Support value based stent-graft marker detection

Receiver Operating Characteristic (ROC) curves can be used to describe detectability of noise-limited signals. The basic concepts underlying the theory of ROC curves and the experimental determination of ROC curves are discussed. ROC curves are used to describe the detectability of the image of 2 mm Lucite beads (similar to certain kinds of small gall-stones) in a noisy background of radiographic mottle. Results are shown for four typical radiographic screen-film combinations of differing optical and noise properties. The qualitative relationship between the ROC curves and the mathematical model describing radiographic mottle is examined. The ROC curves describing the detectability of the bad images when radiographed with two screen-film combinations of equal system speed but different optical characteristics are compared to the quality of blood vessel images obtained with the same screen-film combinations. It is concluded that it is important to distinguish between the assessment of image quality based on the relative sharpness of high contrast images, versus the assessment of image quality based on the relative detectability of low contrast noise-limited images.

BackgroundExtended-Spectrum β-Lactamase-producing Enterobacterales (ESBL-E) infections are associated with high morbidity and mortality, often due to delayed initiation of carbapenem therapy. Carbapenems must be used judiciously to prevent emergence of resistant organisms. We developed a machine learning algorithm (MLA) to identify patients at increased risk for ESBL-E infections in the emergency department (ED).Figure 1:Receiver Operating Characteristic (ROC) curve for predicting Extended-Spectrum β-Lactamase-producing Enterobacterales (ESBL-E)Receiver Operating Characteristic (ROC) curve for predicting Extended-Spectrum β-Lactamase-producing Enterobacterales (ESBL-E) infections in Emergency Department Patients using a gradient boosting machine learning model. The area under the curve (AUC) is 0.8, indicating good discrimination between patients with and without ESBL-E. Higher True Positive Rate (TPR) at a low False Positive Rate (FPR) signifies better model performance.MethodsAdults (≥18) who visited an ED in the Johns Hopkins Health System between 1/2019 and 4/2024 and received Gram-negative therapies appropriate for sepsis or had documented ESBL-E infections were included. Timestamped ED vital signs, healthcare utilization, healthcare worker (HCW)-mediated patient connections, medication history, comorbidities and laboratory results populated prior to first antibiotic order were used as predictors; our primary outcome was ESBL-E infection defined by positive culture. The probability of ESBL-E infection was estimated using a gradient boosting machine learning algorithm (MLA). Because of the time-dependent nature of increasing ESBL-E infections, the MLA was derived using data from encounters prior to 7/1/2023 and evaluated in the remaining encounters using area under the operating characteristics curve (AUC) analysis.Table 1:Patient Cohort DemographicsResultsIn total there were 129,773 patient encounters included, of which 4289 (3.3%) had a positive ESBL-E sample collected during their ED encounter. Out-of-sample predictive accuracy was high (AUC 0.80; Figure). Prior HCW-mediated connections to ESBL-positive patients and prior exposure (≤6 months) to carbapenems, cephalosporins and proton pump inhibitors were the strongest predictors of ESBL-E infection. Abnormal vital signs, increased age, and prior exposure to any antibiotic (prior 6 months) were also important factors in prediction accuracy.ConclusionAn MLA using data readily available from the EHR at the time of antibiotic ordering provided good discrimination between ED patients with and without ESBL-E infection. Further refinement and implementation of MLA-driven decision-support in the EDs could drive more targeted use of broad-spectrum antibiotics and improved patient outcomes.DisclosuresJeremiah S. Hinson, MD, PhD, Beckman Coulter: Advisor/Consultant|Beckman Coulter: Grant/Research Support|Beckman Coulter: Stocks/Bonds (Private Company)

