scholarly journals Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

2010 ◽  
Vol 5 ◽  
pp. BMI.S4877 ◽  
Author(s):  
Emanuel Schwarz ◽  
Rauf Izmailov ◽  
Michael Spain ◽  
Anthony Barnes ◽  
James P. Mapes ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Area Under The Curve ◽  
Healthy Controls ◽  
Receiver Operating Characteristic Area ◽  
Specific Prediction ◽  
Characteristic Area ◽  
Disease Signature ◽  
Based Medicine ◽  
And Control ◽  
Independent Cohort

We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls.

scholarly journals Feasibility and visualization of deep learning detection and classification of inferior vena cava filters

2020 ◽  
Author(s):  
Brian J. Park ◽  
Vlasios S. Sotirchos ◽  
Jason Adleberg ◽  
S. William Stavropoulos ◽  
Tessa S. Cook ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inferior Vena Cava ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Inferior Vena ◽  
Vena Cava ◽  
Area Under The Curve ◽  
Receiver Operating Characteristic Area ◽  
Inferior Vena Cava Filters ◽  
Characteristic Area

AbstractPurposeThis study assesses the feasibility of deep learning detection and classification of 3 retrievable inferior vena cava filters with similar radiographic appearances and emphasizes the importance of visualization methods to confirm proper detection and classification.Materials and MethodsThe fast.ai library with ResNet-34 architecture was used to train a deep learning classification model. A total of 442 fluoroscopic images (N=144 patients) from inferior vena cava filter placement or removal were collected. Following image preprocessing, the training set included 382 images (110 Celect, 149 Denali, 123 Günther Tulip), of which 80% were used for training and 20% for validation. Data augmentation was performed for regularization. A random test set of 60 images (20 images of each filter type), not included in the training or validation set, was used for evaluation. Total accuracy and receiver operating characteristic area under the curve were used to evaluate performance. Feature heatmaps were visualized using guided backpropagation and gradient-weighted class activation mapping.ResultsThe overall accuracy was 80.2% with mean receiver operating characteristic area under the curve of 0.96 for the validation set (N=76), and 85.0% with mean receiver operating characteristic area under the curve of 0.94 for the test set (N=60). Two visualization methods were used to assess correct filter detection and classification.ConclusionsA deep learning model can be used to automatically detect and accurately classify inferior vena cava filters on radiographic images. Visualization techniques should be utilized to ensure deep learning models function as intended.

Clinical and Host Biological Factors Predict Colectomy Risk in Children Newly Diagnosed With Ulcerative Colitis

2021 ◽  
Author(s):  
Jeffrey S Hyams ◽  
Michael Brimacombe ◽  
Yael Haberman ◽  
Thomas Walters ◽  
Greg Gibson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Confidence Interval ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Newly Diagnosed ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Receiver Operating

Abstract Background Develop a clinical and biological predictive model for colectomy risk in children newly diagnosed with ulcerative colitis (UC). Methods This was a multicenter inception cohort study of children (ages 4-17 years) newly diagnosed with UC treated with standardized initial regimens of mesalamine or corticosteroids (CS) depending upon initial disease severity. Therapy escalation to immunomodulators or infliximab was based on predetermined criteria. Patients were phenotyped by clinical activity per the Pediatric Ulcerative Colitis Activity Index (PUCAI), disease extent, endoscopic/histologic severity, and laboratory markers. In addition, RNA sequencing defined pretreatment rectal gene expression and high density DNA genotyping by the Affymetrix UK Biobank Axiom Array. Coprimary outcomes were colectomy over 3 years and time to colectomy. Generalized linear models, Cox proportional hazards multivariate regression modeling, and Kaplan-Meier plots were used. Results Four hundred twenty-eight patients (mean age 13 years) started initial theapy with mesalamine (n = 136), oral CS (n = 144), or intravenous CS (n = 148). Twenty-five (6%) underwent colectomy at ≤1 year, 33 (9%) at ≤2 years, and 35 (13%) at ≤3 years. Further, 32/35 patients who had colectomy failed infliximab. An initial PUCAI ≥ 65 was highly associated with colectomy (P = 0.0001). A logistic regression model predicting colectomy using the PUCAI, hemoglobin, and erythrocyte sedimentation rate had a receiver operating characteristic area under the curve of 0.78 (95% confidence interval [0.73, 0.84]). Addition of a pretreatment rectal gene expression panel reflecting activation of the innate immune system and response to external stimuli and bacteria to the clinical model improved the receiver operating characteristic area under the curve to 0.87 (95% confidence interval [0.82, 0.91]). Conclusions A small group of children newly diagnosed with severe UC still require colectomy despite current therapies. Our gene signature observations suggest additional targets for management of those patients not responding to current medical therapies.

