Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

Prognostic effect of microscopically negative but close resection margin in gastric cancer.

INTRODUCTION: Microscopically positive resection margin (RM) following curative surgery has been linked to disease recurrence in gastric cancer (GC), but the impact of microscopically negative but close RM (CRM) remains unclear. This study aimed to evaluate the prognostic implications of a CRM of ≤0.5 cm in GC patients. METHODS: A retrospective review of the institutional GC database identified 1958 patients who underwent curative gastrectomy for pathologically proven GC between January 2011 and December 2015. The patients were categorized into CRM (RM ≤0.5 cm) and sufficient RM (SRM, RM >0.5 cm) groups. The impact of CRM on recurrence-free survival (RFS) and overall survival (OS) was analyzed compared to the SRM group. RESULTS: The cohort comprised 1264 patients with early GC (EGC, 64.6%) and 694 with advanced GC (AGC, 35.4%). Forty-four patients (2.2%) had RM of ≤0.5 cm. CRM was associated with worse RFS in AGC (5-year RFS in the CRM vs. SRM groups; 41.6% vs. 68.7%, p = 0.011); however, the effect on OS was not significant (p = 0.159). Multivariate analysis revealed that CRM was an independent prognostic factor for RFS (hazard ratio [HR] 2.035, 95% confidence interval [CI] 1.097-3.776). In AGC, the locoregional recurrence rate was significantly higher in the CRM group than in the SRM group (15.4% vs. 4.9%, p = 0.044). CONCLUSION: CRM of ≤0.5 cm was a significant prognostic factor for RFS in GC patients and was associated with a significant increase in locoregional recurrence in AGC.

Impact of reproductive traits on productive life in Iranian Holstein dairy cows.

Enhancing reproductive performance is a key strategy to mitigate involuntary culling rates, thereby extending productive life (PL) and ultimately improving profitability in dairy cattle herds. A piecewise Weibull proportional hazards model was used to investigate the effect of several important reproductive traits on PL in Holstein dairy cows. Data comprised 200,747 lactation records from 82,505 cows sired by 1952 bulls across 36 dairy herds. PL was defined as the number of days from the first calving to the last milk record or censoring. The statistical model accounted for the time-dependent fixed effects of changes in herd size, year-season, milk production, fat and protein contents, and the time-independent fixed effect of age at first calving. Herd-year and sire effects were also included as random effects. Reproductive traits include calving traits such as calving ease (CE), calf size (CZ), and calf survival (CS), as well as female fertility traits such as number of inseminations per conception (NI), days from calving to first service (CFS), days from first service to conception (FSC), and days open (DO). All reproductive traits had a significant effect on PL (p < 0.001). Each reproductive trait was analysed separately. The relative risk (RR) of being culled increased as the severity of calving difficulties increased in both primiparous and multiparous cows. Cows that calved small or large calves showed a higher risk of being culled compared with those that calved medium size calves. The increased RR of culling was observed only for primiparous cows that gave birth to dead calves. In addition, cows that required more NI, a longer CFS, FSC, and DO had shorter longevity. These insights can deepen our comprehension of the factors affecting PL and provide information for refining management and breeding strategies, which could lead to increased profitability and sustainability in Iranian dairy farming.

Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method's relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.

Survival among people with HIV and their families in Denmark 1995-2021: a nationwide population-based cohort study.

