Individual Survival Time Research Articles

The universality of physical images at relative timescales on multiplex networks

The duration of the accumulation rate (physical image) is a key factor in analysis of counterintuitive phenomena involving relative timescales on multiplex networks. Typically, the relative timescales are represented by multiplying any layer by the same factor. However, researchers often overlook the changes in the relative timescales caused by local parameters, resulting in incomplete analysis of phenomena. This paper examines the survival time of stifler individuals in the information-epidemic model on multiplex networks. The relative timescales can be affected by the survival time (only one parameter), reversing the monotonically increasing phenomenon into a monotonically decreasing one, that is, a counterintuitive phenomenon under incomplete analysis. Additionally, the relative timescales can influence the epidemic threshold, which is different from the previous studies. Our work suggests that considering the physical image of relative timescales is crucial when analyzing multiplex networks, even when only one parameter is altered.

A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients

Background and Objective: Survival analysis plays an essential role in the medical field for optimal treatment decision-making. Recently, survival analysis based on the deep learning (DL) approach has been proposed and is demonstrating promising results. However, developing an ideal prediction model requires integrating large datasets across multiple institutions, which poses challenges concerning medical data privacy.Methods: In this paper, we propose FedSurv, an asynchronous federated learning (FL) framework designed to predict survival time using clinical information and positron emission tomography (PET)-based features. This study used two datasets: a public radiogenic dataset of non-small cell lung cancer (NSCLC) from the Cancer Imaging Archive (RNSCLC), and an in-house dataset from the Chonnam National University Hwasun Hospital (CNUHH) in South Korea, consisting of clinical risk factors and F-18 fluorodeoxyglucose (FDG) PET images in NSCLC patients. Initially, each dataset was divided into multiple clients according to histological attributes, and each client was trained using the proposed DL model to predict individual survival time. The FL framework collected weights and parameters from the clients, which were then incorporated into the global model. Finally, the global model aggregated all weights and parameters and redistributed the updated model weights to each client. We evaluated different frameworks including single-client-based approach, centralized learning and FL.Results: We evaluated our method on two independent datasets. First, on the RNSCLC dataset, the mean absolute error (MAE) was 490.80±22.95 d and the C-Index was 0.69±0.01. Second, on the CNUHH dataset, the MAE was 494.25±40.16 d and the C-Index was 0.71±0.01. The FL approach achieved centralized method performance in PET-based survival time prediction and outperformed single-client-based approaches.Conclusions: Our results demonstrated the feasibility and effectiveness of employing FL for individual survival prediction in NSCLC patients, using clinical information and PET-based features.

Use of Residuals and Rank Product in Detection of Outlier in Survival Analysis with Crimean-Congo Hemorrhagic Fever Data

Purpose: Survival analysis is a statistical method used in many fields, especially in the field of health. It involves modeling the relationship between the survival time of individuals after a treatment or procedure and the event called response. The presence of outliers in the data may cause biased parameter estimations of the established models. Also, this situation causes the proportional hazards assumption to be violated especially in Cox regression analysis. Outlier(s) are identified with the help of residuals, Bootstrap Hypothesis test and Rank product test. Method: In R.4.0.3 software, outlier(s) are determined on a clinical dataset by the Schoenfeld residual, Martingale residual, Deviance residual method and Bootstrap Hypothesis test (BHT) based on Concordance index, and Rank product test. Results: After the cox regression established by the backward stepwise and robust cox regression, it was observed that the established models did not fit. So, the outlier(s) determined by the methods mentioned. Conclusion: It was decided that only one observation could be excluded from the study. As in the survival data, in many data types, outliers can be detected and further analyzes can be applied by using the methods mentioned.

Long-Term Impact of D2 Lymphadenectomy during Gastrectomy for Cancer: Individual Patient Data Meta-Analysis and Restricted Mean Survival Time Estimation.

