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BACKGROUND: The exact role of the levator ani (LA) muscle in male continence remains unclear, and so this study aims to shed light on the topic by characterizing MRI-derived radiomic features of LA muscle and their association with postoperative incontinence in men undergoing prostatectomy. METHOD: In this retrospective study, 140 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer using preoperative MRI were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features from MRI images, including morphological, intensity-based, and texture-based features of the LA muscle, along with clinical variables. Mathematical models were created using subsets of features and were evaluated based on their ability to predict continence outcomes. RESULTS: Univariate analysis showed that the best discriminators between continent and incontinent patients were patients age and features related to LA muscle texture. The proposed feature selection approach found that the best classifier used six features: age, LA muscle texture properties, and the ratio between LA size descriptors. This configuration produced a classification accuracy of 0.84 with a sensitivity of 0.90, specificity of 0.75, and an area under the ROC curve of 0.89. CONCLUSION: This study found that certain patient factors, such as increased age and specific texture properties of the LA muscle, can increase the odds of incontinence after RARP. The results showed that the proposed approach was highly effective and could distinguish and predict continents from incontinent patients with high accuracy.

CT measurement of body composition may improve lung transplant candidate selection. We assessed whether skeletal muscle adipose deposition on abdominal and thigh CT scans was associated with 6 min walk distance (6MWD) and wait-list survival in lung transplant candidates. Each ½-SD decrease in abdominal muscle attenuation (indicating greater lipid content) was associated with 14 m decrease in 6MWD (95% CI -20 to -8) and 20% increased risk of death or delisting (95% CI 10% to 40%). Each ½-standard deviation decrease in thigh muscle attenuation was associated with 15 m decrease in 6MWD (95% CI -21 to -10). CT imaging may improve candidate risk stratification.

BackgroundObesity is associated with an increased risk of primary graft dysfunction (PGD) after lung transplantation. The contribution of specific adipose tissue depots is unknown.MethodsWe performed a prospective cohort study of adult lung transplant recipients at 4 U.S. transplant centers. We measured cross-sectional areas of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) on chest and abdominal computed tomography (CT) scans and indexed each measurement to height.2 We used logistic regression to examine the associations of adipose indices and adipose classes with grade 3 PGD at 48 or 72 hours, and Cox proportional hazards models to examine survival. We used latent class analyses to identify the patterns of adipose distribution. We examined the associations of adipose indices with plasma biomarkers of obesity and PGD.ResultsA total of 262 and 117 subjects had available chest CT scans and underwent protocol abdominal CT scans, respectively. In the adjusted models, a greater abdominal SAT index was associated with an increased risk of PGD (odds ratio 1.9, 95% CI 1.02-3.4, p = 0.04) but not with survival time. VAT indices were not associated with PGD risk or survival time. A greater abdominal SAT index correlated with greater pre- and post-transplant leptin (r = 0.61, p < 0.001, and r = 0.44, p < 0.001), pre-transplant IL-1RA (r = 0.25, p = 0.04), and post-transplant ICAM-1 (r = 0.25, p = 0.04). We identified 3 latent patterns of adiposity. The class defined by high thoracic and abdominal SAT had the greatest risk of PGD.ConclusionsSubcutaneous, but not visceral, adiposity is associated with an increased risk of PGD after lung transplantation.

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects. [ABSTRACT FROM AUTHOR]