Accuracy and cut-off point selection in three-class classification problems using a generalization of the Youden index

Interval estimation for the Youden index of a continuous diagnostic test with verification biased data

In medical diagnostic studies, the Youden index plays a crucial role as a comprehensive measurement of the diagnostic test effectiveness, aiding in determining the optimal threshold values by maximizing the sum of sensitivity and specificity. However, in clinical practice, verification of true disease status might be partially missing and estimators based on partially validated subjects are usually biased. While verification bias-corrected estimation methods for the receiver operating characteristic curve have been widely studied, no such results have been specifically developed for the Youden index. In this paper, we propose bias-corrected interval estimation methods for the Youden index of a continuous test under the missing-at-random assumption. Based on four estimators (full imputation (FI), mean score imputation, inverse probability weighting, and the semiparametric efficient (SPE)) introduced by Alonzo and Pepe for handling verification bias, we develop multiple confidence intervals for the Youden index by applying bootstrap resampling and the method of variance estimates recovery (MOVER). Extensive simulation and real data studies show that when the disease model is correctly specified, MOVER-FI intervals yield better coverage probability. We also observe a tradeoff between methods when the verification proportion is low: Bootstrap approaches achieve higher accuracy, while MOVER approaches deliver greater precision. Remarkably, bootstrap-SPE interval exhibit appealing doubly robustness to model misspecification and perform adequately across almost all scenarios considered. Based on our findings, we recommend using the bootstrap-SPE intervals when the true disease model is unknown, and the MOVERws-FI interval if the true disease model can be well approximated.

Artificial intelligence: a useful tool in active tuberculosis screening among vulnerable groups in Romania - advantages and limitations

Introduction

Despite advances in diagnostic technologies for tuberculosis (TB), global control of this disease requires improved technologies for active case finding in selected vulnerable populations. The integration of artificial intelligence (AI) into imaging modalities has been anticipated to assume a pivotal position in conjunction with traditional bacteriological diagnostic approaches, especially in the active diagnosis of vulnerable groups.

Methods

The study was conducted as a prospective investigation spanning from November 2019 to October 2023, in Romania's national TB screening project. From total of 92,368 tested participants, 404 patients were included in the study, with 202 individuals diagnosed with TB and 202 individuals serving as controls. The initial interpretation of radiological images was performed by AI X-Vision software and patients with suspicious findings were confirmed to have TB using GeneXpert testing. The objective of this study is to discover a threshold at which the AI score can accurately assess the risk of TB, regardless of the patient's medical background.

Results

The study involved a number of 404 people, among whom 202 were diagnosed with TB out of a total of 92,368 participants, and the remaining 202 patients represent the control group. The current study highlighted, at an AI threshold value of 0.4, that 89% of the screened people benefited from a correct assessment using the AI associated with the radiological examination. ROC curve analysis indicates an AUC of 0.923 (95% CI:0.893-0.947; significance level p < 0.0001) which shows that the test has a high capacity to accurately detect individuals with TB and also to rule out those who do not have the disease, with sensitivity 87.1, specificity 91.6 and criterion >0.3585.

Conclusion

Our study brings to the fore the significance of integrating AI software X-vision with bacteriological diagnosis in detecting TB among vulnerable groups in Romania. This underscores the imperative at the global level to develop solutions in the prompt diagnosis of TB, particularly within vulnerable groups.

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model

Purpose

Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.

Methods

A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China. After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. The area under the receiver operating characteristic curve (AUC) was used to evaluate model's performance, and then the TreeShap algorithm was employed to interpret the variable contributions.

Results

A total of 184 children were enrolled in this study, of whom 19 (10.33%) developed ATB-DILI. Univariate analysis showed that seven variables were risk factors for ATB-DILI, including the plasma peak concentration (Cmax) of rifampicin, body mass index (BMI), alanine aminotransferase, total bilirubin, total bile acids, aspartate aminotransferase and creatinine. Among the numerous predictive models constructed by the "H2O" AutoML platform, the gradient boost machine (GBM) model exhibited the superior performance with AUCs of 0.838 and 0.784 on the training and testing sets, respectively. The TreeShap algorithm showed that Cmax of rifampicin and BMI were important features that affect the AutoML model's performance.

Conclusion

The GBM model established by AutoML technology shows high predictive accuracy and interpretability for ATB-DILI in children. The prediction model can assist clinicians to implement timely interventions and mitigation strategies, and formulate personalized medication regimens, thereby minimizing potential harm to high-risk children of ATB-DILI.

Statistical Inference for Box-Cox based Receiver Operating Characteristic Curves

Receiver operating characteristic (ROC) curve analysis is widely used in evaluating the effectiveness of a diagnostic test/biomarker or classifier score. A parametric approach for statistical inference on ROC curves based on a Box-Cox transformation to normality has frequently been discussed in the literature. Many investigators have highlighted the difficulty of taking into account the variability of the estimated transformation parameter when carrying out such an analysis. This variability is often ignored and inferences are made by considering the estimated transformation parameter as fixed and known. In this paper, we will review the literature discussing the use of the Box-Cox transformation for ROC curves and the methodology for accounting for the estimation of the Box-Cox transformation parameter in the context of ROC analysis, and detail its application to a number of problems. We present a general framework for inference on any functional of interest, including common measures such as the AUC, the Youden index, and the sensitivity at a given specificity (and vice versa). We further developed a new R package (named 'rocbc') that carries out all discussed approaches and is available in CRAN.

Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the empirical likelihood

The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart-the parametric likelihood-preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class receiver operating characteristic analysis, where a variety of inferential tasks could be of interest. We present novel theoretical results and tailored techniques studied to efficiently solve some of such tasks. Extensive simulation experiments are provided in a supporting role, with our novel proposals compared to existing competitors, when possible. It emerges that our new proposals are extremely flexible, being able to compete with contestants and appearing suited to accommodating several distributions, such, for example, mixtures, for target populations. We illustrate the application of the novel proposals with a real data example. The article ends with a discussion and a presentation of some directions for future research.