Debate exists concerning the impact of D2 vs. D1 lymphadenectomy on long-term oncological outcomes after gastrectomy for cancer. PubMed, MEDLINE, Scopus, and Web of Science were searched and randomized controlled trials (RCTs) analyzing the effect of D2 vs. D1 on survival were included. Overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS) were assessed. Restricted mean survival time difference (RMSTD) and 95% confidence intervals (CI) were used as effect size measures. Five RCTs (1653 patients) were included. Overall, 805 (48.7%) underwent D2 lymphadenectomy. The RMSTD OS analysis shows that at 60-month follow-up, D2 patients lived 1.8 months (95% CI -4.2, 0.7; p = 0.14) longer on average compared to D1 patients. Similarly, 60-month CSS (1.2 months, 95% CI -3.9, 5.7; p = 0.72) and DFS (0.8 months, 95% CI -1.7, 3.4; p = 0.53) tended to be improved for D2 vs. D1 lymphadenectomy. Compared to D1, D2 lymphadenectomy is associated with a clinical trend toward improved OS, CSS, and DFS at 60-month follow-up.

Survival analysis of older adults with dementia: predicting factors after unplanned hospitalization in Maharaj Nakorn Chiang Mai Hospital

BackgroundHospitalization in individuals with dementia can be associated with negative and unintended outcomes. Research indicates that people with dementia experience more hospital admissions in comparison to individuals without dementia. This study aims to assess the survival time of individuals with dementia who experience unplanned hospitalization and examine the factors that are associated with mortality in this population.MethodsThis retrospective cohort study was conducted using data from older adults with dementia who survived unplanned hospitalizations at Maharaj Nakorn Chiang Mai Hospital between January 1, 2009, and December 31, 2016. The association between factors and mortality were analyzed using a multivariable Cox proportional hazards model.ResultsOne hundred and eighty-one cases were included. The mean age of the study population was 80.07 (SD 7.49) years, and the majority were female (56.91%). The median survival time of the studied cohort was 3.06 years (95% CI 3.14–3.60). The multivariable analysis revealed that older age (aHR = 1.02, 95% CI 1.00-1.05), a diagnosis of mixed-type dementia (aHR = 3.45, 95% CI 1.17–10.14), higher Charlson comorbidity index score (aHR = 1.19, 95% CI 1.04–1.36), higher serum creatinine level (aHR = 1.35, 95% CI 1.10–1.66), insertion of endotracheal tube (aHR = 1.95, 95% CI 1.07–3.54), and readmission within 30 days (aHR = 1.88, 95% CI 1.18–2.98) were associated with an increased risk of mortality.ConclusionsWe identified several notable predictors of mortality. Healthcare providers can use the findings of this study to identify patients who may be at higher risk of mortality and develop targeted interventions which may improve patient outcomes.

Two-sample survival tests based on control arm summary statistics.

The one-sample log-rank test is the preferred method for analysing the outcome of single-arm survival trials. It compares the survival distribution of patients with a prefixed reference survival curve that usually represents the expected outcome under standard of care. However, classical one-sample log-rank tests assume that the reference curve is known, ignoring that it is frequently estimated from historical data and therefore susceptible to sampling error. Neglecting the variability of the reference curve can lead to an inflated type I error rate, as shown in a previous paper. Here, we propose a new survival test that allows to account for the sampling error of the reference curve without knowledge of the full underlying historical survival time data. Our new test allows to perform a valid historical comparison of patient survival times when only a historical survival curve rather than the full historic data is available. It thus applies in settings where the two-sample log-rank test is not applicable as method of choice due to non-availability of historic individual patient survival time data. We develop sample size calculation formulas, give an example application and study the performance of the new test in a simulation study.

