scholarly journals GIÁ TRỊ CỦA PHÂN LOẠI IOTA ADNEX TRONG ĐÁNH GIÁ KHỐI U BUỒNG TRỨNG BẰNG SIÊU ÂM TẠI BỆNH VIỆN K

2021 ◽  
Vol 507 (1) ◽  
Author(s):  
Nguyễn Duy Thái ◽  
Dương Đức Hữu ◽  
Hoàng Thị Vi Hương ◽  
Ngô Đức Anh ◽  
Ngô Đức Anh ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Ca 125 ◽  
Roc Area

Mục tiêu: Nghiên cứu nhằm đánh giá giá trị của mô hình IOTA ADNEX trong siêu âm chẩn đoán mức độ lành tính – ác tính của khối u buồng trứng tại bệnh viện K. Đối tượng và phương pháp: Nghiên cứu được thực hiện trên 54 bệnh nhân trong khoảng thời gian từ tháng 12 năm 2020 đến tháng 05 năm 2021 tại bệnh viện K với lâm sàng nghi ngờ u buồng trứng, được siêu âm trước phẫu thuật và thu thập số liệu theo mô hình IOTA ADNEX, được phẫu thuật với chẩn đoán sau phẫu thuật là u buồng trứng. Đối chiếu kết quả phẫu thuật, kết quả giải phẫu bệnh với mô hình IOTA ADNEX thu thập trước phẫu thuật. Từ đó đánh giá giá trị của mô hình IOTA ADNEX trong siêu âm chẩn đoán mức độ lành tính – ác tính u buồng trứng. Kết quả: Mô hình IOTA ADEX có CA 125 và mô hình IOTA ADNEX không có CA 125 có giá trịtốt trong chẩn đoán phân biệt u buồng trứng lành tính và ác tính với diện tích dưới đường cong ROC (Area under the curve – AUC) lần lượt là 0,977 và 0,968. Ngưỡng cắt tối ưu của mô hình IOTA ADNEX có CA 125 và mô hình IOTA ADNEX không có CA125 lần lượt là 24,5 và 25,2. Tại ngưỡng cắt tối ưu, cả hai mô hình này đều có độ nhạy, độ đặc hiệu, giá trị dự báo dương tính, giá trị dự báo âm tính, độ chính xác lần lượt là 92,3%, 96,8%,96%, 93,8%, 94,7%. Kết luận:Mô hình IOTA ADNEX có CA 125 và mô hình IOTA ADNEX không có CA 125 đều có giá trị cao và tương đồng trong chẩn đoán phân biệt u buồng trứng lành tính và ác tính ở bệnh viện K. 

scholarly journals Early serum tumor marker levels after fourteen days of tyrosine kinase inhibitor targeted therapy predicts outcomes in patients with advanced lung adenocarcinoma

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0240736
Author(s):  
Hung-Jen Chen ◽  
Chih-Yen Tu ◽  
Kuo-Yang Huang ◽  
Chun-Ru Chien ◽  
Te-Chun Hsia
Keyword(s):  
Targeted Therapy ◽  
Lung Adenocarcinoma ◽  
Kinase Inhibitor ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Growth Factor Receptor ◽  
Progression Free Survival ◽  
Ca 125 ◽  
Mutation Status ◽  
Anaplastic Lymphoma

Objective Image evaluation strategy for lung cancer patients has difficulty obtaining the appropriate quantity of diffuse lung nodules and bone metastases. The study was to demonstrate whether early variations in the levels of serum 4-tumor markers (4-TMs)(carcinoembryonic antigen [CEA], cancer antigen [CA]125, CA19-9, and CA15-3) after TKI targeted therapy were associated with treatment response in patients with lung adenocarcinoma. Methods Patients with stage IIIB-IV lung adenocarcinoma taking epidermal growth factor receptor (EGFR) TKIs or anaplastic lymphoma kinase (ALK) inhibitors were enrolled prospectively from June 2012 to February 2015. According to the variations of the percentage of change in 4-TM levels (4-TMpc), we divided patients into ascending (increases in 4-TMpc over the 7th- 14th day) and descending (decreases in 4-TMpc over the 7th- 14th day) groups. Results 184 patients were enrolled, and 89% had at least one of the pre-treatment evaluable TMs and were further analyzed. An excellent response to the TKI targeted therapy was accurately predicted in the descending group, as determined using receiver operating characteristic curve analysis (an area under the curve, 0.83). Multivariate Cox hazards model analyses demonstrated that the type of 4-TMpc and mutation status were the strongest predictors of progression-free survival (PFS)(descending versus ascending, hazard ratios [HR] 0.30, 95% confidence interval [CI], 0.19–0.47; sensitive mutation versus wide type, HR 0.30, 95% CI, 0.19–0.48). Conclusions Type of 4-TMpc 14 days after TKI targeted therapy is associated with an image response and PFS, without regarding mutation status, in patients with advanced lung adenocarcinoma.