scholarly journals A MYC-Driven Plasma Polyamine Signature for Early Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 913
Author(s):  
Johannes Fahrmann ◽  
Ehsan Irajizad ◽  
Makoto Kobayashi ◽  
Jody Vykoukal ◽  
Jennifer Dennison ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Stage ◽  
Area Under The Curve ◽  
Polyamine Metabolism ◽  
Healthy Controls ◽  
Test Set ◽  
Exact Test ◽  
Oncogenic Driver ◽  
Characteristic Area ◽  
Validation Set

MYC is an oncogenic driver in the pathogenesis of ovarian cancer. We previously demonstrated that MYC regulates polyamine metabolism in triple-negative breast cancer (TNBC) and that a plasma polyamine signature is associated with TNBC development and progression. We hypothesized that a similar plasma polyamine signature may associate with ovarian cancer (OvCa) development. Using mass spectrometry, four polyamines were quantified in plasma from 116 OvCa cases and 143 controls (71 healthy controls + 72 subjects with benign pelvic masses) (Test Set). Findings were validated in an independent plasma set from 61 early-stage OvCa cases and 71 healthy controls (Validation Set). Complementarity of polyamines with CA125 was also evaluated. Receiver operating characteristic area under the curve (AUC) of individual polyamines for distinguishing cases from healthy controls ranged from 0.74–0.88. A polyamine signature consisting of diacetylspermine + N-(3-acetamidopropyl)pyrrolidin-2-one in combination with CA125 developed in the Test Set yielded improvement in sensitivity at >99% specificity relative to CA125 alone (73.7% vs 62.2%; McNemar exact test 2-sided P: 0.019) in the validation set and captured 30.4% of cases that were missed with CA125 alone. Our findings reveal a MYC-driven plasma polyamine signature associated with OvCa that complemented CA125 in detecting early-stage ovarian cancer.

scholarly journals Machine Learning Models for Diagnostic Classification of Hepatitis C Tests

2021 ◽  
Vol 10 (1) ◽  
pp. 70
Author(s):  
Oladosu Oyebisi Oladimeji ◽  
Abimbola Oladimeji ◽  
Oladimeji Olayanju
Keyword(s):  
Hepatitis C ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Matthews Correlation Coefficient ◽  
Receiver Operating Characteristic Area ◽  
Precision Recall Curve ◽  
Characteristic Area ◽  
F Measure ◽  
Machine Learning Models ◽  
Recall Curve

Introduction: Hepatitis C is a chronic infection caused by hepatitis c virus - a blood borne virus. Therefore, the infection occurs through exposure to small quantities of blood. It has been estimated by World Health Organization (WHO) to have affected 71 million people worldwide. This infection costs individual, groups and government a lot because no vaccine has been gotten yet for the treatment. This disease is likely to continue to affect more people because it’s long asymptotic phase which makes its early detection not feasible.Material and Methods: In this study, we have presented machine learning models to automatically classify the diagnosis test of hepatitis and also ranked the test features in order to know how they contribute to the classification which help in decision making process by the health care industry. The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset.Results: The models were evaluated based on metrics such as Matthews correlation coefficient, F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve.  We found that using SMOTE techniques helped raise performance of the predictive models. Also, random forest (RF) had the best performance based on Matthews correlation coefficient (0.99), F-measure (0.99), Precision-Recall curve (1.00) and Receiver Operating Characteristic Area Under Curve (0.99).Conclusion: This discovery has the potential to impact on clinical practice, when health workers aim at classifying diagnosis result of disease at its early stage.

Evaluation of neutrophil–lymphocyte ratio in predicting bloodstream infection

2019 ◽  
Vol 13 (15) ◽  
pp. 1255-1261 ◽  
Author(s):  
Jian Qu ◽  
Hai-Yan Yuan ◽  
Ying Huang ◽  
Qiang Qu ◽  
Zhan-Bo Ou-Yang ◽  
...  
Keyword(s):  
Bloodstream Infection ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Study Group ◽  
Healthy Controls ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Gram Positive Bacteria ◽  
Diagnostic Indicator

