Survival Analysis Models Research Articles

Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data.

Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2months prior to treatment and once within 10months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling.

Prognostic implications of STK11 with different mutation status and its relationship with tumor-infiltrating immune cells in non-small cell lung cancer.

Mutations in STK11 (STK11Mut) gene may present a negative impact on survival in Non-small Cell Lung Cancer (NSCLC) patients, however, its relationship with immune related genes remains unclear. This study is to unveil whether overexpressed- and mutated-STK11 impact survival in NSCLC and to explore whether immune related genes (IRGs) are involved in STK11 mutations. 188 NSCLC patients with intact formalin-fixed paraffin-embedded (FFPE) tissue available for detecting STK11 protein expression were included in the analysis. After immunohistochemical detection of STK11 protein, patients were divided into high STK11 expression group (STK11High) and low STK11 expression group (STK11Low), and then Kaplan-Meier survival analysis and COX proportional hazards model were used to compare the overall survival (OS) and progression-free survival (PFS) of the two groups of patients. In addition, the mutation data from the TCGA database was used to categorize the NSCLC population, namely STK11 Mutated (STK11Mut) and wild-type (STK11Wt) subgroups. The difference in OS between STK11Mut and STK11Wt was compared. Finally, bioinformatics analysis was used to compare the differences in IRGs expression between STK11Mut and STK11Wt populations. The median follow-up time was 51.0 months (range 3.0 - 120.0 months) for real-life cohort. At the end of follow-up, 64.36% (121/188) of patients experienced recurrence or metastasis. 64.89% (122/188) of patients ended up in cancer-related death. High expression of STK11 was a significant protective factor for NSCLC patients, both in terms of PFS [HR=0.42, 95% CI= (0.29-0.61), P<0.001] and OS [HR=0.36, 95% CI= (0.25, 0.53), P<0.001], which was consistent with the finding in TCGA cohorts [HR=0.76, 95%CI= (0.65, 0.88), P<0.001 HR=0.76, 95%CI= (0.65, 0.88), P<0.001]. In TCGA cohort, STK11 mutation was a significant risk factor for NSCLC in both lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) histology in terms of OS [HR=6.81, 95%CI= (2.16, 21.53), P<0.001; HR=1.50, 95%CI= (1.00, 2.26), P=0.051, respectively]. Furthermore, 7 IRGs, namely CALCA, BMP6, S100P, THPO, CGA, PCSK1 and MUC5AC, were found significantly overexpressed in STK11-mutated NSCLC in both LUSC and LUAD histology. Low STK11 expression at protein level and presence of STK11 mutation were associated with poor prognosis in NSCLC, and mutated STK11 might probably alter the expression IRGs profiling.

Banknote Life in India: A Survival Analysis Approach

India’s currency-to-GDP ratio indicates a strong and persistent demand for cash. Despite the withdrawal of two high-value banknotes in 2016 and the recent economic contraction due to the COVID-19 pandemic, cash continues to rule in India. Although there is extensive work on the demand for cash in India, relatively little is known about the quality and supply of banknotes. This paper provides an overview of currency management policies in India using publicly available annual aggregate issuances and disposals data from the Reserve Bank of India (RBI). We model the life of a banknote using survival analysis models, finding that low-value banknotes (INR 10 and 20) have a median life of 4–5 years. The estimates of longevity of a banknote are significantly associated with velocity of circulation as well as exogenous shocks. We demonstrate the value of survival analysis methods in informing currency management policies in India and providing avenues for future work in this domain.

LGR6 is a prognostic biomarker for less differentiated tumors in lymph nodes of colon cancer patients.

