Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations.

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Comparative Effectiveness of Surgical Approaches for Lung Cancer.

BACKGROUND: The magnitude of association and quality of evidence comparing surgical approaches for lung cancer resection has not been analyzed. This has resulted in conflicting information regarding the relative superiority of the different approaches and disparate opinions on the optimal surgical treatment. We reviewed and systematically analyzed all published data comparing near- (30-d) and long-term mortality for minimally invasive to open surgical approaches for lung cancer. METHODS: Comprehensive search of EMBASE, MEDLINE, and the Cochrane Library, from January 2009 to August 2019, was performed to identify the studies and those that passed bias assessment were included in the analysis utilizing propensity score matching techniques. Meta-analysis was performed using random-effects and fixed-effects models. Risk of bias was assessed via the Newcastle-Ottawa Scale and the ROBINS-I tool. The study was registered in PROSPERO (CRD42020150923) prior to analysis. RESULTS: Overall, 1382 publications were identified but 19 studies were included encompassing 47,054 patients after matching. Minimally invasive techniques were found to be superior with respect to near-term mortality in early and advanced-stage lung cancer (risk ratio 0.45, 95% confidence interval [CI] 0.21-0.95, I2 = 0%) as well as for elderly patients (odds ratio 0.45, 95% CI 0.31-0.65, I2 = 30%), but did not demonstrate benefit for high-risk patients (odds ratio 0.74, 95% CI 0.06-8.73, I2 = 78%). However, no difference was found in long-term survival. CONCLUSIONS: We performed the first systematic review and meta-analysis to compare surgical approaches for lung cancer which indicated that minimally invasive techniques may be superior to thoracotomy in near-term mortality, but there is no difference in long-term outcomes.

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.

BACKGROUND: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. METHODS: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes "Prior interaction map + TCGA data → Posterior interaction map." RESULTS: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. CONCLUSIONS: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.

Estimating screening-mammography receiver operating characteristic (ROC) curves from stratified random samples of screening mammograms: a simulation study.

RATIONALE AND OBJECTIVES: To evaluate stratified random sampling (SRS) of screening mammograms by (1) Breast Imaging Reporting and Data System (BI-RADS) assessment categories, and (2) the presence of breast cancer in mammograms, for estimation of screening-mammography receiver operating characteristic (ROC) curves in retrospective observer studies. MATERIALS AND METHODS: We compared observer study case sets constructed by (1) random sampling (RS); (2) SRS with proportional allocation (SRS-P) with BI-RADS 1 and 2 noncancer cases accounting for 90.6% of all noncancer cases; (3) SRS with disproportional allocation (SRS-D) with BI-RADS 1 and 2 noncancer cases accounting for 10%-80%; and (4) SRS-D and multiple imputation (SRS-D + MI) with missing BI-RADS 1 and 2 noncancer cases imputed to recover the 90.6% proportion. Monte Carlo simulated case sets were drawn from a large case population modeled after published Digital Mammography Imaging Screening Trial data. We compared the bias, root-mean-square error, and coverage of 95% confidence intervals of area under the ROC curve (AUC) estimates from the sampling methods (200-2000 cases, of which 25% were cancer cases) versus from the large case population. RESULTS: AUC estimates were unbiased from RS, SRS-P, and SRS-D + MI, but biased from SRS-D. AUC estimates from SRS-P and SRS-D + MI had 10% smaller root-mean-square error than RS. CONCLUSIONS: Both SRS-P and SRS-D + MI can be used to obtain unbiased and 10% more efficient estimate of screening-mammography ROC curves.

Estimating sensitivity and specificity for technology assessment based on observer studies.