THE VALUE OF HIGH-RESOLUTION T2-WEIGHTED MRI TO EVALUATE LOCAL STAGING OF RECTAL CANCER

Purpose: To describe imaging characteristics of rectal cancer on high-resolution T2-weighted MRI in predicting local staging of rectal cancer
 Materials and methods: A cross-sectional descriptive study aimed at evaluating the sensitivity, specificity, accuracy, and correlation coefficient of MRI in staging T (tumor) and N (lymph node involvement) in patients with rectal cancer. The study used a high-resolution T2 weighted on 3 Tesla MRI to assess the pre-treatment stage compared with histopathology results after surgery.
 Results: The study involved a sample of 48 patients, consisting of 27 men and 21 women. The pre-treatment stage was assessed using magnetic resonance imaging (MRI), considering stages as T1, T2, T3, and T4 were 2 (4.2%), 6 (12.5%), 36 (75%) and 4 (8.3%); staged as N0, N1, N2 were 19 (39.6%), 17 (35.4%), 4 (8.3%). A total of 21 patients underwent surgical procedures, and this study aimed to evaluate the sensitivity, specificity, and accuracy of MRI with a high-resolution T2 weighted in comparison to the overall postoperative pathology were 100%, 75%, 90.5% for T1-3 staging and 75%, 76.92%, 76.19% for N0-2 staging. The κ values were 0.714 and 0.199 for T1-3 and N0-2 staging, respectively.
 Conclusion: MRI with a high-resolution T2 weighted has significant value in the diagnosis of local staging of rectal cancer, particularly in determining the T stage. The precise local staging plays a crucial role in selecting the appropriate treatment methods, and prolongation of survival time for individuals with rectal cancer.

The Geometric Generalized Birnbaum–Saunders model with long-Term Survivors

Introduction: A cure rate survival model was developed based on the assumption that the number of competing reasons for the event of interest has the Geometric distribution and the time allocated to the event of interest follows the Generalized Birnbaum-Saunders distribution.
 Methods: The Geometric Generalized Birnbaum–Saunders distribution was defined and two useful representations were represented for its density function which contributes to the creation of some mathematical properties. Furthermore, the parameters of the model with cure rate were estimated by using the maximum likelihood method.
 Results: Several simulations were performed and a real data set was analyzed from the medical area for different sample sizes and censoring percentages.In the melanoma data set and regarding the AIC and SBC selection criteria, the Geometric Generalized Birnbaum–Saunders distribution model was preferred and was selected as the appropriate model in the present study.
 Conclusion: Geometric Generalized Birnbaum–Saunders distribution is a highly flexible lifetime model which allows for different degrees of Kurtosis and asymmetry.by considering the advantages of the Geometric Generalized Birnbaum–Saunders distribution model, the model can be implemented as an appropriate alternative to explain or predict the survival time for long-term individuals.

Survival Analysis and Hazard of Log Logistic Distribution on Type I Censored Data Parametrically

Survival Analysis is a research method that examines the survival time of individuals or experimental units in relation to events such as death, disease, recovery, or other experiences. This study utilizes a parametric survival analysis model with a 2 parameter log logistic distribution and Maximum Likelihood Estimation (MLE) method to analyze the survival of students during their study period. The log logistic distribution is chosen due to its ability to capture early or late failure patterns. The objective of this research is to analyze type I censored survival data using the log logistic distribution applied to secondary data on student study duration. The dataset consists of 98 observations. The calculated values for the β and γ parameters of the 2 parameters log logistic distribution are 2.12831 and 0.0918891, respectively. The probability of students completing their studies by semester 8 (hazard function h(8)) is 0.370102, while the probability of students continuing their studies in semester 9 (survival function s(9)) is 0.320817.

Anti-tumor effects of ketogenic diets and their synergism with other treatments in mice: Bayesian evidence synthesis of 1755 individual mouse survival data

BackgroundKetogenic diets (KDs) are high-fat diets with putative anti-tumor effects. The aim of this study was to synthesize the evidence for the anti-tumor effects of KDs in mice, with a focus on their possible synergism with chemotherapy (CT), radiotherapy (RT), or targeted therapies (TT). MethodsRelevant studies were retrieved from a literature search. A total of 43 articles reporting on 65 mouse experiments fulfilled the inclusion criteria, and 1755 individual mouse survival times were collated from the study authors or the publications. The restricted mean survival time ratio (RMSTR) between the KD and control groups served as the effect size. Bayesian evidence synthesis models were used to estimate pooled effect sizes and to assess the impact of putative confounders and synergism between KD and other therapies. ResultsOverall, there was a significant survival-prolonging effect of KD monotherapy (RMSTR = 1.161 ± 0.040), which was confirmed in meta-regression accounting for syngeneic versus xenogeneic models, early versus late KD start and subcutaneous versus other organ growth. Combining the KD with RT or TT, but not CT, was associated with a further 30% (RT) or 21% (TT) prolongation of survival. An analysis accounting for 15 individual tumor entities showed that KDs exerted significant survival-prolonging effects in pancreatic cancer (all treatment combinations), gliomas (KD + RT and KD + TT), head and neck cancer (KD + RT), and stomach cancer (KD+RT and KD + TT). ConclusionsThis analytical study confirmed the overall anti-tumor effects of KDs in a large number of mouse experiments and provides evidence for synergistic effects with RT and TT.

