Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: a consensus opinion from the Morphological Uterus Sonographic Assessment (MUSA) group.

The MUSA (Morphological Uterus Sonographic Assessment) statement is a consensus statement on terms, definitions and measurements that may be used to describe and report the sonographic features of the myometrium using gray-scale sonography, color/power Doppler and three-dimensional ultrasound imaging. The terms and definitions described may form the basis for prospective studies to predict the risk of different myometrial pathologies, based on their ultrasound appearance, and thus should be relevant for the clinician in daily practice and for clinical research. The sonographic features and use of terminology for describing the two most common myometrial lesions (fibroids and adenomyosis) and uterine smooth muscle tumors are presented.

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.

OBJECTIVE: Two logistic regression models have been developed for the characterization of adnexal masses. The goal of this prospective analysis was to see whether these models perform differently according to the prevalence of malignancy and whether the cut-off levels of risk assessment for malignancy by the models require modification in different centers. METHODS: Centers were categorized into those with a prevalence of malignancy below 15%, between 15 and 30% and above 30%. The areas under the receiver-operating characteristics curves (AUC) were compared using bootstrapping. The optimal cut-off level of risk assessment for malignancy was chosen per center, corresponding to the highest sensitivity level possible while still keeping a good specificity. RESULTS: Both models performed better in centers with a lower prevalence of malignant cases. The AUCs of the two models for centers with fewer than 15% malignant cases were 0.97 and 0.95, those of centers with 15-30% malignancy were 0.95 and 0.93 and those of centers with more than 30% malignant cases were 0.94 and 0.92. This decrease in performance was due mainly to the decrease in specificity from over 90 to around 76%. In the centers with a higher percentage of malignant cases, a sensitivity of at least 90% with a good specificity could not be obtained by choosing a different cut-off level. CONCLUSIONS: Overall the models performed well in all centers. The performance of the logistic regression models worsened with increasing prevalence of malignancy, due to a case mix with more borderline and complex benign masses seen in those centers. Because the cut-off of 0.10 is optimal for all three types of center, it seems reasonable to use this cut-off for both models in all centers.

Functional linear discriminant analysis: a new longitudinal approach to the assessment of embryonic growth.

BACKGROUND: Functional linear discriminant analysis (FLDA) is a new growth assessment technique using serial measurements to discriminate between normal and abnormal fetal growth. We used FLDA to assess and compare growth in live pregnancies destined to miscarry with those remaining viable. METHODS: This was a prospective cohort study of women with ultrasound scans on at least two separate occasions showing live pregnancies. Serial crown-rump length (CRL), mean gestational sac diameter and mean yolk sac diameter measurements were recorded. The ability of FLDA to predict subsequent miscarriage was compared with that of a single CRL measurement. RESULTS: Of 521 included pregnancies, 493 (94.6%) remained viable at 14 weeks and 28 (5.4%) miscarried. The CRL growth rate was significantly lower in those that miscarried (one-sample t-test, P = 2.638E-22). The sensitivity of FLDA in predicting miscarriage from serial CRL measurements was 60.7% and specificity was 93.1% [positive predictive value (PPV) 33.3%, negative predictive value (NPV) 97.7%]. This was significantly better for predicting miscarriage than a single CRL observation of more than 2SD below that expected (sensitivity 53.6%, specificity 72.2%, PPV 9.9%, NPV 96.5%). CONCLUSIONS: FLDA discriminates between normal and abnormal growth to predict miscarriage with high specificity. FLDA predicts miscarriage better than a single observation of a small CRL.

Building decision trees for diagnosing intracavitary uterine pathology.

OBJECTIVES: To build decision trees to predict intrauterine disease, based on a clinical data set, and using mathematical software. METHODS: Diagnostic algorithms were built and validated using the data of 402 consecutive patients who underwent grey scale ultrasound, followed by colour Doppler, saline infusion sonography (SIS), office hysteroscopy and endometrial-- sampling. The "final diagnosis" was classified as "abnormal" in case of endometrial polyps, hyperplasia or malignancy or intracavitary myoma. "Pre-test parameters" included patient's age, weight, length, parity, menopausal status, bleeding symptoms and cervical cytology; "post-test parameters" included ultrasound-, color Doppler-, SIS-, hysteroscopy- findings and histology results after endometrial sampling. Decision Tree #1 was built using both "pre-test" and "post-test" parameters; Tree #2 was only based on "post-test" parameters; Tree #3 was designed without using the hysteroscopy variables. The Waikato Environment for Knowledge Analysis (Weka) software was used for the development of decision trees. RESULTS: All trees started with an imaging technique: hysteroscopy or SIS. The diagnostic accuracy was 88.3%, 88.3% and 84.0% for Tree #1, #2 and #3 respectively, the sensitivity and specificity was 95.5% and 82%, 97.7% and 80.0, 93.2 and 76.0%, respectively. CONCLUSION: The method used in this study enables the comparison between different decision trees containing multiple tests.