RATIONALE AND OBJECTIVES: The goal of this study was to determine the accuracy and precision of using scores from a receiver operating characteristic rating scale to estimate sensitivity and specificity. MATERIALS AND METHODS: We used data collected in a previous study that measured the improvements in radiologists' ability to classify mammographic microcalcification clusters as benign or malignant with and without the use of a computer-aided diagnosis scheme. Sensitivity and specificity were estimated from the rating data from a question that directly asked the radiologists their biopsy recommendations, which was used as the "truth," because it is the actual recall decision, thus it is their subjective truth. By thresholding the rating data, sensitivity and specificity were estimated for different threshold values. RESULTS: Because of interreader and intrareader variability, estimated sensitivity and specificity values for individual readers could be as much as 100% in error when using rating data compared to using the biopsy recommendation data. When pooled together, the estimates using thresholding the rating data were in good agreement with sensitivity and specificity estimated from the recommendation data. However, the statistical power of the rating data estimates was lower. CONCLUSIONS: By simply asking the observer his or her explicit recommendation (eg, biopsy or no biopsy), sensitivity and specificity can be measured directly, giving a more accurate description of empirical variability and the power of the study can be maximized.

Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts.

RATIONALE AND OBJECTIVES: To evaluate variability in the clinical assessment of breast images, we evaluated scoring behavior of radiologists in a retrospective reader study combining x-ray mammography (XRM) and three-dimensional automated breast ultrasound (ABUS) for breast cancer detection in women with dense breasts. METHODS: The study involved 17 breast radiologists in a sequential study design with readers first interpreting XRM-alone followed by an interpretation of combined XRM + ABUS. Each interpretation included a forced Breast Imaging Reporting and Data System scale and a likelihood that the woman had breast cancer. The analysis included 164 asymptomatic patients, including 31 breast cancer patients, with dense breasts and a negative screening XRM. Of interest were interreader scoring variability for XRM-alone, XRM + ABUS, and the sequential effect. In addition, a simulated double reading by pairs of readers of XRM + ABUS was investigated. Performance analysis included receiver operating characteristic analysis, percentile analysis, and κ statistics. Bootstrapping was used to determine statistical significance. RESULTS: The median change in area under the receiver operating characteristic curve after ABUS interpretation was 0.12 (range 0.04-0.19). Reader agreement was fair with the median interreader κ being 0.26 (0.05-0.48) for XRM-alone and 0.34 (0.11-0.55) for XRM + ABUS (95% confidence interval for the difference in κ, 0.06-0.11). Simulated double reading of XRM + ABUS demonstrated tradeoffs in sensitivity and specificity, but conservative simulated double reading resulted in a significant improvement in both sensitivity (16.7%) and specificity (7.6%) with respect to XRM-alone. CONCLUSION: A modest, but statistically significant, increase in interreader agreement was observed after interpretation of ABUS.

A scaling transformation for classifier output based on likelihood ratio: applications to a CAD workstation for diagnosis of breast cancer.

PURPOSE: The authors developed scaling methods that monotonically transform the output of one classifier to the "scale" of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers. METHODS: Using both simulated and radiologists' rating data of breast imaging cases, the authors investigated a likelihood-ratio-scaling transformation, based on "matching" classifier likelihood ratios. For comparison, three other scaling transformations were investigated that were based on matching classifier true positive fraction, false positive fraction, or cumulative distribution function, respectively. The authors explored modifying the computer output to reflect the scale of the radiologist, as well as modifying the radiologist's ratings to reflect the scale of the computer. They also evaluated how dataset size affects the transformations. RESULTS: When ROC curves of two classifiers differed substantially, the four transformations were found to be quite different. The likelihood-ratio scaling transformation was found to vary widely from radiologist to radiologist. Similar results were found for the other transformations. Our simulations explored the effect of database sizes on the accuracy of the estimation of our scaling transformations. CONCLUSIONS: The likelihood-ratio-scaling transformation that the authors have developed and evaluated was shown to be capable of transforming computer and radiologist outputs to a common scale reliably, thereby allowing the comparison of the computer and radiologist outputs on the basis of a clinically relevant statistic.

Risk factors for perioperative acute kidney injury after adult cardiac surgery: role of perioperative management.

