scholarly journals Prognostic value of cytokines in COVID-19 associated pneumonia

2021 ◽  
Vol 13 (1) ◽  
pp. 59-69
Author(s):  
Olga Yu. Tkachenko ◽  
Margarita Yu. Pervakova ◽  
Sergey V. Lapin ◽  
Aleksandra V. Mazing ◽  
Darya A. Kuznetsova ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Value ◽  
Roc Analysis ◽  
Area Under The Curve ◽  
Superior Performance ◽  
Cytokine Storm ◽  
Point Scale ◽  
Single Marker ◽  
Commercial Kits ◽  
Poor Outcomes

BACKGROUND: Coronavirus disease 2019 (COVID-19) is often complicated by cytokine storm syndrome. Although many interleukins (IL) have predictive value, the sensitivity and specificity of a single marker is limited. AIM: The purpose of the study is to develop an objective and informative cytokine storm scale for assessing the risk of developing a critical course in patients with COVID-19 associated pneumonia. MATERIALS AND METHODS: A total of 226 cases of COVID-19 were investigated, 36 (16 %) of which were with poor outcomes. The cytokines IL-1b, IL-2, IL-6, IL-8, IL-10, IL-18, TNF-, IFN, IFN- were studied by enzyme immunoassay, commercial kits manufactured by Vector-Best, RF. RESULTS: Since IL-6, IL-10, IL-18, and procalcitonin were associated with disease severity and death, these indicators were integrated into a 12-point scale called the cytokine storm scale. The patients who scored more than 6 points had a high risk of a poor outcome of the disease. According to ROC analysis, the area under the curve for the cytokine storm scale was larger than for each of the four markers separately [AUC 0.90 (95% CI 0.84550.9592), p 0.001]. CONCLUSIONS: Thus, the cytokine storm scale system presents superior performance in determining patients with favorable and fatal outcomes to each individual cytokine.

scholarly journals Clinical Validity of a Prognostic Gene Expression Cluster-Based Model in Human Papillomavirus–Positive Oropharyngeal Carcinoma

2021 ◽  
pp. 1666-1676
Author(s):  
Stefano Cavalieri ◽  
Mara S. Serafini ◽  
Andrea Carenzo ◽  
Silvana Canevari ◽  
Ruud H. Brakenhoff ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Clinical Practice ◽  
High Risk ◽  
Cluster Model ◽  
Prognostic Value ◽  
Prognostic Significance ◽  
Area Under The Curve ◽  
Oropharyngeal Carcinoma ◽  
Curative Intent ◽  
Hpv Dna

PURPOSE Under common therapeutic regimens, the prognosis of human papillomavirus (HPV)–positive squamous oropharyngeal carcinomas (OPCs) is more favorable than HPV-negative OPCs. However, the prognosis of some tumors is dismal, and validated prognostic factors are missing in clinical practice. The present work aimed to validate the prognostic significance of our published three-cluster model and to compare its prognostic value with those of the 8th edition of the tumor-node-metastasis staging system (TNM8) and published signatures and clustering models. METHODS Patients with HPV DNA-positive OPCs with locoregionally advanced nonmetastatic disease treated with curative intent (BD2Decide observational study, NCT02832102 ) were considered as validation cohort. Patients were treated in seven European centers, with expertise in the multidisciplinary management of patients with head and neck cancer. The median follow-up was 46.2 months (95% CI, 41.2 to 50), and data collection was concluded in September 2019. The primary end point of this study was overall survival (OS). Three-clustering models and seven prognostic signatures were compared with our three-cluster model. RESULTS The study population consisted of 235 patients. The three-cluster model confirmed its prognostic value. Two-year OS in each cluster was 100% in the low-risk cluster, 96.6% in the intermediate-risk cluster, and 86.3% in the high-risk cluster ( P = .00074). For the high-risk cluster, we observed an area under the curve = 0.832 for 2-year OS, significantly outperforming TNM 8th edition (area under the curve = 0.596), and functional and biological differences were identified for each cluster. CONCLUSION The rigorous clinical selection of the cases included in this study confirmed the robustness of our three-cluster model in HPV-positive OPCs. The prognostic value was found to be independent and superior compared with TNM8. The next step includes the translation of the three-cluster model in clinical practice. This could open the way to future exploration of already available therapies in HPV-positive OPCs tailoring de-escalation or intensification according to the three-cluster model.

Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction

2020 ◽  
Vol 14 (9) ◽  
pp. 775-784
Author(s):  
Johannes T Neumann ◽  
Nils A Sörensen ◽  
Tanja Zeller ◽  
Craig A Magaret ◽  
Grady Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Clinical Trial ◽  
High Risk ◽  
Cardiovascular Events ◽  
Prognostic Value ◽  
Prognostic Score ◽  
Area Under The Curve ◽  
Major Adverse Cardiovascular Events ◽  
Clinical Trial Registration

Background: In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). Methodology & results: A previously described prognostic panel for MACE consisting of four biomarkers was measured in 748 patients with suspected MI. The investigated end point was incident MACE within 1 year. The prognostic value of a continuous score and an optimal cut-off was investigated. The area under the curve was 0.86 for the overall model. Using the optimal cut-off resulted in a negative predictive value of 99.4% for incident MACE. Patients with an elevated prognostic score were at high risk for MACE. Conclusion: Among patients with suspected MI, we validated a multibiomarker panel for predicting 1-year MACE. Clinical Trial Registration: NCT02355457 (ClinicalTrials.gov)

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

scholarly journals Serum Extracellular Vesicle-Derived circHIPK3 and circSMARCA5 Are Two Novel Diagnostic Biomarkers for Glioblastoma Multiforme

2021 ◽  
Vol 14 (7) ◽  
pp. 618
Author(s):  
Michele Stella ◽  
Luca Falzone ◽  
Angela Caponnetto ◽  
Giuseppe Gattuso ◽  
Cristina Barbagallo ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Roc Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Digital Pcr ◽  
Extracellular Vesicle ◽  
Size Exclusion ◽  
Circular Rnas ◽  
Diagnostic Biomarkers ◽  
Exclusion Chromatography

Glioblastoma multiforme (GBM) is the most frequent and deadly human brain cancer. Early diagnosis through non-invasive biomarkers may render GBM more easily treatable, improving the prognosis of this currently incurable disease. We suggest the use of serum extracellular vesicle (sEV)-derived circular RNAs (circRNAs) as highly stable minimally invasive diagnostic biomarkers for GBM diagnosis. EVs were isolated by size exclusion chromatography from sera of 23 GBM and 5 grade 3 glioma (GIII) patients, and 10 unaffected controls (UC). The expression of two candidate circRNAs (circSMARCA5 and circHIPK3) was assayed by droplet digital PCR. CircSMARCA5 and circHIPK3 were significantly less abundant in sEVs from GBM patients with respect to UC (fold-change (FC) of −2.15 and −1.92, respectively) and GIII (FC of −1.75 and −1.4, respectively). Receiver operating characteristic curve (ROC) analysis, based on the expression of sEV-derived circSMARCA5 and circHIPK3, allowed us to distinguish GBM from UC (area under the curve (AUC) 0.823 (0.667–0.979) and 0.855 (0.704 to 1.000), with a 95% confidence interval (CI), respectively). Multivariable ROC analysis, performed by combining the expression of sEV-derived circSMARCA5 and circHIPK3 with preoperative neutrophil to lymphocyte (NLR), platelet to lymphocyte (PLR) and lymphocyte to monocyte (LMR) ratios, three known diagnostic and prognostic GBM markers, allowed an improvement in the GBM diagnostic accuracy (AUC 0.901 (0.7912 to 1.000), 95% CI). Our data suggest sEV-derived circSMARCA5 and circHIPK3 as good diagnostic biomarkers for GBM, especially when associated with preoperative NLR, PLR and LMR.

scholarly journals Prognostic Value of Plasma hPG80 (Circulating Progastrin) in Metastatic Renal Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 375
Author(s):  
Manish Kohli ◽  
Winston Tan ◽  
Bérengère Vire ◽  
Pierre Liaud ◽  
Mélina Blairvacq ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Prognostic Impact ◽  
Significant Difference

Precise management of kidney cancer requires the identification of prognostic factors. hPG80 (circulating progastrin) is a tumor promoting peptide present in the blood of patients with various cancers, including renal cell carcinoma (RCC). In this study, we evaluated the prognostic value of plasma hPG80 in 143 prospectively collected patients with metastatic RCC (mRCC). The prognostic impact of hPG80 levels on overall survival (OS) in mRCC patients after controlling for hPG80 levels in non-cancer age matched controls was determined and compared to the International Metastatic Database Consortium (IMDC) risk model (good, intermediate, poor). ROC curves were used to evaluate the diagnostic accuracy of hPG80 using the area under the curve (AUC). Our results showed that plasma hPG80 was detected in 94% of mRCC patients. hPG80 levels displayed high predictive accuracy with an AUC of 0.93 and 0.84 when compared to 18–25 year old controls and 50–80 year old controls, respectively. mRCC patients with high hPG80 levels (>4.5 pM) had significantly lower OS compared to patients with low hPG80 levels (<4.5 pM) (12 versus 31.2 months, respectively; p = 0.0031). Adding hPG80 levels (score of 1 for patients having hPG80 levels > 4.5 pM) to the six variables of the IMDC risk model showed a greater and significant difference in OS between the newly defined good-, intermediate- and poor-risk groups (p = 0.0003 compared to p = 0.0076). Finally, when patients with IMDC intermediate-risk group were further divided into two groups based on hPG80 levels within these subgroups, increased OS were observed in patients with low hPG80 levels (<4.5 pM). In conclusion, our data suggest that hPG80 could be used for prognosticating survival in mRCC alone or integrated to the IMDC score (by adding a variable to the IMDC score or by substratifying the IMDC risk groups), be a prognostic biomarker in mRCC patients.

scholarly journals Can sonographic features of microcalcification predict thyroid nodule malignancy? a prospective observational study

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mehrdad Nabahati ◽  
Rahele Mehraeen ◽  
Zoleika Moazezi ◽  
Naser Ghaemian
Keyword(s):  
Thyroid Nodule ◽  
Roc Analysis ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Positive Association ◽  
Needle Aspiration ◽  
Northern Iran ◽  
Significant Positive Association ◽  
Irregular Margin ◽  
Benign Nodules

Abstract Background The aim of this study was to investigate the diagnostic accuracy of microcalcification, as well as its associated sonographic features, for prediction of thyroid nodule malignancy. We prospectively assessed the patients with thyroid nodule, who underwent ultrasound-guided fine-needle aspiration during 2017–2020 in Babol, northern Iran. The ultrasonographic characteristics of the nodules, as well as their cytological results, were recorded. We used regression analysis to evaluate the relation between sonographic findings and nodule malignancy. A receiver operator characteristics (ROC) analysis was also used to estimate the ability of ultrasound to predict the characteristic features of malignancy, as estimated by the area under the curve (AUC). Results Overall, 1129 thyroid nodules were finally included in the study, of which 452 (40%) had microcalcification. A significant positive association was found between nodule malignancy and microcalcification in both univariate (OR=3.626, 95% CI 2.258–5.822) and multivariable regression analyses (OR=1.878, 95% CI 1.095–3.219). In the nodules with microcalcification, significant positive relations were seen between malignancy and hypoechogenicity (OR=3.833, 95% CI 1.032–14.238), >5 microcalcification number (OR=3.045, 95% CI 1.328–6.982), irregular margin (OR=3.341, 95% CI 1.078–10.352), and lobulated margin (OR=5.727, 95% CI 1.934–16.959). The ROC analysis indicated that AUC for hypoechogenicity, >5 microcalcification number, irregular margin, and lobulated margin were 60%, 62%, 55%, and 60%, respectively, in predicting malignant thyroid nodules. Conclusion The findings indicated that microcalcification can be a potential predictor of thyroid nodule malignancy. Also, the presence of irregular or lobulated margins, multiple intranodular microcalcification (>5 microcalcifications), and/or hypoechogenicity can improve the ability of microcalcification in distinguishing malignant from benign nodules.

scholarly journals A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Susanne F. Awad ◽  
Soha R. Dargham ◽  
Amine A. Toumi ◽  
Elsy M. Dumit ◽  
Katie G. El-Nahas ◽  
...  
Keyword(s):  
High Risk ◽  
Sensitivity And Specificity ◽  
Risk Score ◽  
Area Under The Curve ◽  
Diabetes Risk ◽  
Predictive Values ◽  
Predictive Variables ◽  
Comparable Performance ◽  
Representative Random Sample ◽  
Diabetes Risk Score

AbstractWe developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.

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