Assessment of CA-125 area under the curve as a prognostic factor in patients with ovarian cancer

2013 ◽  
Vol 30 (1) ◽  
Author(s):  
Sernaz Uzunoglu ◽  
Aysun Aybatlı ◽  
Petek Balkanlı Kaplan ◽  
Irfan Cicin ◽  
Necdet Sut ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Factor ◽  
Area Under The Curve ◽  
Ca 125

scholarly journals Impact of Renal Disease on Natriuretic Peptide Testing for Diagnosing Decompensated Heart Failure and Predicting Mortality

2007 ◽  
Vol 53 (8) ◽  
pp. 1511-1519 ◽  
Author(s):  
Christopher R deFilippi ◽  
Stephen L Seliger ◽  
Susan Maynard ◽  
Robert H Christenson
Keyword(s):  
Heart Failure ◽  
Renal Function ◽  
Natriuretic Peptide ◽  
Estimated Glomerular Filtration Rate ◽  
Area Under The Curve ◽  
Decompensated Heart Failure ◽  
Multivariate Regression Analysis ◽  
Poor Outcomes ◽  
One Year ◽  
Roc Area

Abstract Background: Concomitant occurrence of kidney disease (KD) and heart failure (HF) is common and associated with poor outcomes. Natriuretic peptide studies have typically excluded many individuals with KD. We compared the accuracy of B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) for diagnosing decompensated HF and predicting mortality across the spectrum of renal function. Methods: BNP and NT-proBNP were prospectively measured in a cohort of 831 dyspnea patients. KD was defined as an estimated glomerular filtration rate <60 mL · min−1 · (1.73 m2)−1. The accuracy and predictive value of each test for diagnosing decompensated HF and predicting all-cause 1-year mortality were assessed by ROC area under the curve (AUC) and multivariate regression analysis. Results: Among the 831 dyspnea patients, 393 (47%) had KD. The diagnostic accuracies of BNP and NT-proBNP in detecting decompensated HF were similar to each other in patients without KD (AUC 0.75 vs 0.74, respectively; P = 0.60) and in patients with KD (AUC 0.68 vs 0.66; P = 0.10). One-year mortality rates were 36.3% and 19.0% in those with and without KD, respectively (P <0.001). Progressively higher BNP and NT-proBNP concentrations remained predictive of increased mortality in KD patients. Compared with the lowest quartile, quartile 4 of BNP had an adjusted hazards ratio (HR) of 2.6 (95% CI 1.4–4.8; P = 0.004 for trend) and NT-proBNP quartile 4 had an HR of 4.5 (95% CI 2.0–10.2; P <0.001 for trend). Only NT-proBNP remained a predictor of death after adjustment for clinical confounders and the other natriuretic peptide marker. Conclusions: NT-proBNP and BNP are equivalent predictors of decompensated HF across a spectrum of renal function, but NT-proBNP is a superior predictor of mortality.

scholarly journals Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization

Cells ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 881 ◽  
Author(s):  
Jihwan Ha ◽  
Chihyun Park ◽  
Chanyoung Park ◽  
Sanghyun Park
Keyword(s):  
Computational Models ◽  
Matrix Completion ◽  
Area Under The Curve ◽  
Excellent Performance ◽  
Novel Mirna ◽  
Lack Of Information ◽  
Disease Associations ◽  
Roc Area ◽  
Leave One Out ◽  
Global And Local

The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.

