Relationship Between Early Functional and Structural Brain Developments and Brain Injury in Preterm Infants.

BACKGROUND: Recent studies explored the relationship between early brain function and brain morphology, based on the hypothesis that increased brain activity can positively affect structural brain development and that excitatory neuronal activity stimulates myelination. OBJECTIVE: To investigate the relationship between maturational features from early and serial aEEGs after premature birth and MRI metrics characterizing structural brain development and injury, measured around 30weeks postmenstrual age (PMA) and at term. Moreover, we aimed to verify whether previously developed maturational EEG features are related with PMA. DESIGN/METHODS: One hundred six extremely preterm infants received bedside aEEGs during the first 72h and weekly until week 5. 3T-MRIs were performed at 30weeks PMA and at term. Specific features were extracted to assess EEG maturation: (1) the spectral content, (2) the continuity [percentage of spontaneous activity transients (SAT%) and the interburst interval (IBI)], and (3) the complexity. Automatic MRI segmentation to assess volumes and MRI score was performed. The relationship between the maturational EEG features and MRI measures was investigated. RESULTS: Both SAT% and EEG complexity were correlated with PMA. IBI was inversely associated with PMA. Complexity features had a positive correlation with the cerebellar size at 30weeks, while event-based measures were related to the cerebellar size at term. Cerebellar width, cortical grey matter, and total brain volume at term were inversely correlated with the relative power in the higher frequency bands. CONCLUSIONS: The continuity and complexity of the EEG steadily increase with increasing postnatal age. Increasing complexity and event-based features are associated with cerebellar size, a structure with enormous development during preterm life. Brain activity is important for later structural brain development.

A spline-based tool to assess and visualize the calibration of multiclass risk predictions.

When validating risk models (or probabilistic classifiers), calibration is often overlooked. Calibration refers to the reliability of the predicted risks, i.e. whether the predicted risks correspond to observed probabilities. In medical applications this is important because treatment decisions often rely on the estimated risk of disease. The aim of this paper is to present generic tools to assess the calibration of multiclass risk models. We describe a calibration framework based on a vector spline multinomial logistic regression model. This framework can be used to generate calibration plots and calculate the estimated calibration index (ECI) to quantify lack of calibration. We illustrate these tools in relation to risk models used to characterize ovarian tumors. The outcome of the study is the surgical stage of the tumor when relevant and the final histological outcome, which is divided into five classes: benign, borderline malignant, stage I, stage II-IV, and secondary metastatic cancer. The 5909 patients included in the study are randomly split into equally large training and test sets. We developed and tested models using the following algorithms: logistic regression, support vector machines, k nearest neighbors, random forest, naive Bayes and nearest shrunken centroids. Multiclass calibration plots are interesting as an approach to visualizing the reliability of predicted risks. The ECI is a convenient tool for comparing models, but is less informative and interpretable than calibration plots. In our case study, logistic regression and random forest showed the highest degree of calibration, and the naive Bayes the lowest.

Different quantification algorithms may lead to different results: a comparison using proton MRS lipid signals.

Proton magnetic resonance spectroscopy (MRS) is a sensitive method for investigating the biochemical compounds in a tissue. The interpretation of the data relies on the quantification algorithms applied to MR spectra. Each of these algorithms has certain underlying assumptions and may allow one to incorporate prior knowledge, which could influence the quality of the fit. The most commonly considered types of prior knowledge include the line-shape model (Lorentzian, Gaussian, Voigt), knowledge of the resonating frequencies, modeling of the baseline, constraints on the damping factors and phase, etc. In this article, we study whether the statistical outcome of a biological investigation can be influenced by the quantification method used. We chose to study lipid signals because of their emerging role in the investigation of metabolic disorders. Lipid spectra, in particular, are characterized by peaks that are in most cases not Lorentzian, because measurements are often performed in difficult body locations, e.g. in visceral fats close to peristaltic movements in humans or very small areas close to different tissues in animals. This leads to spectra with several peak distortions. Linear combination of Model spectra (LCModel), Advanced Method for Accurate Robust and Efficient Spectral fitting (AMARES), quantitation based on QUantum ESTimation (QUEST), Automated Quantification of Short Echo-time MRS (AQSES)-Lineshape and Integration were applied to simulated spectra, and area under the curve (AUC) values, which are proportional to the quantity of the resonating molecules in the tissue, were compared with true values. A comparison between techniques was also carried out on lipid signals from obese and lean Zucker rats, for which the polyunsaturation value expressed in white adipose tissue should be statistically different, as confirmed by high-resolution NMR measurements (considered the gold standard) on the same animals. LCModel, AQSES-Lineshape, QUEST and Integration gave the best results in at least one of the considered groups of simulated or in vivo lipid signals. These outcomes highlight the fact that quantification methods can influence the final result and its statistical significance.