The aim was to investigate whether the stem cell marker LGR6 has prognostic value in colon cancer, alone or in combination with the prognostic biomarkers CEA and CXCL16. LGR6 mRNA levels were determined in 370 half lymph nodes of 121 colon cancer patients. Ability to predict relapse after curative surgery was estimated by Kaplan-Meier survival model and Cox regression analyses. Patients with high LGR6 levels [LGR6(+)] had a decreased mean survival time of 11 months at 5-year follow-up and 47 months at 12-year follow-up, respectively, with hazard ratios of 3.2 and 2.8. LGR6 mRNA analysis added prognostic value to CEA and CXCL16 mRNA analysis. In the poor prognosis groups CEA(+) and CXCL16(+), further division was achieved by LGR6 analysis. LGR6(+) patients had a very poor prognosis. LGR6 also identified a small number of CEA(-), TNM stage I patients who relapsed suggesting stem cell origin of these tumors. LGR6 and LGR5 levels correlated strongly in lymph nodes of stage I and IV patients but not in stage II patients, suggesting that these stem cell markers are differentially regulated. This study highlights LGR6 as a useful prognostic biomarker independently and in combination with CEA, CXCL16 or LGR5 identifying different risk groups.

Cost-effectiveness of adding quizartinib to induction chemotherapy for patients with FLT3-mutant acute myeloid leukemia

The FLT3 inhibitor quizartinib has been shown to improve overall survival when added to intensive induction chemotherapy (“7 + 3”) in patients 18-75 years old with newly diagnosed AML harboring a FLT3-ITD mutation. However, the health economic implications of this approval are unknown. We evaluated the cost-effectiveness of quizartinib using a partitioned survival analysis model. One-way and probabilistic sensitivity analyses were conducted. In the base case scenario, the addition of quizartinib to 7 + 3 resulted in incremental costs of $289,932 compared with 7 + 3 alone. With an incremental gain of 0.84 quality-adjusted life years (QALYs) with quizartinib + 7 + 3 induction vs. 7 + 3 alone, the incremental cost-effectiveness ratio for the addition of quizartinib to standard 7 + 3 was $344,039/QALY. Only an 87% reduction in the average wholesale price of quizartinib or omitting quizartinib continuation therapy after completion of consolidation therapy and allogeneic hematopoietic cell transplant would make quizartinib a cost-effective option.

Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data

Interval-censored data frequently arise in various biomedical areas involving periodical follow-ups where the failure or event time of interest cannot be observed exactly but is only known to fall into a time interval. This article considers a semiparametric probit regression model, a valuable alternative to other commonly used semiparametric models in survival analysis, to investigate potential risk factors for the interval-censored failure time of interest. We develop an expectation-maximization (EM) algorithm to conduct the pseudo maximum likelihood estimation (MLE) using the working independence strategy for general or mixed-case interval-censored data. The resulting estimators of regression parameters are shown to be consistent and asymptotically normal with the empirical process techniques. In addition, we propose a novel penalized EM algorithm for simultaneously achieving variable selection and parameter estimation in the case of high-dimensional covariates. The proposed variable selection method can be readily implemented with some existing software and considerably reduces the estimation error of the proposed pseudo-MLE approach. Simulation studies demonstrate the satisfactory performance of the proposed methods. An application to a set of interval-censored data on prostate cancer further confirms the utility of the methodology. Supplementary materials for this article are available online.

A randomized controlled trial evaluating the efficacy of systemic ceftiofur administration for metritis therapy in dairy cows and the effect of metritis cure on economically important outcomes

