Accuracy of placental growth factor alone or in combination with soluble fms-like tyrosine kinase-1 or maternal factors in detecting preeclampsia in asymptomatic women in the second and third trimesters: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to: (1) identify all relevant studies reporting on the diagnostic accuracy of maternal circulating placental growth factor) alone or as a ratio with soluble fms-like tyrosine kinase-1), and of placental growth factor-based models (placental growth factor combined with maternal factors±other biomarkers) in the second or third trimester to predict subsequent development of preeclampsia in asymptomatic women; (2) estimate a hierarchical summary receiver-operating characteristic curve for studies reporting on the same test but different thresholds, gestational ages, and populations; and (3) select the best method to screen for preeclampsia in asymptomatic women during the second and third trimester of pregnancy by comparing the diagnostic accuracy of each method. DATA SOURCES: A systematic search was performed through MEDLINE, Embase, CENTRAL, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform databases from January 1, 1985 to April 15, 2021. STUDY ELIGIBILITY CRITERIA: Studies including asymptomatic singleton pregnant women at >18 weeks' gestation with risk of developing preeclampsia were evaluated. We included only cohort or cross-sectional test accuracy studies reporting on preeclampsia outcome, allowing tabulation of 2×2 tables, with follow-up available for >85%, and evaluating performance of placental growth factor alone, soluble fms-like tyrosine kinase-1- placental growth factor ratio, or placental growth factor-based models. The study protocol was registered on the International Prospective Register Of Systematic Reviews (CRD 42020162460). METHODS: Because of considerable intra- and interstudy heterogeneity, we computed the hierarchical summary receiver-operating characteristic plots and derived diagnostic odds ratios, ß, θi, and Λ for each method to compare performances. The quality of the included studies was evaluated by the QUADAS-2 tool. RESULTS: The search identified 2028 citations, from which we selected 474 studies for detailed assessment of the full texts. Finally, 100 published studies met the eligibility criteria for qualitative and 32 for quantitative syntheses. Twenty-three studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the second trimester, including 16 (with 27 entries) that reported on placental growth factor test alone, 9 (with 19 entries) that reported on the soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 6 (16 entries) that reported on placental growth factor-based models. Fourteen studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the third trimester, including 10 (with 18 entries) that reported on placental growth factor test alone, 8 (with 12 entries) that reported on soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 7 (with 12 entries) that reported on placental growth factor-based models. For the second trimester, Placental growth factor-based models achieved the highest diagnostic odds ratio for the prediction of early preeclampsia in the total population compared with placental growth factor alone and soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 63.20; 95% confidence interval, 37.62-106.16 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 6.96; 95% confidence interval, 1.76-27.61 vs placental growth factor alone, 5.62; 95% confidence interval, 3.04-10.38); placental growth factor-based models had higher diagnostic odds ratio than placental growth factor alone for the identification of any-onset preeclampsia in the unselected population (28.45; 95% confidence interval, 13.52-59.85 vs 7.09; 95% confidence interval, 3.74-13.41). For the third trimester, Placental growth factor-based models achieved prediction for any-onset preeclampsia that was significantly better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 27.12; 95% confidence interval, 21.67-33.94 vs placental growth factor alone, 10.31; 95% confidence interval, 7.41-14.35 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 14.94; 95% confidence interval, 9.42-23.70). CONCLUSION: Placental growth factor with maternal factors ± other biomarkers determined in the second trimester achieved the best predictive performance for early preeclampsia in the total population. However, in the third trimester, placental growth factor-based models had predictive performance for any-onset preeclampsia that was better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio. Through this meta-analysis, we have identified a large number of very heterogeneous studies. Therefore, there is an urgent need to develop standardized research using the same models that combine serum placental growth factor with maternal factors ± other biomarkers to accurately predict preeclampsia. Identification of patients at risk might be beneficial for intensive monitoring and timing delivery.

Analysis of retest reliability for pregnant women undergoing cfDNA testing with a no-call result.