Background: Survival among people with HIV (PWH) has vastly improved globally over the last few decades but remains lower than among the general population. We aimed to estimate time trends of survival among PWH and their families from 1995 to 2021. Methods: We conducted a registry-based, nationwide, population-based, matched cohort study. We included all Danish-born PWH from 1995 to 2021 who had been on antiretroviral therapy for 90 days, did not report intravenous drug use, and were not co-infected with hepatitis C (n = 4168). We matched population controls from the general population 10:1 to PWH by date of birth and sex (n = 41,680). For family cohorts, we identified siblings, mothers, and fathers of PWH and population controls. From Kaplan-Meier tables with age as time scale, we estimated survival from age 25. We compared PWH with population controls and families of PWH with families of population controls to calculate mortality rate ratios adjusted for sex, age, comorbidities, and education (aMRR). Findings: The median age of death among PWH increased from 27.5 years in 1995-1997 to 73.9 years (2010-2014), but thereafter survival increased only marginally. From 2015 to 2021, mortality was increased among PWH (aMRR 1.87 (95% CI: 1.65-2.11)) and siblings (aMRR: 1.25 (95% CI: 1.07-1.47)), mothers (aMRR: 1.30 (95% CI: 1.17-1.43)), and fathers (aMRR: 1.15 (95% CI: 1.03-1.29)) of PWH compared to their respective control cohorts. Mortality among siblings of PWH who reported heterosexual route of HIV transmission (aMRR: 1.51 (95% CI: 1.16-1.96)) was higher than for siblings of PWH who reported men who have sex with men as route of HIV transmission (aMRR 1.19 (95% CI: 0.98-1.46)). Interpretation: Survival among PWH improved substantially until 2010, after which it increased only marginally. This may partly be due to social and behavioural factors as PWH families also had higher mortality. Funding: Preben and Anna Simonsen's Foundation and Independent Research Fund Denmark.

Survival Analysis of Patients with Severe Acute Malnutrition Admitted at the In-patient Therapeutic Care of the Bicol Regional Training and Teaching Hospital.

Background: Severe acute malnutrition (SAM) in children under five years remains a major global health concern. It carries a burden to the overall health of a child, contributes to mortality, and adds financial strain to the family and the hospital. The Philippine Integrated Management of Acute Malnutrition was established to address acute malnutrition in Filipino children. Objective: This study aimed to determine the factors affecting survival of patients admitted at Bicol Regional Training and Teaching Hospital (BRTTH) In-patient Therapeutic Care (ITC). Methods: This is a retrospective cohort study design utilizing survival analysis. Accrual period was from January 1, 2018 to December 31, 2018. Follow-up ended on March 31, 2019. There were 154 admissions and excluded 17 missing charts. Survival analysis was done utilizing STATA 14. Results: The prevalence of SAM requiring ITC admission was 3.0 percent. Majority belonged to 6-59 months of age (63%), with equal predilection for both sexes (1:1) and 71% came from the home province, Albay. Most of patients' caretakers had middle educational attainment. Sixty-eight percent (68%) were new patients, 16% readmitted, 15% transferred from the Out-patient Therapeutic Care (OTC) and <1% relapsed. The top three most common complications and co-morbidities include: pneumonia, low electrolytes, and fever. Sixty-three percent (63%) of patients at the ITC had a desirable treatment outcome, of which, 8% were cured and 55% transferred to OTC. Undesirable outcomes accounted for 37% of the cases which included non-cured, defaulter, and died at 12%, 8%, and 17%, respectively. The risk of dying was higher in SAM patients with parents having middle and low educational attainment as compared to those with high educational attainment (2-5 folds to 100-200 folds). SAM patients presenting with hypovolemic shock were likely to die by 1.5-19 times (1.5-19x) as compared to those without. SAM patients with malignancy were more likely to die 4-44 folds as compared to patients without malignancy. Conclusion and Recommendations: Educational attainment of parents, malignancy, and hypovolemic shock were significant predictors of mortality. We recommend prompt intervention by educating families, strengthen policies targeting socio-economic determinants, capacitate medical staff, refine current clinical practice guidelines and treatment pathways to reduce the number of children who die from severe acute malnutrition.

A deep learning approach for overall survival prediction in lung cancer with missing values.

BACKGROUND AND OBJECTIVE: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. METHODS: We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. RESULTS: We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. CONCLUSIONS: The results show that our model not only outperforms the state-of-the-art's performance but also simplifies the analysis in the presence of missing data, by effectively eliminating the need to identify the most appropriate imputation strategy for predicting OS in NSCLC patients.

