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Genitourinary rhabdomyosarcoma (GU-RMS) is a rare, pediatric malignancy originating from embryonic mesenchyme. Current approaches to prognostication rely upon conventional statistical methods such as Cox proportional hazards (CPH) models and have suboptimal predictive ability. Given the success of deep learning approaches in other specialties, we sought to develop and compare deep learning models with CPH models for the prediction of 5-year survival in pediatric GU-RMS patients.

Patients less than 20 years of age with GU-RMS were identified within the Surveillance, Epidemiology, and End Results (SEER) database (1998-2011). Deep neural networks (DNN) were trained and tested on an 80/20 split of the dataset in a 5-fold cross-validated fashion. Multivariable CPH models were developed in parallel. The primary outcomes were 5-year overall survival (OS) and disease-specific survival (DSS). Variables used for prediction were age, sex, race, primary site, histology, degree of tumor extension, tumor size, receipt of surgery, and receipt of radiation. Receiver operating characteristic curve analysis was conducted, and DNN models were tested for calibration.

277 patients were included. The area under the curve (AUC) for the DNN models was 0.93 for OS and 0.91 for DSS. PF-04691502 price AUC for the CPH models was 0.82 for OS and 0.84 for DSS. The DNN models were well-calibrated OS model (slope=1.02, intercept=-0.06) and DSS model (slope=0.79, intercept=0.21).

A deep learning-based model demonstrated excellent performance, superior to that of CPH models, in the prediction of pediatric GU-RMS survival. Deep learning approaches may enable improved prognostication for patients with rare cancers.

A deep learning-based model demonstrated excellent performance, superior to that of CPH models, in the prediction of pediatric GU-RMS survival. Deep learning approaches may enable improved prognostication for patients with rare cancers.

The superiority of anatomic resection (AR) over non-anatomic resection (NAR) for very early-stage hepatocellular carcinoma (HCC) has remained a topic of debate. Thus, this study aimed to compare the prognosis after AR and NAR for single HCC less than 2cm in diameter.

Consecutive patients with single HCC of diameter less than 2cm who underwent curative hepatectomy between 1997 and 2017 were included in this retrospective study.

In total, 159 patients were included in this study. Of these, 52 patients underwent AR (AR group) and 107 patients underwent NAR (NAR group). No significant differences were noted in recurrence-free survival (RFS) and overall survival (OS) between the AR and NAR groups (P=0.236 and P=0.363, respectively). Multivariate analysis revealed that low preoperative platelet count and presence of satellite nodules were independent prognostic factors of RFS and OS. Wide surgical resection margin did not affect RFS (P=0.692) in the AR group; however, in the NAR group, RFS was found to be higher with surgical resection margin widths ≥1cm than with surgical resection margin widths <1cm(P=0.038).

Prognosis was comparable between the NAR and AR groups for very early-stage HCC with well-preserved liver function. For better oncologic outcomes, surgeons should endeavor in keeping the surgical resection margin widths during NAR ≥1cm.

Prognosis was comparable between the NAR and AR groups for very early-stage HCC with well-preserved liver function. For better oncologic outcomes, surgeons should endeavor in keeping the surgical resection margin widths during NAR ≥1 cm.We examine regional differences in diabetes within Europe, and relate them to variations in socio-economic conditions, comorbidities, health behaviour and diabetes management. We use the SHARE (Survey of Health, Ageing and Retirement in Europe) data of 15 European countries and 28,454 individuals, who participated both in the 4th and 7th (year 2011 and 2017) waves of the survey. First, we estimate multivariate regressions, where the outcome variables are diabetes prevalence, diabetes incidence, and weight loss due to diet as an indicator of management. Second, we study the heterogeneous impact of demographic, socio-economic, health and lifestyle indicators on the regional differences in diabetes incidence with causal random forests. Compared to Western Europe, the odds of a new diabetes diagnosis over a six-year horizon is 2.2-fold higher in Southern and 2.6-fold higher in Eastern Europe. Adjusting for individual characteristics, the odds ratio decreases to 1.8 in the South-West and to 2.0 in the East-West dimension. These remaining differences are mostly explained by country-specific healthcare indicators. Based on the causal forest approach, the adjusted East-West difference is essentially zero for the lowest risk groups (tertiary education, employment, no hypertension, no overweight) and increases substantially with these risk factors, but the South-West difference is much less heterogeneous. The prevalence of diet-related weight loss around the time of diagnosis also exhibits regional variation. The results suggest that the regional differences in diabetes incidence could be reduced by putting more emphasis on diabetes prevention among high-risk individuals in Eastern and Southern Europe.

Natural ankle quasi-stiffness (NAS) is a key metric used to personalize orthotic and prosthetic ankle-foot devices. NAS has traditionally been defined as the average slope (i.e. linear regression) of the net ankle moment vs. ankle angle curve during stance. However, NAS appears to have nonlinear characteristics. Characterizing nonlinear NAS across a wide range of tasks will enable us to incorporate these attributes into future orthotic and prosthetic ankle-foot device designs.

Does nonlinear NAS change across multiple intensities of walking, running, and load carriage tasks?

This observational study examined 22 young, healthy individuals as they walked, ran, and walked while carrying a load at three intensities (speed or load). Linear, quadratic, and cubic regressions were done on the net ankle moment vs. ankle angle curve over three phases of stance impact, loading, and push-off. RMSE between regressions and measured data were computed to determine regression accuracy, and multilevel linear models (MLMs) were used to determine significant differences between coefficients across intensities.