AQP4-DARPin1: A Chimeric Antigen Based on Scaffold Protein DARPin for Efficient Detection of AQP4-IgG in NMOSD

AQP4-IgG is an autoantibody associated with neuromyelitis optica spectroscopic disorder (NMOSD), a central nervous system inflammatory disease that requires early diagnosis and treatment. We designed two fusion proteins, AQP4-DARPin1 and AQP4-DARPin2, comprising the complete antigenic epitopes of aquaporin-4 (AQP4) and the constant region of the scaffold protein DARPin. These fusion proteins were expressed and purified from Escherichia coli and coated on microplates to develop an efficient method for detecting AQP4-IgG. Molecular dynamics simulation revealed that the fusion of AQP4 extracellular epitopes with DARPin did not alter the main structure of DARPin. The purified AQP4-DARPins bound recombinant antibody rAb-53 (AQP4-IgG) with affinities of 135 and 285 nM, respectively. Enzyme-linked immunosorbent assay (ELISA) and immunoprecipitation demonstrated that AQP4-DARPin1 specifically recognized AQP4-IgG in the NMOSD patient serum. AQP4-DARPin1 as a coated antigen showed higher ELISA signal and end point dilution ratio than full-length AQP4. Our AQP4-DARPin1-coated AQP4-IgG ELISA had 100% specificity and 90% sensitivity. These results indicate that AQP4-DARPin1, compared to existing detection strategies that use full-length or extracellular loop peptides of AQP4, provides a new and more effective approach to the ELISA detection of NMOSD.

Evaluating joint confidence region of hypervolume under ROC manifold and generalized Youden index

In biomarker evaluation/diagnostic studies, the hypervolume under the receiver operating characteristic manifold (HUM K $$ {\mathrm{HUM}}_K $$ ) and the generalized Youden index (J K $$ {J}_K $$ ) are the most popular measures for assessing classification accuracy under multiple classes. WhileHUM K $$ {\mathrm{HUM}}_K $$ is frequently used to evaluate the overall accuracy,J K $$ {J}_K $$ provides direct measure of accuracy at the optimal cut-points. Simultaneous evaluation ofHUM K $$ {\mathrm{HUM}}_K $$ andJ K $$ {J}_K $$ provides a comprehensive picture about the classification accuracy of the biomarker/diagnostic test under consideration. This article studies both parametric and non-parametric approaches for estimating the confidence region ofHUM K $$ {\mathrm{HUM}}_K $$ andJ K $$ {J}_K $$ for a single biomarker. The performances of the proposed methods are investigated by an extensive simulation study and are applied to a real data set from the Alzheimer's Disease Neuroimaging Initiative.

Construction of joint confidence spaces for the optimal true class fraction triplet in the ROC space using alternative biomarker cutoffs

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver. Finding new biomarkers for its early detection is of high clinical importance. As with many other diseases, cancer has a progressive nature. In cancer biomarker studies, it is often the case that the true disease status of the recruited individuals exhibits more than two classes. The receiver operating characteristic (ROC) surface is a well-known statistical tool for assessing the biomarkers' discriminatory ability in trichotomous settings. The volume under the ROC surface (VUS) is an overall measure of the discriminatory ability of a marker. In practice, clinicians are often in need of cutoffs for decision-making purposes. A popular approach for computing such cutoffs is the Youden index and its recent three-class generalization. A drawback of such a method is that it treats the data in a pairwise fashion rather than consider all the data simultaneously. The use of the minimized Euclidean distance from the perfection corner to the ROC surface (also known as closest to perfection method) is an alternative to the Youden index that may be preferable in some settings. When such a method is employed, there is a need for inferences around the resulting true class rates/fractions that correspond to the optimal operating point. In this paper, we provide an inferential framework for the derivation of marginal confidence intervals (CIs) and joint confidence spaces (CSs) around the corresponding true class fractions, when dealing with trichotomous settings. We explore parametric and nonparametric approaches for the construction of such CIs and CSs. We evaluate our approaches through extensive simulations and apply them to a real data set that refers to HCC patients.

Usefulness of the SF-36 Health Survey questionnaire in screening for health-related quality of life among parents of children with cancer: Latent profile analysis

Background

Poor health-related quality of life (HRQOL) of parents of children with cancer as their main caregivers can adversely affect child's HRQOL. Short Form-36 Health Survey (SF-36) is a widely used instrument to measure HRQOL. However, there are no clearly defined cut-off points for screening for parents with poor HRQOL. This study aimed to find appropriate cut-off points for the SF-36 questionnaire in a sample of parents of children with cancer using latent profile analysis to add another possibility to use it.

Methods

In this cross-sectional study, a number of 108 couples (108 mothers and 108 fathers), who had children with cancer, were selected by simple random sampling method from the patients' files. The study was conducted at two settings, pediatric hematology/oncology wards of BESAT hospital (a hospital related to Hamadan University of Medical Sciences, Iran) in 2017. Latent Profile analysis (LPA) method was used to determine appropriate cut-off points for the SF-36 questionnaire. Data was analyzed by Mplus and R3.3.0 software.

Results

Based on the results, scores ≤ 44, 45-63 and ≥ 64 for mental health, and scores ≤ 43, 44-59 and ≥ 60 for physical health classes indicate weak, medium, and good, respectively. These cut-off points showed acceptable accuracy in classification of individuals. For the total quality of life, correct classification rates were 88%, 65% and 53% for each class respectively. For mental health (physical health), they were 79(63), 50(62) and 52(63) for each class respectively.

Conclusions

The cut-off points for the classes identified here can be useful in screening parents of children with cancer in clinical setting to provide clinical interventions to protect vulnerable parents from negative outcomes.

Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data

Statistical evaluation of diagnostic tests, and, more generally, of biomarkers, is a constantly developing field, in which complexity of the assessment increases with the complexity of the design under which data are collected. One particularly prevalent type of data is clustered data, where individual units are naturally nested into clusters. In these cases, Bias can arise from omission, in the evaluation process, of cluster-level effects and/or individual covariates. Focusing on the three-class case and for continuous-valued diagnostic tests, we investigate how to exploit the clustered structure of data within a linear-mixed model approach, both when the assumption of normality holds and when it does not. We provide a method for the estimation of covariate-specific receiver operating characteristic surfaces and discuss methods for the choice of optimal thresholds, proposing three possible estimators. A proof of consistency and asymptotic normality of the proposed threshold estimators is given. All considered methods are evaluated by extensive simulation experiments. As an application, we study the use of the Lysosomal Associated Membrane Protein Family Member 5 gene expression as a biomarker to distinguish among three types of glutamatergic neurons.

Application of the skew exponential power distribution to ROC curves

The bi-Normal ROC model and corresponding metrics are commonly used in medical studies to evaluate the discriminatory ability of a biomarker. However, in practice, many clinical biomarkers tend to have skewed or other non-Normal distributions. And while the bi-Normal ROC model's AUC tends to be unbiased in this setting, providing a reasonable measure of global performance, the corresponding decision thresholds tend to be biased. To correct this bias, we propose using an ROC model based on the skew exponential power (SEP) distribution, whose additional parameters can accommodate skewed, heavy tailed, or other non-Normal distributions. Additionally, the SEP distribution can be used to evaluate whether the bi-Normal model would be appropriate. The performance of these ROC models and the non-parametric approach are evaluated via a simulation study and applied to a real data set involving infections from Klebsiella pneumoniae. The SEP based ROC-model provides some efficiency gains with respect to estimation of the AUC and provides cut-points with improved classification rates. As such, in the presence non-Normal data, we suggest using the proposed SEP ROC model.