Prediction of lifespan and assessing risk factors of large-sample implant prostheses: a multicenter study.

PURPOSE: This study aimed to analyze factors influencing the success and failure of implant prostheses and to estimate the lifespan of prostheses using standardized evaluation criteria. An online survey platform was utilized to efficiently gather large samples from multiple institutions. MATERIALS AND METHODS: During the one-year period, patients visiting 16 institutions were assessed using standardized evaluation criteria (KAP criteria). Data from these institutions were collected through an online platform, and various statistical analyses were conducted. Risk factors were assessed using both the Cox proportional hazard model and Cox regression analysis. Survival analysis was conducted using Kaplan-Meier analysis and nomogram, and lifespan prediction was performed using principal component analysis. RESULTS: The number of patients involved in this study was 485, with a total of 841 prostheses evaluated. The median survival was estimated to be 16 years with a 95% confidence interval. Factors found to be significantly associated with implant prosthesis failure, characterized by higher hazard ratios, included the 'type of clinic', 'type of antagonist', and 'plaque index'. The lifespan of implant prostheses that did not fail was estimated to exceed the projected lifespan by approximately 1.34 years. CONCLUSION: To ensure the success of implant prostheses, maintaining good oral hygiene is crucial. The estimated lifespan of implant prostheses is often underestimated by approximately 1.34 years. Furthermore, standardized form, online platform, and visualization tool, such as nomogram, can be effectively utilized in future follow-up studies.

Comprehensive transcriptomic analysis identifies SLC25A4 as a key predictor of prognosis in osteosarcoma.

Background: Osteosarcoma (OS) is highly malignant and prone to local infiltration and distant metastasis. Due to the poor outcomes of OS patients, the study aimed to identify differentially expressed genes (DEGs) in OS and explore their role in the carcinogenesis and progression of OS. Methods: RNA sequencing was performed to identify DEGs in OS. The functions of the DEGs in OS were investigated using bioinformatics analysis, and DEG expression was verified using RT-qPCR and Western blotting. The role of SLC25A4 was evaluated using gene set enrichment analysis (GSEA) and then investigated using functional assays in OS cells. Results: In all, 8353 DEGs were screened. GO and KEGG enrichment analyses indicated these DEGs showed strong enrichment in the calcium signaling pathway and pathways in cancer. Moreover, the Kaplan-Meier survival analysis showed ten hub genes were related to the outcomes of OS patients. Both SLC25A4 transcript and protein expression were significantly reduced in OS, and GSEA suggested that SLC25A4 was associated with cell cycle, apoptosis and inflammation. SLC25A4-overexpressing OS cells exhibited suppressed proliferation, migration, invasion and enhanced apoptosis. Conclusion: SLC25A4 was found to be significantly downregulated in OS patients, which was associated with poor prognosis. Modulation of SLC25A4 expression levels may be beneficial in OS treatment.

Application of Mixture and Non-mixture Cure Models in Survival Analysis of Patients With COVID-19.

Background Due to the emergence of new COVID-19 mutations and an increase in re-infection rates, it has become an important priority for the medical community to identify the factors affecting the short- and long-term survival of patients. This study aimed to determine the risk factors of short- and long-term survival in patients with COVID-19 based on mixture and non-mixture cure models. Methodology In this study, the data of 880 patients with COVID-19 confirmed with polymerase chain reaction in Fereydunshahr city (Isfahan, Iran) from February 20, 2020, to December 21, 2021, were gathered, and the vital status of these patients was followed for at least one year. Due to the high rate of censoring, mixture and non-mixture cure models were applied to estimate the survival. Akaike information criterion values were used to evaluate the fit of the models. Results In this study, the mean age of the patients was 48.9 ± 21.23 years, and the estimated survival rates on the first day, the fourth day, the first week, the first month, and at one year were 0.997, 0.982, 0.973, 0.936, and 0.928, respectively. Among the parametric models, the log-logistic mixed cure model with the logit link, which showed the lowest Akaike information criterion value, demonstrated the best fit. The variables of age and prescribed medication type were significant predictors of long-term survival, while occupation was influential in the short-term survival of patients. Conclusions According to the results of this study, it can be concluded that elderly patients should observe health protocols more strictly and consider receiving booster vaccine doses. The log-logistic cure model with a logit link can be used for survival analysis in COVID-19 patients, a fraction of whom have long-term survival.