Aim: The prognostic role of neutrophil-to-lymphocyte ratio (NLR) in bloodstream infection (BSI) deserves further investigation. Patients & methods: The NLR values were measured and compared in BSI patients and healthy controls. The receiver operating characteristic of NLR and cut-off values were measured in BSI patients and subgroups. Results: We have measured the NLR of study group with 2160 BSI patients and normal group with 2523 healthy controls, which was significantly high in study group (11.36 ± 21.38 vs 2.53 ± 0.86; p < 0.001) and the area under the curve was 0.834 (95% CI: 0.825–0.842; p < 0.001). The critical value of NLR for diagnosis of BSI was 3.09, with a sensitivity of 75.3%, and a specificity of 93.6%. Conclusion: NLR is an effective diagnostic indicator of including BSIs of Gram-negative bacteria, Gram-positive bacteria and fungus.

scholarly journals Diagnostic Performance of Bronchoalveolar Lavage (1,3)-β-d-Glucan Assay for Pneumocystis jirovecii Pneumonia

2020 ◽  
Vol 6 (4) ◽  
pp. 200
Author(s):  
Shiwei Zhou ◽  
Kathleen A. Linder ◽  
Carol A. Kauffman ◽  
Blair J. Richards ◽  
Steve Kleiboeker ◽  
...  
Keyword(s):  
Bronchoalveolar Lavage ◽  
Test Performance ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Lavage Fluid ◽  
Pneumocystis Pneumonia ◽  
Pneumocystis Jirovecii ◽  
Pneumocystis Jirovecii Pneumonia ◽  
Characteristic Area

We evaluated the performance of the (1,3)-β-d-glucan (BDG) assay on bronchoalveolar lavage fluid (BALF) as a possible aid to the diagnosis of Pneumocystis jirovecii pneumonia. BALF samples from 18 patients with well-characterized proven, probable, and possible Pneumocystis pneumonia and 18 well-matched controls were tested. We found that the best test performance was observed with a cut-off value of 128 pg/mL; receiver operating characteristic/area under the curve (ROC/AUC) was 0.70 (95% CI 0.52–0.87). Sensitivity and specificity were 78% and 56%, respectively; positive predictive value was 64%, and negative predictive value was 71%. The low specificity that we noted limits the utility of BALF BDG as a diagnostic tool for Pneumocystis pneumonia.

scholarly journals Optimal Deep Learning Model to Identify the Development of Pomegranate Fruit in Farms

2020 ◽  
Vol 9 (3) ◽  
pp. 2352-2356
Keyword(s):  
Operating Characteristic ◽  
Market Value ◽  
Water Shortage ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Post Harvesting ◽  
Accuracy Of Results ◽  
Cultivation Techniques ◽  
Pomegranate Fruit ◽  
Deep Learning Model

In this study, estimating the maturing condition in gardens helps to enhance the process of post-harvesting. Collecting fruits on the basis of their developmental stage will minimize storage costs and maximize market value. Additionally, estimated ripeness of the fruits can be more useful for indicators for detecting water shortage and to determine the water used during irrigation. The purpose of the study is to develop the new direction of technology to detect the ripeness stage between two classes: ripe and unripe. We employ deep Neural Network (DNN) classifiers for the prediction of ripe and unripe class. The results of our proposed classifiers give the sensitivity 96.2%, specificity 94.2% with accuracy of results 94.5%, over a dataset of 200 images of each class. The ROC (receiver operating characteristic) area values curve close to 0.98 in all-class during training. We believe this is a notable performance that allows a suitable non-intrusive maturing prediction that will enhance cultivation techniques.

scholarly journals Potential Non-Invasive Biomarkers for Early Diagnosis of Oral Squamous Cell Carcinoma

2021 ◽  
Vol 10 (8) ◽  
pp. 1658
Author(s):  
Valentina Dikova ◽  
Eloisa Jantus-Lewintre ◽  
Jose Bagan
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Area Under The Curve ◽  
Healthy Controls ◽  
Tnf Α ◽  
Neck Metastases ◽  
Unstimulated Saliva ◽  
And Control

