scholarly journals Correlation of free-response and receiver-operating-characteristic area-under-the-curve estimates: Results from independently conducted FROC/ROC studies in mammography

2012 ◽  
Vol 39 (10) ◽  
pp. 5917-5929 ◽  
Author(s):  
Federica Zanca ◽  
Stephen L. Hillis ◽  
Filip Claus ◽  
Chantal Van Ongeval ◽  
Valerie Celis ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Receiver Operating Characteristic Area ◽  
Free Response ◽  
Characteristic Area ◽  
Receiver Operating

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 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.

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<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<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<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<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.

Serum syndecan-1, hyaluronan and thrombomodulin levels in patients with lupus nephritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Kelvin Y C Yu ◽  
Susan Yung ◽  
Mel K M Chau ◽  
Colin S O Tang ◽  
Desmond Y H Yap ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Receiver Operating Characteristic ◽  
Healthy Subjects ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Renal Lupus ◽  
Receiver Operating

Abstract Objectives We investigated circulating syndecan-1, HA and thrombomodulin levels in patients with biopsy-proven Class III/IV ± V LN and their clinico-pathological associations. Patients with non-renal SLE or non-lupus chronic kidney disease, and healthy subjects served as controls. Methods Serum syndecan-1, HA and thrombomodulin levels were determined by ELISAs. Results Syndecan-1, HA and thrombomodulin levels were significantly higher during active LN compared with remission (P < 0.01, for all), and correlated with the level of proteinuria, estimated glomerular filtration rate, anti-dsDNA antibodies, complement 3 and serum creatinine. Longitudinal studies showed that syndecan-1 and thrombomodulin levels increased prior to clinical renal flare by 3.6 months, while HA level increased at the time of nephritic flare, and the levels decreased in parallel with treatment response. Receiver operating characteristic curve analysis showed that syndecan-1 and thrombomodulin levels distinguished patients with active LN from healthy subjects, LN patients in remission, patients with active non-renal lupus and patients with non-lupus chronic kidney disease (receiver operating characteristic area under curve of 0.98, 0.91, 0.82 and 0.95, respectively, for syndecan-1; and area under curve of 1.00, 0.84, 0.97 and 0.79, respectively, for thrombomodulin). HA level distinguished active LN from healthy subjects, LN patients in remission and non-lupus chronic kidney disease (receiver operating characteristic area under curve of 0.82, 0.71 and 0.90, respectively) but did not distinguish between renal vs non-renal lupus. Syndecan-1 and thrombomodulin levels correlated with the severity of interstitial inflammation, while HA level correlated with chronicity grading in kidney biopsies of active LN. Conclusion Our findings suggest potential utility of serum syndecan-1, thrombomodulin and HA levels in clinical management, and their potential contribution to LN pathogenesis.

scholarly journals Identification of an optimal prolactin threshold to determine prolactinoma size using receiver operating characteristic analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bianca M. Leca ◽  
Maria Mytilinaiou ◽  
Marina Tsoli ◽  
Andreea Epure ◽  
Simon J. B. Aylwin ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Tumour Size ◽  
Area Under The Curve ◽  
Serum Prolactin ◽  
Pituitary Neoplasms ◽  
Characteristic Analysis ◽  
Roc Curve Analysis ◽  
Baseline Serum ◽  
Receiver Operating

AbstractProlactinomas represent the most common type of secretory pituitary neoplasms, with a therapeutic management that varies considerably based on tumour size and degree of hyperprolactinemia. The aim of the current study was to evaluate the relationship between serum prolactin (PRL) concentrations and prolactinoma size, and to determine a cut-off PRL value that could differentiate micro- from macro-prolactinomas. A retrospective cohort study of 114 patients diagnosed with prolactinomas between 2007 and 2017 was conducted. All patients underwent gadolinium enhanced pituitary MRI and receiver operating characteristic (ROC) analyses were performed. 51.8% of patients in this study were men, with a mean age at the time of diagnosis of 42.32 ± 15.04 years. 48.2% of the total cohort were found to have microadenomas. Baseline serum PRL concentrations were strongly correlated to tumour dimension (r = 0.750, p = 0.001). When performing the ROC curve analysis, the area under the curve was 0.976, indicating an excellent accuracy of the diagnostic method. For a value of 204 μg/L (4338 mU/L), sensitivity and specificity were calculated at 0.932 and 0.891, respectively. When a cut off value of 204 μg/L (4338 mU/L) was used, specificity was 93.2%, and sensitivity 89.1%, acceptable to reliably differentiate between micro- and macro- adenomas.