To assess examiner reliability when scoring dental fluorosis in Malaysian children using clinical (Dean's Index) and photographic methods. The upper central incisors of 111 children were examined both clinically and photographically for fluorosis status using Dean's index. Twenty children were re-examined after a two-week interval for intra-examiner reliability by a single examiner. In addition, two independent examiners and the clinical examiner scored 111 photographic images of the same children in a standardized manner. Fluorosis scores were compared individually between examiners for both clinical and photographic scoring. Examiner reliability was assessed using both simple and weighted kappa statistics at tooth level. Sensitivity, specificity, positive-negative predictive values and a Receiver Operating Characteristic (ROC) curve were also calculated to determine the accuracy of the test. Across the three examiners, the prevalence of fluorosis (Dean's score ≥ 2) using photographs was lower (ranged from 23% to 26%) than the prevalence recorded by clinical examination (30%). The kappa score for intra-examiner reliability for the duplicate clinical examination was excellent (0.89). Inter-examiner reliability between the photographic method and the clinical examination (gold standard) for each examiner was substantial with weighted kappa values ranging from 0.74 to 0.77. The photographic method indicated higher specificity (99%) than sensitivity (79%) and the area under the ROC curve was also high (0.89) which suggests good accuracy of the diagnostic test. These results suggest that photographic examination of fluorosis on central incisors can be recorded with good examiner reliability. The recorded fluorosis prevalence was lower using the photographic scores.

Objective To investigate features of fasting plasma glucose (FPG) during different trimesters and hemoglobin A1c (HbA1c) in the third trimester of pregnant women in Zhujing Region of Shanghai. Methods A total of 606 cases of pregnant women who received prenatal examination and 75 g oral glucose tolerance test (OGTT) in Jinshan Branch of Shanghai Sixth People′s Hospital from January 1 to December 31, 2014 were collected as research subjects. According to the results of 75 g OGTT, they were divided into gestational diabetes mellitus (GDM) group (n=136) and non-GDM group (n=470). The clinical data, such as age, body mass index (BMI), as well as the concentration of FPG during different pregnancy trimesters and level of HbA1c in the third trimester were collected retrospectively in both two groups by retrospective analysis method. According to the quartile method, level of HbA1c in the third trimester of all subjects were divided into four ranges: Q1 (HbA1c level<5.1%), Q2 (5.1%≤HbA1c level<5.2%), Q3 (5.2%≤HbA1c level<5.5%), and Q4 (HbA1c level≥5.5%), respectively. The age, BMI, concentrations of FPG and the level of HbA1c between two groups were compared by independent-samples t test. Chi-square test was used to compare the GDM incidence of all subjects with different ranges of HbA1c level in the third trimester, and further comparison was conducted by adjusting inspection level. Then receiver operator characteristic (ROC) curve of HbA1c level in the third trimester for predicting the incidence of GDM was drawn, and the area under ROC curve (ROC-AUC) was calculated. The optimal critical value of HbA1c level in the third trimester for predicting the incidence of GDM was obtained when the Youden index reaching the maximum value. And its sensitivity and specificity were calculated. This study met the requirements of the World Medical Association Declaration of Helsinki revised in 2013. Results ①The age and BMI of GDM group were higher than those in non-GDM group, and there were statistically significant differences (t=5.306, 5.250; P<0.001). Among the subjects in GDM group, the concentration of FPG in second trimester was the highest among 3 trimesters, which was (4.9±0.6) mmol/L, and the concentration of FPG in second trimester was higher than that in first and third trimester, respectively, and both the differences were statistically significant (t=2.087, 1.960; P=0.039, 0.041). Among the subjects in non-GDM group, the concentration of FPG in first trimester was the highest among 3 trimesters, which was (4.6±0.3) mmol/L, and the concentration of FPG in first trimester was higher than that in second and third trimester, respectively, and both the differences were statistically significant (t=15.230, 5.613; P<0.001). The concentration of FPG in first, second and third trimesters of GDM group were higher than those of non-GDM group, respectively, and there were statistically significant differences (t=5.416, 15.526, 4.471; P<0.001). Besides, HbA1c level in third trimester of GDM group was (5.6±0.4)%, which was higher than that of non-GDM group (5.4±0.4)%, and there was significant difference (t=5.845, P<0.01). ②There was statistical difference in GDM incidences in all subjects with different ranges of HbA1c levels in third trimester (χ2=22.707, P<0.001). Multiple comparison results showed that the incidences of GDM in all subjects with Q1 and Q2 ranges of HbA1c levels in third trimester were higher than that in subjects with Q4 range of HbA1c levels in third trimester, respectively, and both the differences were statistically significant (χ2=15.071, 16.785; P<0.001). ③The results of ROC curve analysis of HbA1c level in third trimester in predicting the incidence of GDM showed that the ROC-AUC was 0.647 (95%CI: 0.625-0.672, P<0.001), and the optimal cut-off value of HbA1c level in third trimester to predict GDM incidence was 5.5%, and the sensitivity of HbA1c level in predicting the incidence of GDM was 63.4%, and the sensitivity was 69.7%. Conclusions The concentrations of FPG in GDM pregnant women in Zhujing Region of Shanghai begin to increase from the first trimester and peak during the second trimester, and decrease during the third trimester after lifestyle intervention. It is advisable for GDM pregnant women to control HbA1c levels below 5.5% during the third trimester. Key words: Diabetes, gestational; Fasting plasma glucose; Hemoglobin A, glycosylated; Glucose tolerance test; ROC curve; Forecasting; Pregnant women

