Best Practice & Research Clinical Obstetrics & Gynaecology
7The use of mathematical models to evaluate pelvic masses; can they beat an expert operator?
Section snippets
Serum CA 125 levels
The CA 125 antigen is a glycoprotein with a high molecular weight that is expressed by most epithelial ovarian cancers and is recognized by a monoclonal antibody (OC 125). Serum CA 125 is the tumour marker with the highest sensitivity for ovarian cancer.1., 2., 3., 4. This tumour marker will detect nearly 80% of advanced (stage ≥III) ovarian cancers, but only 40–44% of patients with stage I disease.5., 6., 7., 8. Some authors use different cut-off levels for pre- and post-menopausal patients.
Risk of malignancy index (RMI)
In order to increase the reliability of serum CA 125 levels to differentiate pre-operatively between benign and malignant pelvic masses, Jacobs and colleagues combined the measurement of CA 125 values with morphological findings and menopausal status of the patient in calculating a risk of malignancy index.15 In a retrospective study using transabdominal ultrasonography the following features suggestive of malignancy have been assessed: multiloculated cysts, evidence of solid areas, evidence of
Morphological scoring systems
More than 10 different morphological scoring systems have been reported and used with varying success.24., 25., 26., 27., 28., 29., 30., 31., 32., 33. Morphological features of pelvic masses are more extensively discussed in Chapter 6 and in other textbooks.34 A summary of clinically useful features is given in Table 3.35
Multivariate logistic regression analysis
Multivariate logistic regression analysis is a statistical tool that can be used to select and combine input variables which are linked to a certain outcome, for example, patient or tumour characteristics that are linked to the presence of malignancy in a pelvic mass. In the logistic regression model, the numerical values x1 to xn associated with observations of selected variables are weighted by coefficients β1 to βn and then summed together. An intercept β0 is subtracted after which the
Artificial neural network (ANN)
The use of artificial neural networks can be seen as a generalization of the methodology of logistic regression analysis described above. Here also an a posteriori probability of malignancy is modelled, but in an ANN the separation in the feature-space is more complex and non-linear in nature.
Artificial neural networks are networks of units (called neurons) that exchange information with each other in the form of numerical values via synaptic interconnections. The neurons take a weighted sum of
New statistical techniques
New statistical models are being developed and tested prospectively. Least squares support vector machine (LS-SVM) classifiers are known to have good generalization performance.45 Graphical models or Bayesian belief networks for grey-box model fitting offer the possibility to integrate expert knowledge, to estimate wrong or missing data, to derive confidence intervals, and to predict subclasses (e.g. borderline tumours, dermoid cysts).46., 47. However, so far these new techniques have not been
New ultrasound-based techniques
Several new ultrasound-based techniques show promise in distinguishing between malignant and benign adnexal tumours: for example, the kinetics of ultrasound contrast agents and three-dimensional power Doppler ultrasound. Orden et al measured uptake and washout times and demonstrated that after microbubble contrast injection malignant and benign adnexal lesions behave differently in degree, onset, and duration of Doppler ultrasound enhancement.48 Further prospective studies are needed to explore
New tumour markers and proteomic patterns
New tumour markers and developments in proteomic patterns50 are beyond the scope of this chapter, but it might be anticipated that quantitative results of these technologies could become incorporated in newly developed statistical models.
Subjective assessment of ultrasound images
One of the first prospective studies assessing the accuracy of subjective impressions of adnexal masses was performed between 1981 and 1985.51 Using transabdominal ultrasonography, an overall accuracy of 91% was obtained (see Table 5). The authors regarded thick septa, irregular solid parts within a mass, indefinite margins, and the presence of ascites and matted bowel loops as malignant patterns.
Using transvaginal grey-scale imaging subjective assessment, Valentin obtained an accuracy of 95%
Prospective comparison of methods
On internal prospective validation all mathematical models performed less well.37 The artificial neural network was significantly better than the three logistic regression models described above, but there was no significant difference between the performance of RMI and artificial neural networks (Timmerman et al, unpublished data).
As expected, external validation usually gives even more variable results in the estimates of diagnostic efficacy for malignancy of mathematical models. Aslam et al
Summary
An ANN can be trained to predict malignancy from the patient's age, CA125 levels, and some simple ultrasonographic criteria with a high degree of accuracy. The most important step in designing a logistic regression or a neural network model is cross-validation. In cross-validation, the ability of a trained network to generalize is evaluated by observing how the network performs on facts in the database that were withheld from the training set.63 Current mathematical models seem not to offer
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Improving diagnostic strategies for ovarian cancer in Filipino women using ultrasound imaging and a multivariate index assay
2022, Cancer EpidemiologyCitation Excerpt :Since then, various studies have been conducted to assess their local diagnostic accuracy and applicability [4,6,7,24,25]. Subjective assessment by a level III expert sonologist [26] has been proven to be the most superior method in distinguishing benign from malignant ovarian masses[8,27–30], which was also the case for our center as IOTA-LR1 and LR2 were the best individual risk prediction classifiers. However, one major limitation of this method is its subjectivity and the requirement for an experienced examiner [31,32].
Diagnosis of adnexal lesions by gynaecology residents using subjective assessment, logistic regression, and Simple Rules
2020, Clinica e Investigacion en Ginecologia y ObstetriciaMethods of Assessing Ovarian Masses: International Ovarian Tumor Analysis Approach
2019, Obstetrics and Gynecology Clinics of North AmericaCitation Excerpt :Various diagnostic approaches have been introduced to characterize ovarian pathology before surgery. Most tests used in clinical practice incorporate findings of transvaginal ultrasound examination, because evidence shows this is the most appropriate first-line imaging technique for the preoperative assessment of women with adnexal pathology,8 with subjective evaluation of ultrasound findings by an experienced examiner being the best method for discriminating between benign and malignant disease.9–11 In a randomized controlled trial, the management of patients with adnexal lesions has been shown to benefit from ultrasound assessment by experienced operators.12
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2019, Obstetrics and Gynecology Clinics of North AmericaClassification systems and prediction of risk of malignancy of the adnexal masses
2018, Clinica e Investigacion en Ginecologia y ObstetriciaOvarian mass–differentiating benign from malignant: the value of the International Ovarian Tumor Analysis ultrasound rules
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