Risikoabschätzung einer akuten Exazerbation bei COPD-Patienten im Rahmen einer pneumologischen Anschluss-Rehabilitation anhand der Prävalenz und Schwergradausprägung von Komorbiditäten

Pneumologie ◽  
2021 ◽  
Author(s):  
P. Luu ◽  
S. Tulka ◽  
S. Knippschild ◽  
W. Windisch ◽  
M. Spielmanns
Keyword(s):  
Receiver Operating Characteristic ◽  
Charlson Comorbidity Index ◽  
Operating Characteristic ◽  
Rating Scale ◽  
Area Under The Curve ◽  
Sensitivität Und Spezifität ◽  
Comorbidity Index ◽  
Receiver Operating

Zusammenfassung Einleitung Akute COPD-Exazerbationen (AECOPD) im Rahmen einer pneumologischen Rehabilitation (PR) sind häufige und gefährliche Komplikationen. Neben Einschränkungen der Lebensqualität führen sie zu einem Unterbrechung der PR und gefährden den PR-Erfolg. Eine Abhängigkeit zwischen dem Krankheitsstatus und einem erhöhten Risiko für eine AECOPD ist beschrieben. Dabei stellt sich die Frage, ob der Charlson Comorbidity Index (CCI) oder die Cumulative Illness Rating Scale (CIRS) dafür geeignet sind, besonders exazerbationsgefährdete COPD-Patienten in der PR im Vorfeld zu detektieren. Patienten und Methoden In einer retrospektiven Untersuchung wurden die Daten von COPD-Patienten, welche im Jahr 2018 eine PR erhielten, analysiert. Primärer Endpunkt der Untersuchung war die Punktzahl im CCI. Alle Daten wurden dem Klinikinformationssystem Phönix entnommen und COPD-Exazerbationen erfasst. Die laut Fallzahlplanung benötigten 44 Patienten wurden zufällig (mittels Zufallsliste für jede Gruppe) aus diesem Datenpool rekrutiert: 22 Patienten mit und 22 ohne Exazerbation während der PR. CCI und CIRS wurden für die eingeschlossenen Fälle für beide Gruppen bestimmt. Die Auswertung des primären Endpunktes (CCI) erfolgte durch den Gruppenvergleich der arithmetischen Mittel und der Signifikanzprüfung (Welch-Tests). Weitere statistische Lage- und Streuungsmaße wurden ergänzt (Median, Quartile, Standardabweichung).Zusätzlich wurde mittels Receiver Operating Characteristic (ROC)-Analyse sowohl für den CCI als auch für den CIRS ein optimaler Cutpoint zur Diskriminierung in AECOPD- und Nicht-AECOPD-Patienten gesucht. Ergebnisse 244 COPD-Patienten erhielten eine stationäre PR von durchschnittlich 21 Tagen, wovon 59 (24 %) während der PR eine behandlungspflichtige AECOPD erlitten. Die ausgewählten 22 Patienten mit einer AECOPD hatten einen mittleren CCI von 6,77 (SD: 1,97) und die 22 Patienten ohne AECOPD von 4,32 (SD: 1,17). Die Differenz von –2,45 war zu einem Signifikanzniveau von 5 % statistisch signifikant (p < 0,001; 95 %-KI: [–3,45 ; –1,46]). Die ROC-Analyse zeigte einen optimalen Cutpoint für den CCI bei 6 mit einer Sensitivität zur Feststellung einer AECOPD von 81,8 % und einer Spezifität von 86.,4 % mit einem Wert der AUC (area under the curve) von 0,87. Der optimale Cutpoint für den CIRS war 19 mit einer Sensitivität von 50 %, einer Spezifität von 77,2 % und einer AUC von 0,65. Schlussfolgerung COPD-Patienten mit einer akuten Exazerbation während der pneumologischen Rehabilitation haben einen höheren CCI. Mithilfe des CCI lässt sich mit einer hohen Sensitivität und Spezifität das Risiko einer AECOPD von COPD-Patienten im Rahmen eines stationären PR-Programms einschätzen.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

scholarly journals miR-142-3p as a novel biomarker for predicting poor prognosis in renal cell carcinoma patients after surgery

2019 ◽  
Vol 34 (3) ◽  
pp. 302-308 ◽  
Author(s):  
Xiqi Peng ◽  
Xiang Pan ◽  
Kaihao Liu ◽  
Chunduo Zhang ◽  
Liwen Zhao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Receiver Operating Characteristic ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Receiver Operating

Background: miR-142-3p has proved to be involved in tumorigenesis and the development of renal cell carcinoma. The present study aimed to explore the prognostic value of miR-142-3p. Methods: Total RNA was extracted from renal cell carcinoma specimens and the expression level of miR-142-3p was measured. Pearson Chi-square test, Kaplan–Meier analysis, as well as univariate and multivariate regression analysis were performed to determine the correlation between miR-142-3p and the prognosis of renal cell carcinoma patients. Receiver operating characteristic curves were constructed to evaluate the predictive efficiency of miR-142-3p for the prognosis of renal cell carcinoma patients. Data from The Cancer Genome Atlas (TCGA) were utilized to validate our findings. Results: Our results demonstrated that upregulation of miR-142-3p was correlated with shorter overall survival (P=0.002) and was, in the meantime, an independent prognostic factor for renal cell carcinoma patients (P=0.002). The receiver operating characteristic curve combining miR-142-3p expression with tumor stage showed an area under the curve of 0.633 (95% confidence interval 0.563, 0.702). The result of TCGA data was consistent with our findings. Conclusions: Our results suggest miR-142-3p expression is correlated with poor prognosis of renal cell carcinoma patients and may serve as a prognostic biomarker in the future.

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