Role of the NF-kB/parkin/vegfr-1 pathway associated with hypoxic-ischemic insult in germinal matrix samples of newborn infants.

Given the high proliferative activity of germinal matrix and its direct correlation with hypoxemia, it is necessary to investigate the possible molecular regulation pathways, to understand the existing clinical relationship between the hypoxic-ischemic insult and the biomarkers NF-kB, AKT-3, Parkin, TRK-C and VEGFR-1. A hundred and eighteen germinal matrix samples of the central nervous system of patients who died in the first 28 days of life were submitted to histological and immunohistochemistry analysis to identify the tissue immunoexpression of those biomarkers related to asphyxia, prematurity, and death events within 24h. A significantly increased tissue immunoexpression of NF-kB, AKT-3 and Parkin was observed in the germinal matrix of preterm infants. In addition, significantly decreased tissue immunoexpression of VEGFR-1 and NF-kB was observed in patients who experienced asphyxia followed by death within 24 hours. The results suggest a direct involvement between the hypoxic-ischemic insult and NF-kB and VEGFR-1 markers since a decreased immunoexpression of these biomarkers was observed in asphyxiated patients. Furthermore, it is suggested that there was not enough time for VEGFR-1 to be transcribed, translated and expressed on the surface of the plasma membrane. This temporality can be observed in the relationship between NF-kB expression and the survival time of individuals who died within 24 hours, suggesting that this factor is essential for the production of VEGFR-1 and, therefore, to carry out the necessary remodeling effect to neovascularize the affected region.

A pan-cancer analysis of the oncogenic role of Holliday junction recognition protein in human tumors.

Although cell-based or animal-based research evidence support the association of Holliday junction recognition protein (HJURP) with cancers, no pan-cancer investigation has been reported. The datasets of Gene Expression Omnibus database along with The Cancer Genome Atlas project were used to evaluate the expression of HJURP in various types of tumors. HJURP is overexpressed in a considerable number of cancers, and some changes in DNA methylation and genetic alterations are discovered in some types of tumors, such as kidney-related and adrenal gland-related tumors. Based on PrognoScan and gene expression profiling interactive analysis (GEPIA), the elevated expression of HJURP worsened the survival time of individuals with cancer. The biological general repository for interaction datasets (BioGRID) and The database for annotation, visualization and integrated discovery (DAVID) were used to establish the functional molecular network. It revealed that the cell cycle and p53 signaling pathway are the key molecular mechanisms that HJURP promotes carcinogenesis. The nomograms between HJURP and clinical pathological factors based on the Cox proportional hazards model showed a good prognostic performance in kidney carcinoma, hepatocellular carcinoma, and lung adenocarcinoma. Our first pan-cancer study provides a relatively profound insights into the oncogenic roles of HJURP across different tumors.

Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction.

Left-truncated data are often encountered in epidemiological cohort studies, where individuals are recruited according to a certain cross-sectional sampling criterion. Length-biased data, a special case of left-truncated data, assume that the incidence of the initial event follows a homogeneous Poisson process. In this article, we consider an analysis of length-biased and interval-censored data with a nonsusceptible fraction. We first point out the importance of a well-defined target population, which depends on the prior knowledge for the support of the failure times of susceptible individuals. Given the target population, we proceed with a length-biased sampling and draw valid inferences from a length-biased sample. When there is no covariate, we show that it suffices to consider a discrete version of the survival function for the susceptible individuals with jump points at the left endpoints of the censoring intervals when maximizing the full likelihood function, and propose an EM algorithm to obtain the nonparametric maximum likelihood estimates of nonsusceptible rate and the survival function of the susceptible individuals. We also develop a novel graphical method for assessing the stationarity assumption. When covariates are present, we consider the Cox proportional hazards model for the survival time of the susceptible individuals and the logistic regression model for the probability of being susceptible. We construct the full likelihood function and obtain the nonparametric maximum likelihood estimates of the regression parameters by employing the EM algorithm. The large sample properties of the estimates are established. The performance of the method is assessed by simulations. The proposed model and method are applied to data from an early-onset diabetes mellitus study.

