Receiver Operating Curve Curve Research Articles

Serum Levels of N-Terminal Pro-Brain Natriuretic Peptide in Gestational Hypertension, Mild Preeclampsia, and Severe Preeclampsia: A Study from a Center in Zhejiang Province, China.

BackgroundPre-eclampsia is one of the primary causes of maternal and newborn mortality. The pathogenesis of pre-eclampsia is still unclear. We aimed to investigate the link between serum N-terminal pro-brain natriuretic peptide (NT-proBNP) levels and pre-eclampsia.Material/MethodsA total of 102 pregnant women with hypertensive disorders of pregnancy who were hospitalized and delivered in Wenzhou People’s Hospital from January 2019 to December 2019 were selected, including 43 patients with gestational hypertension, 32 patients with mild pre-eclampsia, and 27 patients with severe pre-eclampsia. Enzyme-linked fluorescence analysis was used to detect the serum NT-proBNP levels of the patients before delivery. We used the t test and ANOVA to compare differences between groups. Receiver operating curve (ROC) and the Youden index were used to evaluate the detection efficiency.ResultsThe average level of serum NT-proBNP in patients with mild pre-eclampsia was 197.12±105.80 pg/mL, which was significantly higher than that of patients with gestational hypertension (48.98±32.45 pg/mL) (P<0.05). Serum NT-proBNP in patients with severe pre-eclampsia (851.04±879.85 pg/mL) was higher than that in patients with moderate pre-eclampsia (P<0.05). The area under the ROC curve for diagnosing pre-eclampsia using NT-proBNP was 0.973, with NT-proBNP of 129.5 pg/mL as the cut-off value. The Youden index was 0.824, with a sensitivity for predicting pre-eclampsia of 84.7% and a specificity of 97.7%.ConclusionsThe level of serum NT-proBNP was as an effective indicator for predicting pre-eclampsia and judging the risk of pre-eclampsia.

Comparison of novel visceral obesity indexes with traditional obesity measurements in predicting of metabolically unhealthy nonobese phenotype in hemodialysis patients

BackgroundNormal-weight maintenance hemodialysis (MHD) patients with abdominal obesity exhibited a more proatherogenic profile than overweight and obesity patients with abdominal obesity, highlighting the importance of early identification of metabolically unhealthy nonobese (MUNO) in this population. Visceral fat accumulation plays a crucial role in the development of MUNO. Lipid accumulation product (LAP), visceral adiposity index (VAI) have been proved as reliable visceral obesity markers. The Chinese visceral adiposity index (CVAI) and a body shape index (ABSI) are newly discovered indexes of visceral obesity and have been reported to be associated with multiple metabolic disorders. There are limited studies investigating the associations between different visceral obesity indices and risk of MUNO, especially in hemodialysis patients. Moreover, no general agreement has been reached to date regarding which of these obesity indices performs best in identifying MUNO. We aimed to investigate the prevalence of MUNO in MHD patients and compare the associations between different adiposity indices (CVAI, ABSI,VAI, LAP, body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHtR)) with MUNO risk in this population.MethodsWe conducted a multi-center cross-sectional study in Guizhou Province, Southwest China. 1302 nonobese adult MHD patients were included in our study. MUNO was defined as being nonobese and having the presence of > = 2 components of metabolic syndrome (MetS). Nonobese was defined as BMI less than 25 kg/m2. VAI, LAP, CVAI, ABSI, BMI, WC and WHtR were calculated. Logistic regression analyses and receiver operator curve (ROC) analyses were performed.Results65.6% participants were metabolically unhealthy. The ROC curve analysis demonstrated that of the seven obesity indices tested, the VAI (AUC 0.84 for women and 0.79 for men) followed by LAP (AUC 0.78 for women and 0.72 for men) had the highest diagnostic accuracy for MUNO phenotype while ABSI exhibited the lowest AUC value for identifying MUNO phenotypeConclusionsMetabolically unhealthy is highly prevalent in nonobese MHD patients. VAI and LAP outperformed CVAI in discriminating MUNO in MHD patients. Though ABSI could be a weak predictor of MUNO, it is not better than WHtR, WC and BMI.

ELN IMDS Flow Working Group Validation of the Monocyte Assay for Chronic Myelomonocytic Leukemia Diagnosis By Flow Cytometry