Survival, mortality and epidemic risk status of COVID-19: a population-based Study in Golestan province, Iran.

BACKGROUND: Appreciating the various dimensions of the coronavirus disease 2019 (COVID-19) pandemic can improve health systems and prepare them to deal better with future pandemics and public health events. This study was conducted to investigate the association between the survival of hospitalized patients with COVID-19 and the epidemic risk stratification of the disease in Golestan province, Iran. METHODS: In this study, all patients with COVID-19 who were hospitalized in the hospitals of Golestan province of Iran from February 20, 2020, to December 19, 2022, and were registered in the Medical Care Monitoring Center (MCMC) system (85,885 individuals) were examined.The community's epidemic risk status (ERS) was determined based on the daily incidence statistics of COVID-19. The survival distribution and compare Survival in different subgroups was investigated using Kaplan-Meier and log-rank test and association between the survival and ERS by multiple Cox regression modeling. RESULTS: Out of 68,983 individuals whose data were correctly recorded, the mean age was 49 (SD = 23.98) years, and 52.8% were women. In total, 11.1% eventually died. The length of hospital stay was varying significantly with age, gender, ERS, underlying diseases, and COVID-19 severity (P < 0.001 for all). The adjusted hazard ratio of death for the ERS at medium, high, and very high-risk status compared to the low-risk status increased by 19%, 26%, and 56%, respectively (P < 0.001 for all). CONCLUSIONS: Enhancing preparedness, facilitating rapid rises in hospital capacities, and developing backup healthcare capacities can prevent excessive hospital referrals during health crises and further deaths.

A joint model of longitudinal pharmacokinetic and time-to-event data to study exposure-response relationships: a proof-of-concept study with alectinib.

PURPOSE: In exposure-response analyses of oral targeted anticancer agents, longitudinal plasma trough concentrations are often aggregated into a single value even though plasma trough concentrations can vary over time due to dose adaptations, for example. The aim of this study was to compare joint models to conventional exposure-response analyses methods with the application of alectinib as proof-of-concept. METHODS: Joint models combine longitudinal pharmacokinetic data and progression-free survival data to infer the dependency and association between the two datatypes. The results from the best joint model and the standard and time-dependent cox proportional hazards models were compared. To normalize the data, alectinib trough concentrations were normalized using a sigmoidal transformation to transformed trough concentrations (TTC) before entering the models. RESULTS: No statistically significant exposure-response relationship was observed in the different Cox models. In contrast, the joint model with the current value of TTC in combination with the average TTC over time did show an exposure-response relationship for alectinib. A one unit increase in the average TTC corresponded to an 11% reduction in progression (HR, 0.891; 95% confidence interval, 0.805-0.988). CONCLUSION: Joint models are able to give insights in the association structure between plasma trough concentrations and survival outcomes that would otherwise not be possible using Cox models. Therefore, joint models should be used more often in exposure-response analyses of oral targeted anticancer agents.

Aetiology and predictors of outcome in non-shockable in-hospital cardiac arrest: A retrospective cohort study from the Swedish Registry for Cardiopulmonary Resuscitation.