Does the presence of a Caesarean section scar affect implantation site and early pregnancy outcome in women attending an early pregnancy assessment unit?

STUDY QUESTION: Are there any differences in the location and distance to the internal cervical ostium of the implantation site of the intrauterine gestation sacs, early pregnancy symptoms and pregnancy outcome at 12 weeks gestation between women with and without a previous Caesarean section (CS)? SUMMARY ANSWER: The presence of a CS scar affects the site of implantation, and the distance between implantation site and the scar is related to the risk of spontaneous abortion. WHAT IS KNOWN ALREADY?: Little is known about the impact of a CS scar on implantation other than the risk of Caesarean scar pregnancy (CSP). Furthermore, there is a paucity of information on how the proximity of implantation to the scar impacts on pregnancy outcome in the first trimester. STUDY DESIGN, SIZE, AND DURATION: A prospective cohort study conducted over 15 months in the early pregnancy unit of a London Teaching Hospital. Three hundred and eighty women underwent a transvaginal scan at 6-11 weeks of gestation. A total of 170 women had undergone ≥1 CS, and 210 women had no history of CS. PARTICIPANTS/MATERIALS, SETTING, METHODS: The 380 women were recruited as consecutive non-selected cases. The relationship between the implanted sac and the CS scar was assessed by quantifiable measures and by subjective impression. Logistic regression analysis was used to determine the influence of the presence of a CS scar on pregnancy outcome. The final outcome of the study was the viability of the pregnancy at 12 weeks. MAIN RESULTS AND THE ROLE OF CHANCE: Implantation was most frequently posterior (53%) in the CS group and fundal in the non-CS group (42%). Gestation sac implantation was 8.7 mm lower in the CS group (95% confidence interval (CI) 6.7-10.7, P < 0.0001). Presenting complaints differed in women with and without a previous CS (P = 0.0009). More frequent vaginal bleeding [73 versus 55%, difference -18, 95% CI (-27 to -8%] yet no clearly increased spontaneous abortion rates were noted in the CS group compared with the non-CS group (adjusted odds ratio = 1.1, 95% CI 0.6-1.9, P = 0.74). Subjective impression showed that in eight cases the implantation site crossed the scar, seven of which resulted in spontaneous abortion, while the remaining case survived to term complicated by placenta praevia and post-partum haemorrhage. The subjective impression of the examiner was supported by the measurements of distance between implantation site and CS scar. LIMITATIONS, REASONS FOR CAUTION: A weakness of the study is the lack of a reference technique to verify the location of implantation. WIDER IMPLICATIONS OF THE FINDINGS: This study adds further support to the hypothesis that the presence of a CS on the uterus impacts on the implantation site of a future pregnancy. The possibility that the CS scar has an impact on the risk of spontaneous abortion should be further studied. Caution must be exercised when implantation occurs near to, and crosses, a CS scar as this is not always associated with the diagnosis of CSP. A potential limitation of the study is that we did not examine scar dimensions and morphology.

Early respiratory dysfunction as a biomarker for epileptic encephalopathy.

OBJECTIVES: West syndrome is an epileptic encephalopathy starting in infancy with almost continuous interictal epileptic activity, so-called hypsarrhythmia pattern, and therefore is an interesting model for investigating the effect of interictal epileptic activity on autonomic function. It is known that autonomic dysfunction contributes to morbidity and mortality in epilepsy. Our aim is to investigate the effect of interictal epileptic activity in West syndrome on respiratory control. MATERIALS AND METHODS: Interictal single-lead ECG signals were extracted from 24-h video-EEG recordings in 10 children suffering from West syndrome and 14 control subjects. RR interval time series were calculated, and respiration was derived from the ECG signal. ECG-derived respiration (EDR) signals were computed and time and frequency domain parameters were extracted to characterize the respiration pattern. RESULTS: In time domain, the standard deviation of the EDR signal is significantly lower in patients with West syndrome compared to control subjects. This finding is an indication of a less variable respiratory rate. In frequency domain, we analyzed the mean power spectrum for the EDR. In patients with West syndrome, there is more activity at the lower frequencies considered to be a risk factor for apneas. Second, there is an attenuated peak at the higher frequency band where normal respiratory rate is to be found, indicating an abnormal breathing pattern. CONCLUSIONS: Our results show that there is a clear dysfunction in autonomic respiratory control in patients with West syndrome, in between the typical ictal epileptic spasms, compared to control subjects. Respiration is more fixed and contains a higher risk of apneas.