▪IntroductionIt was proposed that peripheral blood (PB) monocyte subset analysis evaluated by flow cytometry, hereafter referred to as “monocyte assay”, could rapidly and efficiently distinguish chronic myelomonocytic leukemia (CMML) from other causes of monocytosis by highlighting an increase in the classical monocyte (cMo) fraction above 94%. However, the robustness of this assay required a large multicenter validation.MethodsPB and/or bone marrow (BM) samples from adult patients displaying monocytosis were assessed with the “monocyte assay” by ten ELN iMDS Flow working group centers (6 equipped with BD FACSCanto™ II (BD Biosciences), 3 with Navios™ (Beckman Coulter) and one with BD™ LSRII (BD Biosciences)) with harmonized protocols. The corresponding files were reanalyzed in a blind fashion by a skilled operator and the cMo (CD14 ++CD16 -) percentages obtained by both analyses were compared. Information regarding age, gender, complete blood count, marrow cytomorphology, cytogenetics and molecular analysis was collected. Confirmed diagnoses were collected when available as well as follow-up for CMML patients.ResultsThe comparison between cMo percentages from 267 PB files provided by the 10 centers and the centralized cMo percentages showed a good global significant correlation (r=0.88; p<0.0001; FigA) with no bias (FigB). Confirmed diagnoses were available for 212 files, namely 101 CMML according to the WHO criteria, 99 reactive monocytosis, and 12 MPN with monocytosis. A phenotype in favor of CMML, either classical with accumulation of cMo ≥94% or a bulbous aspect (FigC), was observed respectively in 81 and 14 patients. Hence, a total of 95 out of the 101 CMML patients translated into a sensitivity of 94% (FigD). Assessment of C reactive protein counts were available in seven of the 14 patients with the characteristic bulbous profile and correlated with an inflammatory state, showing a median of 93.0 [7.0-157.4] mg/L. Conversely, a phenotype not in favor of CMML (FigC) was observed in 83 of the 99 patients with reactive monocytosis and in 10 of 12 patients with MPN with monocytosis, leading to a 84% specificity (FigD). We established a Receiver Operator Curve (ROC) and again obtained a 94% cut-off value of cMo with an area under the ROC curve (AUC) of 0.865 (FigE).The second aim of this multicenter study was to assess the feasibility of the monocyte assay on 117 BM samples provided by 7 out of the 10 ELN centers, 43 of which being paired to PB samples. The comparison between cMo percentages provided by the 7 centers and the centralized cMo percentages showed a lower global significant correlation compared to PB samples (r=0.74; p<0.0001; FigF) with a slight underestimation of cMo percentage by the participating centers (FigG). The comparison between PB and BM samples cMo% obtained by centralized reanalysis showed an excellent global correlation (r=0.93; p<0.0001; FigH) with a higher percentage in the marrow (FigI). Seventy-nine files were associated to a confirmed diagnosis, as expected mostly CMML (n=69), only seven reactive monocytosis and three MPN with monocytosis. Thus, we determined a sensitivity of the “monocyte assay” on BM samples of 87% (a phenotype in favor of CMML being observed in 60 out of the 69 CMML with 6 bulbous aspect profiles) and a specificity of 80% (a phenotype not in favor of CMML being observed in 5 of the 7 patients with reactive monocytosis and in 3 of the 3 patients with MPN with monocytosis).ConclusionsThis ELN multicenter study demonstrates the robustness of the monocyte assay with only limited variability of cMo percentages, validates the 94% cutoff value, confirms its high sensitivity and specificity in PB and finally, also confirms the possibility of its use in BM samples. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership.

Predicting Chronic Immune Thrombocytopenia in Pediatric Patients at Disease Presentation: Leveraging Clinical and Laboratory Characteristics Via Machine Learning Models

Introduction: Pediatric immune thrombocytopenia (ITP) is the most common acquired bleeding disorder of childhood with 3-4,000 new cases annually. While most children experience self-resolving disease, 25% go on to have chronic ITP (cITP) with thrombocytopenia persisting beyond one year. Given the likelihood of spontaneous resolution and potential side effects of initial treatments, standard management for patients without severe or life-threatening bleeding is often observation. However, if it were possible to predict development of chronic disease, earlier initiation of long-term therapies could minimize bleeding risk, reduce fatigue, ease activity restrictions, and mitigate the poorer health-related quality of life that characterizes cITP.Though associations between variables and cITP have been reported, none are strong enough alone to dictate clinical decisions regarding ITP management. Machine learning (ML) is a set of statistical tools that can be utilized to make predictions or cluster data from large datasets. ML models are well suited to complex patterns within clinical datasets. ML can incorporate greater numbers of variables than could be used with traditional statistical models and can assess the impact of variables contingent upon the state of multiple other variables. In this study, we used a large clinical dataset to test a series of ML models on their ability to predict cITP development.Methods: Our group identified 696 pediatric ITP patients cared for at Texas Children's Hematology Center (TXCH) from 2012 to 2020. Of these, 332 had confirmed acute ITP (self-resolved disease in <1 year), and 253 were diagnosed with cITP. Demographic information, presenting clinical features, and laboratory data drawn within 1 month of diagnosis were tabulated for this cohort. Variables included age, gender, race, ethnicity, presence of primary ITP (defined as ITP that is not caused by another underlying disorder), presenting platelet count, absolute leukocyte count, absolute lymphocyte count, absolute eosinophil count, immature platelet fraction (IPF), mean platelet volume (MPV), direct antiglobulin test (DAT), anti-nuclear antibody (ANA) titer, and immunoglobulin levels.We tested the capabilities of several ML methods in predicting cITP using these presenting clinical and laboratory parameters. We performed a 10-fold cross validation to compare average performance metrics of a 100 tree random forest method against logistic ridge regression, support vector machine (SVM), naïve bayes, and AdaBoost methods. We tested feature importance of clinical variables with relation to cITP using the Gini index. Cross-validated ML method performance was compared using the area under the curve (AUC) receiver operator curve (ROC), as well as F1 statistic, classification accuracy (CA), precision or positive predictive value, and recall or sensitivity. Analyses was performed using Orange v2.7 (https://orangedatamining.com).Results: The top five most informative clinical features by Gini index were primary ITP, MPV, IPF, absolute lymphocyte count, and ANA titer. Comparing our five ML methods after 10-fold cross validation, the 100 tree random forest model was the top performing method on average (AUC = 0.795, CA = 0.737, F1=0.734, Precision = 0.738, Recall = 0.737). With an AUC of approximately 0.8, there is an 80% chance the model will accurately distinguish cITP from aITP. A close second performing method was the naïve bayes (AUC 0.792, CA = 0.698, F1 = 0.671, Precision = 0.737, Recall = 0.698). We present the average cross validated AUC ROC curves and the full ML method test statistics in Figure 1.Conclusions: Clinical and laboratory features present at the time of initial ITP diagnosis can be utilized to predict the development of cITP in pediatric patients using ML models. Ensemble decision tree methods are promising candidates for further ML method refinement, as AUC ROC of predicting cITP with a 100 tree RF model is > 0.7. Our group is expanding this model through incorporation of genotyping data from both acute and cITP patients. Ultimately, these ML models, in the form of an online tool, could be applied to predict cITP, allowing providers to initiate upfront interventions for those ITP patients who are unlikely to experience spontaneous disease resolution. [Display omitted] DisclosuresKirk: Biomarin: Honoraria. Powers: American Regent: Research Funding. Despotovic: Agios: Consultancy; Apellis: Consultancy; UpToDate: Patents & Royalties: Royalties; Novartis: Consultancy, Research Funding.

Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features.

Purpose: The aims of this study were to combine CT images with Ki-67 expression to distinguish various subtypes of lung adenocarcinoma and to pre-operatively predict the Ki-67 expression level based on CT radiomic features.Methods: Data from 215 patients with 237 pathologically proven lung adenocarcinoma lesions who underwent CT and immunohistochemical Ki-67 from January 2019 to April 2021 were retrospectively analyzed. The receiver operating curve (ROC) identified the Ki-67 cut-off value for differentiating subtypes of lung adenocarcinoma. A chi-square test or t-test analyzed the differences in the CT images between the negative expression group (n = 132) and the positive expression group (n = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset (n = 165) and a validation dataset (n = 72) in a ratio of 7:3. A total of 1,316 quantitative radiomic features were extracted from the Analysis Kinetics (A.K.) software. Radiomic feature selection and radiomic classifier were generated through a least absolute shrinkage and selection operator (LASSO) regression and logistic regression analysis model. The predictive capacity of the radiomic classifiers for the Ki-67 levels was investigated through the ROC curves in the training and testing groups.Results: The cut-off value of the Ki-67 to distinguish subtypes of lung adenocarcinoma was 5%. A comparison of clinical data and imaging features between the two groups showed that histopathological subtypes and air bronchograms could be used as risk factors to evaluate the expression of Ki-67 in lung adenocarcinoma (p = 0.005, p = 0.045, respectively). Through radiomic feature selection, eight top-class features constructed the radiomic model to pre-operatively predict the expression of Ki-67, and the area under the ROC curves of the training group and the testing group were 0.871 and 0.8, respectively.Conclusion: Ki-67 expression level with a cut-off value of 5% could be used to differentiate non-invasive lung adenocarcinomas from invasive lung adenocarcinomas. It is feasible and reliable to pre-operatively predict the expression level of Ki-67 in lung adenocarcinomas based on CT radiomic features, as a non-invasive biomarker to predict the degree of malignant invasion of lung adenocarcinoma, and to evaluate the prognosis of the tumor.

Are there predictor variables for the diagnosis of transthyretin cardiac amyloidosis?

Abstract Introduction Transthyretin cardiac amyloidosis (ATTR) can be reliably diagnosed in the absence of histology if grade 2 or 3 cardiac uptake is demonstrated on 99mTc-DPD scan (DPD) in the absence of a detectable monoclonal component. Diagnosis requires a high degree of clinical suspicion in the presence of often non-specific findings and that it may be one of the reasons to under-diagnose ATTR. The aim of the study is to identify clinical, analytical and ECG variables that best predict a positive DPD result. Methods This is a multicentre retrospective study including all patients undergoing consecutive 99mTc-DPD scintigraphy in a reference area of 750,000 inhabitants between January 2016 and January 2021 for suspected ATTR. AL amyloidosis patients were excluded. Clinical, analytical, ECG and echocardiographic data were analyzed. We identified variables that independently predicted a positive DPD study using a multivariable logistic regression analysis. Receiver Operating Curve (ROC) analysis and the Area under the Curve (AUC) were calculated to assess the discrimination capacity of the model to predict a positive DPD study. Results DPD scans from a total of 181 patients were analyzed. Mean age of the sample: 78 years (42–96), 100% caucasians, 77% male. 54.7% (N=99) had a positive DPD study (defined as grade 2 or 3 Perugini uptake) and 45.3% (N=82) were negative. Independent predictors of a positive study were age, male gender, left ventricular septum thickness, any grade of atrioventricular block, low QRS voltage, Carpal tunnel syndrome, history of hypotension or need to lower antihypertensive drugs and a NT-proBNP value above 1800 pg/ml (See Table 1). The diagnostic accuracy of the model was excellent, with an AUC of 0.92 (IC 95% 0.87–0.96) (see Figure 1). Conclusions There are clinical-analytical factors and ECG and echocardiogram findings accessible in daily clinical practice that are able to predict a positive result on cardiac scintigraphy requested for suspected ATTR. Identifying these factors may improve the non-invasive diagnosis of ATTR. Funding Acknowledgement Type of funding sources: None. Table 1. Multivariable logistic regression analysFigure 1. ROC curve

Atherogenic Index of Plasma Predicts Outcomes in Acute Ischemic Stroke.

Aim: The atherogenic index of plasma (AIP) was significantly related to adverse outcomes in patients with cardiovascular disease. Our aim was to investigate the association between AIP and adverse outcomes in acute ischemic stroke.Methods: Patients with acute ischemic stroke (AIS) admitted between 2015 and 2018 were prospectively enrolled in this study. Functional outcomes were evaluated by the modified Rankin Scale (mRS). Poor outcomes were defined as mRS 3–6. The relationship of AIP with the risk of outcomes was analyzed by multivariate logistic regression models.Results: A total of 1,463 patients with AIS within 24 h of symptom onset were enrolled. The poor outcome group had a significantly higher level of AIP [0.09 (−0.10 to 0.27) vs. 0.04 (−0.09 to 0.18), p < 0.001] compared with the good outcome group. Multivariable logistic regression analysis showed that higher AIP was associated with poor outcomes in all the stroke patients (OR 1.84, 95% CI, 1.23–2.53, p = 0.007), which was more evident in patients with large-artery atherosclerosis subtype (OR 1.90, 95% CI, 1.53–2.62, p = 0.002), but not in the other subtypes. Receiver operating curve (ROC) analysis revealed that the best predictive cutoff value of AIP was 0.112, with a sensitivity of 70.8% and a specificity of 59.2%, and the area under the ROC curves for AIP was 0.685.Conclusion: AIP may be an important and independent predictor of the outcome of dysfunction in patients with AIS, especially the stroke subtype of large-artery atherosclerosis.

646 PREDICTING RESPONSE TO NEOADJUVANT CHEMOTHERAPY IN PATIENTS WITH OESOPHAGEAL ADENOCARCINOMA

Abstract Neoadjuvant chemotherapy is often used prior to surgical resection for oesophageal adenocarcinoma but remains ineffective in a high proportion of patients. The histological Mandard tumour regression grade is used to determine chemoresponse but is not available at the time of treatment decision-making. The aim of this study was to identify factors that predict chemotherapy response prior to surgery. Methods A prospectively collected database of patients undergoing surgical resection for oesophageal adenocarcinoma from a high-volume UK institution was used. Patients were subcategorised using pathological tumour response into ‘responders’ (Mandard grade 1–3) and ‘non-responders’ (Mandard grade 4&amp;5). Multivariable logistic regression analysis was performed to calculate crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) for responder status adjusting for a variety of parameters. Receiver-operator curves (ROC) were calculated. Results Among 315 patients included, 102 (32%) were responders and 213 (68%) non-responders. A decrease in radiological tumour volume (OR 1.92 95%CI 1.02–3.62; p = 0.05), a ‘partial response’ RECIST score (OR 7.16 95%CI 1.49–34.36; p = 0.01), a clinically improved dysphagia score (OR 2.79 95%CI 1.05–7.04; p = 0.04) and lymphovascular invasion (OR 0.06 95%CI 0.02–0.13; p = 0.000) influenced responder status. ROC curve analysis for responder status utilising all available parameters had an area under the curve (AUC) of 0.86. Conclusion This study has highlighted the potential for using pre-defined factors to identify those patients who have responded to neoadjuvant chemotherapy, prior to surgical resection, potentially facilitating a more individualised therapeutic approach.

Serum markers change for intraocular metastasis in renal cell carcinoma.

Objective: Renal cell carcinoma is prone to early metastasis. In general, intraocular metastasis (IOM) is not common. In the present study, we studied the relationship between different biochemical indicators and the occurrence of IOM in renal cancer patients, and identified the potential risk factors.Methods: A retrospective analysis of the clinical data of 214 patients with renal cell carcinoma from October 2001 to August 2016 was carried out. The difference and correlation of various indicators between the two groups with or without IOM was analyzed, and binary logistic regression analysis was used to explore the risk factors of IOM in renal cancer patients. The diagnostic value of each independent related factor was calculated according to the receiver operating curve (ROC).Results: The level of neuron-specific enolase (NSE) in renal cell carcinoma patients with IOM was significantly higher than that in patients without IOM (P<0.05). There was no significant difference in alkaline phosphatase (ALP), hemoglobin (Hb), serum calcium concentration, α fetoprotein (AFP), carcinoembryonic antigen (CEA), CA-125 etc. between IOM group and non-IOM (NIOM) group (P>0.05). Binary logistic regression analysis showed that NSE was an independent risk factor for IOM in renal cell carcinoma patients (P<0.05). ROC curve shows that the factor has high accuracy in predicting IOM, and the area under the curve (AUC) is 0.774. The cut-off value of NSE was 49.5 U/l, the sensitivity was 72.2% and the specificity was 80.1%.Conclusion: NSE concentration is a risk factor for IOM in patients with renal cell cancer. If the concentration of NSE in the patient’s body is ≥49.5 U/l, disease monitoring and eye scans should be strengthened.

The Value of Magnetic Resonance Imaging Histograms in the Preoperative Differential Diagnosis of Endometrial Stromal Sarcoma and Degenerative Hysteromyoma.

Objective: The present study aimed to explore the application value of magnetic resonance imaging (MRI) histograms with multiple sequences in the preoperative differential diagnosis of endometrial stromal sarcoma (ESS) and degenerative hysteromyoma (DH).Methods: The clinical and preoperative MRI data of 20 patients with pathologically confirmed ESS and 24 patients with pathologically confirmed DH were retrospectively analyzed, forming the two study groups. Mazda software was used to select the MRI layer with the largest tumor diameter in T2WI, the apparent diffusion coefficient (ADC), and enhanced T1WI (T1CE) images. The region of interest (ROI) was outlined for gray-scale histogram analysis. Nine parameters—the mean, variance, kurtosis, skewness, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile—were obtained for intergroup analysis, and the receiver operating curves (ROCs) were plotted to analyze the differential diagnostic efficacy for each parameter.Results: In the T2WI histogram, the differences between the two groups in seven of the parameters (mean, skewness, 1st percentile, 10th percentile, 50th percentile, 90th percentile, and 99th percentile) were statistically significant (P < 0.05). In the ADC histogram, the differences between the two groups in three of the parameters (skewness, 10th percentile, and 50th percentile) were statistically significant (P < 0.05). In the T1CE histogram, no significant differences were found between the two groups in any of the parameters (all P > 0.05). Of the nine parameters, the 50th percentile was found to have the best diagnostic efficacy. In the T2WI histogram, ROC curve analysis of the 50th percentile yielded the best area under the ROC curve (AUC; 0.742), sensitivity of 70%, and specificity of 83.3%. In the ADC histogram, ROC curve analysis of the 50th percentile yielded the best area under the ROC curve (AUC; 0.783), sensitivity of 81%, and specificity of 76.9%.Conclusion: The parameters of the mean, 10th percentile and 50th percentile in the T2WI histogram have good diagnostic efficacy, providing new methods and ideas for clinical diagnosis.

Potential of testis-derived circular RNAs in seminal plasma to predict the outcome of microdissection testicular sperm extraction in patients with idiopathic non-obstructive azoospermia.

Do testis-derived circular RNAs (circRNAs) in seminal plasma have potential as biomarkers to predict the outcome of microdissection testicular sperm extraction (micro-TESE) in patients with idiopathic non-obstructive azoospermia (NOA)? Testis-derived circRNAs in the seminal plasma can indeed be used for predicting the outcome of micro-TESE in patients with idiopathic NOA. Micro-TESE is an effective method to obtain sperm samples from patients with idiopathic NOA. However, its success rate is only 40-50% in such patients. Six idiopathic NOA patients with different micro-TESE results were included as the discovery cohort. Their testicular tissues were used for extracting and sequencing circRNAs. Five circRNAs with the most significantly different expression levels were selected for further verification. Fifty-two patients with idiopathic NOA were included as the validation cohort. Preoperative seminal plasma samples of 52 patients with idiopathic NOA and 25 intraoperative testicular tissues were collected and divided into 'success' and 'failure' groups according to the results of micro-TESE. Quantitative real-time polymerase chain reaction was performed to verify differences in the expression levels of the selected circRNAs between the two groups in the testicular tissues and seminal plasma. Whether at the seminal plasma or testicular tissue level, the differences in the expression levels of the three circRNAs (hsa_circ_0000277, hsa_circ_0060394 and hsa_circ_0007773) between the success and failure groups were consistent with the sequencing results. A diagnostic receiver operating curve (ROC) analysis of the AUC indicated excellent diagnostic performance of these circRNAs in seminal plasma in predicting the outcome of micro-TESE (AUC values: 0.920, 0.928 and 0.891, respectively). On the basis of least absolute shrinkage and selection operator (LASSO) logistic regression, the three circRNAs were combined to construct a new prediction model. The diagnostic ROC curve analysis of the model showed an AUC value of 0.958. The expression levels of these circRNAs in seminal plasma using three normospermic volunteer samples remained stable after 48 h at room temperature. NA. This was a single-center retrospective study with relatively few cases. The functions of these circRNAs, as well as their relationship with spermatogenesis, have not yet been established. Testis-derived circRNAs in seminal plasma can reflect the microenvironment of the testis and can be used as reliable biomarkers to screen patients with idiopathic NOA who might be suitable for micro-TESE. This article was funded by the National Natural Science Foundation of China (Grant no. 81871151). There were no competing interests.

18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma

PurposeThis article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery.MethodsA retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified.ResultsIn the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively.ConclusionPET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade.Graphic abstract

Serum IL-6/IL-10 ratio as a biomarker for the diagnosis and severity assessment of primary-open angle glaucoma.

To assess the performance of serum cytokine IL-6 and IL-6/IL-10 ratio as biomarkers for the diagnosis of primary open-angle glaucoma (POAG) and for determining its progression. In this study, 20 POAG patients and 21 healthy individuals from the Indonesian population were enrolled. The serum concentration of IL-6 and IL-10 were quantified. Comparative analysis was performed in addition to assessment of the diagnostic performance of cytokines using receiver-operating-curve (ROC) analysis. POAG patients had a higher IL-6 (p < 0.0001) and IL-6/IL-10 ratio (p < 0.0001) than controls. Among the POAG subjects, advanced-stage patients exhibited a higher IL-6/IL-10 ratio than those in the early-moderate stage (p = 0.001; p = 0.006). The ROC curve analysis showed that both IL-6 level and IL-6/IL-10 ratio exhibited an excellent capability of diagnosing POAG (cut-off of 20.5 pg/mL (100% sensitivity and 94% specificity) and 4.4 (88% sensitivity and 94% specificity), respectively). Serum IL-6/IL-10 ratio displayed a better performance than IL-6 in discriminating POAG severity with cut-off of at least 6.6 (sensitivity of 86% and specificity of 90%) and 9.1 (sensitivity of 89% and specificity of 78%) classified according to C/D ratio and MD of VF, respectively. The balance between IL-6 and IL-10 serum levels is potentially useful in discriminating POAG severity.

Clinical profile and outcome of elderly COVID-19 patients

Background: Emerging and reemerging infectious disease have plagued mankind and have been potential killer since historic times. The current pandemic of COVID-19 is the latest crisis that has challenged leadership and health infrastructure globally. Since, elderly are more susceptible, understanding risk factor and clinical feature in them is critically important to improve the outcome and efficacy of treatment.Methods: The prospective study included 1187 patients, aged ≥65 years diagnosed with COVID-19. Necessary record like demographic data, comorbidities and clinical features were collected. Relevant laboratory investigation done. Patients were grouped as survival and death. Different parameters were compared using t-test for continuous variables and Chi-square test for categorical data. Receiver operating curve (ROC) drawn using SPSS software.Results: Out of 1187 elderly people,316 (26%) have succumbed to death. Mortality was more in Male (68.7%). Fever (42%), cough (33.2%), dyspnea (26.9%) were the major symptom. Among patient who succumbed to death 45.9% people had diabetes with hypertension, 15.2% had only diabetes, 17.1% had only hypertension, 12.3% had chronic kidney disease and 15.56% had IHD. ROC curve drawn for TLC (AUC=0.72), NLR ratio (AUC=0.844), LDH (AUC=0.842), D-dimer (AUC=0.851), CRP (AUC=0.82), ferritin (AUC=0.823) which were statistically significant as predictor of disease outcome. D-dimer at cut-off 1 mcg/ml has specificity 89% and sensitivity 76% which is highest among the parameter, followed by NLR ratio at 6.9 has specificity (82%), sensitivity (73%).Conclusions: D-dimer of 1 mcg/ml, NLR ratio of 6.9 may assist in triaging patient requiring ICU care and deciding intervention. Also, elderly with multiple comorbidity should be treated aggressively, so that chance of survival can be increased.

Value of PET/CT Combined with CT Three-dimensional Reconstruction in Distinguishing Different Pathological Subtypes of Early Lung Adenocarcinoma

背景与目的早期肺腺癌中病理亚型以贴壁为主型的浸润性腺癌（lepidic predominant invasive adenocarcinoma, LPA）与原位腺癌（adenocarcinoma in situ, AIS）、微浸润性腺癌（microinvasive adenocarcinoma, MIA）的良好预后相似，临床上也迫切需要能够区分LPA与非LPA型浸润性腺癌（non-lepidic predominant invasive adenocarcinoma, non-LPA）的手段，本研究拟通过正电子发射型计算机断层显像（positron emission computed tomography, PET）/计算机断层扫描（computed tomography, CT）的最大标准化摄取值（maximal standard uptake value, SUVmax）和CT三维重建后参数探讨术前影像学表现为部分实性结节（part-solid nodules, PSNs）的早期肺腺癌不同病理亚型间的关系。方法回顾性分析2016年1月-2019年1月于江苏省苏北人民医院胸外科行解剖性肺切除术且影像学表现为PSNs的早期肺腺癌患者资料，所有患者胸部增强CT和PET/CT资料完整可获取，利用Mimics软件行三维重建，获取肿瘤体积、肿瘤三维平均CT值（3-dimensional mean-CT value, 3Dm-CT）、SUVmax等数据，采用SPSS 25.0进行统计分析，GraphPad Prism 8.3.0绘制受试者工作曲线（receiver operating curve, ROC），P < 0.05为差异有统计学意义。结果最终共计67例患者纳入本研究，按病理亚型不同将所有患者分为两组，AIS、MIA及浸润性腺癌（invasive adenocarcinoma, IAC）中的LPA归为低危组28例（41.8%），其余non-LPA如腺泡型（acinar pattern-predominant adeno-carcinoma, APA）、乳头型（papillary pattern-predominant adenocarcinoma, PPA）、微乳头型（micropapillary pattern-predominant adeno-carcinoma, MPA）归为高危组39例（58.2%），两组间SUVmax（t=3.153, P=0.002）、肿瘤体积（t=3.331, P=0.001)、实性/磨玻璃成分体积（t=2.74, P=0.006) /（t=3.127, P=0.002）、实性/磨玻璃成分3Dm-CT（t=3.655, P < 0.001) /（t=7.082, P < 0.001) 均具有显著统计学意义。ROC曲线提示：SUVmax[曲线下面积（area under curve, AUC）=0.727]、肿瘤体积（AUC=0.740）、磨玻璃成分体积（AUC=0.725）、实性成分3Dm-CT（AUC=0.763）、磨玻璃成分3Dm-CT（AUC=0.756）预测效能最佳。将上述AUC > 0.7的协变量纳入多因素ROC曲线分析，获得联合预测因子（AUC=0.835）具有中等以上预测价值。结论PET/CT中SUVmax和CT三维重建参数与影像学表现为PSNs的早期肺腺癌的不同病理亚型具有显著相关性，联合SUVmax、肿瘤体积、磨玻璃成分体积和实性/磨玻璃成分3Dm-CT对鉴别表现为PSNs的早期肺腺癌的病理亚型具有一定价值。

Glycemic control, depression, diabetes distress among adolescents with type 1 diabetes: effects of sex, race, insurance, and obesity.

To determine whether diabetes distress or depression screening better predict increased hemoglobin A1c (HbA1c) and to assess interactions with age, sex, race, obesity, and insurance status. Diabetes distress is a negative emotional reaction to diabetes, diabetes complications, self-management demands, unresponsive providers, and/or poor interpersonal relationships. Guidelines recommend annual depression screening, however diabetes distress may be mistaken for depression. Depression (PHQ-9) and diabetes distress (PAID-T) scores from self-administered tests were studied in 313 patients with type 1 diabetes (T1D) between the ages of 13-17. Spearman correlations and robust rank order multivariable regression analysis were used to assess relationships to age, duration, HbA1c. Kruskal-Wallis test was used to assess differences between sexes, races, and insurance status. Receiver operator curves (ROC) were constructed to see whether PAID-T or PHQ-9 scores more closely predicted HbA1c greater than 9%. HbA1c was more strongly correlated with PAID-T (rs = 0.37, p < 0.01), than PHQ-9 (rs = 0.27, p < 0.01) scores. Area under ROC curve for poor HbA1c was 0.75 for PAID and 0.64 for PHQ-9. PAID-T and PHQ-9 scores were increased in females and subjects with public insurance and both were significantly related to HbA1c even when accounting for age, sex, race obesity, and insurance status. PHQ-9 and PAID-T scores correlated with BMI-Z scores in Blacks, but not Whites. Both depression and diabetes distress are associated with increased HbA1c in adolescents with T1D, though distress is more so. Diabetes distress and depression should be routinely assessed in T1D adolescents, particularly those with public insurance.

Predicting Severe Radiation Esophagitis in Patients With Locally Advanced Esophageal Squamous Cell Carcinoma Receiving Definitive Chemoradiotherapy: Construction and Validation of a Model Based in the Clinical and Dosimetric Parameters as Well as Inflammatory Indexes.

ObjectiveRadiation esophagitis (RE) is common in patients treated with radiotherapy (RT) for locally advanced esophageal squamous cell carcinoma (ESCC). We aim to construct a nomogram predicting the severe RE (grade ≥2) in patients with ESCC receiving definitive chemoradiotherapy (dCRT).Materials and MethodsLogistic regression was performed to evaluate the risk factors in predicting RE. Nomogram was built based on the multivariate analysis result. The model was validated using the area under the receiver operating curve (ROC) curve (AUC), calibration curves, and decision curve analyses (DCA). Spearman correlation analysis was used to evaluate the correlation between inflammation indexes.ResultsA total of 547 patients with stage II–IVA ESCC treated with dCRT from the retrospective study were included. Two hundred and thirty-two of 547 patients (42.4%) developed grade ≥2 RE. Univariate analysis indicated that gender (p = 0.090), RT dose (p < 0.001), targeted therapy (p = 0.047), tumor thickness (p = 0.013), lymphocyte-monocyte ratio (LMR, p = 0.016), neutrophil-lymphocyte ratio (NLR, p < 0.001), and platelet-lymphocyte ratio (PLR, p < 0.001) were the significant factors for a higher incidence of RE. In multivariate analysis, RT dose [p < 0.001; odds ratio (OR), 4.680; 95% confidence interval (CI), 2.841–6.709], NLR (p < 0.001; OR, 0.384; 95% CI, 0.239–0.619), and PLR (p < 0.001; OR, 3.539; 95% CI: 2.226–5.626) were independently associated grade ≥2 RE and were involved in the nomogram. ROC curves showed the AUC of the nomogram was 0.714 (95% CI, 0.670–0.757), which was greater than each factor alone (RT dose: 0.615; NLR: 0.596; PLR: 0.590). Calibration curves showed good consistency between the actual observation and the predicted RE. DCA showed satisfactory positive net benefits of the nomogram among most threshold probabilities.ConclusionsThe study demonstrated that RT dose, NLR, and PLR were independent risk factors for grade ≥2 RE in patients with locally advanced ESCC receiving dCRT. A predictive model including all these factors was built and performed better than it based on each separately. Further validation in large patient populations is still warranted.

Elevated Serum Human Epididymis Protein 4 Is Associated With Disease Activity and Systemic Involvements in Primary Sjögren's Syndrome.

BackgroundWe aimed to investigate the clinical utility of human epididymis protein 4, a tumor biomarker being widely utilized in clinical practice in the diagnosis of ovarian cancer, in primary Sjögren’s Syndrome (pSS).MethodsA total of 109 pSS patients and 113 healthy controls (HCs) were included in the study. HE4 were determined by Roche Cobas E601 electrochemical luminescence analyzer. Clinical and laboratory findings were reviewed, and the relationships between HE4 and clinical parameters were determined by Spearman’s correlation test. The European league against rheumatism Sjögren’s syndrome disease activity index (ESSDAI) was utilized to evaluate disease activity.FindingsThe levels of HE4 were significantly elevated in patients with pSS compared to HCs (103.65 pmol/L vs. 46.52 pmol/L, p<0.001). The levels of HE4 were positively correlated with ESSDAI scores (r=0.462, p<0.001). Significant positive correlations between the levels of HE4 with pulmonary involvements (r=0.442, p<0.001) and renal involvements (r=0.320, p=0.001) were observed. Receiver operating curve (ROC) analysis revealed an optimal cut-off value of 104.90 pmol/L and 128.05 pmol/L for distinguishing patients with pulmonary and renal involvements, with the areas under the ROC curve (AUCs) of 0.778 (95%CI 0.685-0.870, p<0.001) and 0.768 (95%CI 0.646-0.891, p=0.001), respectively. Among patients with pulmonary involvement, the levels of HE4 were positively correlated with the semiquantitative HRCT grade (r=0.417, p=0.016), and negatively correlated with the percentage of forced vital capacity (FVC) (r= -0.460, p=0.047) and diffusing capacity of the lung for carbon monoxide (DLco) (r= -0.623, p=0.004). For patients with renal involvement, HE4 was positively correlated with creatinine (r=0.588, p=0.021) and negatively correlated with estimated glomerular filtration rate (r= -0.599, p=0.030).ConclusionsOur findings demonstrated a novel role of HE4 in clinical stratification of pSS, suggesting that introducing HE4 to the current pSS test panel may provide additional diagnostic value, particularly in evaluating disease activity and pulmonary/renal involvements.