We consider the distributed detection problem, in which a set of decision makers (DMs) receive observations of the environment and transmit finite-valued messages to other DMs according to prespecified communication protocols. A designated primary DM makes the final decision on one out of two alternative hypotheses. All DMs make decisions, in order to maximize a measure of organizational performance. We discuss three different types of decision rules (deterministic, independent randomization, and dependent randomization), and their implications on the organizational performance. Each DM is described by its individual Receiver Operating Characteristic (ROC) curve, which is concave. We determine that concavity is not guaranteed for the case of the ROC curve of a team of DMs, even if the decision rules are perfectly continuous and if the individual ROC curves are strictly concave and smooth, unless dependent randomization is allowed.

In power distribution fault data, the percentage of faults with different causes could be very different and varies from region to region. This data imbalance issue seriously affects the performance evaluation of fault diagnosis algorithms. Due to the limitations of conventional accuracy (ACC) and geometric mean (G-mean) measures, this paper discusses the application of Receiver Operating Characteristic (ROC) curves in evaluating distribution fault diagnosis performance. After introducing how to obtain ROC curves, Artificial Neural Networks (ANN), Logistic Regression (LR), Support Vector Machines (SVM), Artificial Immune Recognition Systems (AIRS), and K-Nearest Neighbor (KNN) algorithm are compared using ROC curves and Area Under the Curve (AUC) on real-world fault datasets from Progress Energy Carolinas. Experimental results show that AIRS performs best most of the time and ANN is potentially a good algorithm with a proper decision threshold.

Cognitive impairment is frequent in patients with high-grade glioma and requires cognitive follow-up. Cognitive screening tools such as the Montreal Cognitive Assessment (MoCA) have been used to assess cognition in these patients. Here we assessed the sensitivity of the MoCA in screening for cognitive impairment in a cohort of 156 patients with newly-diagnosed high-grade glioma, after surgery and before radiochemotherapy. We assessed cognitive performance with the MoCA and a neuropsychological battery. Cognitive scores were analyzed in terms of a previously validated framework designed to control false positives and data for 1003 control participants from the GRECOGVASC study. After comparison of performance on the tests, we used stepwise logistic regression to produce a cognitive summary score from the neuropsychological battery. Then we analyzed sensitivity and specificity of the MoCA with receiver operator characteristic (ROC) curve analysis. Both raw and adjusted MoCA scores showed only moderate sensitivity. The area under the ROC curve was 0.759 (95% CI 0.703-0.815) for the raw score and 0.788 (95% CI 0.734-0.842) for the adjusted score. Optimal discrimination was obtained with a raw score ≤ 25 (sensitivity: 0.526; specificity: 0.832; positive predictive value: 0.2; negative predictive value: 0.96) and an adjusted score - 0.603 (sensitivity: 0.716; specificity: 0.768; positive predictive value: 0.24; negative predictive value: 0.96). The moderate sensitivity of MoCA indicates that it is not a suitable screening tool for detecting cognitive impairment in patients with newly-diagnosed high-grade glioma.