Predicting successful vaginal birth after Cesarean section using a model based on Cesarean scar features examined by transvaginal sonography.

OBJECTIVE: To develop a model to predict the success of a trial of vaginal birth after Cesarean section (VBAC) based on sonographic measurements of Cesarean section (CS) scar features, demographic variables and previous obstetric history. METHODS: We used transvaginal sonography (TVS) to examine the CS scar of 320 consecutive pregnant women. TVS was carried out at 11-13, 19-21 and 34-36 weeks' gestation and prospective measurements of the scar were recorded at each visit according to a defined protocol. A logistic regression model to predict success of VBAC was developed for those patients with a visible scar on ultrasound and only one previous CS. The model was evaluated using bootstrap validation. RESULTS: There were 131 women with one previous CS and a visible scar, of whom 10 underwent CS prior to labor and were excluded from analysis. Successful VBAC was achieved in 74/121 (61%) of the remaining cases. The prediction model developed was based on patient age, previous history of VBAC, residual myometrial thickness (RMT) and the change in RMT from the first to the second trimester (ΔRMT). The internally validated area under the receiver-operating characteristics curve was 0.62 when measurements of RMT and ΔRMT were excluded, but 0.94 when scar information was incorporated into the model. CONCLUSION: Ultrasound measurements of CS scar, namely RMT and the change in RMT from the first to the second trimester of pregnancy, when incorporated into a mathematical model, can predict accurately a successful trial of labor in patients with one previous CS.

Risk of malignancy in unilocular cysts: a study of 1148 adnexal masses classified as unilocular cysts at transvaginal ultrasound and review of the literature.

OBJECTIVES: The aim of this study was to estimate the rate of malignancy in adnexal lesions described as unilocular cysts at transvaginal ultrasound examination and to investigate if there are differences in clinical and ultrasound characteristics between benign and malignant unilocular cysts. METHODS: A total of 3511 patients with an adnexal mass underwent transvaginal ultrasound examination between 1999 and 2007. Sonologists used the International Ovarian Tumor Analysis terms and definitions to describe their ultrasound findings. Only masses operated on within 120 days after the ultrasound examination were included in the analysis and the histopathological diagnosis of the mass was used as the gold standard. RESULTS: Of the 3511 masses, 1148 (33%) were classified as unilocular cysts on ultrasound. Of these, 11 (0.96% (95% CI, 0.48-1.71)) were malignant. The malignancy rate was lower in premenopausal than in postmenopausal women: 0.54% (5/931; 95% CI, 0.17-1.25) vs. 2.76% (6/217; 95% CI, 1.02-5.92); P = 0.009. More patients with malignant unilocular cysts had a personal history of breast cancer (18% vs. 2%; P = 0.02) or ovarian cancer (18% vs 0.6%; P = 0.003). Hemorrhagic cyst contents on ultrasound were more common in malignant than in benign unilocular cysts (18% vs. 2%; P = 0.03). In seven of the 11 malignancies judged to be unilocular cysts at scan, papillary projections or other solid components were seen at macroscopic inspection of the surgical specimen. CONCLUSIONS: The malignancy rate in surgically removed adnexal lesions judged to be unilocular cysts at transvaginal scan is c 1%. Postmenopausal status, personal history of breast or ovarian cancer and hemorrhagic cyst contents on ultrasound increase the risk of malignancy. To avoid misclassifying adnexal lesions as unilocular cysts at scan, it is important to scrutinize unilocular cysts for the presence of solid components.

Unilocular adnexal cysts with papillary projections but no other solid components: is there a diagnostic method that can classify them reliably as benign or malignant before surgery?