AKR1C1/2 inhibition by MPA sensitizes platinum resistant ovarian cancer towards carboplatin

In recurrent epithelial ovarian cancer (EOC) most patients develop platinum-resistance. On molecular level the NRF2 pathway, a cellular defense mechanism against reactive oxygen species, is induced. In this study, we investigate AKR1C1/2, target of NRF2, in a well-established EOC collective by immunohistochemistry and in a panel of ovarian cancer cell lines including platinum-resistant clones. The therapeutic effect of carboplatin and MPA as monotherapy or in combination was assessed by functional assays, using OV90 and OV90cp cells. Molecular mechanisms of action of MPA were investigated by NRF2 silencing and AKR activity measurements. Immunohistochemical analysis revealed that AKR1C1/2 is a key player in the development of chemoresistance and an independent indicator for short PFS (23.5 vs. 49.6 months, p = 0.013). Inhibition of AKR1C1/2 by MPA led to a concentration- and time-dependent decline of OV90 viability and to an increased response to CP in vitro. By NRF2 silencing, however, the effects of MPA treatment were reduced. Concludingly, our data suggest that a combination therapy of carboplatin and MPA might be a promising therapeutic approach to increase response rates of EOC patients, which should be explored in clinical context.

A family of estimators to diagnostic accuracy when candidate tests are subject to detection limits-Application to diagnosing early stage Alzheimer disease

In disease diagnosis, individuals are usually assumed to be one of the two basic types, healthy or diseased, as typically based on an established gold standard. Candidate markers for diagnosing a disease often are much cheaper and less invasive than the gold standard but must be evaluated against the gold standard for their sensitivity and specificity to accurately diagnose the disease. When candidate diagnostic markers are fully measured, receiver operating characteristic curves have been the standard approaches for assessing diagnostic accuracy. However, full measurements of diagnostic markers may not be available above or below certain limits due to various practical and technical limitations. For example, in the diagnosis of Alzheimer disease using cerebrospinal fluid biomarkers, the Roche Elecsys® immunoassays have a measuring range for multiple cerebrospinal fluid molecular concentrations. Many cognitive tests used in diagnosing dementia due to Alzheimer disease are also subject to detection limits, often referred to as the floor and ceiling effects in the neuropsychological literature. We propose a new statistical methodology for estimating the diagnostic accuracy when a diagnostic marker is subject to detection limits by dividing the entire study sample into two sub-samples by a threshold of the diagnostic marker. We then propose a family of estimators to the area under the receiver operating characteristic curve by combining a conditional nonparametric estimator and another conditional semi-parametric estimator derived from Cox's proportional hazards model. We derive the variance to the proposed estimators, and further, assess the performance of the proposed estimators as a function of possible thresholds through an extensive simulation study, and recommend the optimum thresholds. Finally, we apply the proposed methodology to assess the ability of several cerebrospinal fluid biomarkers and cognitive tests in diagnosing early stage Alzheimer disease dementia.

The Platelet-Activating Factor Receptor's Association with the Outcome of Ovarian Cancer Patients and Its Experimental Inhibition by Rupatadine

The platelet-activating factor receptor (PAFR) and its ligand (PAF) are important inflammatory mediators that are overexpressed in ovarian cancer. The receptor is an important player in ovarian cancer development. In this study, we aimed to evaluate the prognostic value of PAFR in epithelial ovarian cancer (EOC) and the potential use of its antagonist, rupatadine, as an experimental treatment. Tissue microarrays of ovarian cancer patients, most markedly those with a non-mucinous subtype, immunohistochemically overexpressed PAFR. Elevated cytoplasmic PAFR expression was found to significantly and independently impair patients' overall and recurrence-free survival (OS: median 83.48 vs. 155.03 months; p = 0.022; RFS: median 164.46 vs. 78.03 months; p = 0.015). In vitro, the serous ovarian cancer subtypes especially displayed an elevated PAFR gene and protein expression. siRNA knockdown of PAFR decreased cell proliferation significantly, thus confirming the receptor's protumorigenic effect on ovarian cancer cells. The clinically approved PAFR antagonist rupatadine effectively inhibited in vitro cell proliferation and migration of ovarian cancer cells. PAFR is a prognostic marker in ovarian cancer patients and its inhibition through rupatadine may have important therapeutic implications in the therapy of ovarian cancer patients.

Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes

Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non-malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi-step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut-point selection methods with great computational ease. The performance of proposed measures and cut-point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut-points were estimated for the risk of ovarian malignancy algorithm score.

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation

When amino acids vary during evolution, the outcome can be functionally neutral or biologically-important. We previously found that substituting a subset of nonconserved positions, "rheostat" positions, can have surprising effects on protein function. Since changes at rheostat positions can facilitate functional evolution or cause disease, more examples are needed to understand their unique biophysical characteristics. Here, we explored whether "phylogenetic" patterns of change in multiple sequence alignments (such as positions with subfamily specific conservation) predict the locations of functional rheostat positions. To that end, we experimentally tested eight phylogenetic positions in human liver pyruvate kinase (hLPYK), using 10-15 substitutions per position and biochemical assays that yielded five functional parameters. Five positions were strongly rheostatic and three were non-neutral. To test the corollary that positions with low phylogenetic scores were not rheostat positions, we combined these phylogenetic positions with previously-identified hLPYK rheostat, "toggle" (most substitution abolished function), and "neutral" (all substitutions were like wild-type) positions. Despite representing 428 variants, this set of 33 positions was poorly statistically powered. Thus, we turned to the in vivo phenotypic dataset for E. coli lactose repressor protein (LacI), which comprised 12-13 substitutions at 329 positions and could be used to identify rheostat, toggle, and neutral positions. Combined hLPYK and LacI results show that positions with strong phylogenetic patterns of change are more likely to exhibit rheostat substitution outcomes than neutral or toggle outcomes. Furthermore, phylogenetic patterns were more successful at identifying rheostat positions than were co-evolutionary or eigenvector centrality measures of evolutionary change.

Differentiating Between Low- and High-grade Glioma Tumors Measuring Apparent Diffusion Coefficient Values in Various Regions of the Brain

Objectives

Our study aimed to apply the apparent diffusion coefficient (ADC) values to quantify the differences between low- and high-grade glioma tumors.

Methods

We conducted a multicenter, retrospective study between September to December 2019. Magnetic resonance imaging (MRI) diffusion-weighted images (DWIs), and the pathologic findings of 56 patients with glioma tumors (low grade = 28 and high grade = 28) were assessed to measure the ADC values in the tumor center, tumor edema, boundary area between tumor with normal tissue, and inside the healthy hemisphere. These values were compared between the two groups, and cut-off values were calculated using the receiver operating characteristic curve.