A CdTe real time X-ray imaging sensor and system

The registration of intraoperative x-ray images with preoperative CT images is an important step in image-guided surgery. However, existing regression-based methods lack an interpretable and stable mechanism when fusing information from intraoperative images and preoperative CT volumes. In addition, existing feature extraction and fusion methods limit the accuracy of poseregression. The objective of this study is to develop a method that leverages both x-ray and computed tomography (CT) images to rapidly and robustly estimate an accurate initial registration within a broad search space. This approach integrates the strengths of learning-based registration with those of traditional registration methodologies, enabling the acquisition of registration outcomes across a wide search space at an acceleratedpace. We introduce a regression-based registration framework to address the aforementioned issues. We constrain the feature fusion process by training the network to reconstruct the high-dimensional feature representation vector of the preoperative CT volume in the embedding space from the input single-view x-ray, thereby enhancing the interpretability of feature extraction. Also, in order to promote the effective fusion and better extraction of local texture features and global information, we propose a lightweight cross-attention mechanism named lite cross-attention(LCAT). Besides, to meet the intraoperative requirements, we employ the intensity-based registration method CMA-ES to refine the result of pose regression. Our approach is verified on both real and simulated x-ray data. Experimental results show that compared with the existing learning-based registration methods, the median rotation error of our method can reach 1.9 and the median translation error can reach 5.6mm in the case of a large search range. When evaluated on 52 real x-ray images, we have a median rotation error of 1.6 and a median translation error of 3.8mm due to the smaller search range. We also verify the role of the LCAT and embedding reconstruction modules in our registration framework. If they are not used, our registration performance will be reduced to approximately random initialization results. During the experiments, our method demonstrates higher accuracy and larger capture range on both simulated images and real x-ray images compared to existing methods. The inspiring experimental results indicate the potential for future clinical application of ourmethod.

Objective To investigate the correlation between serum endocan level and risk of diabetic peripheral neuropathy(DPN). Methods A total of 67 patients with type 2 diabetes mellitus (T2DM) were recruited from the Department of Endocrinology in Gansu Provincial Hospital From April 2015 to November 2015. All patients were divided into DPN group (n=25) and simple T2DM group (n=42). Meanwhile, gender and age matched healthy subjects were included as the normal control group(NC group, n=28). Serum endocan was measured by ELISA, and other metabolic parameters were also tested. Pearson correlation analysis, logistic regression analysis and area of receiver operator characteristic (ROC) curve analysis were used to analyze the correlation between endocan level and DPN risk. Results Compared with NC group, the serum level of endocan was significantly increased in simple T2DM group and DPN group(F=24.26, P<0.05). In addition, compared with simple T2DM group, the serum level of endocan in DPN group was markedly higher (t=5.668, P<0.05). Endocan was positively correlated with the course of disease and age(r=0.438, 0.394, all P<0.05). Endocan level, smoking and age were the independent risk factors for DPN(OR=1.692, 9.880, 1.093, 95% CI: 1.279-2.237, 2.039-47.878, 1.024-1.168, all P<0.05). The results of the ROC analysis revealed that the area under ROC curve was 0.824(95% CI: 0.727-0.921, P=0.000). Conclusion Serum level of endocan is related to DPN, and it may be used as a novel biomarker for DPN. Key words: Endocan; Diabetic peripheral neuropathy; Type 2 diabetes mellitus

Objectives: Herpes virus entry mediator (HVEM) is a costimulatory molecule, and has been proved to play an important role in airway inflammatory and remodeling processes of asthma. We aimed to investigate the expression of HVEM gene in patients with asthma as a means of assessing disease severity.Methods: This study was carried out on 59 subjects, 16 patients with mild persistent asthma, 11 patients with moderate persistent asthma, 13 patients with severe persistent asthma, and 19 age and gender matched healthy controls. The HVEM mRNA expressions of all subjects were determined by real time PCR. Correlations between HVEM mRNA expression and fractional exhaled nitric oxide (FeNO), pulmonary function test values, total blood white cell count and differential, total immunoglobulin E (IgE) level, and Asthma Control Test (ACT) score were analyzed, respectively. The discrimination abilities of HVEM mRNA between different groups were tested using receiver operating characteristics (ROC) curve analyses.Results: This study showed the expressions of HVEM mRNA were significantly higher in the patients with severe and moderate persistent asthma than in patients with mild persistent asthma and healthy subjects (2.97 ± 1.23 vs. 1.17 ± 0.42 vs. 0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p < 0.001), but there was no significant difference between patients with mild persistent asthma and health controls (0.62 ± 0.38 vs. 0.46 ± 0.18/NAPDH, p = 0.557). HVEM mRNA expression at cut off point [1.01/NAPDH, area under the ROC curve (AUC) = 0.99] is sufficient to discriminate severe patients from mild-to-moderate patients, and at cut off point (0.93/NAPDH, AUC = 0.91) for discrimination of moderate-to-severe patients from mild ones, while at cut off point (0.76/NAPDH, AUC = 0.75) for discrimination of asthmatic patients from controls. Furthermore, HVEM mRNA expression was positively correlated with FeNO level (r = 0.524, p = 0.015), and total lymphocyte count (r = 0.426, p = 0.017) in patients with persistent asthma.Conclusions: HVEM gene expressions can be used as a potential biomarker for evaluating the severity of patients with persistent asthma.

