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

scholarly journals External validation of the adapted Risk of Malignancy Index incorporating tumor size in the preoperative evaluation of adnexal masses

2011 ◽  
Vol 159 (2) ◽  
pp. 422-425 ◽  
Author(s):  
Petronella A.J. van den Akker ◽  
Petra L.M. Zusterzeel ◽  
Anette L. Aalders ◽  
Marc P.L.M. Snijders ◽  
Rahul A.K. Samlal ◽  
...  
Keyword(s):  
Tumor Size ◽  
Preoperative Evaluation ◽  
External Validation ◽  
Adnexal Masses ◽  
Risk Of Malignancy ◽  
Risk Of Malignancy Index

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 Development and external validation of new ultrasound-based mathematical models for preoperative prediction of high-risk endometrial cancer

2014 ◽  
Vol 43 (5) ◽  
pp. 586-595 ◽  
Author(s):  
C. Van Holsbeke ◽  
L. Ameye ◽  
A. C. Testa ◽  
F. Mascilini ◽  
P. Lindqvist ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Mathematical Models ◽  
External Validation ◽  
Preoperative Prediction

scholarly journals EP34.09: A retrospective external validation of the GI‐RADS classification to differentiate between benign and malignant adnexal masses

2019 ◽  
Vol 54 (S1) ◽  
pp. 442-443
Author(s):  
A. Esquivel Villabona ◽  
N. Rodriguez ◽  
C. Buritica ◽  
N. Rodríguez ◽  
A. Gomez ◽  
...  
Keyword(s):  
External Validation ◽  
Adnexal Masses

scholarly journals Logistic models and artificial intelligence in the sonographic assessment of adnexal masses – a systematic review of the literature

2020 ◽  
Vol 22 (4) ◽  
pp. 469
Author(s):  
Mihaela Grigore ◽  
Razvan Mihai Popovici ◽  
Dumitru Gafitanu ◽  
Loredana Himiniuc ◽  
Mara Murarasu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
High Performance ◽  
Ultrasound Examination ◽  
External Validation ◽  
Logistic Models ◽  
Medical Practitioners ◽  
Adnexal Masses ◽  
Surgical Evaluation ◽  
Malignant Condition

Adnexal masses are common, yet challenging, in gynecological practice. Making the differential diagnosis between their benign and malignant condition is essential for optimal surgical management, but reliable pre-surgical differentiation is sometimes difficult using clinical features, ultrasound examination, or tumor markers alone. A possible way to improve the diagnosis is using artificial intelligence (AI) or logistic models developed based on compiling and processing clinical, ultrasound, and tumor marker data together. Ample research has already been conducted in this regard that medical practitioners could benefit from. In this systematic review, we present logistic models and methods using AI, chosen based on their demonstrated high performance in clinical practice. Although some external validation of these models has been performed, further prospective studies are needed in order to select the best model or to create a new, more efficient, one for the pre-surgical evaluation of ovarian masses. 

External validation of the MR imaging scoring system for the management of adnexal masses

2016 ◽  
Vol 205 ◽  
pp. 115-119 ◽  
Author(s):  
Mireille Ruiz ◽  
Pénélope Labauge ◽  
Anne Louboutin ◽  
Olivier Limot ◽  
Arnaud Fauconnier ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Scoring System ◽  
External Validation ◽  
Adnexal Masses

scholarly journals EP34.03: A retrospective external validation of the ADNEX model to differentiate between benign and malignant adnexal masses

2019 ◽  
Vol 54 (S1) ◽  
pp. 441-441
Author(s):  
A. Esquivel Villabona ◽  
N. Rodriguez ◽  
C. Buritica ◽  
N. Rodríguez ◽  
A. Velandia ◽  
...  
Keyword(s):  
External Validation ◽  
Adnexal Masses
Sign in / Sign up

Export Citation Format

Share Document