Epirubicin With Cyclophosphamide Followed by Docetaxel With Trastuzumab and Bevacizumab as Neoadjuvant Therapy for HER2-Positive Locally Advanced Breast Cancer or as Adjuvant Therapy for HER2-Positive Pathologic Stage III Breast Cancer: A Phase II Trial of the NSABP Foundation Research Group, FB-5

Background The purpose of this study was to determine the cardiac safety and clinical activity of trastuzumab and bevacizumab with docetaxel after epirubicin with cyclophosphamide (EC) in patients with HER2-positive locally advanced breast cancer (LABC) or pathologic stage 3 breast cancer (PS3BC). Patients and Methods Patients received every 3 week treatment with 4 cycles of EC (90/600 mg/m2) followed by 4 cycles of docetaxel (100 mg/m2). Targeted therapy with standard-dose trastuzumab with bevacizumab 15 mg/kg was given for a total of 1 year. Coprimary end points were (1) rate of cardiac events (CEs) in all patients defined as clinical congestive heart failure with a significant decrease in left ventricular ejection fraction or cardiac deaths; and (2) pathologic complete response (pCR) in breast and nodes in the neoadjuvant cohort. An independent cardiac review panel determined whether criteria for a CE were met. Results A total of 105 patients were accrued, 76 with LABC treated with neoadjuvant therapy and 29 with PS3BC treated with adjuvant therapy. Median follow-up was 59.2 months. Among 99 evaluable patients for cardiac safety, 4 (4%; 95% confidence interval [CI], 1.1%-10.0%) met CE criteria. The pCR percentage in LABC patients was 46% (95% CI, 34%-59%). Five-year recurrence-free survival (RFS) and overall survival (OS) for all patients was 79.9% and 90.8%, respectively. Conclusion The regimen met predefined criteria for activity of interest with an acceptable rate of CEs. Although the pCR percentage was comparable with chemotherapy regimens with trastuzumab alone the high RFS and OS are of interest in these high-risk populations

Is There a Relationship Between a MLB Team’s Payroll & Their Performance?

Major League Baseball (MLB) is a 10 billion-dollar industry with billions of dollars going to players each year. The best players receive the most money. There is a preconceived notion that more money translates to more wins and therefore more championships. However, there have been an increasing number of individuals who believe that all 30 teams have a chance to win their respective games regardless of the amount of money spent on players. The objective of this research was to explore the relationship between team payroll and team wins. Independent t-test and regression analyses were conducted using data for the 1995-2019 time period. The results herein show that teams with the top 10 highest payrolls had a better chance of winning the world series than teams with the lowest payrolls. This finding supports the claim that payroll is a predictor of success but the causal factors are yet to be explored; a topic for future research. The focus of this research was professional baseball. Future research may also be extended to explore the implications of compensating college athletes

The impact of patterns in linkage disequilibrium and sequencing quality on the imprint of balancing selection

Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima\u27s D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Social Security and Divorce Decisions

People who have divorced are entitled to Social Security spousal benefits if their marriages lasted at least ten years. This paper uses 1985–1995 Vital Statistics data and the 2008–2011 American Community Surveys to analyze how this rule affects divorce decisions. I find evidence that the ten-year rule results in a small increase in divorces for the general population; however, the effects vary greatly by age. Divorce decisions change very little for people under the age of 35. For people 55 and older, however, divorces increase by approximately 20 percent around the ten-year cutoff, which leads to an increase in the likelihood of being divorced of 11.7 percent at ten years of marriage. For people between the ages of 35 and 55, who account for over half of divorces, the likelihood of being divorced increases by almost 6 percent as marriages cross the ten-year mark. This heterogeneity across ages likely exists because older people are more focused on retirement and have less time to remarry. These results indicate many people delay divorcing because they need Social Security benefits

Robustness of a Restriction Spectrum Imaging (RSI) quantitative MRI biomarker for prostate cancer: assessing for systematic bias due to age, race, ethnicity, prostate volume, medication use, or imaging acquisition parameters