BACKGROUND: Determining the reasons for unreportable or no-call cell-free DNA (cfDNA) test results has been an ongoing issue, and a consensus on subsequent management is still lacking. This study aimed to explore potential factors related to no-call cfDNA test results and to discuss whether retest results are reliable. METHODS AND RESULTS: This was a retrospective study of women with singleton pregnancies undergoing cfDNA testing in 2021. Of the 9871 pregnant patients undergoing cfDNA testing, 111 had a no-call result, and their results were compared to those of 170 control patients. The no-call rate was 1.12% (111/9871), and the primary cause for no-call results was data fluctuation (88.29%, 98/111). Medical conditions were significantly more frequent in the no-call group than in the reportable results group (P < 0.001). After retesting, 107 (107/111, 96.40%) patients had a result, and the false-positive rate (FPR) of retesting was 10.09% (10.09%, 11/109). In addition, placental lesions were more frequent in the no-call group than in the reportable results group (P = 0.037), and 4 patients, all in the no-call group, experienced pregnancy loss. CONCLUSIONS: Pregnant women with medical conditions are more likely to have a no-call result. A retest is suggested for patients with a no-call result, but retests have a high FPR. In addition, pregnant women with a no-call result are at increased risk of adverse pregnancy outcomes. In conclusion, more attention should be given to pregnant women for whom a no-call cfDNA result is obtained.

Reverse Bayesian Implications of p-Values Reported in Critical Care Randomized Trials.

BACKGROUND: Misinterpretations of the p-value in null-hypothesis statistical testing are common. We aimed to determine the implications of observed p-values in critical care randomized controlled trials (RCTs). METHODS: We included three cohorts of published RCTs: Adult-RCTs reporting a mortality outcome, Pediatric-RCTs reporting a mortality outcome, and recent Consecutive-RCTs reporting p-value ≤.10 in six higher-impact journals. We recorded descriptive information from RCTs. Reverse Bayesian implications of obtained p-values were calculated, reported as percentages with inter-quartile ranges. RESULTS: Obtained p-value was ≤.005 in 11/216 (5.1%) Adult-RCTs, 2/120 (1.7%) Pediatric-RCTs, and 37/90 (41.1%) Consecutive-RCTs. An obtained p-value .05-.0051 had high False Positive Rates; in Adult-RCTs, minimum (assuming prior probability of the alternative hypothesis was 50%) and realistic (assuming prior probability of the alternative hypothesis was 10%) False Positive Rates were 16.7% [11.2, 21.8] and 64.3% [53.2, 71.4]. An obtained p-value ≤.005 had lower False Positive Rates; in Adult-RCTs the realistic False Positive Rate was 7.7% [7.7, 16.0]. The realistic probability of the alternative hypothesis for obtained p-value .05-.0051 (ie, Positive Predictive Value) was 28.0% [24.1, 34.8], 30.6% [27.7, 48.5], 29.3% [24.3, 41.0], and 32.7% [24.1, 43.5] for Adult-RCTs, Pediatric-RCTs, Consecutive-RCTs primary and secondary outcome, respectively. The maximum Positive Predictive Value for p-value category .05-.0051 was median 77.8%, 79.8%, 78.8%, and 81.4% respectively. To have maximum or realistic Positive Predictive Value >90% or >80%, RCTs needed to have obtained p-value ≤.005. The credibility of p-value .05-.0051 findings were easy to challenge, and the credibility to rule-out an effect with p-value >.05 to .10 was low. The probability that a replication study would obtain p-value ≤.05 did not approach 90% unless the obtained p-value was ≤.005. CONCLUSIONS: Unless the obtained p-value was ≤.005, the False Positive Rate was high, and the Positive Predictive Value and probability of replication of "statistically significant" findings were low.

Critical CHD screening programme: a 3-year multicentre experience in Mexico.

INTRODUCTION: CHDs are the most common type of birth defect. One in four newborns with a heart defect has a critical CHD. In Mexico, there is a lack of data available to determine its prevalence. Pulse oximetry screening programmes have been implemented worldwide, reporting opportunity areas in algorithm interpretation and data management. Our study aims to share preliminary results of a 3-year experience of a multicentre pulse oximetry screening programme that addresses critical challenges. MATERIALS AND METHODS: This retrospective study examined the reports of newborns screened from February 2016 to July 2019 from five hospitals. Two algorithms -the New Jersey and the American Academy of Pediatrics- were implemented over consecutive periods. The algorithms' impact was assessed through the calculation of the false-positive rate in an eligible population. RESULTS: A total of 8960 newborns were eligible for the study; from it, 32.27% were screened under the New Jersey and 67.72% under the American Academy of Pediatrics algorithm - false-positive rate: 1% (CI 95: ± 0.36%) and 0.71% (CI 95: ± 0.21%), respectively. Seventy-nine newborns were referred, six were diagnosed with critical CHD, and six with CHD. The critical CHD estimated prevalence was 6.69:10,000 newborns (CI 95: ± 5.36). Our results showed that the algorithm was not related to the observable false-positive rate reduction. DISCUSSION: Other factors may play a role in decreasing the false-positive rate. Our experience implementing this programme was that a systematic screening process led to more confident results, newborn's report interpretation, and follow-up.