Genome-wide association study reveals multiple novel SNPs and putative candidate genes associated with low oxygen tolerance in golden pompano Trachinotus ovatus (Linnaeus 1758)

Golden pompano (Trachinotus ovatus) is one of the most economically critical marine fishes in South China. Low oxygen stress, which is caused by a variety of factors, has resulted in substantial economic losses to the aquaculture of golden pompano. Genome-wide association study (GWAS) has been widely used to study the genetic basis of important economic traits in aquatic animals. In the present study, 100 individuals were used for genome-wide association study of hypoxia tolerance trait based on genome-wide resequencing technology to explore the SNPs and related candidate genes affecting hypoxia tolerance trait in golden pompano. Four significant SNPs were identified to be suggestively significantly associated with the hypoxia tolerance trait. In the 50 kb window surrounding the suggestive SNPs, 16 potential candidate genes affecting hypoxia tolerance trait were detected. Moreover, validation results of the screened significant loci in the large population were consistent with the results of the GWAS. Among these results, the survival times of individuals with different genotypes at SNP24101852 and SNP24194184 were significantly different (p < 0.01). These results have specific guiding significance for the genetic improvement and determining the molecular mechanism of hypoxia tolerance. These SNPs could be used as molecular markers to screen for hypoxia tolerance be used for further applications in the heritage breeding of golden pompano.

A novel immune-related gene signature predicts survival in esophageal squamous cell carcinoma.

BackgroundImmune-related genes (IRGs) are highly relevant to the progression and prognosis of esophageal squamous cell carcinoma (ESCC). A prognostic signature could be reliable in stratifying ESCC patients according to the risk score, which may help manage systematic treatments. In this study, a systematic and reliable immune signature was developed to estimate the prognostic stratification in ESCC.MethodsRibonucleic acid (RNA) expression data of 79 ESCC samples from the Cancer Genome Atlas (TCGA) database and 269 normal esophageal mucosal samples from the Genotype-Tissue Expression (GTEx) project database were downloaded from the University of California, Santa Cruz (UCSC) website to form a TCGA-GTEx dataset. First, we screened differentially expressed genes (DEGs) and then filtered IRGs based on the Immunology Database and Analysis Portal (ImmPort) database to obtain immune-related DEGs (IRDEGs). Next, a novel prognostic signature based on IRDEGs was developed using multivariable Cox analysis. Immune infiltration status was evaluated via single-sample gene set enrichment analysis (ssGSEA). ESCC tissues were grouped into three clusters in terms of immune infiltration (Immunity-L, Immunity-M, and Immunity-H) by applying an unsupervised hierarchical clustering algorithm. Finally, the samples were divided into high- and low-risk groups using the median of the risk score scores for GSEA pathway enrichment analysis in the three clusters.ResultsThe prognostic signature based on IRDEGs (FCER1G, ISG20, and EGFR) performed moderately in prognostic predictions, with a concordance index (C-index) value of 0.73 [95% (confidence interval) CI: 0.63–0.84, P=2.02E-05] and an area under the curve (AUC) value of 0.817. The xenobiotic metabolism pathway was significantly enriched and up-regulated both in the high-risk group of the immunity-M and immunity-H clusters.ConclusionsThe novel immune-related prognostic signature we constructed has a good prognostic, predictive ability and can be used as an independent prognostic indicator. Our study provides clinicians with a quantitative tool to predict the probability of individual survival time and helps clinicians select targets for immunotherapies and individualized treatment strategies for ESCC patients.

