Journal: Cancer Management and Research

Article Title: High Expression of lncRNA HEIH is Helpful in the Diagnosis of Non-Small Cell Lung Cancer and Predicts Poor Prognosis

doi: 10.2147/CMAR.S320965

Figure Lengend Snippet: LncRNA HEIH has a high diagnostic efficacy in NSCLC patients. ( A ) The diagnostic efficacy of lncRNA HEIH and CEA in LUSC patients was evaluated by ROC curve analysis; ( B ) the diagnostic efficacy of lncRNA HEIH and CEA in LUAD patients was evaluated by ROC curve analysis. MedCalc-comparison of ROC curves was used to compare and analyze the area difference under the ROC curve.

Article Snippet: MedCalc-comparison of ROC curves showed that the area under ROC curve of lncRNA HEIH was significantly higher than that of CEA ( P = 0.0011; 95% CI = 0.057–0.228), indicating that lncRNA HEIH had a higher diagnostic efficacy than CEA for LUAD.

Techniques: Diagnostic Assay, Comparison

Journal: Frontiers in Oncology

Article Title: Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients

doi: 10.3389/fonc.2022.882584

Figure Lengend Snippet: ROC curves of every continuous variable. (A) , primary tumor CT signs. (B) , texture features. The area under the curve (AUC) represented the accuracy of predicting for occult PM.

Article Snippet: The efficiency of the models was tested by the AUC of the ROC curve and compared by the DeLong test in MedCalc.

Techniques:

Journal: Frontiers in Oncology

Article Title: Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients

doi: 10.3389/fonc.2022.882584

Figure Lengend Snippet: ROC curves parameters of different models.

Article Snippet: The efficiency of the models was tested by the AUC of the ROC curve and compared by the DeLong test in MedCalc.

Techniques:

Journal: Frontiers in Oncology

Article Title: Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients

doi: 10.3389/fonc.2022.882584

Figure Lengend Snippet: Comparison of ROC curves AUC areas of different models.

Article Snippet: The efficiency of the models was tested by the AUC of the ROC curve and compared by the DeLong test in MedCalc.

Techniques: Comparison

Journal: 3 Biotech

Article Title: Diagnostic and prognostic potential clustered miRNAs in bladder cancer

doi: 10.1007/s13205-022-03225-z

Figure Lengend Snippet: Prognostic feature analysis of three miRNA cluster candidates in BCa patients. The Kaplan–Meier survival curves show overall survival outcomes of miRNA cluster candidates. a mir-200c/mir-141, b mir-216a/mir-217, c mir-15b/mir-16-2 according to their expression in high and low-risk patient groups (black: low expression; red: high expression). The ROC curve showing diagnostic values of miRNA cluster candidates, d mir-200c/mir-141, e mir-15b/mir-16-2, and f mir-216a/mir-217. The ROC and AUROC curves are generated through MedCalc software

Article Snippet: The ROC and AUROC curves are generated through MedCalc software

Techniques: Expressing, Diagnostic Assay, Generated, Software

Journal: PLoS Neglected Tropical Diseases

Article Title: Multi-parameter approach to evaluate the timing of memory status after 17DD-YF primary vaccination