Adjusting Coronavirus Prevalence Estimates for Laboratory Test Kit Error.

Testing representative populations to determine the prevalence or the percentage of the population with active severe acute respiratory syndrome coronavirus 2 infection and/or antibodies to infection is being recommended as essential for making public policy decisions to ease restrictions or to continue enforcing national, state, and local government rules to shelter in place. However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100%-in some cases, considerably less than 100%. That error will lead to biased prevalence estimates. If the true prevalence is low, possibly in the range of 1%-5%, then testing error will lead to a constant background of bias that most likely will be larger, and possibly much larger, than the true prevalence itself. As a result, what is needed is a method for adjusting prevalence estimates for testing error. Methods are outlined in this article for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted. If used, these methods also would help harmonize study results within countries and worldwide. Adjustment can lead to more accurate prevalence estimates and to better policy decisions. However, adjustment will not improve the accuracy of an individual test.

Noninvasive prenatal screening in twin pregnancies with cell-free DNA using the IONA test: a prospective multicenter study.

BACKGROUND: In singleton pregnancies, studies investigating cell-free DNA in maternal blood have consistently reported high detection rate and low false-positive rate for the 3 common fetal trisomies (trisomies 21, 18, and 13). The potential advantages of noninvasive prenatal testing in twin pregnancies are even greater than in singletons, in particular lower need for invasive testing and consequent fetal loss rate. However, several organizations do not recommend cell-free DNA in twin pregnancies and call for larger prospective studies. OBJECTIVE: In response to this, we undertook a large prospective multicenter study to establish the screening performance of cell-free DNA for the 3 common trisomies in twin pregnancies. Moreover, we combined our data with that reported in published studies to obtain the best estimate of screening performance. STUDY DESIGN: This was a prospective multicenter blinded study evaluating the screening performance of cell-free DNA in maternal plasma for the detection of fetal trisomies in twin pregnancies. The study took place in 6 fetal medicine centers in England, United Kingdom. The primary outcome was the screening performance and test failure rate of cell-free DNA using next generation sequencing (the IONA test). Maternal blood was taken at the time of (or after) a conventional screening test. Data were collected at enrolment, at any relevant invasive testing throughout pregnancy, and after delivery until the time of hospital discharge. Prospective detailed outcome ascertainment was undertaken on all newborns. The study was undertaken and reported according to the Standards for Reporting of Diagnostic Accuracy Studies. A pooled analysis was also undertaken using our data and those in the studies identified by a literature search (MEDLINE, Embase, CENTRAL, Cochrane Library, and ClinicalTrials.gov) on June 6, 2020. RESULTS: A total of 1003 women with twin pregnancies were recruited, and complete data with follow-up and reference data were available for 961 (95.8%); 276 were monochorionic and 685 were dichorionic. The failure rate was 0.31%. The mean fetal fraction was 12.2% (range, 3%-36%); all 9 samples with a 3% fetal fraction provided a valid result. There were no false-positive or false-negative results for trisomy 21 or trisomy 13, whereas there was 1 false-negative and 1 false-positive result for trisomy 18. The IONA test had a detection rate of 100% for trisomy 21 (n=13; 95% confidence interval, 75-100), 0% for trisomy 18 (n=1; 95% confidence interval, 0-98), and 100% for trisomy 13 (n=1; 95% confidence interval, 3-100). The corresponding false-positive rates were 0% (95% confidence interval, 0-0.39), 0.10% (95% confidence interval, 0-0.58), and 0% (95% confidence interval, 0-0.39), respectively. By combining data from our study with the 11 studies identified by literature search, the detection rate for trisomy 21 was 95% (n=74; 95% confidence interval, 90-99) and the false-positive rate was 0.09% (n=5598; 95% confidence interval, 0.03-0.19). The corresponding values for trisomy 18 were 82% (n=22; 95% confidence interval, 66-93) and 0.08% (n=4869; 95% confidence interval, 0.02-0.18), respectively. There were 5 cases of trisomy 13 and 3881 non-trisomy 13 pregnancies, resulting in a computed average detection rate of 80% and a false-positive rate of 0.13%. CONCLUSION: This large multicenter study confirms that cell-free DNA testing is the most accurate screening test for trisomy 21 in twin pregnancies, with screening performance similar to that in singletons and very low failure rates (0.31%). The predictive accuracy for trisomies 18 and 13 may be less. However, given the low false-positive rate, offering first-line screening with cell-free DNA to women with twin pregnancy is appropriate in our view and should be considered a primary screening test for trisomy 21 in twins.