Results

We saw significant differences between the mean ADC values measured in the tumor center and edema between high- and low-grade tumors (p< 0.005). The ADC values in the boundary area between tumors with normal tissue and inside healthy hemisphere did not significantly differ in the groups. The ADC values at tumor center and edema were higher than 1.12 × 10^-3 mm²/s (sensitivity = 100% and specificity = 96.0%) and 1.15 × 10^-3 mm²/s (sensitivity = 75.0% and specificity = 64.0%), respectively, could be classified as low-grade tumors.

Conclusions

The ADC values from the MRI DWIs in the tumor center and edema could be used as an appropriate method for investigating the differences between low- and high-grade glioma tumors. The ADC values in the boundary area and healthy tissues had no diagnostic values in grading the glioma tumors.

Combining multiple biomarkers to linearly maximize the diagnostic accuracy under ordered multi-class setting

Either in clinical study or biomedical research, it is a common practice to combine multiple biomarkers to improve the overall diagnostic performance. Despite the fact there exist a large number of statistical methods for biomarker combination under binary classification, research on this topic under multi-class setting is sparse. The overall diagnostic accuracy, i.e. the sum of correct classification rates, directly measures the classification accuracy of the combined biomarkers. Hence the overall accuracy can serve as an important objective function for biomarker combination, especially when the combined biomarkers are used for the purpose of making medical diagnosis. In this paper, we address the problem of combining multiple biomarkers to directly maximize the overall diagnostic accuracy by presenting several grid search methods and derivation-based methods. A comprehensive simulation study was conducted to compare the performances of these methods. An ovarian cancer data set is analyzed in the end.

Nuclear Enolase-1/ MBP-1 expression and its association with the Wnt signaling in epithelial ovarian cancer

BACKGROUND

Enolase-1, primarily known for its role in glucose metabolism, is overexpressed in various cancer entities. In contrast its alternative spliced nuclear isoform MBP-1 acts as a tumor suppressor. The aim of this study is to analyze the prognostic impact of Enolase-1/ MBP-1 and its functional significance in epithelial ovarian cancer (EOC).

METHODS

By immunohistochemistry, Enolase-1 staining was examined in 156 EOC samples. Evaluation of Enolase-1 staining was conducted in the nucleus and the cytoplasm using the semi-quantitative immunoreactive score. Expression levels were correlated with clinical and pathological parameters as well as with overall survival to assess for prognostic impact.

RESULTS

Cytoplasmic and nuclear Enolase-1 expression did not show a significant difference between the histological subtypes (p = 0.1). High nuclear Enolase-1/ MBP-1 staining negativly correlated with the tumor grading (p<0.001; Cc= -0.318). Cytoplasmic Enolase-1 did not correlate with clinicopathological data. Higher nuclear Enolase-1/ MBP-1 staining was detected in low-grade serous cancer cases compared to high-grade ones (median IRS 3 (range 0-8) vs. median IRS 2 (range 0-4), p<0.001). Nuclear Enolase-1/ MBP-1 expression correlated with the Wnt signaling markers membranous beta-catenin (p = 0.007; Cc=0.235), serine residue 9-phosphorylated glycogen synthase kinase 3 beta (p<0.001; Cc=0.341) and snail/slug (p = 0.004; Cc= -0.257). High nuclear Enolase-1/ MBP-1 expression was associated with improved overall survival (88.6 vs. 33.1 months, median; p = 0.013).

CONCLUSION

Additional knowledge of Enolase-1/ MBP-1 as a biomarker and its interactions within the Wnt signaling pathway and epithelial-mesenchymal transition potentially improve the prognosis of therapeutic approaches in EOC.

Statistical Methodology for Multiclass Classifications: Applications to Dementia

Alzheimer's disease (AD) is a common, devastating disease which carries a heavy economic burden. Accelerated efforts to identify presymptomatic stages of AD and biomarkers to classify the disease are urgent needs. Currently, no biomarkers can perfectly discriminate individuals into multiple disease categories of AD (no cognitive impairment, mild cognitive impairment, and dementia). Although many biomarkers for diagnosis and their various features are being studied, we lack advanced statistical methods which can fully utilize biomarkers to classify AD accurately, thereby facilitating evaluation of putative markers both alone and in combination. In this paper, we propose two approaches: 1) a forward addition procedure in which we adapt an additive logistic regression model to the setting for disease with ordered multiple categories. Using this approach, we select and combine multiple cross-sectional biomarkers to improve diagnostic accuracy, and 2) a method by extending the Neyman-Pearson Lemma to the ordered three disease categories to construct optimal cutoff points to distinguish multiple disease categories. We evaluate the robustness of the proposed model using a simulation study. Then we apply these two methods to data from the Religious Orders Study to examine the feasibility of combining biomarkers, and compare the diagnostic accuracy between the proposed methods and existing methods including model-based methods (ordinal logistic regression and quadratic discriminant analysis), a tree-based method CART, and the Youden index method. The two proposed methods facilitate evaluations of biomarkers for conditions with graded, rather than binary, classifications. The evaluation of the performance of different approaches provides guidance of how to choose approaches to address research questions.

Cytoplasmic VDR expression as an independent risk factor for ovarian cancer

The vitamin D receptor (VDR), primarily known as a crucial mediator of calcium homeostasis and metabolism, has been shown to play a significant role in various cancer entities. Previous studies have focused on vitamin D and its receptor in gynecological cancers, noting that the receptor is upregulated in epithelial ovarian cancer (EOC). The aim of this study is to analyze the prognostic impact of VDR and its functional significance in ovarian cancer. Through immunohistochemistry, VDR staining was examined in 156 ovarian cancer samples. Evaluation of VDR staining was conducted in the nucleus and the cytoplasm using the semi-quantitative immunoreactive score, and the scores were classified into high- and low-level expressions. Expression levels were correlated with clinical and pathological parameters as well as with overall survival to assess for prognostic impact. Differences in cytoplasmic VDR expression were identified between the histological subtypes (p = 0.001). Serous, clear cell, and endometrioid subtypes showed the highest staining, while the mucinous subtype showed the lowest. Cytoplasmic VDR correlated with higher FIGO stage (p = 0.013; Cc = 0.203), positive lymph node status (p = 0.023; Cc = 0.236), high-grade serous histology (p = 0.000; Cc = 0.298) and grading from the distinct histological subtypes (p = 0.006; Cc = − 0.225). Nuclear VDR did not correlate with clinicopathological data. High cytoplasmic expression of VDR was associated with impaired overall survival (HR 2.218, 32.5 months vs. median not reached; p < 0.001) and was confirmed as a statistically independent prognostic factor in the Cox regression multivariate analysis. Additional knowledge of VDR as a biomarker and its interactions within the mitogen-activated protein kinase (MAPK) signaling pathway could potentially improve the prognosis of therapeutic approaches for specific subgroups in EOC.