Real-time ultrasound vs. evaluation of static images in the preoperative assessment of adnexal masses.

OBJECTIVE: To determine if the prediction of the malignancy of an adnexal mass using pattern recognition, i.e. subjective evaluation of gray-scale and Doppler ultrasound findings, is as accurate when based on static images as it is when based on a real-time ultrasound examination. METHODS: The static images of 166 non-consecutive patients with 'difficult' adnexal masses, who all underwent surgery and for whom a histopathological diagnosis was available, were evaluated by three experts in gynecological ultrasound ('image experts'). All ultrasound examinations had been performed and the static images saved by a fourth expert sonologist ('real-time' sonologist). All four sonologists classified the adnexal masses as benign or malignant based on their subjective impression and stated with what degree of confidence their diagnosis was made. The diagnostic performance of the real-time sonologist was compared with that of each of the three image experts and with that of the 'consensus opinion' of the image experts (i.e. the diagnosis suggested by at least two of the latter). RESULTS: The real-time sonologist correctly predicted the diagnosis with an accuracy of 89% (148/166) vs. 85% (141/166) for the consensus opinion of static images (P = 0.0707). Equivalent values for sensitivity and specificity were 80% (56/70) vs. 83% (58/70) (P = 0.4142) and 96% (92/96) vs. 86% (83/96) (P = 0.0027), respectively. CONCLUSIONS: The preoperative diagnosis of an adnexal mass made on the basis of a real-time ultrasound examination is more precise than a diagnosis made on the basis of saved static ultrasound images. Evaluation of static images is associated with lower diagnostic specificity.

Three-dimensional ultrasound assessment of the cervix for predicting time to spontaneous onset of labor and time to delivery in prolonged pregnancy.

OBJECTIVES: To determine whether three-dimensional (3D) ultrasound including power Doppler examination of the cervix is useful for predicting time to spontaneous onset of labor or time to delivery in prolonged pregnancy. METHODS: A prospective study was conducted in 60 women who went into spontaneous labor. All underwent transvaginal 3D power Doppler ultrasound examination of the cervix immediately before a prolonged-pregnancy check-up at > or = 41 + 5 gestational weeks. Univariate and multivariate logistic regression analysis was used to determine which of the following variables predicted spontaneous onset of labor > 24 h and > 48 h and vaginal delivery > 48 h and > 60 h: length, anteroposterior (AP) diameter and width of the cervix and of any cervical funneling; cervical volume (cm3); vascularization index (VI); flow index (FI); vascularization flow index (VFI); parity; and Bishop score. Multivariate logistic regression analysis was carried out both with and without Bishop score as a predictive variable. Receiver-operating characteristics (ROC) curves were used to describe the diagnostic performance of the tests. RESULTS: The areas under the ROC curves for Bishop score, cervical length, and logistic regression models did not differ significantly (areas ranging from 0.72 to 0.82). If Bishop score was not included in the logistic regression model, cervical length, VI and FI independently predicted delivery > 48 h, the likelihood increasing with increasing cervical length, decreasing VI and increasing FI. CONCLUSIONS: In prolonged pregnancy cervical vascularization as estimated by 3D power Doppler ultrasound is related to time to delivery > 48 h, but the likelihood of delivery > 48 h can be predicted equally well using Bishop score alone or sonographic cervical length alone.

Financial time series prediction using least squares support vector machines within the evidence framework.

The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropriate choice of several hyper-parameters. The hyper-parameters of the model are inferred on the second level of inference. The inferred hyper-parameters, related to the volatility, are used to construct a volatility model within the evidence framework. Model comparison is performed on the third level of inference in order to automatically tune the parameters of the kernel function and to select the relevant inputs. The LS-SVM formulation allows one to derive analytic expressions in the feature space and practical expressions are obtained in the dual space replacing the inner product by the related kernel function using Mercer's theorem. The one step ahead prediction performances obtained on the prediction of the weekly 90-day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic.