Ovarian cancer screening in women with a family history of breast or ovarian cancer

OBJECTIVE: To evaluate positive predictive values of CA 125 or transvaginal ultrasonography screening for ovarian cancer according to family history of breast or ovarian cancer. METHODS: In the screening arm of a randomized controlled trial of screening compared with usual care, 28,460 women with family history data received baseline and annual CA 125 and transvaginal ultrasonography examinations. We analyzed CA 125 and transvaginal ultrasonography results from the first four rounds of screening. We classified women as average (n=22,687), moderate (n=2,572), or high (n=2,163) risk based on family history, or high risk due to a personal history of breast cancer (n=1,038). Cancers were identified by active follow-up of women with abnormal screening results and annual questionnaires. We calculated positive predictive values for screening combinations. RESULTS: Similar proportions (4.8-5.0%) of women in each group had abnormal screening results. Higher-risk women were more likely than lower-risk women to undergo biopsy after a positive screen. Screening identified 43 invasive ovarian cancers. The positive predictive values for abnormal screening results were 0.7% in average-risk, 1.3% in moderate-risk, and 1.6% in high-risk groups; one ovarian cancer occurred among the breast cancer survivors. The positive predictive values for postbaseline abnormal screening results were also higher in the higher-risk groups. The positive predictive values did not significantly differ across risk groups. CONCLUSION: Probabilities of abnormal annual CA 125 and transvaginal ultrasonography screens were similar across groups based on family history of breast or ovarian cancer. However, ovarian cancer was more likely to be diagnosed after an abnormal screening result among women at higher family history-based risk than among women at lower risk. LEVEL OF EVIDENCE: I.     

Deterministic center of pressure patterns characterize postural instability in Parkinson’s disease

Static posturographic recordings were obtained from six Parkinson’s patients and six age-matched, healthy control participants. The availability of vision and visuo-spatial cognitive load were manipulated. Postural sway patterns were analyzed using recurrence quantification analysis (RQA), which revealed differences in center of pressure (COP) dynamics between Parkinson’s and control participants. AP COP trajectories for the Parkinson’s group were not only significantly more variable than for the control group, but also exhibited distinct patterns of temporal dynamics. The visual manipulation did not differentially affect the two groups. No cognitive load effects were found. The results are generally consistent with the hypothesis that pathological physiological systems exhibit a tendency for less flexible, more deterministic dynamic patterns.     

Effect of cardiac motion on body surface electrocardiographic potentials: an MRI-based simulation study

This paper describes an electrical model of cardiac ventricles incorporating real geometry and motion. The heart anatomy and its motion through the cardiac cycle are obtained from segmentations of multiple-slice MRI time sequences; the special conduction system is constructed using an automated mapping procedure from an existing static heart model. The heart model is mounted in an anatomically realistic voxel model of the human body. The cardiac electrical source and surface potentials are determined numerically using both a finite-difference scheme and a boundary-element method with the incorporation of the motion of the heart. The electrocardiograms (ECG) and body surface potential maps are calculated and compared to the static simulation in the resting heart. The simulations demonstrate that introducing motion into the cardiac model modifies the ECG signals, with the most obvious change occurring during the T-wave at peak contraction of the ventricles. Body surface potential maps differ in some local positions during the T-wave, which may be of importance to a number of cardiac models, including those incorporating inverse methods.     

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the ‘Gail 2’ model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the ‘Gail 2’ model showed the average C statistic was 0.63 (95% CI 0.59–0.67), and the expected/observed ratio of events varied considerably across studies (95% prediction interval for E/O ratio when the model was applied in practice was 0.75–1.19). There is a need for models with better predictive performance but, given the large amount of work already conducted, further improvement of existing models based on conventional risk factors is perhaps unlikely. Research to identify new risk factors with large additionally predictive ability is therefore needed, alongside clearer reporting and continual validation of new models as they develop.     

First-year students' perceptions about pediatric dental behavior guidance techniques: the effect of education

The purpose of this study was to evaluate the effect of a dental education component on dental students' perceptions toward behavior guidance techniques in pediatric dentistry. A questionnaire was completed by seventy-three first-year dental students, before and after a course on human development and behavior guidance techniques in pediatric dentistry. The acceptability of behavior guidance techniques and situations in pediatric dentistry was scored with a visual analog scale before and after the course, compared, and evaluated in relation to demographic data. After the course, statistically significant increases (ANOVA) in the acceptability of aversive behavior guidance (voice control, hand over mouth, and immobilization), sedation, general anesthesia, and modeling were found. Statistically significant decreases (ANOVA) in acceptability were found in mentioning the possibility of pain during treatment and with a parent being in the clinic or talking with the child during treatment. Female or married students, those who had previously received dental treatment, or those who had a dentist in the family showed statistically significant changes that indicated more empathy toward the children. We conclude that undergraduate dental education may have a significant short-term influence on dental students' perceptions of behavior guidance in pediatric dentistry.     

Identification of soluble tissue-derived biomarkers from human thyroid tissue explants maintained on a microfluidic device

Although a large cohort of potential biomarkers for thyroid cancer aggressiveness have been tested in various formats in recent years, to the best of our knowledge, thyroglobulin and calcitonin remain the only two established biomarkers associated with thyroid cancer management. Our group has recently validated a novel means of maintaining live, human ex vivo thyroid tissue within a tissue-on-chip format. The present pilot study aimed to interrogate the tissue effluent, containing all the soluble markers released by the tissue samples maintained within the devices'' tissue chamber, for the presence of markers potentially associated with thyroid cancer aggressiveness. Culture effluent from tissue samples harvested from 19 individual patients who had undergone thyroidectomy for the treatment of suspected thyroid cancer was assessed, first using a proteome profiler™ angiogenesis array kit. Patients were subcategorised as ‘aggressive’ if they possessed a minimum of N1b level metastases, whilst ‘non-aggressive’ samples were T3 or lower without evidence of multifocality; and contralateral healthy thyroid tissue was harvested for comparative studies. Levels of Serpin-F1, vascular endothelial growth factor, Thrombospondin-1 and chemokine (C-C motif) ligand were significantly altered and, thus, were further investigated using ELISA to allow for quantitative analysis. The concentration of serpin-F1 was significantly increased in the effluent of aggressive thyroid cancer tissue when compared with levels released by both non-aggressive and benign samples. The present study demonstrated the usability of microfluidic technology for the analysis of the ex vivo tissue secretome in order to identify novel biomarkers.