The main objective of this study was to evaluate the effect of ceftiofur on metritis cure, milk yield, reproductive performance, and culling up to 300 DIM. The secondary objective was to evaluate the effect of metritis cure at 5 (early cure [ECURE]) and 14 (late cure [LCURE]) days after diagnosis on milk production, reproduction, and culling. A total of 422 Holstein cows diagnosed with metritis from 4 herds located in Texas, California, and Florida were enrolled in a randomized clinical trial. Cows diagnosed with metritis (fetid, watery, reddish or brownish uterine discharge) were blocked by herd and parity and were randomly allocated to receive systemic administration of ceftiofur (CEF) or to remain untreated (CON). In addition, 399 nonmetritic cows (NMET) were included for comparison purposes. Metritis cure was evaluated at 5 and 14 d after diagnosis and was defined as the absence of metritis clinical signs. Logistic regression models were fitted to the data to assess the effect of treatment on metritis cure. Milk yield was analyzed using a mixed linear model, while logistic regression, Cox proportional hazard, and Kaplan-Meier survival analysis models were fitted to culling and reproduction data. Cows treated with CEF had 1.86 (95% CI: 1.22-2.81) and 1.68 (95% CI: 1.02-2.75) greater odds of being cured than CON cows at 5 and 14 d after diagnosis, respectively. No effect of CEF was observed for milk yield; however, NMET cows had greater milk yield compared with metritic cows (CEF = 36.0, 95% CI = 33.8-38.1; CON = 36.1, 95% CI = 33.9-38.2; NMET = 36.9 kg/d, 95% CI = 34.8-39.4). Likewise, no effect of CEF was observed on reproductive performance and culling. Nonetheless, the likelihood of conceiving for NMET cows was 1.72 (95% CI = 1.41-2.12) and 1.64 (95% CI = 1.33-2.00) times greater than for CEF and CON cows, respectively. Ceftiofur-treated and CON cows had 2.93 (95% CI = 1.90-4.51) and 2.37 (95% CI = 1.51-3.71) greater hazard of culling compared with NMET, respectively. Regardless of treatment, no differences between ECURE and LCURE were observed on milk yield, reproduction, and culling throughout the entire lactation, but cows that cured at 5 or 14 d after diagnosis had greater milk production in the first 60 DIM compared with cows that did not cure (NCURE). Cows in ECURE and LCURE also had a 1.59 (95% CI = 1.16-2.16) and 1.49 (95% CI = 1.08-2.05) greater hazard of pregnancy and 0.43 (95% CI = 0.26-0.71) and 0.56 (95% CI = 0.34-0.92) hazard of culling compared with NCURE. Ceftiofur therapy increased metritis cure, but benefits to productivity and longevity were not observed. Also, cows that fail to cure have impaired lactation performance, but no differences regarding timing of cure were observed.

Analysis of predictors of clinical pregnancy and live birth in patients with RIF treated with IVF-ET technology: a cohort study based on a propensity score approach.

To investigate the predictors of clinical pregnancy and live birth rate in patients with recurrent embryo implantation failure (RIF) treated with in vitro fertilization-embryo transfer (IVF-ET) technique. This retrospective cohort study was conducted in Jinjiang District Maternal and Child Health Hospital, Chengdu City, Sichuan Province, China. Patients were recruited who were enrolled at this hospital between November 1, 2019 and August 31, 2022, and who met the following criteria: a frozen embryo transfer (FET) at day 5 or 6 blastocyst stage was performed and the number of transfer cycles was not less than two. We collected information on age, height, weight, number of embryo transfer cycles, and information related to clinical outcomes. We used the group of patients who underwent ERA testing as the study group and those who underwent FET only as the control group, and matched baseline characteristics between the two groups by propensity score to make them comparable. We compared the differences in clinical outcomes between the two groups and further explored predictors of pregnancy and live birth using survival analysis and COX regression modeling. The success rate of clinical pregnancy in RIF patients was 50.74% and the live birth rate was 33.09%. Patients in the FET group were less likely to achieve clinical pregnancy compared to the ERA group (HR = 0.788, 95%CI 0.593-0.978, p < 0.05). Patients with >3 previous implantation failures had a lower probability of achieving a clinical pregnancy (HR = 0.058, 95%CI 0.026-0.128, p < 0.05) and a lower likelihood of a live birth (HR = 0.055, 95%CI 0.019-0.160, p < 0.05), compared to patients with ≤3 previous implantation failures. Patients who had two embryos transferred were more likely to achieve a clinical pregnancy (HR = 1.357, 95%CI 1.079-1.889, p < 0.05) and a higher likelihood of a live birth (HR = 1.845, 95%CI 1.170-2.910, p < 0.05) than patients who had a single embryo transfer. Patients with concomitant high-quality embryo transfer were more likely to achieve a clinical pregnancy compared to those without high-quality embryo transfer (HR = 1.917, 95%CI 1.225-1.863, p < 0.05). Not receiving an ERA, having >3 previous implantation failures, using single embryo transfer and not transferring quality embryos are predictors for clinical pregnancy in patients with RIF. Having>3 previous implantation failures and using single embryo transfer were predictors for live birth in patients with RIF.