Survival time prediction by integrating cox proportional hazards network and distribution function network

BackgroundThe Cox proportional hazards model is commonly used to predict hazard ratio, which is the risk or probability of occurrence of an event of interest. However, the Cox proportional hazard model cannot directly generate an individual survival time. To do this, the survival analysis in the Cox model converts the hazard ratio to survival times through distributions such as the exponential, Weibull, Gompertz or log-normal distributions. In other words, to generate the survival time, the Cox model has to select a specific distribution over time.ResultsThis study presents a method to predict the survival time by integrating hazard network and a distribution function network. The Cox proportional hazards network is adapted in DeepSurv for the prediction of the hazard ratio and a distribution function network applied to generate the survival time. To evaluate the performance of the proposed method, a new evaluation metric that calculates the intersection over union between the predicted curve and ground truth was proposed. To further understand significant prognostic factors, we use the 1D gradient-weighted class activation mapping method to highlight the network activations as a heat map visualization over an input data. The performance of the proposed method was experimentally verified and the results compared to other existing methods.ConclusionsOur results confirmed that the combination of the two networks, Cox proportional hazards network and distribution function network, can effectively generate accurate survival time.

Protective and Antitumor Effects of Oxidal and Pyrucet in Hamsters with Experimental Graffi Tumor

The use of combination drug therapy instead of monotherapy is a new positive and promising approach in the treatment of cancer. Oxidal® and Pyrucet® are food supplements containing Methylene blue (USP), Caffeine (USP), Salicylic acid (USP) and Ethyl Acetoacetate and Ethyl Pyruvate respectively. The therapeutic significance of each of these compounds (their derivatives) is well established, but no data are available on their combined action in in vivo experimental tumor models.&#x0D; The aim of the present study was to investigate the in vivo protective effect of mono- and combined experimental therapy with dietary supplements Oxidal® and Pyrucet® in the Graffi myeloid tumor model in hamsters. For this purpose, the two drugs were administered alone or in combination in two treatment regimens - prophylactic (before) and therapeutic (simultaneously) with the transplantation of Graffi tumor cells. The protective effect was determined by the biometric parameters of tumor growth (transplantability, tumor size, mortality, mean survival time, survival rate) recorded during the experiment. The results showed a favorable effect of both drugs, administered alone or in combination, before and simultaneously with the transplantation of tumor cells on the appearance and development of Graffi tumor in hamsters. About 2-fold lower transplantability, prolongation of the latency period by 7 days, inhibition of tumor growth till 30thday of experiments, reduced mortality and increased individual and overall survival time between 7 to 10 days were observed in the Graffi tumor-bearing hamsters with experimental therapy compared to control-Graffi tumor-bearing hamsters, without therapy. The obtained data revealed that Oxidal® and Pyrucet® could be a promising candidate for the treatment of tumor diseases.

Abstract PO-038: Improving lung cancer survival analysis from CT images by saliency sampling

Abstract Background: Survival analysis of the patient has an important role in the cancer treatment process. Traditional models based on clinical information, signs, and symptoms to predict the outcome of the patient. Radiomics is another approach based on image features and machine learning methods. Both approaches need hand-craft features and expertise of medical doctors which time-consuming task. Recently, deep learning (DL) has more and more success in many medicine tasks. In this report, we investigate the DL model architecture to non-small cell lung cancer (NSCLC) survival analysis based on a CT scan in the end-to-end strategy. As much medical imaging technique, a CT scan includes three-dimension. 3D CT images are used to large and require much computing, space, and time-consuming. Most studies scale down the CT image size by linear methods, e.g. 128 × 128 × 128 pixels, to fit the available computing resource. However, down-scaling images to low-resolution using linear methods lose information, then affect the final output. We deal with this problem by proposed a non-linear scale down DL network architecture CT image to fit the resource problem while trying to keep the most important information in the original image. Material and method: The CT images (n=1861) from our hospital about non-small cell lung cancer (NSCLC) were collected between 2013 and 2020. All patients are independent then we the dataset into two separate subsets including training and testing with the number of samples are 1516 and 345, respective. Training subsets were used to build a model and testing was leaving for test purposes. Most studies scale down the CT image to low-resolution by linear approach before applying 3DCNN to prediction. We assume that in each slice, there are some regions that contain more information than others. Then, we apply saliency sampling approach to sampling the original CT image to low-resolution. The network architecture has two blocks including saliency sampling block (SS) and classification block, named Saliency Sampling per Slice (SSPS). Firstly, a localization part detects the important regions in a slice and return the map that scores each pixel. After that, a grid is built and used to sample the original image to a low-resolution image. This way, the most important part of the image are less scale, and less important get more scale. Results: For individual survival time prediction, we compare only non-censoring data. Our SSPS approach gets 347.1 days in mean absolute error (MAE). Our results are far better than the naive predictions based on the mean value (389.2 days). Compare to the base-line architecture of the 3DCNN model, SSPS with SS block improve by reducing 43.5 days from 391.6 days of 3DCNN without SS part. Conclusions: Our results suggest that the saliency sampling approach has an effect when dealing with the big size image like CT. The network with SS block likely learns to focus on some important regions of image instead of linear scale-down image. Citation Format: Hung Thanh Vo, Sae-Ryung Kang, In-Jae Oh, Soo-Hyung Kim. Improving lung cancer survival analysis from CT images by saliency sampling [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-038.