OBJECTIVES: To develop a logistic regression model for discrimination between benign and malignant unilocular solid cysts with papillary projections but no other solid components, and to compare its diagnostic performance with that of subjective evaluation of ultrasound findings (subjective assessment), CA 125 and the risk of malignancy index (RMI). METHODS: Among the 3511 adnexal masses in the International Ovarian Tumor Analysis (IOTA) database there were 252 (7%) unilocular solid cysts with papillary projections but no other solid components ('unilocular cysts with papillations'). All had been examined with transvaginal ultrasound using the IOTA standardized research protocol. The ultrasound examiner had also classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for all unilocular cysts with papillations (175 tumors in the training set and 77 in the test set) and for unilocular cysts with papillations for which the ultrasound examiner was not certain about benignity/malignancy (113 tumors in the training set and 53 in the test set). The gold standard was the histological diagnosis of the surgically removed adnexal mass. RESULTS: A model containing six variables was developed for all unilocular cysts with papillations. The model had an area under the receiver-operating characteristics curve (AUC) on the test set of 0.83 (95% CI, 0.74-0.93). The optimal risk cut-off, as defined on the training set (0.35), resulted in sensitivity 69% (20/29), specificity 79% (38/48), positive likelihood ratio (LR +) 3.31 and negative likelihood ratio (LR-) 0.39 on the test set. The corresponding values for subjective assessment when using the ultrasound examiner's dichotomous classification of the mass as benign or malignant were 97% (28/29), 79% (38/48), 4.63 and 0.04. A model containing four variables was developed for unilocular cysts with papillations for which the ultrasound examiner was not certain about benignity/malignancy. The model had an AUC of 0.74 (95% CI, 0.60-0.88) on the test set. The optimal risk cut-off of the model, as defined on the training set (0.30), resulted in sensitivity 57% (12/21), specificity 78% (25/32), LR + 2.61 and LR- 0.55 on the test set. The corresponding values for subjective assessment were 95% (20/21), 78% (25/32), 4.35 and 0.06. CA 125 and RMI had virtually no diagnostic ability. CONCLUSIONS: Even though logistic regression models to predict malignancy in unilocular cysts with papillations can be developed, they have at most moderate performance and are not superior to subjective assessment for discrimination between benignity and malignancy.

Improved performance on high-dimensional survival data by application of Survival-SVM.

MOTIVATION: New application areas of survival analysis as for example based on micro-array expression data call for novel tools able to handle high-dimensional data. While classical (semi-) parametric techniques as based on likelihood or partial likelihood functions are omnipresent in clinical studies, they are often inadequate for modelling in case when there are less observations than features in the data. Support vector machines (svms) and extensions are in general found particularly useful for such cases, both conceptually (non-parametric approach), computationally (boiling down to a convex program which can be solved efficiently), theoretically (for its intrinsic relation with learning theory) as well as empirically. This article discusses such an extension of svms which is tuned towards survival data. A particularly useful feature is that this method can incorporate such additional structure as additive models, positivity constraints of the parameters or regression constraints. RESULTS: Besides discussion of the proposed methods, an empirical case study is conducted on both clinical as well as micro-array gene expression data in the context of cancer studies. Results are expressed based on the logrank statistic, concordance index and the hazard ratio. The reported performances indicate that the present method yields better models for high-dimensional data, while it gives results which are comparable to what classical techniques based on a proportional hazard model give for clinical data.

Clinically oriented three-step strategy for assessment of adnexal pathology.

OBJECTIVE: To determine the diagnostic performance of ultrasound-based simple rules, risk of malignancy index (RMI), two logistic regression models (LR1 and LR2) and real-time subjective assessment by experienced ultrasound examiners following the exclusion of masses likely to be judged as easy and 'instant' to diagnose by an ultrasound examiner, and to develop a new strategy for the assessment of adnexal pathology based on this. METHODS: 3511 patients with at least one persistent adnexal mass preoperatively underwent transvaginal ultrasonography to assess tumor morphology and vascularity. They were included in two consecutive prospective studies by the International Ovarian Tumor Analysis (IOTA) group: Phase 1 (1999-2005), development of the simple rules and logistic regression models LR1 and LR2, and Phase 2, a validation study (2005-2007). RESULTS: Almost half of the cases (43%) were identified as 'instant' to diagnose on the basis of descriptors applied to the database. To assess diagnostic performance in the more difficult 'non-instant' masses, we used only Phase 2 data (n = 1036). The sensitivity of LR2 was 88%, of RMI it was 41% and of subjective assessment it was 87%. The specificity of LR2 was 67%, of RMI it was 90% and of subjective assessment it was 86%. The simple rules yielded a conclusive result in almost 2/3 of the masses, where they resulted in sensitivity and specificity similar to those of real-time subjective assessment by experienced ultrasound examiners: sensitivity 89 vs 89% (P = 0.76), specificity 91 vs 91% (P = 0.65). When a three-step strategy was applied with easy 'instant' diagnoses as Step 1, simple rules where conclusive as Step 2 and subjective assessment by an experienced ultrasound examiner in the remaining masses as Step 3, we obtained a sensitivity of 92% and specificity of 92% compared with sensitivity 90% (P = 0.03) and specificity 93% (P = 0.44) when using real-time subjective assessment by experts in all tumors. CONCLUSION: A diagnostic strategy using simple descriptors and ultrasound rules when applied to the variables contained in the IOTA database obtains results that are at least as good as those obtained by subjective assessment of a mass by an expert.

Detection of intracavitary uterine pathology using offline analysis of three-dimensional ultrasound volumes: interobserver agreement and diagnostic accuracy.

OBJECTIVE: To estimate the diagnostic accuracy and interobserver agreement in predicting intracavitary uterine pathology at offline analysis of three-dimensional (3D) ultrasound volumes of the uterus. METHODS: 3D volumes (unenhanced ultrasound and gel infusion sonography with and without power Doppler, i.e. four volumes per patient) of 75 women presenting with abnormal uterine bleeding at a 'bleeding clinic' were assessed offline by six examiners. The sonologists were asked to provide a tentative diagnosis. A histological diagnosis was obtained by hysteroscopy with biopsy or operative hysteroscopy. Proliferative, secretory or atrophic endometrium was classified as 'normal' histology; endometrial polyps, intracavitary myomas, endometrial hyperplasia and endometrial cancer were classified as 'abnormal' histology. The diagnostic accuracy of the six sonologists with regard to normal/abnormal histology and interobserver agreement were estimated. RESULTS: Intracavitary pathology was diagnosed at histology in 39% of patients. Agreement between the ultrasound diagnosis and the histological diagnosis (normal vs abnormal) ranged from 67 to 83% for the six sonologists. In 45% of cases all six examiners agreed with regard to the presence/absence of intracavitary pathology. The percentage agreement between any two examiners ranged from 65 to 91% (Cohen's κ, 0.31-0.81). The Schouten κ for all six examiners was 0.51 (95% CI, 0.40-0.62), while the highest Schouten κ for any three examiners was 0.69. CONCLUSION: When analyzing stored 3D ultrasound volumes, agreement between sonologists with regard to classifying the endometrium/uterine cavity as normal or abnormal as well as the diagnostic accuracy varied substantially. Possible actions to improve interobserver agreement and diagnostic accuracy include optimization of image quality and the use of a consistent technique for analyzing the 3D volumes.

The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task.

Simultaneous EEG-fMRI measurements can combine the high spatial resolution of fMRI with the high temporal resolution of EEG. Therefore, we applied this approach to the study of peripheral vision. More specifically, we presented visual field quadrant fragments of checkerboards and a full central checkerboard in a simple detection task. A technique called "integration-by-prediction" was used to integrate EEG and fMRI data. In particular, we used vectors of single-trial ERP amplitude differences between left and right occipital electrodes as regressors in an ERP-informed fMRI analysis. The amplitude differences for the regressors were measured at the latencies of the visual P1 and N1 components. Our results indicated that the traditional event-related fMRI analysis revealed mostly activations in the vicinity of the primary visual cortex and in the ventral visual stream, while both P1 and N1 regressors revealed activation of areas in the temporo-parietal junction. We conclude that simultaneous EEG-fMRI in a spatial detection task can separate visual processing at 100-200 ms from stimulus onset from the rest of the information processing in the brain.

Lobular and non-lobular breast cancers differ regarding axillary lymph node metastasis: a cross-sectional study on 4,292 consecutive patients.

Invasive lobular carcinoma (ILC) accounts for 8-14% of all breast cancers and carries distinct prognostic and biologic implications. The goal of our study was to investigate the impact of lobular histology on axillary lymph node (ALN) involvement. This is a cross-sectional study of 4,292 consecutive patients surgically treated for breast carcinoma at the University Hospitals Leuven. Logistic regression analysis was used to relate ILC to lymph node involvement while controlling for the following clinicopathologic features: tumor size, multifocal disease, tumor grade, lobular subtype and the combined steroid, and Her-2 status. Odds ratios (ORs) and 95% confidence intervals (CIS) were computed. A subgroup analysis was performed for patients that underwent a sentinel lymph node (SLN) procedure. The observed incidence of ILC was 13%. ILCs were larger, were more often grade II, multifocal, steroid receptor positive and Her-2 negative, and tended to be present in older patients. Incidence of ALN involvement was 42.0% for ILCs versus 38.3% for other tumors (OR 1.17, 95% CI 0.97-1.40). For the SLN subgroup, ILCs were less often ALN positive than non-ILCs (20.5% versus 28.3%, OR 0.66, 95% CI: 0.41-1.00). In the multivariable analysis, the lobular subtype was identified as less likely to have ALN involvement (adjusted OR 0.66, 95% CI 0.53-0.82). The analysis for the SLN subgroup showed comparable results (adjusted OR 0.49, 95% CI 0.30-0.78). This study has demonstrated that the lobular subtype is an independent predictor of lymph node involvement with ILC having a lower incidence of involved lymph nodes. The mildly higher incidence of ALN metastasis in lobular cancers in univariable analysis is not due to the lobular subtype, but due to confounding factors that interact with lymph node involvement.

Ex vivo high resolution magic angle spinning metabolic profiles describe intratumoral histopathological tissue properties in adult human gliomas.

In gliomas one can observe distinct histopathological tissue properties, such as viable tumor cells, necrotic tissue or regions where the tumor infiltrates normal brain. A first screening between the different intratumoral histopathological tissue properties would greatly assist in correctly diagnosing and prognosing gliomas. The potential of ex vivo high resolution magic angle spinning spectroscopy in characterizing these properties is analyzed and the biochemical differences between necrosis, high cellularity and border tumor regions in adult human gliomas are investigated. Statistical studies applied on sets of metabolite concentrations and metabolite ratios extracted from 52 high resolution magic angle spinning recordings coming from patients with different grades of glial tumors show a strong correlation between the histopathological tissue properties and the considered metabolic profiles, regardless of the malignancy grade. The results are in agreement with the pathology obtained by the histopathological examination that succeeded the high resolution magic angle spinning measurements. The metabolite concentration set can better differentiate between the considered histopathological tissue properties compared to the ratios. Representative reference tissue models describing the metabolic behavior are extracted for characterizing the intratumoral tissue properties. The proposed metabolic profiles reflect that the metabolites behavior is interconnected, and typical biochemical patterns emerge for each histopathological tissue property.

A model and scoring system to predict outcome of intrauterine pregnancies of uncertain viability.

OBJECTIVES: To define the incidence and outcome of intrauterine pregnancy of uncertain viability (PUV) and to develop and assess the performance of a model and a scoring system to predict ongoing viability. METHODS: Of 1881 consecutive women undergoing transvaginal ultrasonography, a cohort of 493 women with an empty gestational sac < 20 mm in mean diameter, gestational sac < 25 mm in mean diameter and containing yolk sac only or an embryonic pole < 6 mm in maximum length and without visible heart activity were followed until the end of the first trimester. Women with multiple pregnancies or who underwent termination of pregnancy were excluded. Outcome measures were pregnancy viability at initial 7-14-day follow-up and first-trimester viability at 11-14 weeks. The data were split randomly into two sets (two-thirds and one-third, respectively) in order to first develop and then test a mathematical model and a 'simple' model in the prediction of viability at each outcome point, based on maternal demographics, ultrasound features and symptoms. The performance of each system was assessed by receiver-operating characteristics (ROC) curve analysis and calibration plots on a test dataset. RESULTS: The incidence of PUV in this population was 29.2% (549/1881). Of the 493 pregnancies with initial (7-14 days) follow-up available, 307 (62.3%) were viable at this time and of the 444 pregnancies with follow-up at the end of the first trimester, 225 (50.7%) were still viable. Initial (7-14-day) viability was predicted by the model with an area under the ROC curve (AUC) of 0.837 (95% CI, 0.791-0.884) in the training dataset and 0.821 (95% CI, 0.756-0.885) in the test dataset. First-trimester (11-14-week) viability was predicted by the model with an AUC of 0.788 (95% CI, 0.734-0.842) in the training dataset and 0.774 (95% CI, 0.701-0.848) in the test dataset. The scoring system performed slightly worse than did the model, but had the advantage of being easily applicable. CONCLUSIONS: When early pregnancy viability cannot be established immediately with ultrasound, use of either a logistic regression model or a scoring system allows an individualized prediction of first-trimester outcome.