BACKGROUND: The development of acute kidney injury (AKI) after adult cardiac surgery is associated with increased morbidity and mortality. Our aim was to assess the risk factors for postoperative AKI and whether the addition of perioperative management variables can improve AKI prediction. METHODS: We studied 3,219 patients operated from January 2006 to December 2009. The AKI was defined as proposed by the Acute Kidney Injury Network. Patient preoperative characteristics, as well as intraoperative, cardiopulmonary bypass (CPB), and postoperative management variables, were evaluated for association with AKI with logistic regression analysis. The model including all variables was assessed first, then separate models including only preoperative variables followed by the sequential addition of intraoperative, CPB, and postoperative management variables were tested; receiver operating characteristic analysis was used to evaluate and compare models' discriminatory power. RESULTS: The AKI occurred in 288 of 3,219 patients (8.9%). Logistic regression analysis identified 15 predictors of AKI; 4 were preoperative (age, diabetes, smoking, and serum creatinine), 4 intraoperative (inotropes, erythrocytes transfusion, cross-clamp time, and need of a new pump run), 2 CPB-related (urine output and furosemide administration during CPB), and 5 postoperative (erythrocytes transfusion, administration of vasoconstrictors, inotropes, diuretics, and antiarrhythmics). Model-discrimination performance improved from an area under the curve of 0.830 (95% confidence interval 0.807 to 0.854) for the model including only preoperative variables to an area under the curve of 0.904 (95% confidence interval 0.886 to 0.921) for the model including all variables (p<0.001). CONCLUSIONS: Several factors influence AKI development after cardiac surgery and perioperative patient management significantly affects AKI occurrence. Predictive models can be sensibly improved by the addition of these variables.

Non-contrast enhanced MRI for evaluation of breast lesions: comparison of non-contrast enhanced high spectral and spatial resolution (HiSS) images versus contrast enhanced fat-suppressed images.

RATIONALE AND OBJECTIVES: The aims of this study were to evaluate high spectral and spatial resolution (HiSS) magnetic resonance imaging (MRI) for the diagnosis of breast cancer without the injection of contrast media by comparing the performance of precontrast HiSS images to that of conventional contrast-enhanced, fat-suppressed, T1-weighted images on the basis of image quality and in the task of classifying benign and malignant breast lesions. MATERIALS AND METHODS: Ten benign and 44 malignant lesions were imaged at 1.5 T with HiSS (precontrast administration) and conventional fat-suppressed imaging (3-10 minutes after contrast administration). This set of 108 images, after randomization, was evaluated by three experienced radiologists blinded to the imaging technique. Breast Imaging Reporting and Data System morphologic criteria (lesion shape, lesion margin, and internal signal intensity pattern) and final assessment were used to measure reader performance. Image quality was evaluated on the basis of boundary delineation and quality of fat suppression. An overall probability of malignancy was assigned to each lesion for HiSS and conventional images separately. RESULTS: On boundary delineation and quality of fat suppression, precontrast HiSS scored similarly to conventional postcontrast MRI. On benign versus malignant lesion separation, there was no statistically significant difference in receiver-operating characteristic performance between HiSS and conventional MRI, and HiSS met a reasonable noninferiority condition. CONCLUSIONS: Precontrast HiSS imaging is a promising approach for showing lesion morphology without blooming and other artifacts caused by contrast agents. HiSS images could be used to guide subsequent dynamic contrast-enhanced MRI scans to maximize spatial and temporal resolution in suspicious regions. HiSS MRI without contrast agent injection may be particularly important for patients at risk for contrast-induced nephrogenic systemic fibrosis or allergic reactions.

Small lung cancers: improved detection by use of bone suppression imaging--comparison with dual-energy subtraction chest radiography.

PURPOSE: To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography. MATERIALS AND METHODS: Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance. RESULTS: The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001). CONCLUSION: Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose.

Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study.