BACKGROUND: Non-shockable in-hospital cardiac arrest (IHCA) is a condition with diverse aetiology, predictive factors, and outcome. This study aimed to compare IHCA with initial asystole or pulseless electrical activity (PEA), focusing specifically on their aetiologies and the significance of predictive factors. METHODS: Using the Swedish Registry of Cardiopulmonary Resuscitation, adult non-shockable IHCA cases from 2018 to 2022 (n = 5788) were analysed. Exposure was initial rhythm, while survival to hospital discharge was the primary outcome. A random forest model with 28 variables was used to generate permutation-based variable importance for outcome prediction. RESULTS: Overall, 60% of patients (n = 3486) were male and the median age was 75 years (IQR 67-81). The most frequent arrest location (46%) was on general wards. Comorbidities were present in 79% of cases and the most prevalent comorbidity was heart failure (33%). Initial rhythm was PEA in 47% (n = 2702) of patients, and asystole in 53% (n = 3086). The most frequent aetiologies in both PEA and asystole were cardiac ischemia (24% vs. 19%, absolute difference [AD]: 5.4%; 95% confidence interval [CI] 3.0% to 7.7%), and respiratory failure (14% vs. 13%, no significant difference). Survival was higher in asystole (24%) than in PEA (17%) (AD: 7.3%; 95% CI 5.2% to 9.4%). Cardiopulmonary resuscitation (CPR) durations were longer in PEA, 18 vs 15 min (AD 4.9 min, 95% CI 4.0-5.9 min). The duration of CPR was the single most important predictor of survival across all subgroup and sensitivity analyses. Aetiology ranked as the second most important predictor in most analyses, except in the asystole subgroup where responsiveness at cardiac arrest team arrival took precedence. CONCLUSIONS: In this nationwide registry study of non-shockable IHCA comparing asystole to PEA, cardiac ischemia and respiratory failure were the predominant aetiologies. Duration of CPR was the most important predictor of survival, followed by aetiology. Asystole was associated with higher survival compared to PEA, possibly due to shorter CPR durations and a larger proportion of reversible aetiologies.

The Survival and Financial Benefit of Investigator-Initiated Trials Conducted by Korean Cancer Study Group.

Purpose: The Korean Cancer Study Group (KCSG) is a nationwide cancer clinical trial group dedicated to advancing investigator-initiated trials (IITs) by conducting and supporting clinical trials. This study aims to review IITs conducted by KCSG and quantitatively evaluate the survival and financial benefits of IITs for patients. Materials and Methods: We reviewed IITs conducted by KCSG from 1998 to 2023, analyzing progression-free survival (PFS) and overall survival (OS) gains for participants. PFS and OS benefits were calculated as the difference in median survival times between the intervention and control groups, multiplied by the number of patients in the intervention group. Financial benefits were assessed based on the cost of investigational products provided. Results: From 1998 to 2023, KCSG conducted 310 IITs, with 133 completed and published. Of these, 21 were included in the survival analysis. The analysis revealed that 1,951 patients in the intervention groups gained a total of 2,558.4 months (213.2 years) of PFS and 2,501.6 months (208.5 years) of OS, with median gains of 1.31 months in PFS and 1.58 months in OS per patient. When analyzing only statistically significant results, PFS and OS gain per patients was 1.69 months and 3.02 months, respectively. Investigational drug cost analysis from 6 available IITs indicated that investigational products provided to 252 patients were valued at 10,400,077,294 won (approximately 8,046,481 US dollars), averaging about 41,270,148 won (approximately 31,930 US dollars) per patient. Conclusion: Our findings, based on analysis of published research, suggest that IITs conducted by KCSG led to survival benefits for participants and, in some studies, may have provided financial benefits by providing investment drugs.

ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.

Subjective cognitive concerns, APOE ε4, PTSD symptoms, and risk for dementia among older veterans.

