injected with KrasLSL-G12D/+;Trp53LSL-R172H/+;Pdx1Cre(KPC) cells were randomized to vehicle, gemcitabine/paclitaxel alone, and NLR-attenuating anti-Ly6G with/without gemcitabine/paclitaxel treatment. Results: In 94 PDAC patients undergoing NAC (median:4 months), pre-chemotherapy NLR (p

PurposeThe purpose of this work is to explore delta-radiomics texture features for predicting response using setup images of pancreatic cancer patients treated with magnetic resonance image guided (MRI-guided) stereotactic ablative radiotherapy (SBRT).MethodsThe total biological effective dose (BED) was calculated for 30 patients treated with MRI-guided SBRT that delivered physical doses of 30–60 Gy in three to five fractions. Texture features were then binned into groups based upon BED per fraction by dividing BED by the number of fractions. Delta-radiomics texture features were calculated after delivery of 20 Gy BED (BED20 features) and 40 Gy BED (BED40 features). A random forest (RF) model was constructed using BED20 and then BED40 features to predict binary outcome. During model training, the Gini Index, a measure of a variable’s importance for accurate prediction, was calculated for all features, and the two features that ranked the highest were selected for internal validation. The two features selected from each bin were used in a bootstrapped logistic regression model to predict response and performance quantified using the area under the receiver operating characteristic curve (AUC). This process was an internal validation analysis.ResultsAfter RF model training, the Gini Index was highest for gray-level co-occurrence matrix-based (GLCM) sum average, and neighborhood gray tone difference matrix-based (NGTDM) busyness for BED20 features and gray-level size zone matrix-based (GLSZM) large zones low gray-level emphasis and gray-level run length matrix-based (GLRLM) run percentage was selected from the BED40-based features. The mean AUC obtained using the two BED20 features was AUC = 0.845 with the 2.5 percentile and 97.5 percentile values ranging from 0.794 to 0.856. Internal validation of the BED40 delta-radiomics features resulted in a mean AUC = 0.567 with a 2.5 and 97.5 percentile range of 0.502–0.675.ConclusionEarly changes in treatment quantified with the BED20 delta-radiomics texture features in low field images acquired during MRI-guided SBRT demonstrated better performance in internal validation than features calculated later in treatment. Further analysis of delta-radiomics texture analysis in low field MRI is warranted.

Purpose: The National Comprehensive Cancer Network (NCCN) clinical guidelines influence medical practice, payor coverage, and standards of care. The levels of evidence underlying radiation therapy recommendations in NCCN have not been systematically explored. Herein, we aim to systematically investigate the NCCN recommendations pertaining to the categories of consensus and evidence (CE) for radiation therapy. Methods and Materials: We evaluated the distribution of CE underlying current treatment recommendations for the 20 most prevalent cancers in the United States with at least 10 radiation therapy recommendations in the NCCN clinical guidelines. For context, the distribution of evidence in the radiation therapy guidelines was compared with that of systemic therapy using a χ2 test. The proportion of category I CE between radiation and systemic therapy was compared using a 2-proportion, 2-tailed z-test in total and for each disease site. A P value of < .05 was considered significant. Results: Among all radiation therapy recommendations, the proportions of category I, IIA, IIB, and III CE were 9.7%, 80.6%, 8.4%, and 1.3%, respectively. When analyzed by disease site, cervix and breast cancer had the highest portion of category I CE (33% and 31%, respectively). There was no radiation therapy category I CE for hepatobiliary, bone, pancreatic, melanoma, and uterine cancers. There was a significant difference in the distribution of CE between the systemic therapy recommendations and the radiation therapy recommendations (χ2 statistic 64.16, P < .001). Overall, there was a significantly higher proportion of category I CE in the systemic therapy recommendations compared with the radiation therapy recommendations (12.3% vs 9.7%, P = .043). Conclusions: Only 9.7% of radiation therapy recommendations in NCCN guidelines are category I CE. The highest levels of evidence for radiation therapy are in breast and cervical cancers. Despite major advances in the field, these data underline that the majority of NCCN radiation therapy recommendations are based on uniform expert opinion and not on higher level evidence.

Background & Aims: Liver cancer is highly fatal and the most rapidly increasing cancer in the US, where chronic hepatitis C (HCV) infection is the leading etiology. HCV is particularly prevalent among the 1945-1965 birth cohort, the so-called “baby boomers”. Focusing on this cohort-etiology link, we aim to characterize liver cancer patterns for 15 unique US populations: White, African American, Mexican Immigrant, Mexican American, Cuban and Chinese, among others. Methods: Individual-level mortality data from 2012–2016 from the health departments of 3 large states – California, Florida, New York – were pooled to compute liver cancer mortality rates for each racial/ethnic group and for 2 birth cohorts of interest: “1945–1965 cohort” and “older cohort”. Results: Liver cancer is a major cause of cancer death among all US male groups and the leading cause in Mexican American men. Over 50% of the age-adjusted liver cancer mortality of White, African American, Mexican American, and Puerto Rican males came from the 1945-1965 birth cohort. In contrast, foreign-born male and all female populations had higher liver cancer mortality originating from the older cohort. Internationally, US White male baby boomers had a 49% higher liver cancer mortality rate than their counterparts in Europe (mortality rate ratio 1.49; 95% CI 1.43–1.56). Conclusions: Populations burdened disproportionately by liver cancer in the 1945–1965 cohort include US-born males who were all present in the US during the 1960s–1990s when significant HCV transmission took place; these individuals will benefit most from HCV screening and treatment. For the others, including all women, Asian subgroups, and especially burgeoning Hispanic immigrant populations, comprehensive liver cancer prevention efforts will require detailed study of the distribution of etiologies. Lay summary: Liver cancer, a major cause of cancer death among US males, is increasing. The causes of liver cancer are varied, including hepatitis C, hepatitis B, alcohol-related liver disease, and non-alcoholic fatty liver disease. Racial/ethnic groups are impacted differently, but the highest rates are seen among US-born men born between 1945–1965, the so-called “baby boomers”, whether White, Black, or Hispanic, likely linked to the known high prevalence of hepatitis C infection among this cohort. Keywords: hepatocellular carcinoma, mortality, etiology, race, ethnicity, liver cancer, Hispanic, Asian, Caucasian, epidemiology