PURPOSE: To evaluate a computer-aided diagnosis (CADx) system for dynamic contrast material-enhanced magnetic resonance (MR) imaging and compare it with a currently used clinical method of interpreting breast MR image findings that includes the use of commercially available automated software for kinetic image data processing and visualization. MATERIALS AND METHODS: In this HIPAA-compliant, institutional review board-approved study, a training set of 121 breast lesions (77 malignant, 44 benign) was used to train the CADx system. After practicing with 10 training cases, six breast imaging radiologists assessed the likelihood of malignancy and the need for biopsy with a separate test set of 60 lesions (30 malignant, 30 benign). Their performances in differentiating between benign and malignant breast lesions both without (conventional lesion viewing, output from commercially available breast MR imaging analysis software) and with the aid of the CADx workstation (with classification yielding an estimation of the probability of malignancy for each lesion) were evaluated with receiver operating characteristic analysis. RESULTS: When CADx was used, the average performance of the radiologists was significantly improved, as indicated by increases in mean area under the receiver operating characteristic curve (from 0.80 to 0.84, P = .007), mean sensitivity (from 83% to 88%, P = .001), and average number of biopsy recommendations for malignant cases (1.7 more biopsies for malignant lesions with use of CADx, P = .032). Although the mean specificity improved (from 50% to 53%), the improvement was not significant (P = .2). CONCLUSION: Use of the CADx system improved the radiologists' performance in differentiating between malignant and benign MR imaging-depicted breast lesions.

Enhancement of breast CADx with unlabeled data.

PURPOSE: Unlabeled medical image data are abundant, yet the process of converting them into a labeled ("truth-known") database is time and resource expensive and fraught with ethical and logistics issues. The authors propose a dual-stage CADx scheme in which both labeled and unlabeled (truth-known and "truth-unknown") data are used. This study is an initial exploration of the potential for leveraging unlabeled data toward enhancing breast CADx. METHODS: From a labeled ultrasound image database consisting of 1126 lesions with an empirical cancer prevalence of 14%, 200 different randomly sampled subsets were selected and the truth status of a variable number of cases was masked to the algorithm to mimic different types of labeled and unlabeled data sources. The prevalence was fixed at 50% cancerous for the labeled data and 5% cancerous for the unlabeled. In the first stage of the dual-stage CADx scheme, the authors term "transductive dimension reduction regularization" (TDR-R), both labeled and unlabeled images characterized by extracted lesion features were combined using dimension reduction (DR) techniques and mapped to a lower-dimensional representation. (The first stage ignored truth status therefore was an unsupervised algorithm.) In the second stage, the labeled data from the reduced dimension embedding were used to train a classifier toward estimating the probability of malignancy. For the first CADx stage, the authors investigated three DR approaches: Laplacian eigen-maps, t-distributed stochastic neighbor embedding (t-SNE), and principal component analysis. For the TDR-R methods, the classifier in the second stage was a supervised (i.e., utilized truth) Bayesian neural net. The dual-stage CADx schemes were compared to a single-stage scheme based on manifold regularization (MR) in a semisupervised setting via the LapSVM algorithm. Performance in terms of areas under the ROC curve (AUC) of the CADx schemes was evaluated in leave-one-out and .632+ bootstrap analyses on a by-lesion basis. Additionally, the trained algorithms were applied to an independent test data set consisting of 101 lesions with approximately 50% cancer prevalence. The difference in AUC (deltaAUC) between with and without the use of unlabeled data was computed. RESULTS: Statistically significant differences in the average AUC value (deltaAUC) were found in many instances between training with and without unlabeled data, based on the sample set distributions generated from this particular ultrasound data set during cross-validation and using independent test set. For example, when using 100 labeled and 900 unlabeled cases and testing on the independent test set, the TDR-R methods produced average deltaAUC=0.0361 with 95% intervals [0.0301; 0.0408] (p-value < 0.0001, adjusted for multiple comparisons, but considering the test set fixed) using t-SNE and average deltaAUC=.026 [0.0227, 0.0298] (adjusted p-value < 0.0001) using Laplacian eigenmaps, while the MR-based LapSVM produced an average deltaAUC=.0381 [0.0351; 0.0405] (adjusted p-value < 0.0001). The authors also found that schemes initially obtaining lower than average performance when using labeled data only showed the most prominent increase in performance when unlabeled data were added in the first CADx stage, suggesting a regularization effect due to the injection of unlabeled data. CONCLUSION: The findings reveal evidence that incorporating unlabeled data information into the overall development of CADx methods may improve classifier performance by non-negligible amounts and warrants further investigation.