Phase II Study of Carboplatin, Paclitaxel, and Bevacizumab With Maintenance Bevacizumab As First-Line Chemotherapy for Advanced Müllerian Tumors

2010 ◽  
Vol 28 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Richard T. Penson ◽  
Don S. Dizon ◽  
Stephen A. Cannistra ◽  
Maria R. Roche ◽  
Carolyn N. Krasner ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Phase Ii ◽  
Single Agent ◽  
Area Under The Curve ◽  
Advanced Ovarian Cancer ◽  
Progression Free Survival ◽  
Ca 125 ◽  
Open Label ◽  
Peritoneal Cancer ◽  
Open Label Phase

Purpose New strategies are needed to improve outcomes for patients with advanced ovarian cancer. Bevacizumab is a recombinant humanized monoclonal antibody that neutralizes vascular endothelial growth factor but is associated with GI perforations (GIPs) in patients with recurrent disease. Patients and Methods An open-label, phase II clinical trial was conducted in newly diagnosed patients with stage ≥ IC epithelial müllerian tumors. Patients received intravenous (IV) carboplatin (area under the curve = 5), paclitaxel (175 mg/m2 IV), and bevacizumab (15 mg/kg IV) for six to eight cycles on day 1 every 21 days. Bevacizumab was omitted in the first cycle and continued as a single agent for 1 year. Results Sixty-two women participated in this study. Fifty-one patients (82%) were optimally surgically cytoreduced before treatment. The median age was 58 years (range, 18 to 77 years). Forty-five women (73%) had ovarian cancer, 10 (16%) had peritoneal cancer, four (6%) had fallopian tube cancers, and three (5%) had uterine papillary serous tumors. The majority of patients (90%) had stage III or IV disease. A median of 17 maintenance cycles (range, 0 to 25+ cycles) of bevacizumab (556 cycles) were administered with mild toxicity. Treatment was associated with two pulmonary embolisms and two GIPs, all occurring during the chemotherapy phase of treatment (364 total cycles). No grade 4 toxicities were seen during maintenance bevacizumab treatment. Radiographic responses were documented in 21 (75%) of 28 women with measurable disease (11 complete responses and 10 partial responses), with CA-125 responses in 76% of patients (11 complete responses, 21%; and 35 partial responses, 55%). The progression-free survival rate at 36 months was 58%. Conclusion The regimen of carboplatin, paclitaxel, and bevacizumab with maintenance bevacizumab is feasible, safe, and worthy of future study in advanced ovarian cancer.

scholarly journals The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Guruduth Banavar ◽  
Oyetunji Ogundijo ◽  
Ryan Toma ◽  
Sathyapriya Rajagopal ◽  
Yen Kai Lim ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Average Risk ◽  
Operating Characteristics ◽  
Early Diagnostics ◽  
New Era ◽  
Oral Cancers ◽  
Non Invasive ◽  
Roc Area ◽  
Stage 1 ◽  
Invasive Diagnostics

AbstractDespite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

scholarly journals A tool to distinguish viral from bacterial pneumonia

2021 ◽  
Author(s):  
Alfredo Tagarro ◽  
Cinta Moraleda ◽  
Sara Dominguez-Rodriguez ◽  
Mario Jose Rodriguez ◽  
Maria Dolores Martin ◽  
...  
Keyword(s):  
Bacterial Pneumonia ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Community Acquired Pneumonia ◽  
Hospitalized Children ◽  
Training Set ◽  
Etiological Diagnosis ◽  
Choice Of Treatment ◽  
Roc Area ◽  
Atypical Bacteria