This study aimed to investigate the role of a panel of salivary cytokines as biomarkers for early detection oral squamous cell carcinoma (OSCC), comparing their levels among healthy individuals, patients with oral leukoplakia (OL), and malignant lesions. Cytokine profiling analysis performed in a minimally invasive sample was correlated with clinicopathological variables in our patient cohorts. Unstimulated saliva was obtained from subjects with OSCC at early (n = 33) and advanced (n = 33) disease, OL with homogeneous (n = 33) and proliferative verrucous (n = 33) clinical presentations, and healthy controls (n = 25). Salivary IL-1α, IL-6, IL-8, IP-10, MCP-1, TNF-α, HCC-1, and PF-4 levels were analyzed by a sensitive bead-based multiplex immunoassay. Mean levels of IL-6, IL-8, TNF-α, HCC-1, MCP-1, and PF-4 differed significantly between OSCC, OL, and control saliva (p < 0.05). We found notably higher IL-6 and TNF-α in advanced compared to early OSCC stages. The area under the curve (AUC) for OSCC vs. control was greater than 0.8 for IL-6, IL-8, TNF-α, and HCC-1, and greater than 0.7 for PF-4. The presence of neck metastases (NM) was associated with increased IL-6 and TNF-α levels. Our findings suggest that salivary IL-6, IL-8, TNF-α, HCC-1, and PF-4 may discriminate between OSCC, OL, and healthy controls. IL-6 and TNF-α may indicate OSCC progression, being distinctive in the presence of NM.

149 Bovine embryo selection can be improved by the characteristics of secreted extracellular vesicles

2019 ◽  
Vol 31 (1) ◽  
pp. 199
Author(s):  
E. Mellisho ◽  
M. Briones ◽  
F. O. Castro ◽  
L. Rodriguez-Alvarez
Keyword(s):  
Receiver Operating Characteristic ◽  
Predictive Power ◽  
Operating Characteristic ◽  
Culture Media ◽  
Area Under The Curve ◽  
Blastocyst Stage ◽  
Blastocyst Quality ◽  
Characteristic Area ◽  
Receiver Operating

Extracellular vesicles (EV) secreted by blastocysts might be relevant to predict competence of embryos produced in vitro. The aim of this study was to develop a model to select competent embryos that combines blastocyst morphokinetics data and morphological parameters of EV secreted during blastulation (Days 5-7.5). Embryos were cultured in groups up to Day 5; morulae were selected and individually cultured in SOFaa depleted of EV until Day 7.5 after IVF. Embryo competence was determined by in vitro post-hatching development up to Day 11. A retrospective classification of blastocyst and culture media was performed based on blastulation time [early (EB) or late (LB)] and competence at Day 11 [competent (C) or non-competent (NC)]. The EV were isolated from culture media of individual embryos, their properties determined by nanoparticle tracking analysis. The model was based on a binary logistic regression to describe the dichotomous-dependent variable of the blastocyst (C=1 and NC=0). A set of independent variables of blastocyst morphokinetics (blastulation time, blastocyst stage, blastocyst quality and blastocyst diameter at Day 7.5) and EV morphological parameters [mean size (ME), mode size (MO) and particle concentration (CO)] were analysed with multiple regression. The analysis generated the coefficients and their standard errors and significance level of an equation to calculate a probability, where values between 0.5 and 1 predict competent embryos. To verify the predictive power of the algorithm, the following indicators were used: the receiver operating characteristic with the determination of area under the curve, percentage correct predictions, and Omnibus tests. Statistical significance was determined at the P&lt;0.05 level. A rough guide for classifying the accuracy of a predictive model is as follows: 0.9 to 1=excellent, 0.8 to 0.9=good, 0.7 to 0.8=fair, 0.6 to 0.7=poor, 0.5 to 0.6=fail. A total of 254 embryos were used in this study; from them, 73 were classified in C-EB, 68 in NC-EB, 61 in C-LB and 52 in NC-LB. Initially, all independent variables were analysed in model 1; the most significant predictors associated with embryo competence were blastocyst stage, blastocyst quality, blastocyst diameter, ME and CO (P&lt;0.05). In model 2 no significant variables were excluded (blastulation time and MO). The statistical test of predictive power indicates that models 1 and 2 achieved a receiver operating characteristic-area under the curve of 0.853 (95% confidence interval, 0.806-0.9; P&lt;0.001) and correct predictions of 77.2 and 77.6%, respectively. When EV characteristics were excluded and the model considers only variables from the embryo, the receiver operating characteristic-area under the curve value was 0.714 (95% confidence interval, 0.651-0.777; P&lt;0.001) and correct predictions was reduced to 65.4. Model 2 was consider the most appropriate from the practical point of view because it avoids disturbing embryo culture during blastulation. The results indicate that incorporating EV properties increases accuracy of embryo selection, supporting the possibility to improve conventional methods by combining blastocyst morphology and characteristics of EV obtained by nanoparticle tracking analysis. This work was supported by Fondecyt 1170310.

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