EuroSCORE performance in valve surgery: a meta-analysis.

BACKGROUND: The European System for Cardiac Operative Risk Evaluation (EuroSCORE) was developed to predict immediate outcomes after adult cardiac operations, but less than 30% of the cases used to develop this score were valve procedures. We studied EuroSCORE performance in valve procedures. METHODS: We performed a meta-analysis of published studies reporting the assessment of discriminatory power of the EuroSCORE by receiver operating characteristics (ROC) curve analysis in adult valve operations. A comparison of observed and predicted mortality rates was also performed. RESULTS: A literature search identified 37 potentially eligible studies, and 12 were selected for meta-analysis comprising 26,621 patients with 1250 events (mortality rate, 4.7%). Meta-analysis of these studies provided an average area under the curve (AUC) value of 0.730 (95% confidence interval [CI], 0.717 to 0.743). The same results were obtained when meta-analyses were performed separately in studies categorized on reliability of uncertainty estimation: in the seven studies reporting reliable uncertainty estimation (8175 patients with 358 events; mortality rate, 4.4%), the ROC curve provided an average AUC value of 0.724 (95% CI, 0.699 to 0.749). The five studies not reporting reliable uncertainty estimation (18,446 patients with 892 events; mortality rate, 4.8%) had an average AUC of 0.732 (95% CI, 0.717 to 0.747). We documented a constant trend to overpredict mortality by EuroSCORE, both in the additive and especially in the logistic form. CONCLUSIONS: The EuroSCORE has low discrimination ability for valve surgery, and it sensibly overpredicts risk. Alternative risk scoring algorithms should be seriously considered.

Safety of pregnancy after primary breast carcinoma in young women: a meta-analysis to overcome bias of healthy mother effect studies.

BACKGROUND: An increased number of women are expected to conceive after the diagnosis of early breast cancer. Most physicians recommend that pregnancy be delayed by 2 to 3 years after diagnosis of early breast cancer, but this recommendation is based on data from trials with small patient cohorts. Furthermore, a healthy mother effect (HME) selection bias may be operative in most of these studies, because women undergoing childbearing after treatment were healthier when compared with the control group. AIM: To perform a systematic review and meta-analysis of published trials corrected for HME bias so as to assess the effect of pregnancy (at least 10 months after diagnosis) versus no pregnancy on overall survival of primary breast cancer patients less than 45 years. METHODS: We searched MEDLINE and Thomson Reuters (ISI) Web of Knowledge for eligible studies. From each study we extracted the relative hazard ratio or, if not provided, all the necessary data to impute it. In cases where the duration from diagnosis to pregnancy was not reported, we extracted relevant data to estimate it. RESULTS: Our electronic search strategy yielded 1623 hits pertaining to 20 potentially eligible studies involving 49,370 premenopausal breast cancer patients. Ten studies were eligible after considering HME potential bias in matching controls. Among these, 9 studies (pregnant 1089, matched-controls 13051) contained data appropriate for analysis. Overall survival was statistically higher among patients who became pregnant compared to controls: fixed effect model estimated pooled hazard ratio for death 0.51 (95% confidence interval: 0.42-0.62). No study heterogeneity was observed: Q = 10.4, P = 0.17; I(2) = 48%. CONCLUSION: The pooled available evidence indicates that in early breast cancer patients, pregnancy that occurs at least 10 months after diagnosis does not jeopardize prognosis and may actually confer significant survival benefit. TARGET AUDIENCE: Obstetricians & Gynecologists, Family Physicians. LEARNING OBJECTIVES: After completing this CME activity, physicians should be better able to assess the effect pregnancy has on long-term survival in primary breast cancer patients under age 45; counsel patients on the safety of pregnancy after breast cancer diagnosis and treatment; and interpret how pregnancy may be associated with improved breast cancer survival.