Establishing the etiology of community-acquired pneumonia (CAP) in children at admission is challenging. As a result, most children receive antibiotics that do not need. This study aims to build and validate a diagnostic tool combining clinical, analytical and radiographical features to sequentially differentiate viral from bacterial CAP, and among bacterial CAP, typical from atypical bacteria, to improve choice of treatment. Methods Consecutive hospitalized children between 1 month and 16 years of age with CAP were enrolled. An extensive microbiological workup was performed. A score was built with a training set of 70% patients, to first differentiate between viral and bacterial CAP and secondly, typical from atypical bacterial CAP. To select variables, a Ridge model was used. Optimal cut-off points were selected to maximize specificity setting a high sensitivity (80%). Weights of each variable were calculated with a multivariable logistic regression. The score was validated with the rest of the participants. Results In total, 262 (53%) children (median age, 2 years, 52.3% male) had an etiological diagnosis. The step 1 discriminates viral from bacterial CAP. Bacterial CAPs were classified with a sensitivity=97%, a specificity=48%, and a ROC area under the curve (AUC)=0.81. If a CAP was classificated as bacterial, it was assessed with step 2. The step 2 differentiates typical vs. atypical bacterial CAP. Typical bacteria were classified with a sensitivity=100%, a specificity=64%, and AUC=0.90. Conclusion This two-steps tool can facilitate the physician decision to prescribe antibiotics without compromising patient safety.

scholarly journals Is there a correlation between inflammatory markers and coagulation parameters in women with advanced ovarian endometriosis?

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Shaojie Ding ◽  
Qiao Lin ◽  
Tianhong Zhu ◽  
Tiantian Li ◽  
Libo Zhu ◽  
...  
Keyword(s):  
Inflammatory Markers ◽  
Area Under The Curve ◽  
Plasma Fibrinogen ◽  
Ca 125 ◽  
Ovarian Cysts ◽  
Control Group ◽  
Thrombin Time ◽  
Ovarian Endometriosis ◽  
Coagulation Parameters ◽  
Lymphocyte Ratio

Abstract Background Endometriosis is defined as a chronic inflammatory disease. Recent studies have shown that increased coagulation parameters including fibrinogen and platelets are associated with endometriosis. The objective of this study was to determine the levels of inflammatory markers and coagulation parameters and their correlations in women with endometriomas compared to those with benign ovarian cysts or normal pelvic anatomy. Methods Between June 2015 and June 2017, a total of 548 women who underwent laparoscopic/laparotomic surgery for ovarian endometriomas (OMA group, n = 226), non-endometriosis benign ovarian cysts (Cyst group, n = 210) and tubal reanastomosis (Control group, n = 112) were recruited in this study. Inflammatory markers including c-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and coagulation parameters including platelet count, thrombin time (TT), prothrombin time (PT), activated partial thromboplastin time, and plasma fibrinogen as well as CA-125 were determined. Results Compared with Cyst group and Control group, TT and PT in OMA group were significantly shorter and plasma fibrinogen levels were significantly higher (P < 0.05). Moreover, the levels of plasma fibrinogen were positively correlated with CRP, NLR and PLR (P < 0.05). In addition, the confidence intervals for the area under the curve (AUC) for CA-125 × fibrinogen were significantly higher than those for CA-125 (0.904–0.952 vs. 0.899–0.949) in the diagnosis of endometrioma. Conclusions These results indicate that women with endometriomas demonstrate a hypercoagulable status due to the inflammatory nature of endometriosis. The combined determination for CA-125 and fibrinogen demonstrate a higher area under the curve than the single detection of CA-125 in those with endometriomas compared to these with benign ovarian cysts. Trial registration This study was approved by the Human Ethics Committee of the Women’s Hospital, School of Medicine, Zhejiang University (No.20170174) and all women provided written informed consent.

scholarly journals Lack of Efficacy of Combined Carbohydrate Antigen Markers for Lung Cancer Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhineng Wen ◽  
Ying Huang ◽  
Zhougui Ling ◽  
Jifei Chen ◽  
Xiaomou Wei ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Markers ◽  
Cell Lung Cancer ◽  
Area Under The Curve ◽  
Nonsmall Cell Lung Cancer ◽  
Diagnostic Value ◽  
Carbohydrate Antigen ◽  
Ca 125 ◽  
Chi Square ◽  
Diagnostic Efficacy