Abstract Background Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies. Main text We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates. Conclusions In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Abstract Background Hepatitis C virus (HCV) treatment regimens (DAAs) are well tolerated, efficacious but costly. Their high cost and restricted availability, raises concerns about the outcome of treatment in uninsured patients. This study investigated sustained virologic response (SVR) outcomes in a predominately uninsured patient population and completion of four steps along the HCV treatment cascade. Methods A retrospective chart review was conducted to characterize the patient population and analyze covariates to determine association with insurance status, attainment of SVR and progression through the HCV treatment cascade. Results Out of a total of 216 patients, 154 (71%) were uninsured. Approximately 50% of patients (109 of 216 patients) were male and 57% were Hispanic (123 of 216 patients). Sex, race, ethnicity, treatment compliance, and rates of complications were not associated with insurance status. Insured patients were older (median 60 years vs 57 years, p-value

Identifying actionable components in castration–resistant prostate cancer (CRPC) is critical for the development of effective treatments. Here, the authors show that the inhibition of the redox-protective protein TRX1 decreases the growth of CRPC cells through the regulation of ROS levels, p53 and androgen receptor expression.

Protein concentrations evolve under greater evolutionary constraint than mRNA levels. Translation efficiency of mRNA represents the chief determinant of basal protein concentrations. This raises a fundamental question of how mRNA and protein levels are coordinated in dynamic systems responding to physiological stimuli. This report examines the contributions of mRNA abundance and translation efficiency to protein output in cells responding to oxygen stimulus. We show that changes in translation efficiencies, and not mRNA levels, represent the major mechanism governing cellular responses to [O2] perturbations. Two distinct cap-dependent protein synthesis machineries select mRNAs for translation: the normoxic eIF4F and the hypoxic eIF4FH. O2-dependent remodeling of translation efficiencies enables cells to produce adaptive translatomes from preexisting mRNA pools. Differences in mRNA expression observed under different [O2] are likely neutral, given that they occur during evolution. We propose that mRNAs contain translation efficiency determinants for their triage by the translation apparatus on [O2] stimulus.

In recent years, there has been an increased interest in using protein mass spectroscopy to identify molecular markers that discriminate diseased from healthy individuals. Existing methods are tailored towards classifying observations into nominal categories. Sometimes, however, the outcome of interest may be measured on an ordered scale. Ignoring this natural ordering results in some loss of information. In this paper, we propose a Bayesian model for the analysis of mass spectrometry data with ordered outcome. The method provides a unifi ed approach for identifying relevant markers and predicting class membership. This is accomplished by building a stochastic search variable selection method within an ordinal outcome model. We apply the methodology to mass spectrometry data on ovarian cancer cases and healthy individuals. We also utilize wavelet-based techniques to remove noise from the mass spectra prior to analysis. We identify protein markers associated with being healthy, having low grade ovarian cancer, or being a high grade case. For comparison, we repeated the analysis using conventional classification procedures and found improved predictive accuracy with our method.

BackgroundThe excess incidence of thyroid cancer in Ukraine and Belarus observed a few years after the Chernobyl accident is considered to be largely the result of 131I released from the reactor. Although the Belarus thyroid cancer prevalence data has been previously analyzed, no account was taken of dose measurement error.MethodsWe examined dose-response patterns in a thyroid screening prevalence cohort of 11,732 persons aged under 18 at the time of the accident, diagnosed during 1996-2004, who had direct thyroid 131I activity measurement, and were resident in the most radio-actively contaminated regions of Belarus. Three methods of dose-error correction (regression calibration, Monte Carlo maximum likelihood, Bayesian Markov Chain Monte Carlo) were applied.ResultsThere was a statistically significant (p0.2).ConclusionsIn summary, the relatively small contribution of unshared classical dose error in the current study results in comparatively modest effects on the regression parameters.

The 1986 accident at the Chernobyl nuclear power plant remains the most serious nuclear accident in history, and excess thyroid cancers, particularly among those exposed to releases of iodine-131 remain the best-documented sequelae. Failure to take dose-measurement error into account can lead to bias in assessments of dose-response slope. Although risks in the Ukrainian-US thyroid screening study have been previously evaluated, errors in dose assessments have not been addressed hitherto. Dose-response patterns were examined in a thyroid screening prevalence cohort of 13,127 persons aged

Background: Immune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients. Methods: Fifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab. Results: Models incorporating baseline NLR and identical radiomics (surface-tomass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank). Discussion: This study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response. [ABSTRACT FROM AUTHOR]