Similar Medium-Term Revision Rates Following Acute Total Hip Arthroplasty Versus Open Reduction and Internal Fixation for Acetabular Fractures in the Elderly.

The management of elderly acetabular fractures is complex, with high rates of conversion total hip arthroplasty (THA) after open reduction and internal fixation (ORIF), but potentially higher rates of complications after acute THA. The California Office of Statewide Health Planning and Development database was queried between 2010 and 2017 for all patients aged 60 years or older who sustained a closed, isolated acetabular fracture and underwent ORIF, THA, or a combination. Chi-square tests and Student t tests were used to identify demographic differences between groups. Multivariate regression was used to evaluate predictors of 30-day readmission and 90-day complications. Kaplan-Meier (KM) survival analysis and Cox proportional hazards model were used to estimate the revision surgery-free survival (revision-free survival [RFS]), with revision surgery defined as conversion THA, revision ORIF, or revision THA. A total of 2,184 surgically managed acetabular fractures in elderly patients were identified, with 1,637 (75.0%) undergoing ORIF and 547 (25.0%) undergoing THA with or without ORIF. Median follow-up was 295 days (interquartile range, 13 to 1720 days). 99.4% of revisions following ORIF were for conversion arthroplasty. Unadjusted KM analysis showed no difference in RFS between ORIF and THA (log-rank test P = 0.27). RFS for ORIF patients was 95.1%, 85.8%, 78.3%, and 71.4% at 6, 12, 24 and 60 months, respectively. RFS for THA patients was 91.6%, 88.9%, 87.2%, and 78.8% at 6, 12, 24 and 60 months, respectively. Roughly 50% of revisions occurred within the first year postoperatively (49% for ORIF, 52% for THA). In propensity score-matched analysis, there was no difference between RFS on KM analysis ( P = 0.22). No difference was observed in medium-term RFS between acute THA and ORIF for elderly acetabular fractures in California. Revision surgeries for either conversion or revision THA were relatively common in both groups, with roughly half of all revisions occurring within the first year postoperatively. III.

Survival analysis of freezing stress in the North American native perennial flax, Linum lewisii Pursh

Abstract The expansive range of Lewis flax (Linum lewisii Pursh.), an herbaceous perennial, exposes the species to a diversity of climatic conditions. As interest in the domestication and adoption of perennial crop alternatives grows and interest in this species for natural area restoration continues, the assurance of a commercial plant variety’s ability to endure the full range of possible climatic extremes is paramount. This study examines the freezing tolerance of a geographically representative sampling of 44 Lewis flax accessions at winter temperature extremes experienced in the northern Great Plains of the United States. Survival analysis models were adapted to include temperature exposure, in replacement of ordinal time typically used in such models, to produce statistics evaluating reactions to extreme temperatures that Lewis flax would encounter in our field environments. Our results revealed Lewis flax is more freezing tolerant than previously reported, and revealed four accessions with significantly superior genetic freezing tolerance than the released ‘Maple Grove’ cultivar. Furthermore, regrowth analyses indicate variation among accessions not associated with survival, which could lead to improving regrowth rate and survival simultaneously. These findings and their methodology expand the understanding of Lewis flax adaptation for winter hardiness and offer an efficient, new model that can be used to evaluate freezing tolerance at ordinal temperatures without requiring extensive prior physiological knowledge for a species.