BACKGROUND: Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are associated with self-reported problems with cognition as well as risk for Alzheimer's disease and related dementias (ADRD). Overlapping symptom profiles observed in cognitive disorders, psychiatric disorders, and environmental exposures (e.g., head injury) can complicate the detection of early signs of ADRD. The interplay between PTSD, head injury, subjective (self-reported) cognitive concerns and genetic risk for ADRD is also not well understood, particularly in diverse ancestry groups. METHODS: Using data from the U.S. Department of Veterans Affairs (VA) Million Veteran Program (MVP), we examined the relationship between dementia risk factors (APOE ε4, PTSD, TBI) and subjective cognitive concerns (SCC) measured in individuals of European (n = 140,921), African (n = 15,788), and Hispanic (n = 8,064) ancestry (EA, AA, and HA, respectively). We then used data from the VA electronic medical record to perform a retrospective survival analysis evaluating PTSD, TBI, APOE ε4, and SCC and their associations with risk of conversion to ADRD in Veterans aged 65 and older. RESULTS: PTSD symptoms (B = 0.50-0.52, p < 1E-250) and probable TBI (B = 0.05-0.19, p = 1.51E-07 - 0.002) were positively associated with SCC across all three ancestry groups. APOE ε4 was associated with greater SCC in EA Veterans aged 65 and older (B = 0.037, p = 1.88E-12). Results of Cox models indicated that PTSD symptoms (hazard ratio [HR] = 1.13-1.21), APOE ε4 (HR = 1.73-2.05) and SCC (HR = 1.18-1.37) were positively associated with risk for ADRD across all three ancestry groups. In the EA group, probable TBI also contributed to increased risk of ADRD (HR = 1.18). CONCLUSIONS: The findings underscore the value of SCC as an indicator of ADRD risk in Veterans 65 and older when considered in conjunction with other influential genetic, clinical, and demographic risk factors.

Use of automated assessment for determining associations of low muscle mass and muscle loss with overall survival in patients with colorectal cancer - a validation study.

BACKGROUND: Low muscle mass and skeletal muscle mass (SMM) loss are associated with adverse patient outcomes, but the time-consuming nature of manual SMM quantification prohibits implementation of this metric in clinical practice. Therefore, we assessed the feasibility of automated SMM quantification compared to manual quantification. We evaluated both diagnostic accuracy for low muscle mass and associations of SMM (change) with survival in colorectal cancer (CRC) patients. METHODS: Computed tomography (CT) images from CRC patients enrolled in two clinical studies were analyzed. We compared i) manual vs. automated segmentation of preselected slices at the third lumbar [L3] vertebra ("semi-automated"), and ii) manual L3-slice-selection + manual segmentation vs. automated L3-slice-selection + automated segmentation ("fully-automated"). Automated L3-selection and automated segmentation was performed with Quantib Body Composition v0.2.1. Bland-Altman analyses, within-subject coefficients of variation (WSCVs) and Intraclass Correlation Coefficients (ICCs) were used to evaluate the agreement between manual and automatic segmentation. Diagnostic accuracy for low muscle mass (defined by an established sarcopenia cut-off) was calculated with manual assessment as the "gold standard". Using either manual or automated assessment, Cox proportional hazard ratios (HRs) were used to study the association between changes in SMM (>5% decrease yes/no) during first-line metastatic CRC treatment and mortality adjusted for prognostic factors. SMM change was also assessed separately in weight-stable (<5%, i.e. occult SMM loss) patients. RESULTS: In total, 1580 CT scans were analyzed, while a subset of 307 scans were analyzed in the fully-automated comparison. Included patients (n=553) had a mean age of 63±9 years and 39% were female. The semi-automated comparison revealed a bias of -2.41 cm2, 95% limits of agreement [-9.02 to 4.20], a WSCV of 2.25%, and an ICC of 0.99 (95% confidence intervals (CI) 0.97 to 1.00). The fully-automated comparison method revealed a bias of -0.08 cm2 [-10.91 to 10.75], a WSCV of 2.85% and an ICC of 0.98 (95% CI 0.98 to 0.99). Sensitivity and specificity for low muscle mass were 0.99 and 0.89 for the semi-automated comparison and 0.96 and 0.90 for the fully-automated comparison. SMM decrease was associated with shorter survival in both manual and automated assessment (n=78/280, HR 1.36 [95% CI 1.03 to 1.80] and n=89/280, HR 1.38 [95% CI 1.05 to 1.81]). Occult SMM loss was associated with shorter survival in manual assessment, but not significantly in automated assessment (n=44/263, HR 1.43 [95% CI 1.01 to 2.03] and n=51/2639, HR 1.23 [95% CI 0.87 to 1.74]). CONCLUSION: Deep-learning based assessment of SMM at L3 shows reliable performance, enabling the use of CT measures to guide clinical decision making. Implementation in clinical practice helps to identify patients with low muscle mass or (occult) SMM loss who may benefit from lifestyle interventions.