Adnexal masses difficult to classify as benign or malignant using subjective assessment of gray-scale and Doppler ultrasound findings: logistic regression models do not help.

OBJECTIVE: To develop a logistic regression model that can discriminate between benign and malignant adnexal masses perceived to be difficult to classify by subjective evaluation of gray-scale and Doppler ultrasound findings (subjective assessment) and to compare its diagnostic performance with that of subjective assessment, serum CA 125 and the risk of malignancy index (RMI). METHODS: We used data from the 3511 patients with an adnexal mass included in the International Ovarian Tumor Analysis (IOTA) studies. All patients had been examined using transvaginal gray-scale and Doppler ultrasound following a standardized research protocol carried out by an experienced ultrasound examiner using a high-end ultrasound system. In addition to prospectively collecting information on > 40 clinical and ultrasound variables, the ultrasound examiner classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for the unclassifiable masses (n = 244, i.e. 7% of all tumors) using a training set (160 tumors, 45 malignancies) and then tested on a test set (84 tumors, 28 malignancies). The gold standard was the histological diagnosis of the surgically removed adnexal mass. The area under the receiver-operating characteristics curve (AUC), sensitivity, specificity, positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were used to describe diagnostic performance and were compared between subjective assessment, CA 125, the RMI and the logistic regression model created. RESULTS: One variable was retained in the logistic regression model: the largest diameter (in mm) of the largest solid component of the tumor (odds ratio (OR) = 1.04; 95% CI, 1.02-1.06). The model had an AUC of 0.68 (95% CI, 0.59-0.78) on the training set and an AUC of 0.65 (95% CI, 0.53-0.78) on the test set. On the test set, a cut-off of 25% probability of malignancy (corresponding to the largest diameter of the largest solid component of 23 mm) resulted in a sensitivity of 64% (18/28), a specificity of 55% (31/56), an LR+ of 1.44 and an LR- of 0.65. The corresponding values for subjective assessment were 68% (19/28), 59% (33/56), 1.65 and 0.55. On the test set of patients with available CA 125 results, the LR+ and LR- of the logistic regression model (cut-off = 25% probability of malignancy) were 1.29 and 0.73, of subjective assessment were 1.45 and 0.63, of CA 125 (cut-off = 35 U/mL) were 1.24 and 0.84 and of RMI (cut-off = 200) were 1.21 and 0.92. CONCLUSIONS: About 7% of adnexal masses that are considered appropriate for surgical removal cannot be classified as benign or malignant by experienced ultrasound examiners using subjective assessment. Logistic regression models to estimate the risk of malignancy, CA 125 measurements and the RMI are not helpful in these masses.

Clinical implications of intra- and interobserver reproducibility of transvaginal sonographic measurement of gestational sac and crown-rump length at 6-9 weeks' gestation.