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.

On an $\left( \iota ,x_{0}\right) $-Generalized Logistic-Type Function

In this article, some mathematical properties of $\left( \iota ,x_{0}\right) $-generalized logistic-type function are presented. This four-parameter generalized function can be considered as a statistical phenomenon enhancing more vigorous survival analysis models. Moreover, the behaviors of the relevant parametric functions obtained are examined with graphics using computer programming language Python 3.9.

Risk analysis of the association between different hemoglobin glycation index and poor prognosis in critical patients with coronary heart disease-A study based on the MIMIC-IV database

BackgroundThe hemoglobin glycation index (HGI) is the difference between the observed and predicted values of glycosylated hemoglobin (HbA1c), which is closely associated with a variety of poor prognoses. However, there are still no studies on the correlation between HGI and poor prognosis in patients with critical coronary artery disease. The purpose of this study was to analyze the correlation between HGI and all-cause mortality in patients with critical coronary artery disease using the MIMIC-IV database.MethodsThe HGI was calculated by constructing a linear regression equation between HbA1c and fasting plasma glucose (FPG). A Kaplan‒Meier survival analysis model was constructed based on the HGI quartiles to clarify the differences in all-cause mortality rates between groups, and the log-rank test was used to assess the differences between groups. The hazard ratio (HR) of HGI as a risk factor for outcome events was assessed using the Cox proportional risk model and restricted cubic spline (RCS), with the Q2 group serving as the reference group.ResultsA total of 5260 patients were included in this study. The 30-day mortality rate of the patients was 4.94% and the mortality rate within 365 days was 13.12%. A low HGI was significantly associated with 30-day mortality (HR, 1.96; 95% CI, (1.38, 2.78); P < 0.001) and 365-day mortality (HR, 1.48; 95% CI, (1.19, 1.85); P < 0.001) in patients with critical coronary artery disease in the completely adjusted Cox proportional risk model. In addition, high levels of HGI were associated with 365-day mortality (HR, 1.31; 95% CI, (1.02, 1.69); P < 0.05). RCS analysis revealed a U-shaped relationship between HGI and outcome events. According to the stratified analysis, the interaction test revealed that the correlation between HGI and outcome events remained stable.ConclusionThere was a significant correlation between HGI and all-cause mortality in patients with critical coronary artery disease, particularly in those with low HGI. HGI can be used as a potential indicator for assessing the short- and long-term risk of mortality in such patients.

Prediction and decision-making based on nonlinear risks model in stomach cancer treatment

Objectives. The goals are to develop a nonlinear risk model and examine its prediction applicability for clinical use.Methods. Methods of survival analysis and regression statistical models were used.Results. A practical approach to assessing nonlinear risks of adverse events using the example of gastric cancer treatment is proposed. A model for predicting the metachronous peritoneal dissemination in patients undergoing radical surgery for gastric cancer was proposed and studied. Assessment of risks for various periods of observation was performed, and the clinical suitability of developed approach was assessed.Conclusion. In clinical oncological practice, not only timely treatment plays an important role, but also the prevention of adverse outcomes after treatment. Individualization of patient monitoring after treatment reduces the risks of fatal outcomes and the costs of additional research and treatment in the event of cancer progression. Based on the results of this study, we propose solutions that should lead to more effective and high-quality treatment tactics and follow-up after treatment for gastric cancer, also to the selection of optimal approaches and to obtaining clinically favorable outcomes of the disease. The proposed risk prediction method will ultimately lead to individualized patient management based on the results of personal data.

Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study.

This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients. Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability. A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70). An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

The characteristic and prognostic role of blood inflammatory markers in patients with Huntington's disease from China.