Survival time and distance to access specialized treatment among people living with HIV/Aids in the state of Alagoas, Brazil.

To evaluate the effect of the distance between the place of residence and the specialized treatment service on the survival time of people living with HIV/Aids in Alagoas, Northeast Brazil. Data from the notification and mortality systems related to individuals aged 13 years or older diagnosed with the infection between 2007 and 2013 were used. The cases were observed for a period of follow-up until December 2017. For the analyses, the Pearson's χ2 test, Kaplan-Meier method, and Cox regression were adopted according to the outcome of the case, place of residence, distance to the health unit, population size of the municipality of residence, sex, skin color/ethnicity, and age. Of the 2,732 analyzed cases, 760 individuals died of Aids-related causes. The average estimate of survival time for individuals residing in the capital was 98.6 months (95%CI 96.1-101). Among residents of inland cities, the estimate was 92.7 months (95%CI 89.3-96.1). There was a significant difference in curves throughout the period. The group residing in inland municipalities and those traveling a distance of >70 km had a higher average relative risk of death (RR=1.21, 95%CI 1.05-1.4 and RR=1.18, 95%CI 1.01-1.39, respectively). Living in or near the capital decreases the average relative risk of death. In order to increase the survival time of HIV/Aids patients in Alagoas, it is suggested to decentralize specialized health care, that is, to create regional centers to care for these people.

Impact of Mechanical Circulatory Support and Heart Transplantation on Survival of the Population of Transplant Eligible Advanced Heart Failure Patients - A Computer Simulation Study

Long term survival after heart transplantation is better than after implantation of VAD, however, the reported survival curves do not represent the whole population of transplant eligible patients, where some die while waiting for the transplantation and do not include the time on the waiting list. The aim of the present study was to use computer simulation model to assess the impact of left ventricular assist device implantation, heart transplantation and combination of both on the overall survival of a group of transplant eligible patients. A computer model was made in Mathematica v12.0 (Wolfram Research, Champaign, IL, USA) to simulate overall survival of a group of virtual patients eligible for heart transplantation in different scenarios, by using Seattle heart failure model score to define baseline survival and published survival curves after heart transplantation, ventricular assist device implantation and time to transplantation to generate individual survival times for each virtual patient. Computer generated data were compared with survival of the transplant eligible patients treated at the University Medical Center Ljubljana, Slovenia. Kaplan-Meier survival curves are presented for baseline, combined waiting list and heart transplantation, waiting list, combined waiting list and high-risk ventricular assist device implantation and combined ventricular assist device implantation and heart transplantation survival. The model has shown that overall group survival after heart transplantation only is much worse than published post-transplant survival due to waiting list mortality. Waiting list mortality is lower than true baseline mortality due to informative censoring. Ventricular assist device implantation is beneficial in higher risk patients but can do more harm in lower risk patients. The combined use of ventricular assist device implantation and heart transplantation has the greatest impact on survival, by decreasing early mortality with ventricular assist device and increasing the long-term survival with transplantation.