Breast US computer-aided diagnosis system: robustness across urban populations in South Korea and the United States.

PURPOSE: To evaluate the robustness of a breast ultrasonographic (US) computer-aided diagnosis (CAD) system in terms of its performance across different patient populations. MATERIALS AND METHODS: Three US databases were analyzed for this study: one South Korean and two United States databases. All three databases were utilized in an institutional review board-approved and HIPAA-compliant manner. Round-robin analysis and independent testing were performed to evaluate the performance of a computerized breast cancer classification scheme across the databases. Receiver operating characteristic (ROC) analysis was used to evaluate performance differences. RESULTS: The round-robin analyses of each database demonstrated similar results, with areas under the ROC curve ranging from 0.88 (95% confidence interval [CI]: 0.820, 0.918) to 0.91 (95% CI: 0.86, 0.95). The independent testing of each database, however, indicated that although the performances were similar, the range in areas under the ROC curve (from 0.79 [95% CI: 0.730, 0.842] to 0.87 [95% CI: 0.794, 0.923]) was wider than that with the round-robin tests. However, the only instances in which statistically significant differences in performance were demonstrated occurred when the Korean database was used in a testing capacity in independent testing. CONCLUSION: The few observed statistically significant differences in performance indicated that while the US features used by the system were useful across the databases, their relative importance differed. In practice, this means that a CAD system may need to be adjusted when applied to a different population.

Osteonecrosis of the jaw and use of bisphosphonates in adjuvant breast cancer treatment: a meta-analysis.

To estimate the cumulative randomized evidence for the overall incidence of bisphosphonates induced jaw osteonecrosis in adjuvant treatment of breast cancer. Systematic review and meta-analysis of randomized clinical trials. Trials were located through PubMed, ISI, Cochrane Library, and major cancer scientific meetings searches. We identified 15 studies reporting data on osteonecrosis of the jaw. A total of 10,694 randomized women were included, of whom 5,312 received bisphosphonates and 5,382 received either placebo or no treatment. Osteonecrosis of the jaw was a rare event, occurring in 13 (0.24%) of the 5,312 patients receiving bisphosphonates, and in one of the 5,382 patients in the control group. All the 13 events of osteonecrosis of the jaw reported among bisphosphonates arms occur in patients undergoing treatment with zoledronic acid (13/3,987, 0.33%). No events of osteonecrosis of the jaw were reported among patients randomized to receive clodronate (n = 669), pamidronate (n = 460), risedronate (n = 171), and ibandronate (n = 25); however, these samples were too small to be able to rule out the condition. Treatment with zoledronic acid was significantly associated to the occurrence of osteonecrosis of the jaw (OR = 3.23, 95% CI = 1.7-8) compared with no use. No significant between-study heterogeneity was observed. Despite use of zoledronic acid is associated to a higher number of events compared with no use, the osteonecrosis of the jaw during the adjuvant treatment of breast cancer is a rare event. At current dosage, adjuvant use of bisphosphonates in breast cancer treatment is safe.

Performance of EuroSCORE in CABG and off-pump coronary artery bypass grafting: single institution experience and meta-analysis.