High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules

ObjectiveTo investigate whether 3D convolutional neural network (CNN) is able to enhance the classification performance of radiologists in classifying pulmonary non-solid nodules (NSNs). Materials and MethodsData of patients with solitary NSNs and diagnosed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC) in pathological after surgical resection were analyzed retrospectively. Ultimately, 532 patients in our institution were included in the study: 427 cases (144 AIS, 167 MIA, 116 IAC) were assigned to training dataset and 105 cases (36 AIS, 41 MIA and 28 IAC) were assigned to validation dataset. For external validation, 177 patients (60 AIS, 69 MIA and 48 IAC) from another hospital were assigned to testing dataset. The clinical and morphological characteristics of NSNs were established as radiologists’ model. The trained classification model based on 3D CNN was used to identify NSNs types automatically. The evaluation and comparison on classification performance of the two models and CNN + radiologists’ model were performed via receiver operating curve (ROC) analysis and integrated discrimination improvement (IDI) index. The Akaike information criterion (AIC) was calculated to find the best-fit model. ResultsIn external testing dataset, radiologists’ model showed inferior classification performance than CNN model both in discriminating AIS from MIA-IAC and AIS-MIA from IAC (the area under the ROC curve (Az value), 0.693 vs 0.820, P = 0.011; 0.746 vs 0.833, P = 0.026, respectively). However, combining CNN significantly enhanced the classification performance of radiologists and exhibited higher Az values than CNN model alone (Az values, 0.893 vs 0.820, P < 0.001; 0.906 vs 0.833, P < 0.001, respectively). The IDI index further confirmed CNN’s contribution to radiologists in classifying NSNs (IDI = 25.8 % (18.3–46.1 %), P < 0.001; IDI = 30.1 % (26.1–45.2 %), P < 0.001, respectively). The CNN + radiologists’ model also provided the best fit over radiologists’ model and CNN model alone (AIC value 63.3 % vs. 29.5 %, 49.5 %, P < 0.001; 69.2 % vs. 34.9 %, 53.6 %, P < 0.001, respectively). ConclusionCNN successfully classified NSNs based on CT images and its classification performance were superior to radiologists’ model. But the classification performance of radiologists can be significantly enhanced when combined with CNN in classifying NSNs.

Analysis of Predictive Value of Laboratory Indexes for Malignant Lymphoma Patients with Bone Marrow Involvement

To analyze the influence of bone marrow involvement (BMI) in patients with malignant lymphoma (ML) on laboratory indexes, and evaluate the laboratory markers that can be used to predict/diagnose BMI. The clinical characteristics and laboratory indexes of 137 ML patients were analyzed retrospectively, from which the indexes of BMI in ML patients was studied. The logistic regression analysis and receiver operating curve (ROC) were used to evaluate independent risk factors and predictors of BMI diagnosis in ML patients. Compared with non-BMI group, the red blood cell distribution width, C-reactive protein, erythrocyte sedimentation rate, D-dimer, lactate dehydrogenase, alkaline phosphatase, β2-microglobulin, transferrin, CA153, CA125, and soluble interleukin-2 receptor (sIL-2R) levels were increased while platelet (PLT) count was decreased in BMI group, and the difference was statistically significant (P<0.05). The blood indexes related to BMI and the statistically significant indexes in the univariate regression analysis were corrected by multivariate logistic regression analysis. The corrected results showed that T cell-related non-Hodgkin lymphoma (adjusted OR=2.18, 95%CI: 1.48-4.90, P＜0.001), clinical stage Ⅲ-Ⅳ (adjusted OR=3.32, 95%CI: 2.16-5.83, P＜0.001), sIL-2R (adjusted OR=4.26, 95%CI: 2.95-12.85, P＜0.001) were the risk factors for ML patients with BMI, while PLT (adjusted OR=0.89, 95%CI: 0.55-0.96, P= 0.003) was a protective factor. ROC analysis showed that the areas under the ROC curve of PLT and sIL-2R predicting BMI in ML patients was 0.712 (95%CI: 0.646-0.776, P<0.001) and 0.796 (95%CI: 0.739-0.853, P<0.001), respectively. The best cut-off point of PLT and sIL-2R was 160×109/L and 2 568 U/ml, respectively. The diagnostic specificities of the two indexes here were both greater than 80%. PLT and sIL2R show good diagnostic value for ML patients with BMI.