Methods for Improving Screening for Vascular Cognitive Impairment Using the Montreal Cognitive Assessment

BACKGROUND

Vascular cognitive impairment (VCI) post-stroke is frequent but may go undetected, which highlights the need to better screen cognitive functioning following a stroke.

AIM

We examined the clinical utility of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairment against a gold-standard neuropsychological battery.

METHODS

We assessed cognitive status with a comprehensive battery of neuropsychological tests in 161 individuals who were at least 3-months post-stroke. We used receiver operating characteristic (ROC) curves to identify two cut points for the MoCA to maximize sensitivity and specificity at a minimum 90% threshold. We examined the utility of the Symbol Digit Modalities Test, a processing speed measure, to determine whether this additional metric would improve classification relative to the MoCA total score alone.

RESULTS

Using two cut points, 27% of participants scored ≤ 23 and were classified as high probability of cognitive impairment (sensitivity 92%), and 24% of participants scored ≥ 28 and were classified as low probability of cognitive impairment (specificity 91%). The remaining 48% of participants scored from 24 to 27 and were classified as indeterminate probability of cognitive impairment. The addition of a processing speed measure improved classification for the indeterminate group by correctly identifying 65% of these individuals, for an overall classification accuracy of 79%.

CONCLUSIONS

The utility of the MoCA in detecting cognitive impairment post-stroke is improved when using a three-category approach. The addition of a processing speed measure provides a practical and efficient method to increase confidence in the determined outcome while minimally extending the screening routine for VCI.

M2 Macrophages Infiltrating Epithelial Ovarian Cancer Express MDR1: A Feature That May Account for the Poor Prognosis

Multi drug resistance protein 1 (MDR1) expression on tumor cells has been widely investigated in context of drug resistance. However, the role of MDR1 on the immune cell infiltrate of solid tumors remains unknown. The aim of this study was to analyze the prognostic significance of a MDR1+ immune cell infiltrate in epithelial ovarian cancer (EOC) and to identify the MDR1+ leucocyte subpopulation. MDR1 expression was analyzed by immunohistochemistry in 156 EOC samples. In addition to MDR1+ cancer cells, we detected a MDR1+ leucocyte infiltrate (high infiltrate >4 leucocytes per field of view). Correlations and survival analyses were calculated. To identify immune cell subpopulations immunofluorescence double staining was performed. The MDR1+ leucocyte infiltrate was associated with human epidermal growth factor receptor 2 (HER2) (cc = 0.258, p = 0.005) and tumor-associated mucin 1 (TA-MUC1) (cc = 0.202, p = 0.022) expression on cancer cells. A high MDR1+ leucocyte infiltrate was associated with impaired survival, especially in patients whose carcinoma showed either serous histology (median OS 28.80 vs. 50.64 months, p = 0.027, n = 91) or TA-MUC1 expression (median OS 30.60 vs. 63.36 months, p = 0.015, n = 110). Similar findings for PFS suggest an influence of MDR1+ immune cells on the development of chemoresistance. A Cox regression analysis confirmed the independency of a high MDR1+ leucocyte infiltrate as prognostic factor. M2 macrophages were identified as main part of the MDR1+ leucocyte infiltrate expressing MDR1 as well as the M2 marker CD163 and the pan-macrophage marker CD68. Infiltration of MDR1+ leucocytes, mostly M2 macrophages, is associated with poor prognosis of EOC patients. Further understanding of the interaction of M2 macrophages, MDR1 and TA-MUC1 appears to be a key aspect to overcome chemoresistance in ovarian cancer.

Usefulness of the 15-item geriatric depression scale (GDS-15) for classifying minor and major depressive disorders among community-dwelling elders

BACKGROUND

The 15-item geriatric depression scale (GDS-15) is a short form of GDS and is used to screen, diagnose, and evaluate depression in elderly individuals. Most previous studies evaluated the ability of GDS-15 to discriminate between depressive and non-depressive states. In this study, we investigated the multi-stage discriminating ability of GDS-15.

METHODS

A total of 774 participants, over 65 years of age were included (normal, n = 650; minor depressive disorder [MnDD], n = 94; major depressive disorder [MDD], n = 30). Multi-category receiver operating characteristic (ROC) surfaces were evaluated to identify three stages of geriatric depression. The optimal cutoff points were selected based on the volume under the ROC surface (VUS) and the Youden index.

RESULTS

In the results of multi-category classification analyses, VUS of the GDS-15 of 0.61 was obtained, and optimal cutoff points of the GDS-15 for multiple stages of depression of 4 (between normal and MnDD) and 11 (between MnDD and MDD) were derived. The Youden index for the GDS-15 was 0.49, and the derived optimal cutoff points were 5 and 10, for the multiple stages, respectively. The overall diagnostic accuracy based on the Youden index was superior to that based on the VUS in the GDS-15.

LIMITATIONS

The participants' cognitive function has potential to affect the GDS-15 score; nevertheless, the study included those with mild cognitive impairment.

CONCLUSIONS

GDS-15 was a useful tool to classify stages of geriatric depression into either minor or major depressive disorder.

Cumulative ROC curves for discriminating three or more ordinal outcomes with cutpoints on a shared continuous measurement scale

Cumulative receiver operator characteristic (ROC) curve analysis extends classic ROC curve analysis to discriminate three or more ordinal outcome levels on a shared continuous scale. The procedure combines cumulative logit regression with a cumulative extension to the ROC curve and performs as expected with ternary (three-level) ordinal outcomes under a variety of simulated conditions (unbalanced data, proportional and non-proportional odds, areas under the ROC curve [AUCs] from 0.70 to 0.95). Simulations also compared several criteria for selecting cutpoints to discriminate outcome levels: the Youden Index, Matthews Correlation Coefficient, Total Accuracy, and Markedness. Total Accuracy demonstrated the least absolute percent-bias. Cutpoints computed from maximum likelihood regression parameters demonstrated bias that was often negligible. The procedure was also applied to publicly available data related to computer imaging and biomarker exposure science, yielding good to excellent AUCs, as well as cutpoints with sensitivities and specificities of commensurate quality. Implementation of cumulative ROC curve analysis and extension to more than three outcome levels are straightforward. The author's programs for ternary ordinal outcomes are publicly available.

Correlation of NRF2 and progesterone receptor and its effects on ovarian cancer biology

Purpose

This study aimed to investigate the potential prognostic impact of nuclear factor erythroid 2-related factor 2 (NRF2) and progesterone receptor A (PRA)/progesterone receptor B (PRB) in ovarian cancer patients which might be the rationale for putative new treatment strategies.