This study aims to elucidate the role of peripheral inflammation in Huntington's disease (HD) by examining the correlation of peripheral inflammatory markers with clinical manifestations and disease prognosis. This investigation involved 92 HD patients and 92 matched healthy controls (HCs). We quantified various peripheral inflammatory markers and calculated their derived metrics including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). Clinical assessments spanning cognitive, motor, and disease severity were administered. Comparative analysis of inflammatory markers and clinical correlations between HD and controls was performed. Kaplan-Meier survival analysis and Cox regression model were used to assess the effect of inflammatory markers on survival. The study revealed that HD patients had significantly reduced lymphocyte counts, and LMR. Conversely, NLR, PLR, and SII were elevated compared to HCs. Lymphocyte levels inversely correlated with the age of onset and monocyte levels inversely correlated with the UHDRS-total functional capacity (TFC) scores. After adjusting for age, sex, and CAG repeat length, lymphocyte count, NLR, PLR, and SII were significantly correlated with the progression rate of TFC scores. Elevated levels of white blood cells and monocytes were associated with an increased risk of disability and mortality in the HD cohort. Our findings indicate that HD patients display a distinct peripheral inflammatory profile with increased NLR, PLR, and SII levels compared to HCs. The peripheral inflammation appears to be linked with accelerated disease progression and decreased survival in HD.

Abstract 5065: Integrating blood based liquid biopsies with TNM stage improves survival prediction model for non-small cell lung cancer

Abstract Introduction:Stage is the most important prognostic factor for survival in non-small cell lung cancer (NSCLC). The current TNM (Tumor, Node, Metastasis) staging system for NSCLC uses physical exam, histology, imaging, and surgical findings to define the extent and spread of a patient’s cancer. Despite advances in molecular testing which can impact treatment and prognosis for NSCLC patients, the current system does not incorporate molecular data into staging. We proposed a novel predictive model for survival analysis which integrates molecular analysis of liquid biopsies (B) into the TNM staging system (1). In this study, we tested this new staging system and our predictive model shows TNM + B (TNMB) provides a better predictive model for survival compared to TNM alone. Methods:176 patients were identified at Atrium Health Wake Forest Baptist with Guardant 360 liquid biopsies sent at diagnosis for NSCLC. Clinical data was obtained through retrospective chart review. Molecular analysis was performed on 81 genes across all Guardant 360 liquid biopsies. Using Cox proportional hazards model, we identified significant gene mutations or genes with significant magnitude of variant allele frequency (VAF) that improved survival prediction. We then designed a novel staging system incorporating whether a patient had a positive liquid biopsy test (B1), defined by the presence of these impact gene variables, to TNM stage. Results: Three impact genes STK11 (p=0.0253), NFE2L2 (p = 0.0025), TP53 (p=0.0129) and one gene with a magnitude of VAF, ARID1A (p=0.0082) was identified which significantly improved the predictive model for survival. Patients with negative liquid biopsy test, B0, had improved survival compared to patients with a positive liquid biopsy test, B1 (p&amp;lt;0.005). In our cohort, TNM staging alone did not show a significant survival difference between stage II and III (p = 0.19). Whereas TNMB staging showed a significant survival difference between stage II, III and IV. Conclusions: While TNM stage remains a major prognostic factor for NSCLC patients, it fails to incorporate molecular data which has a critical impact on management and prognosis. We designed a novel method to analyze molecular data obtained by liquid biopsy to identify significant gene mutations and gene magnitude of VAF that impacts survival prediction. In our cohort, we showed 4 impact genes which we used to create a new TNMB staging system which led to improved survival prediction compared to TNM alone. Acknowledgments: Research partly supported by NIH T32 Grant (T32CA247819) and Cancer Center Support Grant (P30CA012197) Reference: (1) Yang M, Forbes ME, Bitting RL, O'Neill SS, Chou PC, Topaloglu U, Miller LD, Hawkins GA, Grant SC, DeYoung BR, Petty WJ, Chen K, Pasche BC, Zhang W. Incorporating Blood-based Liquid Biopsy Information into Cancer Staging: Time for a TNMB System? Ann Oncol. 2018 Feb 1;29(2):311-323. Citation Format: Lauren Schmalz, Cetin Urtis, Liang Liu, Elizabeth Forbes, Fang-Chi Hsu, Nury Steuerwald, Alberto de Hoyos, Thomas Lycan, Jimmy Ruiz, William Petty, Wei Zhang. Integrating blood based liquid biopsies with TNM stage improves survival prediction model for non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5065.