Background. Lung cancer (LC) is top-ranked in cancer incidence and is the leading cause of cancer death globally. Combining serum biomarkers can improve the accuracy of LC diagnosis. The identification of the best potential combination of traditional tumor markers is essential for LC diagnosis. Patients and Methods. Blood samples were collected from 132 LC cases and 118 benign lung disease (BLD) controls. The expression levels of ten serum tumor markers (CYFR21, CEA, NSE, SCC, CA15-3, CA 19-9, CA 125, CA50, CA242, and CA724) were assayed, and that the expression in the levels of tumor markers were evaluated, isolated, and combined in different patients. The performance of the biomarkers was analyzed by receiver operating characteristic (ROC) analyses, and the difference between combinations of biomarkers was compared by Chi-square ( χ 2 ) tests. Results. As single markers, CYFR21 and CEA showed good diagnostic efficacy for nonsmall cell lung cancer (NSCLC) patients, while NSE and CEA were the most sensitive in the diagnosis of small cell lung cancer (SCLC). The area under the curve (AUC) value was 0.854 for the panel of four biomarkers (CYFR21, CEA, NSE, and SCC), 0.875 for the panel of six biomarkers (CYFR21, CEA, NSE, SCC, CA125, and CA15-3), and 0.884 for the panel of ten markers (CYFR21, CEA, NSE, SCC, CA125, CA15-3, CA19-9, CA50, CA242, and CA724). With a higher sensitivity and negative predictive value (NPV), the diagnostic accuracy of the three panels was better than that of any single biomarker, but there were no statistically significant differences among them (all P values > 0.05). However, the panel of six carbohydrate antigen (CA) biomarkers (CA125, CA15-3, CA19-9, CA50, CA242, and CA724) showed a lower diagnostic value (AUC: 0.776, sensitivity: 59.8%, specificity: 73.0%, and NPV: 60.4%) than the three panels ( P value < 0.05). The performance was similar even when analyzed individually by LC subtypes. Conclusion. The biomarkers isolated are elevated for different types of lung cancer, and the panel of CYFR21, CEA, NSE, and SCC seems to be a promising serum biomarker for the diagnosis of lung cancer, while the combination with carbohydrate antigen markers does not improve the diagnostic efficacy.

Paediatric pneumonia: deriving a model to identify severe disease

2021 ◽  
pp. archdischild-2021-322665
Author(s):  
Stuart Haggie ◽  
Elizabeth H Barnes ◽  
Hiran Selvadurai ◽  
Hasantha Gunasekera ◽  
Dominic A Fitzgerald
Keyword(s):  
Intensive Care ◽  
Work Of Breathing ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Community Acquired Pneumonia ◽  
Sensitivity Analyses ◽  
Intensive Care Treatment ◽  
Clinical Tool ◽  
Clinical Variables ◽  
Roc Area

BackgroundCommunity-acquired pneumonia (CAP) is a leading cause of childhood hospitalisation. Limited data exist on factors predicting severe disease with no paediatric-specific predictive tools.MethodsRetrospective cohort (2011–2016) of hospitalised CAP cases. We analysed clinical variables collected at hospital presentation against outcomes. Stratified outcomes were mild (hospitalised), moderate (invasive drainage procedure, intensive care) or severe (mechanical ventilation, vasopressors, death).ResultsWe report 3330 CAP cases, median age 2.0 years (IQR 1–5 years), with 2950 (88.5%) mild, 305 (9.2%) moderate and 75 (2.3%) severe outcomes. Moderate-severe outcomes were associated with hypoxia (SaO2 <90%; OR 6.6, 95% CI 5.1 to 8.5), increased work of breathing (severe vs normal OR 5.8, 95% CI 4.2 to 8.0), comorbidities (4+ comorbidities vs nil; OR 8.8, 95% CI 5.5 to 14) and being indigenous (OR 4.7, 95% CI 2.6 to 8.4). Febrile children were less likely than afebrile children to have moderate-severe outcomes (OR 0.57 95% CI 0.44 to 0.74). The full model receiver operating characteristic (ROC) area under the curve (AUC) was 0.78. Sensitivity analyses showed similar results with clinical or radiological CAP definitions. We derived a clinical tool to stratify low, intermediate or high likelihood of severe disease (AUC 0.72). High scores (≥5) had nearly eight times higher odds of moderate-severe disease than those with a low (≤1) score (OR 7.7 95% CI 5.6 to 10.5).ConclusionsA clinical risk prediction tool is needed for child CAP. We have identified risk factors and derived a simple clinical tool using clinical variables at hospital presentation to determine a child’s risk of invasive or intensive care treatment with an ROC AUC comparable with adult pneumonia tools.

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