Patients and methods

The presence of NRF2 and PRA/PRB was investigated in 156 ovarian cancer samples using immunohistochemistry (IHC). Staining of NRF2 and PRA/PRB was rated using the semi-quantitative immunoreactive score (IR score, Remmele’s score) and correlated to clinical and pathological data. NRF2 and PRA/PRB expression were compared with respect to the overall survival (OS).

Results

NRF2 staining was different in both, the cytoplasm and nucleus between the histological subtypes (p=0.001 and p=0.02, respectively). There was a significant difference in the PRA expression comparing all histological subtypes (p=0.02). Histological subtypes showed no significant differences in the PRB expression. A strong correlation of cytoplasmic NRF2 and PRA expression was detected (cc=0.247, p=0.003) as well as of cytoplasmic NRF2 and PRB expression (cc=0.25, p=0.003), confirmed by immunofluorescence double staining. Cytoplasmic NRF2 expression was associated with a longer OS (median 50.6 vs 32.5 months; p=0.1) as it was seen for PRA expression (median 63.4 vs 33.1 months; p=0.08), although not statistically significant. In addition, high PRB expression (median 80.4 vs 32.5 months; p=0.04) and concurrent expression of cytoplasmic NRF2 and PRA were associated with a significantly longer OS (median 109.7 vs 30.6 months; p=0.02). The same relationship was also noted for NRF2 and PRB with improved OS for patients expressing both cytoplasmic NRF2 and PRB (median 153.5 vs 30.6 months; p=0.009). Silencing of NFE2L2 induced higher mRNA expression of PGR in the cancer cell line OVCAR3 (p>0.05) confirming genetic interactions of NRF2 and PR.

Conclusion

In this study, the combination of cytoplasmic NRF2 and high PRA/PRB expression was demonstrated to be associated with improved overall survival in ovarian cancer patients. Further understanding of interactions within the NRF2/AKR1C1/PR pathway could open new additional therapeutic approaches.

Correlation of the Aryl Hydrocarbon Receptor with FSHR in Ovarian Cancer Patients

Expression of the aryl hydrocarbon receptor (AhR) has been described in various tumor entities from different organs. However, its role in ovarian cancer has not been thoroughly investigated. We aimed to elucidate the prognostic impact of AhR, its correlation with the follicle-stimulating hormone receptor (FSHR), and their functional role in ovarian cancer. By immunohistochemistry, AhR staining was analyzed in a subset of 156 samples of ovarian cancer patients. AhR staining was assessed in the nucleus and the cytoplasm using the semi-quantitative immunoreactive score (IRS), and the scores were grouped into high- and low-level expression. AhR expression was detected in all histological subtypes, with clear cell ovarian cancer displaying the highest staining intensity. Low cytoplasmic expression of AhR was associated with longer overall survival (median 183.46 vs. 85.07 months; p = 0.021). We found a positive correlation between AhR and FSHR (p = 0.005). Ovarian cancer patients with high cytoplasmic AhR and concurrent FSHR expression had the worst outcome (median 69.72 vs. 43.32 months; p = 0.043). Consequently, low cytoplasmic AhR expression seems to be associated with improved survival in ovarian cancer patients. Our data suggest that AhR and FSHR levels correlate with each other, and their concurrent expression was observed in ovarian cancer patients with the worst outcome. Further investigation of the interaction of both receptors and their functional role might better predict the impact of endocrine therapy in ovarian cancer.

The Youden Index in the Generalized Receiver Operating Characteristic Curve Context

The receiver operating characteristic (ROC) curve and their associated summary indices, such as the Youden index, are statistical tools commonly used to analyze the discrimination ability of a (bio)marker to distinguish between two populations. This paper presents the concept of Youden index in the context of the generalized ROC (gROC) curve for non-monotone relationships. The interval estimation of the Youden index and the associated cutoff points in a parametric (binormal) and a non-parametric setting is considered. Monte Carlo simulations and a real-world application illustrate the proposed methodology.

The Uropathogenic Escherichia coli Subclone Sequence Type 131-H30 Is Responsible for Most Antibiotic Prescription Errors at an Urgent Care Clinic

BACKGROUND

The pandemic spread of antibiotic resistance increases the likelihood of ineffective empirical therapy. The recently emerged fluoroquinolone-resistant Escherichia coli sequence type (ST) 131-H30R subclone (H30) is a leading cause of multidrug-resistant urinary tract infection (UTI) and bloodstream infection worldwide.

METHODS

We studied the relative impact of H30 on the likelihood that bacteria isolated from urine of urgent care patients would be resistant to the empirically prescribed antibiotic regimen for UTI.

RESULTS

Of 750 urinalysis-positive urine samples from urgent care patients with suspected UTI, 306 (41%) yielded E. coli, from 35 different clonal groups (clonotypes). H30 predominated (14% prevalence overall), especially among older patients (age ≥70 years: 26%) and those with diabetes (43%) or urinary catheterization (60%). Resistance to the empirically selected antibiotic regimen occurred in 16% (40/246) of patients overall, 28% (20/71) of older patients, 30% (8/27) of patients with diabetes, 60% (3/5) of catheterized patients, and 71% (22/30) of those with H30. H30's contribution to such mismatched antibiotic selection was 55% overall, 70% among older patients, and 100% among patients with diabetes or a urinary catheter. Among patients with ≥2 of these factors (older age, diabetes, or urinary catheter), 24% of all urinalysis-positive urine samples yielded H30, with a 92% likelihood of resistance to the selected empirical therapy.

CONCLUSIONS

The multidrug-resistant H30 subclone of E. coli ST131 is responsible for the great majority of mismatched empirical antibiotic prescriptions for suspected UTI at an urgent care clinic among patients ≥70 years old or with diabetes or urinary catheterization.

Rapid and Extensive Expansion in the United States of a New Multidrug-resistant Escherichia coli Clonal Group, Sequence Type 1193

We describe the rapid and ongoing emergence across multiple US cities of a new multidrug-resistant Escherichia coli clone-sequence type (ST) 1193-resistant to fluoroquinolones (100%), trimethoprim-sulfamethoxazole (55%), and tetracycline (53%). ST1193 is associated with younger adults (age <40 years) and currently comprises a quarter of fluoroquinolone-resistant clinical E. coli urine isolates.