A technical and clinical evaluation of the new ThermoFisher BRAHMS unconjugated estriol and inhibin-A assays and their use in second trimester Down syndrome screening.

To evaluate second-trimester Down syndrome screening performance of the new ThermoFisher BRAHMS GOLD unconjugated estriol (uE3) and inhibin-A assays. Serum samples were analyzed for levels of uE3 and inhibin-A using the ThermoFisher BRAHMS GOLD immunoanalyzer and compared to other platforms. Levels were transformed to multiples of the median (MoM) in unaffected pregnancies. Log10 MoM distributions in unaffected and Down syndrome pregnancies were assessed for central tendency (mean) and dispersion (SD). Empirical and estimated screening performances were determined. Correlation between BRAHMS and AutoDELFIA® uE3 and inhibin-A were 0.63 and 0.97, respectively, the respective mean difference was 31.3% [95%CI 50.2% to -112.8%] and -23.3% [95%CI -41.9% to -4.7%]. Passing-Bablok indicated significant systematic (-2.78 [95%CI -3.57 to -2.04]) and proportional bias (1.30 [95%CI 1.15 to -1.47]) between uE3 assays and significant proportional bias (0.71[95%CI 0.65-0.78]) between inhibin-A assays. The uE3 and inhibin-A log10 MoM distribution mean [SD] in unaffected and Down syndrome pregnancies were 0.0024 [SD = 0.2341] and -0.0001 [SD = 0.2078], and -0.2028 [SD = 0.2495] and 0.3645 [SD = 0.2576], respectively. The new BRAHMS uE3 and inhibin-A assays had an 81-83% detection rate for Trisomy21 for a 5% false-positive rate. The new BRAHMS assays achieved the expected screening performance provided the risk estimation model is adjusted to account for the higher BRAHMS uE3 MoM measurement distribution variance.

The incidence and incubation period of false-positive culture results in shoulder surgery.

BACKGROUND: Postoperative shoulder infection is a significant complication requiring timely identification and treatment. Indolent infections such as those involving Cutibacterium acnes (formerly Propionibacterium acnes) provide a diagnostic dilemma as they present differently, without the acute symptoms associated with most postoperative bone and joint infections. Furthermore, Cacnes is thought to be a common contaminant isolated from intraoperative cultures. With no consensus algorithm, long-held cultures play a major role in guiding management decisions in potential postoperative shoulder infection. Our study sought to determine the incidence of positive culture results in both open and arthroscopic procedures in noninfected patients, as well as to clarify whether an increase in the incubation time frame leads to an increased rate of culture growth. METHODS: One hundred patients were prospectively enrolled into either the open or arthroscopic procedure group. Patients with abnormal inflammatory laboratory findings, a history of shoulder surgery, or corticosteroid injection within 6 months of surgery were excluded from the study. Three cultures were obtained for each patient: superficial tissue culture, tissue culture, and "sterile" control swab. Cultures were held for 28 days and checked at regular intervals. All patients were followed up clinically for 6 months to ensure no signs of postoperative infection occurred. RESULTS: Ultimately, 95 patients were included in the final analysis. The false-positive rate was 17.0% in those who underwent open shoulder surgery and 10.4% in those who underwent arthroscopic shoulder surgery. The incidence of positive Cacnes culture results was 6.4% in the open group, whereas Cacnes was not isolated in the arthroscopic group. All positive bacterial culture results were reported within 7 days of collection. One culture result was positive for mold at 26 days. CONCLUSION: A relatively high false-positive culture rate occurred in both open and arthroscopic shoulder surgery. Cacnes was the most commonly identified bacterium in cultures in the open surgery group. Knowledge of one's institutional false-positive culture rate could be important in avoiding potentially inappropriate treatment. Additionally, we found that holding cultures longer than 14 days did not lead to an increased rate of false-positive culture results.