Unraveling the Influence of Social, Economic, and Demographic Factors in Texas on Breast Cancer Survival.

Background: According to the Centers for Disease Control (CDC), breast cancer is the second most common cancer among women in the United States. Affected people are financially challenged due to the high out-of-pocket cost of breast cancer treatment, as it is the most expensive treatment. Using a 16-year cohort study of breast cancer survival data in Texas, we investigate the factors that might explain why some breast cancer patients live longer than others. Methods: Performing a survival analysis consisting of the log-rank test, a survival time regression, and Cox proportional hazards regression, we explore the breast cancer survivors' specific attributes to identify the main determinants of survival time. Results: Analyses show that the factors: stage, grade, primary site of the cancer, number of cancers each patient has, histology of the cancer, age, race, and income are among the main variables that enlighten why some breast cancer survivors live much longer than others. For instance, compared to White non-Hispanics, Black non-Hispanics have a shorter length of survival with a hazard ratio of (1.282). The best prognostic for White non-Hispanics, Hispanics (all races), and Black non-Hispanics is a woman aged between 40 to 49 years old, diagnosed with localized stage and grade one with Axillary tail of breast as a primary site with only one cancer and with a household income of 75,000.00 and over. Conclusion: Policymakers should promote early diagnosis and screening and better assist the older and the poor to improve the survival time for breast cancer patients.

Reproductive potential and implant loss in female hamadryas baboons (Papio hamadryas) previously contracepted with melengestrol acetate contraceptive implants at AZA institutions.

Melengestrol acetate (MGA) implants are a progestin-based reversible contraceptive used to manage fertility in animals. MGA implants are recommended for replacement every 2 years; however, reproduction may be suppressed longer if implants are not removed. In this study, we investigated whether the probability of reproducing (pR) differed among nonimplanted females, females with MGA implants removed, and females whose implants were not removed. In addition, since implant loss in hamadryas baboons is a concern, we explored whether female age, institution, implant placement year, implant location, or implant placement type (intramuscular vs. subcutaneous) differed for females whose implants were lost compared to those that were not. The pR differed significantly across all three treatment conditions with the nonimplanted group having the highest pR. The pR plateaued at 63% after 40 months for the implant-removed group compared to 96% after 84 months in the nonimplanted group. There was no reproduction after contraception if implants were not removed (7.83-45.53 months). In the nonimplanted group, pR was significantly higher for older and parous females. In terms of implant loss, we found that implant placement type was significantly associated with implant loss, such that there were fewer losses when implants were placed intramuscularly (IM) as compared to subcutaneously. Our results suggest that placing MGA implants IM is likely to reduce loss. When loss is prevented, MGA implants are an effective form of contraception and are reliably reversibly in most individuals when removed. However, if not removed, they can prevent reproduction longer than 2 years.

Phylogenetic Survival Analysis.

Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future. Here, we present a novel genome-wide survival analysis and use the emergence of drug resistance in Mycobacterium tuberculosis as an example to demonstrate the potential and utility of the technique.