AIMS: To assess EuroSCORE performance in predicting in-hospital mortality in on-pump coronary artery bypass grafting (CABG) and off-pump coronary artery bypass grafting (OPCAB). METHODS AND RESULTS: Additive and logistic EuroSCORE were computed for consecutive patients undergoing CABG (n = 3440, 75%) or OPCAB (n = 1140, 25%) at our hospital from 1999 to September 2007. The areas under the receiver operating characteristic (ROC) curves (AUCs) were used to describe performance and accuracy. No difference in performance between CABG and OPCAB and between additive and logistic EuroSCORE (additive EuroSCORE AUCs of 0.808 and 0.779 for CABG and OPCAB, respectively; logistic EuroSCORE AUCs of 0.813 and of 0.773 for CABG and OPCAB, respectively) was found, although a marked tendency to overpredict mortality by both models was evident. A meta-analysis of previously published data was done, and a total of eight studies representing 19 212 and 5461 patients undergoing CABG and OPCAB, respectively, met inclusion criteria. Meta-analysis confirmed similar performance of EuroSCORE in CABG and OPCAB: estimated AUCs were 0.767 and 0.766 for CABG and OPCAB, respectively, with an estimated difference of 0.001 (95% CI -0.061 to 0.063). CONCLUSION: Additive and logistic EuroSCORE algorithms performed similarly, and cumulative evidence suggests comparable performance in CABG and OPCAB procedures; both risk models, however, significantly overestimated mortality.

Comparison of spin echo T1-weighted sequences versus fast spin-echo proton density-weighted sequences for evaluation of meniscal tears at 1.5 T.

PURPOSE: At our institution, fast spin-echo (FSE) proton density (PD) imaging is used to evaluate articular cartilage, while conventional spin-echo (CSE) T1-weighted sequences have been traditionally used to characterize meniscal pathology. We sought to determine if FSE PD-weighted sequences are equivalent to CSE T1-weighted sequences in the detection of meniscal tears, obviating the need to perform both sequences. METHOD AND MATERIALS: We retrospectively reviewed the records of knee arthroscopies performed by two arthroscopy-focused surgeons from an academic medical center over a 2-year period. The preoperative MRI images were interpreted independently by two fellowship-trained musculoskeletal radiologists who graded the sagittal CSE T1 and FSE PD sequences at different sittings with grades 1-5, where 1 = normal meniscus, 2 = probable normal meniscus, 3 = indeterminate, 4 = probable torn meniscus, and 5 = torn meniscus. Each meniscus was divided into an anterior and posterior half, and these halves were graded separately. Operative findings provided the gold standard. Receiver operating characteristic (ROC) analysis was performed to compare the two sequences. RESULTS: There were 131 tears in 504 meniscal halves. Using ROC analysis, the reader 1 area under curve for FSE PD was significantly better than CSE T1 (0.939 vs. 0.902, >95% confidence). For reader 2, the difference met good criteria for statistical non-inferiority but not superiority (0.913 for FSE PD and 0.908 for CSE T1; >95% non-inferiority for difference at most of -0.027). CONCLUSION: FSE PD-weighted sequences, using our institutional protocol, are not inferior to CSE T1-weighted sequences for the detection of meniscal tears and may be superior.

Performance of breast ultrasound computer-aided diagnosis: dependence on image selection.

RATIONALE AND OBJECTIVES: The automated classification of sonographic breast lesions is generally accomplished by extracting and quantifying various features from the lesions. The selection of images to be analyzed, however, is usually left to the radiologist. Here we present an analysis of the effect that image selection can have on the performance of a breast ultrasound computer-aided diagnosis system. MATERIALS AND METHODS: A database of 344 different sonographic lesions was analyzed for this study (219 cysts/benign processes, 125 malignant lesions). The database was collected in an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant manner. Three different image selection protocols were used in the automated classification of each lesion: all images, first image only, and randomly selected images. After image selection, two different protocols were used to classify the lesions: (a) the average feature values were input to the classifier or (b) the classifier outputs were averaged together. Both protocols generated an estimated probability of malignancy. Round-robin analysis was performed using a Bayesian neural network-based classifier. Receiver-operating characteristic analysis was used to evaluate the performance of each protocol. Significance testing of the performance differences was performed via 95% confidence intervals and noninferiority tests. RESULTS: The differences in the area under the receiver-operating characteristic curves were never more than 0.02 for the primary protocols. Noninferiority was demonstrated between these protocols with respect to standard input techniques (all images selected and feature averaging). CONCLUSION: We have proved that our automated lesion classification scheme is robust and can perform well when subjected to variations in user input.