Background: HER2+ breast cancer (BC) is a heterogeneous disease. Despite the novel targeted therapies against HER2, 20% of patients relapse. In this context, the identification of prognostic and predictor biomarkers is needed.MicroRNAs are involved in BC development and prognosis. We aim at assessing the prognostic significance of two micro-RNAs (named as A and B) in HER2+ BC tissue, to propose signature to predict relapse in this setting of patients.Methods: This study was conducted in a cohort of 46 non-consecutive HER2+ BC patients diagnosed at Hospital Clínico València-INCLIVA. All BC patients received standard treatment for localized disease. RNA was isolated from formalin-fixed paraffin-embedded primary tumor and retro-transcribed to cDNA. MicroRNA expression levels were measured by real-time qPCR and results were normalized according to the expression of housekeeping mir-16 miRNA.Non-parametric Mann-Whitney U test was used to ascertain the statistical significance of differences in miRNA expression levels between disease-free and relapse. Receiver operator characteristics (ROC) curves were constructed and AUC, accuracy, specificity and sensitivity, were calculated as a biomarker performance parameter. The best cutoff value was established based on the highest value obtained in ROC curve analysis according to the maximization of the sum of sensitivity and specificity. Kaplan-Meier curves were plotted for disease-free survival (DFS) based on the best cutoff value. As both microRNA showed high correlation (r=0.85), a genetic signature was developed using Principal Component Analysis (PCA) and extracting the first component. MicroRNA targeted genes were downloaded from miRTarBase (release 7.0). Cytoscape (v3.8.0) and ClueGo (v2.5.6) and were used to perform functional enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. To select significant terms/pathways, p-values were adjusted by the Benjamni-Hochberg method (p&amp;lt;0.05). Results: The expression of both microRNA-A and B was higher in the primary tumor of those patients who relapsed after treatment compared to those free of disease (p= 0.003 and 0.0002, respectively). ROC curves demonstrated that microRNA-A and B might discriminate those HER2+ BC patients who relapse. MicroRNA-A predicts relapse with an AUC 0.740, sensitivity of 0.810, specificity of 0.583 and accuracy of 0.689, and microRNA-B presented an AUC 0.821, sensitivity of 0.810, specificity of 0.750, and accuracy of 0.778. The survival analysis showed that microRNA-A and B strongly predicted shorter DFS (p= 0.014 and 0.0012, respectively).Two-microRNA signature combining (PCA1= 99.4% absorbed variance) microRNA-A and microRNA-B expression levels had high performance in discriminating relapsed from non-relapsed HER2+ BC patients (AUC 0.748, accuracy 0.689, sensitivity 1, specificity 0.417). Signature predicted DFS (p= 0.009). Functional enrichment analysis returned 55 significant pathways. Of these, important pathways related to tumorigenesis and response to treatment were enriched. Interestingly, P53 pathway, apoptosis, and cell cycle were in the top 5 enriched pathways. Conclusions: We demonstrated the value of microRNA A and B as potential prognostic biomarkers. High expression was related to poor prognosis in over cohort of HER2+ BC patients. Both microRNAs were able to strongly discriminate patients who will relapse and predict shorter DFS. Moreover, our signature was able to predict the outcome, suggesting a potential prognostic role at diagnosis. Both microRNAs are related to important biological pathways associated to BC progression. Further investigations to optimize the micro-RNA signature are on-going. Citation Format: Anna Adam-Artigues, Juan Antonio Carbonell-Asíns, Sheila Zúñiga-Trejos, Santiago Moragón, Maria Teresa Martínez, Cristina Hernando, Eduardo Tormo, Octavio Burgués, Ana Lluch, Begoña Bermejo, Pilar Eroles, Juan Miguel Cejalvo. Identification of a novel two-microRNA signature for recurrence prediction in HER2 positive breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS19-20.