OBJECTIVES: To assess intra- and interobserver agreement of routinely performed measurements-crown-rump length (CRL) and mean gestational sac diameter (MSD)-for assessing the likelihood of miscarriage in the first trimester of pregnancy using transvaginal sonography. METHODS: A cross-sectional study of CRL and gestational sac measurements in first-trimester pregnancies was conducted in a fetal medicine referral center with a predominantly Caucasian population. Gestational age ranged from 6 to 9 weeks. All patients underwent a transvaginal ultrasound examination using a high-resolution ultrasound machine. Two measurements of CRL and measurements of three diameters of the gestational sac were obtained by two observers. Agreement within and between observers for CRL and between observers for MSD was analyzed using 95% prediction intervals, Bland-Altman plots with 95% limits of agreement and the intraclass correlation coefficient (ICC). RESULTS: In total 54 patients were included in the study, with measurements obtained by both observers in 44 of these. Intra- and interobserver ICCs were high for CRL measurements, with values of 0.992 and 0.993 for intraobserver agreement and 0.993 for interobserver agreement. For the MSD, the interobserver ICC was 0.952. Limits of agreement were ± 8.91 and ± 11.37% for intraobserver agreement of CRL and ± 14.64% for interobserver agreement of CRL. For MSD, the interobserver limits of agreement were ± 18.78%. For an MSD measurement of 20 mm by the first observer, the prediction interval for the second observer was 16.8-24.5 mm. For a CRL measurement of 6 mm, the prediction interval for the second observer was 5.4-6.7 mm. CONCLUSION: For dating purposes, there is reasonable reproducibility of CRL measurements using transvaginal ultrasonography at 6-9 weeks' gestation. When diagnosing miscarriage based on measurements of CRL care must be taken for values close to any decision boundary. The higher interobserver variability that we observed for MSD has implications for the diagnosis of miscarriage based on this measurement in the absence of a visible embryo or yolk sac.

Effect of gel-instillation sonography on Doppler ultrasound findings in endometrial polyps.

OBJECTIVE: Saline infusion sonohysterography has been reported to suppress the color signal within the endometrium at color or power Doppler evaluation. The aim of this study was to evaluate if gel-instillation sonography (GIS) affects the power Doppler signal in patients with endometrial polyps. METHODS: Ultrasound volumes of the uterus, obtained by three-dimensional ultrasound imaging of 25 women with histologically confirmed endometrial polyps, were assessed offline by six gynecologists with a special interest in gynecological ultrasound. Each woman contributed four volumes: one gray-scale volume and one power Doppler volume before GIS, and one gray-scale volume and one power Doppler volume at GIS. Power Doppler features before and after gel infusion were compared. RESULTS: At unenhanced ultrasound a pedicle artery was seen in 27-46% of cases, whereas, after gel infusion the examiners reported a pedicle artery in 30-46% of cases (Exact McNemar's test P-values ranged from 0.50 to 1.00). The level of agreement between unenhanced ultrasound and GIS ranged from 59 to 91% (Cohen's kappa values ranged from 0.17 to 0.79). There was no tendency for a pedicle artery to be identified less often at GIS than before gel instillation. CONCLUSION: Gel infusion does not affect the power Doppler signal in patients with endometrial polyps.

Does a prediction model for pregnancy of unknown location developed in the UK validate on a US population?

BACKGROUND: A logistic regression model (M4) was developed in the UK to predict the outcome for women with a pregnancy of unknown location (PUL) based on the initial two human chorionic gonadotrophin (hCG) values, 48 h apart. The purpose of this paper was to assess the utility of this model to predict the outcome for a woman (PUL) in a US population. METHODS: Diagnostic variables included log-transformed serum hCG average of two measurements, and linear and quadratic hCG ratios. Outcomes modeled were failing PUL, intrauterine pregnancy (IUP) and ectopic pregnancy (EP). This model was applied to a US cohort of 604 women presenting with symptomatic first-trimester pregnancies, who were followed until a definitive diagnosis was made. The model was applied before and after correcting for differences in terminology and diagnostic criteria. RESULTS: When retrospectively applied to the adjusted US population, the M4 model demonstrated lower areas under the curve compared with the UK population, 0.898 versus 0.988 for failing PUL/spontaneous miscarriage, 0.915 versus 0.981 for IUP and 0.831 versus 0.904 for EP. Whereas the model had 80% sensitivity for EP using UK data, this decreased to 49% for the US data, with similar specificities. Performance only improved slightly (55% sensitivity) when the US population was adjusted to better match the UK diagnostic criteria. CONCLUSIONS: A logistic regression model based on two hCG values performed with modest decreases in predictive ability in a US cohort for women at risk for EP compared with the original UK population. However, the sensitivity for EP was too low for the model to be used in clinical practice in its present form. Our data illustrate the difficulties of applying algorithms from one center to another, where the definitions of pathology may differ.

Additive survival least-squares support vector machines.

This work studies a new survival modeling technique based on least-squares support vector machines. We propose the use of a least-squares support vector machine combining ranking and regression. The advantage of this kernel-based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non-linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high-dimensional Norway/Stanford Breast Cancer Data set.