scholarly journals Effect of cancer prevalence on the use of risk-assessment cut-off levels and the performance of mathematical models to distinguish malignant from benign adnexal masses

2011 ◽  
Vol 37 (2) ◽  
pp. 226-231 ◽  
Author(s):  
A. Daemen ◽  
D. Jurkovic ◽  
C. Van Holsbeke ◽  
S. Guerriero ◽  
A. C. Testa ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Mathematical Models ◽  
Cancer Prevalence ◽  
Adnexal Masses

How to model the impact of climate change on vector-borne diseases?

2021 ◽  
pp. 26-31
Author(s):  
Cyril Caminade
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Mathematical Models ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Change Risk ◽  
Vector Borne Diseases ◽  
Impact Of Climate Change ◽  
Vector Borne ◽  
The Impact

Abstract This expert opinion provides an overview of mathematical models that have been used to assess the impact of climate change on ticks and tick-borne diseases, ways forward in terms of improving models for the recent context and broad guidelines for conducting future climate change risk assessment.

scholarly journals Adnexal mass risk assessment: a multivariate index assay for malignancy risk stratification

2019 ◽  
Vol 15 (33) ◽  
pp. 3783-3795
Author(s):  
Zhen Zhang ◽  
Rowan G Bullock ◽  
Herbert Fritsche
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Risk Stratification ◽  
Predictive Value ◽  
Adnexal Mass ◽  
Clinical Performance ◽  
Risk Group ◽  
High Risk Group ◽  
Adnexal Masses ◽  
Immediate Surgery

Aims: Adnexal mass risk assessment (AMRA) stratifies patients with adnexal masses, identifying the relatively small number of malignancies from benigns which might take a ‘watchful waiting’ approach. Methods: AMRA uses seven biomarkers and derived from women with adnexal masses scheduled for surgery. Estimated clinical performance was calculated using fixed prevalence. Results: At 5% prevalence, the high-risk group, 7.9% total, captured 75.9% of invasive malignancies at a positive predictive value of 35.8%. High risk/intermediate risk combined had a sensitivity of 89.7 and 95.6% for pre- and post-menopausal cancers, respectively. The low-risk group, 67.8% total, had an negative predictive value of 99.0%. Conclusion: With highly differentiating risk stratification capability across histological subtypes and stages, AMRA is potentially applicable to patients with adnexal masses to assist deciding whether immediate surgery is recommended.

Mathematical Models to Discriminate Between Benign and Malignant Adnexal Masses: Potential Diagnostic Improvement Using Ovarian HistoScanning

2011 ◽  
Vol 21 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Evelien Vaes ◽  
Ranjit Manchanda ◽  
Rina Nir ◽  
Dror Nir ◽  
Harry Bleiberg ◽  
...  
Keyword(s):  
Mathematical Models ◽  
New Technology ◽  
Characteristic Curve ◽  
Model Performance ◽  
Diagnostic Value ◽  
Ca 125 ◽  
Transvaginal Sonography ◽  
Future Research ◽  
Initial Report ◽  
Adnexal Masses

Purpose:Accurate preoperative clinical assessment of adnexal masses can optimize outcomes by ensuring appropriate and timely surgery. This article addresses whether a new technology, ovarian HistoScanning, has an additional diagnostic value in mathematical models developed for the differential diagnosis of adnexal masses.Patients and Methods:Transvaginal sonography-based morphological variables were obtained through blinded analysis of archived images in 199 women enrolled in a prospective study to assess the performance of ovarian HistoScanning. Logistic regression (LR) and neural network (NN) models including these variables and clinical and patient data along with the HistoScanning score (HSS) (range, 0-125; based on mathematical algorithms) were developed in a learning set (60% patients). The remaining 40% patients (evaluation set) were used to assess model performance.Results:Of all morphological and clinical variables tested, serum CA-125, presence of a solid component, and HSS were most significant and used to develop the LR model. The NN model included all variables. The novel variable, HSS, offered significant improvement in the LR and NN models' performance. The LR and NN models in an independent evaluation set were found to have area under the receiver operating characteristic curve = 0.97 (95% confidence interval [CI], 94-99) and 0.93 (95% CI, 88-98), sensitivities = 83% (95% CI, 71%-91%) and 80% (95% CI, 67%-89%), and specificities = 98% (95% CI, 89%-99%) and 86% (95% CI, 72%-95%), respectively. In addition, these models showed an improved performance when compared with 3 other existing models (allP< 0.05).Conclusions:This initial report shows a clear benefit of including ovarian HistoScanning into mathematical models used for discriminating benign from malignant ovarian masses. These models may be specifically helpful to the less experienced examiner. Future research should assess performance of these models in prospective clinical trials in different populations.

scholarly journals OC05.05: External validation of “old” and new mathematical models to distinguish between benign and malignant adnexal masses

2009 ◽  
Vol 34 (S1) ◽  
pp. 8-8
Author(s):  
C. Van Holsbeke ◽  
B. Van Calster ◽  
G. B. Melis ◽  
A. Testa ◽  
S. Guerriero ◽  
...  
Keyword(s):  
Mathematical Models ◽  
External Validation ◽  
Adnexal Masses

The Development of the PIPESAFE Risk Assessment Package for Gas Transmission Pipelines

1998 ◽  
Author(s):  
Michael R. Acton ◽  
Phil J. Baldwin ◽  
Tim R. Baldwin ◽  
Eric E. R. Jager
Keyword(s):  
Risk Assessment ◽  
Mathematical Models ◽  
Integrated Assessment ◽  
Large Scale ◽  
Assessment Tool ◽  
International Group ◽  
Knowledge Based ◽  
Hazard And Risk ◽  
Hazard And Risk Assessment ◽  
Transmission Pipelines

PIPESAFE is a knowledge based hazard and risk assessment package for gas transmission pipelines, which has been developed jointly by an international group of gas transmission companies. PIPESAFE has been developed from the BG (formerly British Gas) TRANSPIRE package, to produce an integrated assessment tool for use on PCs. which includes a range of improvements and additional models backed by large scale experimentation. This paper describes the development of the PIPESAFE package, and the formulation and validation of the mathematical models included within it.

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