Introduction Currently, the Dukes and AJCC TNM staging systems are widely used to predict survival from colon cancer and assist with clinical decision making. The Memorial Sloan Kettering cancer Centre (MSKCC) nomogram has been developed as an adjunct to these, to predict the five and ten year recurrence free survival probability for patients who had curative resection for colon cancer. The aim of this study is to externally validate the MSKCC nomogram in the UK population, and determine its usefulness. Method The colon cancer database from a district general hospital in England was used to extract all patients who had a curative colon cancer resection. Inclusion criteria were all patients who had curative elective colon cancer resection over a five year period. Patients included in the study were followed up for up to ten years. Variables required for the nomogram were: age, sex, tumour location, CEA levels, T stage, positive lymph nodes, negative lymph nodes, tumour differentiation, lymphovascular invasion, perineural invasion and whether post-operative chemotherapy was given. Five and ten year predictions were calculated for each patient, and plotted against the actual recurrence using a Receiver Operator Characteristic (ROC) curve, and Area Under the Curve (AUC) was calculated for both the five and ten year nomogram. Two cut off points were identified for each nomogram, and this helped to stratify our cohort into three risk groups depending on risk of recurrence. Results 138 patients were included in the study. 49.2% were males with mean age of 69.47 years (SD= 10.81). Overall five year recurrence rate was 26.8% with a mean follow up of 60.24 months (SD= 38.6). 118 patients were included in the five year nomogram validation, and 102 patients were included in the ten year nomogram validation. A ROC curve was plotted for both the five and ten year nomogram and AUC was calculated. For the five year nomogram AUC was 0.673, and for the ten year nomogram AUC was 0.687. Two cut off points were identified for each nomogram and this allowed the groups to be stratified into low, medium and high risk for recurrence. Cox regression showed a significant difference between all groups for both nomograms. Conclusion The MSKCC colon cancer nomogram was validated in our cohort, but is recommended to be used in conjunction with AJCC TNM staging system. The use of risk groups to stratify patients may allow the nomogram to be used simply and effectively in clinical practice. Disclosure of interest None Declared.

Semen parameters were evaluated by a variety of tests. A polarizing microscope (PM) which allows, in a single step, for the evaluation of the number, motility, viability of sperm using the phenomenon of birefringence was used. This approach avoids a Papanicolaou staining procedure modified for sperm (PAP) performed in fixed material. The aim of this study was to examine the birefringence of sperm structures, in live cells, for the direct analysis of morphology on fresh samples. Semen samples from 15 men of recently proven fertility and from 66 male patients who attended our center for semen analysis were examined by polarization microscope (PM) analysis in order to find an index representing a pool of sperm with normal morphology and progressive motility. The receiver operating characteristics (ROC) curves were used to determine the performance of the proposed diagnostic method. The difference between the two areas under the ROC curves (PAP=0.76 and PM=0.82) was quantitatively not significant (P=0.308); however, the curve of the PM method was always higher than the curve of PAP, revealing that, qualitatively, PM was more sensitive than PAP. The PM index can represent the percentage of motile sperm with normal morphology, which is the actual pool of sperm that can reach and fertilize the oocyte. We suggest a PM diagnostic cut-off value of 20%, since this value was the lowest found for individuals of proven fertility.

Objective To analyze the expression of interleukin (IL)-31 in tuberculous pleural effusion, and to evaluate its diagnostic value of tuberculous effusion. Methods Seventy-one patients with pleural effusion were enrolled, including 40 cases of tuberculous pleural effusion and 31 cases of malignant pleural effusion. Luminex method was applied to detect the IL-31 expression in pleural effusion. IL-31 levels were compared using non-parametric Mann-Whitney U test, and the receiver operator characteristic (ROC) curve was used to elvaluate the diagnostic value of IL-31. Results IL-31 expression in tuberculous pleural effusion was significantly higher than that in malignant pleural effusion with statistical significance (529.4 ng/L vs 13.8 ng/L, U=62, P<0.01). Based on the level of IL-31 expression, area under the ROC curve was 0.95 with the optimum cut-off value of 67.5 ng/L. Thus, the sensitivity and specificity of IL-31≥67.5 ng/L for diagnosis of tuberculous pleurisy were 82.5% (95%CI: 73.3%-94.2%) and 100.0% (95%CI: 91.4%-100.0%), respectively. Conclusion IL-31 is highly sensitive and specific for the diagnosis of tuberculous pleural effusion, which favors the differentiation of tuberculosis from malignance. Key words: Interleukin-31; Pleural effusion, malignant; Tuberculosis, pleural; Differential diagnosis