doi: 10.1371/journal.pntd.0006462

Figure Lengend Snippet: (A) Venn diagram analysis was carried out to identify the intersection of phenotypic/functional biomarkers amongst NV(day0), PV(day30-45), PV(year1-9) and PV(year10-11) and select those common attributes between PV(day30-45) and PV(year1-9), considered biomarkers of protection. Detailed Venn Diagram report is provided in the . (B) The performance indicators (cut-off, accuracy, sensitivity-Se and specificity-Sp) for the ten selected common biomarkers (eEfCD4, EMCD4, CMCD19, EMCD8, IFNCD4, IL-5CD8, TNFCD4, IFNCD8, TNFCD8 and IL-5CD4) are provided in the inserted table. (C) Heatmap analysis was carried out to illustrate the profile of phenotypic/functional biomarkers common at PV(day30-45) and PV(year1-9) as well as the mean index calculated considering all 10 phenotypic/functional biomarkers together and demonstrate the proportion (%) of volunteers presenting low (Green), median (Black) or high (Red) YF-Ag/CC index. Histograms were constructed to highlight the frequency of volunteers above the median YF-Ag/CC index (Red Curves). (D) Scatter plot were built to demonstrate the sensitivity (Red Circles) and specificity (Green Circles) of the mean index of 10 phenotypic/functional biomarkers, using the cut-off edge (Mean Index = 1.3) provided by the ROC curve analysis. (E) Memory diagram were constructed using the defined cut-off for phenotypic/functional biomarkers, the memory status was defined for each subject, considering the phenotypic/functional (Mean Index of 10 biomarkers >1.3) and PRNT (>2.9 Log mIU/mL, according to Simões et al., 2012). Column statistics were used to calculate the proportion of subjects displaying distinct status of resultant memory, referred as none, phenotypic/functional biomarkers—P&F, PRNT and both. (F) The resultant memory for the 10 phenotypic/functional biomarkers and PRNT was displayed on bar charts. Significant differences at p<0.05 (Chi-square test) of resultant status amongst study groups were represented by letters “a”, b”, “c” and “d” in comparison to NV(day0), PV(day30-45), PV(year1-9) and PV(year10-11), respectively.

Article Snippet: Additional analysis was carried out employing Venn diagram ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ), ROC curve (MedCalc software package, Version 7.3.0.0), heatmap (R Project for Statistical Computing Version 3.0.1) and decision tree J48 algorithm (present in WEKA software version 3.6.11).

Techniques: Functional Assay, Construct, Comparison

Journal: PLoS Neglected Tropical Diseases

Article Title: Multi-parameter approach to evaluate the timing of memory status after 17DD-YF primary vaccination

doi: 10.1371/journal.pntd.0006462

Figure Lengend Snippet: (A) Decision tree analysis was carried out to identify root attributes [Ellipses] for phenotypic/functional (P&F) biomarkers amongst NV(day0)&PV(year10-11) [white rectangle] and PV(day30-45)&PV(year1-9) [black rectangle], considered biomarkers to discriminate unprotected from protected subjects. Leave-one-out-cross-validation analysis (LOOCV) was employed to minimize biased performance estimates by using all data set for decision tree model fitting. EMCD8 and IL-5CD4 were selected as major phenotypic and functional 17DD-YF Memory-related biomarkers, respectively. (B) Heatmaps were built, taking the mean index of the top-two phenotypic/functional biomarkers (EMCD8 & IL-5CD4) and demonstrating the proportion (%) of volunteers ranging from low (White) to high (Gray) YF-Ag/CC index. A scatter plot was constructed to show the sensitivity (Gray Circle) and specificity (White Circle) of the top-two biomarkers, employing the cut-off edge (Mean Index = 1.3) provided by the ROC curve analysis. (C) Resultant memory status was defined for each subject, considering the top-two biomarkers (Mean Index >1.3) and PRNT (>2.9 Log mIU/mL, according to Simões et al., 2012). Column statistics were used to calculate the proportion of subjects displaying differing categories of resultant memory, referred as none, top-two biomarkers—P&F, PRNT and both. (D) Pie charts illustrated the overall resultant memory status within each category, as determined by the top-two biomarkers and PRNT. Significant differences at p<0.05 (Chi-square test) of resultant memory status amongst study groups were represented by letters “a”, b”, “c” and “d” in comparison to NV(day0), PV(day30-45), PV(year1-9) and PV(year10-11), respectively.

Article Snippet: Additional analysis was carried out employing Venn diagram ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ), ROC curve (MedCalc software package, Version 7.3.0.0), heatmap (R Project for Statistical Computing Version 3.0.1) and decision tree J48 algorithm (present in WEKA software version 3.6.11).

Techniques: Functional Assay, Biomarker Discovery, Construct, Comparison