ResDeepSurv: A Survival Model for Deep Neural Networks Based on Residual Blocks and Self-attention Mechanism.

Survival analysis, as a widely used method for analyzing and predicting the timing of event occurrence, plays a crucial role in the medicine field. Medical professionals utilize survival models to gain insight into the effects of patient covariates on the disease, and the correlation with the effectiveness of different treatment strategies. This knowledge is essential for the development of treatment plans and the enhancement of treatment approaches. Conventional survival models, such as the Cox proportional hazards model, require a significant amount of feature engineering or prior knowledge to facilitate personalized modeling. To address these limitations, we propose a novel residual-based self-attention deep neural network for survival modeling, called ResDeepSurv, which combines the benefits of neural networks and the Cox proportional hazards regression model. The model proposed in our study simulates the distribution of survival time and the correlation between covariates and outcomes, but does not impose strict assumptions on the basic distribution of survival data. This approach effectively accounts for both linear and nonlinear risk functions in survival data analysis. The performance of our model in analyzing survival data with various risk functions is on par with or even superior to that of other existing survival analysis methods. Furthermore, we validate the superior performance of our model in comparison to currently existing methods by evaluating multiple publicly available clinical datasets. Through this study, we prove the effectiveness of our proposed model in survival analysis, providing a promising alternative to traditional approaches. The application of deep learning techniques and the ability to capture complex relationships between covariates and survival outcomes without relying on extensive feature engineering make our model a valuable tool for personalized medicine and decision-making in clinical practice.

Survival analysis and prognostic model establishment of secondary osteosarcoma: a SEER-based study.

Surgical excision is considered one of the most effective treatments for secondary osteosarcoma (SO). It remains unclear whether the survival of patients with secondary osteosarcoma (SO) could be associated with their surgical willingness. The statistics of the patients diagnosed with SO between 1975 and 2008 were gathered from the surveillance epidemiology and end results (SEER) database. The patients were divided into three subgroups according to their surgical compliance. The authors used the multivariable Logistic regression analysis and cox regression method to reveal the influence of surgical compliance on prognosis and the risk factors of surgical compliance. Additionally, the authors formulated a nomogram model to predict the overall survival (OS) of patients. The concordance index (C-index) was used to evaluate the accuracy and practicability of the above prediction model. Sixty-three (9.2%) of the 688 patients with SO who were recommended for surgical treatment refused to undergo surgery. Lower surgical compliance can be ascribed to an earlier time of diagnosis and refusal of chemotherapy. The lower overall survival (OS) {[hazard ratio (HR)] 1.733, [CI] 1.205-2.494, P value [P]=0.003} of not surgical compliant patients was verified by the multivariate cox regression method, compared with surgical compliant patients. In addition, the discernibility of the nomogram model was proven to be relatively high (C-index=0.748), by which we can calibrate 3-year- and 5-year OS prediction plots to obtain good concordance to the actual situation. Surgical compliance was proved to be an independent prognostic factor in the survival of patients with SO.

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs.

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE-Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate's probability of HCV infection during the trial. The decision to recruit considers both the candidate's predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642-1010) to 278 (95%: 264-294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356-0.568) to 0.754 (95%: 0.685-0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642-1010) to 304 (95%: 288-322) while improving PPR to 0.807 (95%: 0.792-0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.