IntroductionProstate multiparametric magnetic resonance imaging (mpMRI) has greatly improved the detection of clinically significant prostate cancer (csPCa). However, the limited number of expert sub-specialist radiologists capable of interpreting conventional prostate mpMRI is a bottleneck for universal access to this healthcare advance. A reliable and reproducible quantitative imaging biomarker could facilitate implementation of accurate prostate MRI at clinical sites with limited experience, thus ensuring more equitable patient care. Restriction Spectrum Imaging restriction score (RSIrs) is an MRI biomarker that has shown the ability to enhance the qualitative and quantitative interpretation of prostate MRI. However, patient-level factors (age, race, ethnicity, prostate volume, and 5-alpha-reductase inhibitor (5-ARI) use) and acquisition-level factors (scanner manufacturer/model and protocol parameters) can affect prostate mpMRI, and their impact on quantitative RSIrs is unknown.MethodsRSI data from patients with known or suspected csPCa were collected from seven centers. We estimated effects of patient and acquisition factors on prostate voxels overall (Method 1: benign patients only) and on only the maximum RSIrs within each prostate (RSIrsmax; Method 2: benign and csPCa patients) using linear models. We then tested whether adjusting for any estimated systematic biases would improve performance of RSIrs for patient-level detection of csPCa, as measured by area under the ROC curve (AUC).ResultsUsing both Method 1 and Method 2, we observed statistically significant effects on RSIrs of age and acquisition group (p < 0.05). Prostate volume had significant effects using only Method 2. All of these effects were small, and adjusting for them did not improve csPCa detection performance (p ≥ 0.05). AUC of RSIrsmaxfor patient-level csPCa detection was 0.77 (95% CI: 0.75, 0.79) unadjusted, compared to 0.77 (0.76, 0.79) and 0.74 (0.72, 0.76) after adjustment using Method 1 and 2 respectively.ConclusionAge, prostate volume, and imaging acquisition factors may lead to systematic differences in RSIrs, but these effects are small and have minimal impact on performance of RSIrs for detection of csPCa. RSIrs can be used as a reliable biomarker across a wide range of patients, centers, scanners, and acquisition factors

Restriction Spectrum Imaging as a quantitative biomarker for prostate cancer with reliable positive predictive value

Abstract: Background and Objective: Positive predictive value of PI-RADS for clinically significant prostate cancer (csPCa, grade group [GG]≥2) varies widely between institutions and radiologists. The Restriction Spectrum Imaging restriction score (RSIrs) is a metric derived from diffusion MRI that could be an objectively interpretable biomarker for csPCa. Methods: In patients scanned for suspected or known csPCa at 7 centers, we calculated patient-level csPCa probability based on maximum RSIrs in the prostate, without relying on subjectively defined lesions. We used area under the ROC curve (AUC) to compare patient-level csPCa detection for RSIrs, ADC, and PI-RADS. Finally, we combined RSIrs with clinical risk factors via multivariable regression, training in a single-center cohort and testing in an independent, multi-center dataset. Key Findings and Limitations: Among all patients (n=1892), probability of csPCa increased with higher RSIrs . GG≥4 csPCa was most common in patients with very high RSIrs. Among biopsy-naïve patients (n=877), AUCs for GG≥2 vs. non-csPCa were 0.73 (0.69-0.76), 0.54 (0.50-0.57), and 0.75 (0.71-0.78) for RSIrs, ADC, and PI-RADS, respectively. RSIrs significantly outperformed ADC (p<0.01) and was comparable to PI-RADS (p=0.31). The combination of RSIrs and PI-RADS outperformed either alone. Combining RSIrs with PI-RADS, age, and PSA density in a multivariable model achieved the best discrimination of csPCa. Conclusions and Clinical Implications: RSIrs is an accurate and reliable quantitative biomarker that performs better than conventional ADC and comparably to expert-defined PI-RADS for patient-level detection of csPCa. RSIrs provides objective estimates of probability of csPCa that do not require radiology expertise

Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study

Purpose Clinical sequencing emerging in health care may result in secondary findings (SFs). Methods Seventy-four of 6240 (1.2%) participants who underwent genome or exome sequencing through the Clinical Sequencing Exploratory Research (CSER) Consortium received one or more SFs from the original American College of Medical Genetics and Genomics (ACMG) recommended 56 gene–condition pair list; we assessed clinical and psychosocial actions. Results The overall adjusted prevalence of SFs in the ACMG 56 genes across the CSER consortium was 1.7%. Initially 32% of the family histories were positive, and post disclosure, this increased to 48%. The average cost of follow-up medical actions per finding up to a 1-year period was $128 (observed, range:$0–$678) and$421 (recommended, range: $141–$1114). Case reports revealed variability in the frequency of and follow-up on medical recommendations patients received associated with each SF gene–condition pair. Participants did not report adverse psychosocial impact associated with receiving SFs; this was corroborated by 18 participant (or parent) interviews. All interviewed participants shared findings with relatives and reported that relatives did not pursue additional testing or care. Conclusion Our results suggest that disclosure of SFs shows little to no adverse impact on participants and adds only modestly to near-term health-care costs; additional studies are needed to confirm these findings

Correction: Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study

Correction to: Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite stud