Interaction of ERα and NRF2 Impacts Survival in Ovarian Cancer Patients

Nuclear factor erythroid 2-related factor 2 (NRF2) regulates cytoprotective antioxidant processes. In this study, the prognostic potential of NRF2 and its interactions with the estrogen receptor α (ERα) in ovarian cancer cells was investigated. NRF2 and ERα protein expression in ovarian cancer tissue was analyzed as well as mRNA expression of NRF2 (NFE2L2) and ERα (ESR1) in four ovarian cancer and one benign cell line. NFE2L2 silencing was carried out to evaluate a potential interplay between NRF2 and ERα. Cytoplasmic NRF2 expression as inactive form had significantly higher expression in patients with low-grade histology (p = 0.03). In the serous cancer subtype, high cytoplasmic NRF2 expression (overall survival (OS), median 50.6 vs. 29.3 months; p = 0.04) and high ERα expression (OS, median 74.5 vs. 27.1 months; p = 0.002) was associated with longer overall survival as well as combined expression of both inactive cytoplasmic NRF2 and ERα in the whole cohort (median 74.5 vs. 37.7 months; p = 0.04). Cytoplasmic NRF2 expression showed a positive correlation with ERα expression (p = 0.004). NFE2L2 was found to be highly expressed in the ovarian cancer cell lines OVCAR3, UWB1.289, and TOV112D. Compared with the benign cell line HOSEpiC, ESR1 expression was reduced in all ovary cancer cell lines (all p < 0.001). Silencing of NFE2L2 induced a higher mRNA expression of ESR1 in the NFE2L2 downregulated cancer cell lines OVCAR3 (p = 0.003) and ES2 (p < 0.001), confirming genetic interactions of NRF2 and ERα. In this study, both inactive cytoplasmic NRF2 and high ERα expression were demonstrated to be associated with improved survival in ovarian cancer patients. Further understanding of interactions within the estradiol⁻ERα⁻NRF2 pathway could better predict the impact of endocrine therapy in ovarian cancer.

A Smooth Nonparametric Approach to Determining Cut-Points of A Continuous Scale

The problem of determining cut-points of a continuous scale according to an establish categorical scale is often encountered in practice for the purposes such as making diagnosis or treatment recommendation, determining study eligibility, or facilitating interpretations. A general analytic framework was recently proposed for assessing optimal cut-points defined based on some pre-specified criteria. However, the implementation of the existing nonparametric estimators under this framework and the associated inferences can be computationally intensive when more than a few cut-points need to be determined. To address this important issue, a smoothing-based modification of the current method is proposed and is found to substantially improve the computational speed as well as the asymptotic convergence rate. Moreover, a plug-in type variance estimation procedure is developed to further facilitate the computation. Extensive simulation studies confirm the theoretical results and demonstrate the computational benefits of the proposed method. The practical utility of the new approach is illustrated by an application to a mental health study.

Public Awareness of Early and Late Complications of Type 2 Diabetes - Application of Latent Profile Analysis in Determining Questionnaire Cut-Off Points

Objectives

A questionnaire was designed to determine public understanding of early and late complications of Type 2 diabetes mellitus (T2DM).

Methods

A cross-sectional study was performed in participants who were selected using a multi-stage sampling method and a standard questionnaire of 67 questions was proposed. An expert panel selected 53 closed-ended questions for content validity to be included in the questionnaire. The reliability of the questionnaire was tested using Cronbach’s alpha coefficient giving a score of 0.84.

Results

Of the 825 participants, 443 (57.6%) were male, and 322 (41.87%) were 40 years or more. The proportion of low-, moderate- and high- awareness about T2DM and its complications was 29.26%, 62.68%, and 8.06%, respectively. Friends (56.31%) and internet and social networks (20.55%) were the 2 major sources of awareness, respectively. The medical staff (e.g., physicians) had the lowest share in the level of public awareness (3.64%) compared to other sources.

Conclusion

These results present data that shows the general population awareness of T2DM is low. Healthcare policymakers need to be effective at raising awarenes of diabetes and it should be through improved education.

The Clinical Relevance of Methods for Handling Inconclusive Medical Test Results: Quantification of Uncertainty in Medical Decision-Making and Screening

BACKGROUND

although the existence of inconclusive medical test results or bio-markers is widely recognized, there are indications that this inherent diagnostic uncertainty is sometimes ignored. This paper discusses three methods for defining and determining inconclusive medical test results, which use different definitions and differ in clinical relevance.

METHODS

the TG-ROC (two graphs receiver operating characteristics) method is the easiest to use, while the grey zone method and the uncertain interval method require more extensive calculations.

RESULTS

this paper discusses the technical details of the methods, as well as advantages and disadvantages for their clinical use. TG-ROC and the grey zone method can help in the acquisition of high rates of diagnostic certainty, but can exclude large groups. The uncertain interval method can prevent decisions that are the most uncertain, invalid and unreliable, while excluding smaller groups.

CONCLUSIONS

the identification of uncertain test scores is relevant, because these scores indicate the need to obtain better information or to await further developments. The methods presented help to determine inconclusive test scores and can help to reduce erroneous decisions. However, further research and development is desirable.

Measuring diagnostic accuracy for biomarkers under tree-ordering

In the field of diagnostic studies for tree or umbrella ordering, under which the marker measurement for one class is lower or higher than those for the rest unordered classes, there exist a few diagnostic measures such as the naive AUC ( NAUC), the umbrella volume ( UV), and the recently proposed TAUC, i.e. area under a ROC curve for tree or umbrella ordering (TROC). However, an important characteristic about tree or umbrella ordering has been neglected. This paper mainly focuses on promoting the use of the integrated false negative rate under tree ordering ( ITFNR) as an additional diagnostic measure besides TAUC, and proposing the idea of using ( TAUC, ITFNR) instead of TAUC to evaluate the diagnostic accuracy of a biomarker under tree or umbrella ordering. Parametric and non-parametric approaches for constructing joint confidence region of ( TAUC, ITFNR) are proposed. Simulation studies under a variety of settings are carried out to assess and compare the performance of these methods. In the end, a published microarray data set is analyzed.

Validation of the trauma mortality prediction scores from a Malaysian population

Background

Well-known trauma mortality prediction scores such as New Injury Severity Score (NISS), Revised Trauma Score (RTS), and Trauma and Injury Severity Score (TRISS) have been externally validated from high-income countries with established trauma databases. However, these scores were never used in Malaysian population. In this current study, we attempted to validate these scoring systems using our regional trauma surgery database.

Methods

A retrospective analysis of the regional Malaysian Trauma Surgery Database was performed over a period of 3 years from May 2011 to April 2014. NISS, RTS, Major Trauma Outcome Study (MTOS)-TRISS, and National Trauma Database (NTrD)-TRISS scores were recorded and calculated. Individual scoring system’s performance in predicting trauma mortality was compared by calculating the area under the receiver operating characteristic (AUC) curve. Youden index and associated optimal cutoff values for each scoring system was calculated to predict mortality. The corresponding positive predictive value, negative predictive value, and accuracy of the cutoff values were calculated.