Combining biomarkers by maximizing the true positive rate for a fixed false positive rate.

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate (TPR) for a biomarker combination at a prespecified, clinically acceptable false positive rate (FPR) is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the TPR while constraining the FPR. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations. Thus, use of our method could lead to the development of better biomarker combinations, increasing the likelihood of clinical adoption.

Statistical Assessment of Discrimination Capabilities of a Fractional Calculus Based Image Watermarking System for Gaussian Watermarks.

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles; then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

Nick Martin as a Mentor - A Perspective.

Nick Martin has had an outsized influence on the field of behavioral genetics. Much of this influence stems from his mentorship of young scientists. I describe Nick's mentorship, and what makes it special, from a personal perspective.

Some thoughts on the QR method for analytical similarity evaluation.

As indicated in a recent published draft guidance on comparative analytical assessment, the United States (US) Food and Drug Administration (FDA) seems to suggest the use of quality range (QR) method for analytical similarity evaluation. It is a concern that the use of QR method for analytical similarity evaluation could potentially approve biological products which are not deemed biosimilar to the reference biological products. In this article, the limitations and potential risk for the use of the QR method for analytical similarity evaluation are discussed. Alternatively, two modified versions of the QR method, which are referred to as effect size (ES) mQR and plausibility interval (PI) mQR methods are suggested. The performance and statistical properties of the mQR methods are evaluated via extensive clinical trial simulation under various scenarios. The results indicate that the modified versions of the QR method not only overcome the limitations of the QR method for analytical similarity evaluation, but also can potentially help in detecting reference product changes during manufacturing process.

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems.

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features.

pValid: Validation Beyond the Target-Decoy Approach for Peptide Identification in Shotgun Proteomics.

As the de facto validation method in mass spectrometry-based proteomics, the target-decoy approach determines a threshold to estimate the false discovery rate and then filters those identifications beyond the threshold. However, the incorrect identifications within the threshold are still unknown and further validation methods are needed. In this study, we characterized a framework of validation and investigated a number of common and novel validation methods. We first defined the accuracy of a validation method by its false-positive rate (FPR) and false-negative rate (FNR) and, further, proved that a validation method with lower FPR and FNR led to identifications with higher sensitivity and precision. Then we proposed a validation method named pValid that incorporated an open database search and a theoretical spectrum prediction strategy via a machine-learning technology. pValid was compared with four common validation methods as well as a synthetic peptide validation method. Tests on three benchmark data sets indicated that pValid had an FPR of 0.03% and an FNR of 1.79% on average, both superior to the other four common validation methods. Tests on a synthetic peptide data set also indicated that the FPR and FNR of pValid were better than those of the synthetic peptide validation method. Tests on a large-scale human proteome data set indicated that pValid successfully flagged the highest number of incorrect identifications among all five methods. Further considering its cost-effectiveness, pValid has the potential to be a feasible validation tool for peptide identification.

A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests.

One-sided t-tests are widely used in neuroimaging data analysis. While such a test may be applicable when investigating specific regions and prior information about directionality is present, we argue here that it is often mis-applied, with severe consequences for false positive rate (FPR) control. Conceptually, a pair of one-sided t-tests conducted in tandem (e.g., to test separately for both positive and negative effects), effectively amounts to a two-sided t-test. However, replacing the two-sided test with a pair of one-sided tests without multiple comparisons correction essentially doubles the intended FPR of statements made about the same study; that is, the actual family-wise error (FWE) of results at the whole brain level would be 10% instead of the 5% intended by the researcher. Therefore, we strongly recommend that, unless otherwise explicitly justified, two-sided t-tests be applied instead of two simultaneous one-sided t-tests.

Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer.