Results

A total of 2208 trauma patients (2004 blunt and 204 penetrating injuries) with mean age of 36 (SD = 16) years were included. There were 239 deaths with a corresponding mortality rate of 10.8%. The AUC calculated for the NISS, RTS, MTOS-TRISS, and NTrD-TRISS were 0.878, 0.802, 0.812, and 0.848, respectively. The NISS score with a cutoff value of 24, sensitivity of 86.6% and specificity of 74.3%, outperformed the rest (p < 0.001). Mortality was predicted by NISS with an overall accuracy of 75.6%; its positive predictive value was at 29.02% and negative predictive value at 97.86%.

Conclusion

Amongst the four scores, the NISS score is the best trauma mortality prediction model suited for a local Malaysian trauma population. Further validation with multicentre data in the country may require to ascertain the finding.

Bayesian nonparametric inference for the three-class Youden index and its associated optimal cutoff points

The three-class Youden index serves both as a measure of medical test accuracy and a criterion to choose the optimal pair of cutoff values for classifying subjects into three ordinal disease categories (e.g. no disease, mild disease, advanced disease). We present a Bayesian nonparametric approach for estimating the three-class Youden index and its corresponding optimal cutoff values based on Dirichlet process mixtures, which are robust models that can handle intricate features of distributions for complex data. Results from a simulation study are presented and an application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is detailed.

Confidence intervals for differences between volumes under receiver operating characteristic surfaces (VUS) and generalized Youden indices (GYIs)

This article explores both existing and new methods for the construction of confidence intervals for differences of indices of diagnostic accuracy of competing pairs of biomarkers in three-class classification problems and fills the methodological gaps for both parametric and non-parametric approaches in the receiver operating characteristic surface framework. The most widely used such indices are the volume under the receiver operating characteristic surface and the generalized Youden index. We describe implementation of all methods and offer insight regarding the appropriateness of their use through a large simulation study with different distributional and sample size scenarios. Methods are illustrated using data from the Alzheimer's Disease Neuroimaging Initiative study, where assessment of cognitive function naturally results in a three-class classification setting.

Prostaglandin receptor EP3 regulates cell proliferation and migration with impact on survival of endometrial cancer patients

Background

Prostaglandin E2 (PGE2) receptor 3 (EP3) regulates tumor cell proliferation, migration, and invasion in numerous cancers. The role of EP3 as a prognostic biomarker in endometrial cancer remains unclear. The primary aim of this study was to analyze the prognostic significance of EP3 expression in endometrial cancer.

Methods

We analyzed the EP3 expression of 140 endometrial carcinoma patients by immunohistochemistry. RL95-2 endometrial cancer cell line was chosen from four endometrial cancer cell lines (RL95-2, Ishikawa, HEC-1-A, and HEC-1-B) according to EP3 expression level. Treated with PGE2 and EP3 antagonist, RL95-2 cells were investigated by MTT, BrdU, and wound healing assay for functional assessment of EP3.

Results

EP3 staining differed significantly according to WHO tumor grading in both whole cohort (p = 0.01) and the subgroup of endometrioid carcinoma (p = 0.01). Patients with high EP3 expression in their respective tumors had impaired progression-free survival as well as overall survival in both cohorts above. EP3 expression in the overall cohort was identified as an independent prognostic marker for progression-free survival (HR 1.014, 95%CI 1.003-1.024, p = 0.01) when adjusted for age, stage, grading, and recurrence. Treatment with EP3 antagonists induced upregulation of estrogen receptor β and decreased activity of Ras and led to attenuated proliferation and migration of RL95-2 cells.

Conclusions

EP3 seems to play a crucial role in endometrial cancer progression. In the context of limited systemic treatment options for endometrial cancer, this explorative analysis identifies EP3 as a potential target for diagnostic workup and therapy.

Bronchiolitis Score of Sant Joan de Déu: BROSJOD Score, validation and usefulness

OBJECTIVE

To validate the bronchiolitis score of Sant Joan de Déu (BROSJOD) and to examine the previously defined scoring cutoff.

PATIENTS AND METHODS

Prospective, observational study. BROSJOD scoring was done by two independent physicians (at admission, 24 and 48 hr). Internal consistency of the score was assessed using Cronbach's α. To determine inter-rater reliability, the concordance correlation coefficient estimated as an intraclass correlation coefficient (CCC) and limits of agreement estimated as the 90% total deviation index (TDI) were estimated. An expert opinion was used to classify patients according to clinical severity. A validity analysis was conducted comparing the 3-level classification score to that expert opinion. Volume under the surface (VUS), predictive values, and probability of correct classification (PCC) were measured to assess discriminant validity.

RESULTS

About 112 patients were recruited, 62 of them (55.4%) males. Median age: 52.5 days (IQR: 32.75-115.25). The admission Cronbach's α was 0.77 (CI95%: 0.71; 0.82) and at 24 hr it was 0.65 (CI95%: 0.48; 0.7). The inter-rater reliability analysis was: CCC at admission 0.96 (95%CI 0.94-0.97), at 24 h 0.77 (95%CI 0.65-0.86), and at 48 hr 0.94 (95%CI 0.94-0.97); TDI 90%: 1.6, 2.9, and 1.57, respectively. The discriminant validity at admission: VUS of 0.8 (95%CI 0.70-0.90), at 24 h 0.92 (95%CI 0.85-0.99), and at 48 hr 0.93 (95%CI 0.87-0.99). The predictive values and PCC values were within 38-100% depending on the level of clinical severity.

CONCLUSION

There is a high inter-rater reliability, showing the BROSJOD score to be reliable and valid, even when different observers apply it. Pediatr Pulmonol. 2017;52:533-539. © 2016 Wiley Periodicals, Inc.

Inverse probability weighting estimation of the volume under the ROC surface in the presence of verification bias

In diagnostic medicine, the volume under the receiver operating characteristic (ROC) surface (VUS) is a commonly used index to quantify the ability of a continuous diagnostic test to discriminate between three disease states. In practice, verification of the true disease status may be performed only for a subset of subjects under study since the verification procedure is invasive, risky, or expensive. The selection for disease examination might depend on the results of the diagnostic test and other clinical characteristics of the patients, which in turn can cause bias in estimates of the VUS. This bias is referred to as verification bias. Existing verification bias correction in three-way ROC analysis focuses on ordinal tests. We propose verification bias-correction methods to construct ROC surface and estimate the VUS for a continuous diagnostic test, based on inverse probability weighting. By applying U-statistics theory, we develop asymptotic properties for the estimator. A Jackknife estimator of variance is also derived. Extensive simulation studies are performed to evaluate the performance of the new estimators in terms of bias correction and variance. The proposed methods are used to assess the ability of a biomarker to accurately identify stages of Alzheimer's disease.