PURPOSE: The high false positive rate (FPR) of 18F-FDG PET/CT in lung cancer screening represents a severe challenge for clinical decision-making. This study aimed to develop a clinical-translatable radiomics nomogram for reducing the FPR of PET/CT in lung cancer diagnosis, and to determine the impact of integrating manual diagnosis to the performance of the radiomics nomogram. METHODS: Among 3,947 18F-FDG PET/CT-screened patients with lung lesion, 157 malignant and 111 benign patients were retrospectively enrolled and divided into training and test cohorts. The data of manual diagnosis were recorded. A total of 4,338 features were extracted from CT, thin-section CT, PET and PET/CT, and the four radiomics signatures (RS) were then generated by LASSO method. Radiomics prediction nomogram integrating imaging-based RS and manual diagnosis was developed using multivariable logistic regression. The performances of RS and prediction nomograms were independently validated through key discrimination index and clinical benefit. RESULTS: The FPR of manual diagnosis was found to be 30.6%. Among the four RS, PET/CT RS exhibited the best performance. By integrating manual diagnosis, the hybrid nomogram integrating PET/CT RS and manual diagnosis demonstrated lowest FPR and highest area under curve (AUC) and Youden index (YI) in both training and test cohorts (FPR: 5.4% and 9.1%, AUC: 0.98 and 0.92, YI: 85.8% and 75.5%, respectively). This hybrid nomogram respectively corrected 78.6% and 37.5% among FPR cases produced by PET/CT RS, without significantly sacrificing its sensitivity. The net benefit of hybrid nomogram appeared highest at <85% threshold probability. CONCLUSION: The established hybrid nomogram integrating PET/CT RS and manual diagnosis can significantly reduce FPR, improve diagnostic accuracy and enhance clinical benefit compared to manual diagnosis. By integrating manual diagnosis, the performance of this hybrid nomogram is superior to PET/CT RS, indicating the importance of clinicians' judgement as an essential information source for improving radiomics diagnostic approaches.

Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population.

BACKGROUND: The administration of aspirin <16 weeks gestation to women who are at high risk for preeclampsia has been shown to reduce the rate of preterm preeclampsia by 65%. The traditional approach to identify such women who are at risk is based on risk factors from maternal characteristics, obstetrics, and medical history as recommended by the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. An alternative approach to screening for preeclampsia has been developed by the Fetal Medicine Foundation. This approach allows the estimation of patient-specific risks of preeclampsia that requires delivery before a specified gestational age with the use of Bayes theorem-based model. OBJECTIVE: The purpose of this study was to examine the diagnostic accuracy of the Fetal Medicine Foundation Bayes theorem-based model, the American College of Obstetricians and Gynecologists, and the National Institute for Health and Care Excellence recommendations for the prediction of preterm preeclampsia at 11-13+6 weeks gestation in a large Asian population STUDY DESIGN: This was a prospective, nonintervention, multicenter study in 10,935 singleton pregnancies at 11-13+6 weeks gestation in 11 recruiting centers across 7 regions in Asia between December 2016 and June 2018. Maternal characteristics and medical, obstetric, and drug history were recorded. Mean arterial pressure and uterine artery pulsatility indices were measured according to standardized protocols. Maternal serum placental growth factor concentrations were measured by automated analyzers. The measured values of mean arterial pressure, uterine artery pulsatility index, and placental growth factor were converted into multiples of the median. The Fetal Medicine Foundation Bayes theorem-based model was used for the calculation of patient-specific risk of preeclampsia at <37 weeks gestation (preterm preeclampsia) and at any gestation (all preeclampsia) in each participant. The performance of screening for preterm preeclampsia and all preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and placental growth factor (triple test) was evaluated with the adjustment of aspirin use. We examined the predictive performance of the model by the use of receiver operating characteristic curve and calibration by measurements of calibration slope and calibration in the large. The detection rate of screening by the Fetal Medicine Foundation Bayes theorem-based model was compared with the model that was derived from the application of American College of Obstetricians and Gynecologists and National Institute for Health and Care Excellence recommendations. RESULTS: There were 224 women (2.05%) who experienced preeclampsia, which included 73 cases (0.67%) of preterm preeclampsia. In pregnancies with preterm preeclampsia, the mean multiples of the median values of mean arterial pressure and uterine artery pulsatility index were significantly higher (mean arterial pressure, 1.099 vs 1.008 [P<.001]; uterine artery pulsatility index, 1.188 vs 1.063[P=.006]), and the mean placental growth factor multiples of the median was significantly lower (0.760 vs 1.100 [P<.001]) than in women without preeclampsia. The Fetal Medicine Foundation triple test achieved detection rates of 48.2%, 64.0%, 71.8%, and 75.8% at 5%, 10%, 15%, and 20% fixed false-positive rates, respectively, for the prediction of preterm preeclampsia. These were comparable with those of previously published data from the Fetal Medicine Foundation study. Screening that used the American College of Obstetricians and Gynecologists recommendations achieved detection rate of 54.6% at 20.4% false-positive rate. The detection rate with the use of National Institute for Health and Care Excellence guideline was 26.3% at 5.5% false-positive rate. CONCLUSION: Based on a large number of women, this study has demonstrated that the Fetal Medicine Foundation Bayes theorem-based model is effective in the prediction of preterm preeclampsia in an Asian population and that this method of screening is superior to the approach recommended by American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. We have also shown that the Fetal Medicine Foundation prediction model can be implemented as part of routine prenatal care through the use of the existing infrastructure of routine prenatal care.

Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut.

BACKGROUND: Detection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples. METHODS: Using 30 isogenic samples, reliable cohorts of validated mutations were created for testing purposes. Optimal values of the filtering parameters of IsoMut were determined in a thorough and strict optimization procedure based on these test sets. RESULTS: We show that IsoMut, when tuned correctly, decreases the false positive rate compared to conventional tools in a 30 sample experimental setup; and detects not only single nucleotide variations, but short insertions and deletions as well. IsoMut can also be run more than a hundred times faster than the most precise state of art tool, due its straightforward and easily understandable filtering algorithm. CONCLUSIONS: IsoMut has already been successfully applied in multiple recent studies to find unique, treatment induced mutations in sets of isogenic samples with very low false positive rates. These types of studies provide an important contribution to determining the mutagenic effect of environmental agents or genetic defects, and IsoMut turned out to be an invaluable tool in the analysis of such data.

Impacting the effect of fMRI noise through hardware and acquisition choices - Implications for controlling false positive rates.

We review the components of time-series noise in fMRI experiments and the effect of image acquisition parameters on the noise. In addition to helping determine the total amount of signal and noise (and thus temporal SNR), the acquisition parameters have been shown to be critical in determining the ratio of thermal to physiological induced noise components in the time series. Although limited attention has been given to this latter metric, we show that it determines the degree of spatial correlations seen in the time-series noise. The spatially correlations of the physiological noise component are well known, but recent studies have shown that they can lead to a higher than expected false-positive rate in cluster-wise inference based on parametric statistical methods used by many researchers. Based on understanding the effect of acquisition parameters on the noise mixture, we propose several acquisition strategies that might be helpful reducing this elevated false-positive rate, such as moving to high spatial resolution or using highly-accelerated acquisitions where thermal sources dominate. We suggest that the spatial noise correlations at the root of the inflated false-positive rate problem can be limited with these strategies, and the well-behaved spatial auto-correlation functions (ACFs) assumed by the conventional statistical methods are retained if the high resolution data is smoothed to conventional resolutions.

Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection.

Detection of circulating tumor cells (CTCs) in a blood sample is limited by the sensitivity and specificity of the biomarker panel used to identify CTCs over other blood cells. In this work, we present Bayesian theory that shows how test sensitivity and specificity set the rarity of cell that a test can detect. We perform our calculation of sensitivity and specificity on our image cytometry biomarker panel by testing on pure disease positive (D+ ) populations (MCF7 cells) and pure disease negative populations (D- ) (leukocytes). In this system, we performed multi-channel confocal fluorescence microscopy to image biomarkers of DNA, lipids, CD45, and Cytokeratin. Using custom software, we segmented our confocal images into regions of interest consisting of individual cells and computed the image metrics of total signal, second spatial moment, spatial frequency second moment, and the product of the spatial-spatial frequency moments. We present our analysis of these 16 features. The best performing of the 16 features produced an average separation of three standard deviations between D+ and D- and an average detectable rarity of ∼1 in 200. We performed multivariable regression and feature selection to combine multiple features for increased performance and showed an average separation of seven standard deviations between the D+ and D- populations making our average detectable rarity of ∼1 in 480. Histograms and receiver operating characteristics (ROC) curves for these features and regressions are presented. We conclude that simple regression analysis holds promise to further improve the separation of rare cells in cytometry applications. © 2017 International Society for Advancement of Cytometry.