Predictor Of Survival Research Articles

Construction of a Cancer Stem Cell Marker Genes-Related 22-Gene Signature for Overall Survival Prediction in High-Risk Wilms' Tumor.

Background: This study aimed to investigate the comprehensive expression profile of cancer stem cell (CSC)-related genes and construct a prognostic signature for overall survival (OS) prediction in high-risk Wilms' tumor (WT). Materials and methods: Gene expression and survival data from 120 high-risk WT cases in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-WT were used. Results: In total, 229 CSC-related genes were found to be significantly dysregulated in WT compared to tumor-adjacent normal tissues, among which 34 were associated with OS. Using LASSO regression, a 22-gene signature was developed, which exhibited excellent performance in 3-, 5-, and 10-year OS predictions (AUC > 0.86). The high-risk score group showed markedly poorer OS compared to the low-risk score group (median separation, HR = 6.41, 95% CI: 3.18-12.92, p = 3.2e - 9). The 22-gene signature was an independent prognostic factor for OS (HR = 5.086, 95% CI: 3.019-8.568, p < 0.001). Conclusion: This study identified a robust prognostic signature that can effectively support OS prediction.

MiRNA expression as outcome predictor in pediatric AML: systematic evaluation of a new model

Accurately predicting patient outcomes is essential for optimizing treatment and improving outcomes in pediatric acute myeloid leukemia (AML). In recent years, microRNAs have emerged as a promising prognostic marker, with a growing body of evidence supporting their potential predictive value. We systematically reviewed all previous studies that have analyzed the expression of microRNAs as predictors of survival in pediatric AML and found 16 microRNAs and 4 microRNA signatures previously proposed as predictors of survival. We then used a public access cohort of 1414 pediatric AML patients from the TARGET project to develop a new predictive model using penalized lasso Cox regression based on microRNA expression. Here we propose a new score based on a 37-microRNA signature that is associated with AML and is able to predict survival more accurately than previous microRNA-based methods.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period

BackgroundSevere heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period.MethodsWe extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values.ResultsA total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality.ConclusionsThe survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Survival status and predictors of mortality among severely acute malnourished under-5 children admitted to stabilisation centers in selected government hospitals in Addis Ababa, Ethiopia, 2022: a retrospective cohort study

ObjectiveThis study aims to assess the survival status and predictors of mortality among under-5 children with severe acute malnutrition in Addis Ababa, Ethiopia.DesignA retrospective cohort study was employed on randomly...

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer

ObjectiveColorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer. MethodsConsensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer. ResultsThe prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT. ConclusionDifferences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non–small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the ‘right patient’ receives the ‘right treatment’. However, survival prediction is a highly complex task, given the large number of ‘omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient’s survival. We introduce a methodology for incorporating prior knowledge into machine learning–based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

The Glasgow Prognostic Score as a Predictor of Survival After Chemoradiotherapy for Limited-Disease Small Cell Lung Cancer.

Established biomarkers for predicting chemoradiotherapy efficacy for limited-disease small cell lung cancer (LD-SCLC) are lacking. The inflammation-based Glasgow Prognostic Score (GPS), comprising serum C-reactive protein (CRP) and albumin levels, can predict survival in advanced cancer. This study investigated whether metabolic and inflammatory markers, including the GPS, can predict the efficacy of chemoradiotherapy in patients with LD-SCLC. We retrospectively analyzed 124 patients who underwent chemoradiotherapy for LD-SCLC at two institutions between April 2007 and June 2021, and assessed the prognostic significance of various metabolic and inflammatory markers. The GPS was calculated using the CRP and albumin concentrations, and categorized as follows: 0, CRP&lt;1.0 mg/dL and albumin≥3.5 mg/dL; 1, elevated CRP or decreased albumin; and 2, CRP≥1.0 mg/dL and albumin&lt;3.5 mg/dL. Differences in progression-free survival (PFS) and overall survival (OS) were examined using Kaplan-Meier curves and Cox proportional-hazard models. The overall response rate was 95.1% (95% confidence interval [CI]: 89.6-97.9%). The median PFS and OS from chemoradiotherapy initiation were 12.6 (95% CI: 9.9-15.4) and 29.0 (95% CI: 24.8-45.5) months, respectively. The GPS demonstrated independent predictive ability for the effectiveness of chemoradiotherapy, wherein favorable scores (GPS 0-1) were significantly correlated with superior PFS and OS compared to unfavorable scores (GPS 2: PFS: 14.8 vs. 6.7 months, p=0.0001; OS: 35.4 vs. 11.0 months, p&lt;0.0001). This preliminary examination revealed that the GPS was significantly associated with PFS and OS in patients undergoing chemoradiotherapy for LD-SCLC, indicating its potential utility in assessing the therapeutic outcomes in LD-SCLC.

Assessment of survival prediction after surgery in spinal metastases patients using the Global Spine Study Tumor Group (GSTSG) risk calculator; an external validation from a tertiary cancer hospital.

We aim to validate the Global Spine Tumor Study Group (GSTSG) score compared to previous prognostic scoring systems in spinal metastasis. We conducted a retrospective study from January 2013 to December 2022. The survival prediction was compared between the GSTSG, Tomita Score, Revised Tokuhashi Score, and Skeletal Oncology Research Group (SORG) Nomogram. Single-variable factors associated with survival rate were analyzed using univariate Cox regression and multivariable Cox proportional hazard model. Receiver operating characteristic was used for external validity analysis at 3, 6, 12, and 24months. The overall survival rate was reported using the Kaplan-Meier survival curve. 248 spinal metastasis patients were included. The mean age was 59.23 ± 12.55years. The mean duration of follow-up time was 470.29 ± 441.98days. The external validity of GSTSG was the highest at all follow-up times (sufficiently accurate AUC > 0.7), which was about the same as SORG at 3months (both AUC of GSTSG and SORG = 0.76) and higher than modified Tokuhashi and Tomita score at 12months (AUC of GSTSG = 0.78, SORG = 0.71, Tomita = 0.64, and modified Tokuhashi = 0.61, respectively). From our study, the Multivariate Cox regression analysis indicates that the significant factors related to survival rate are regular analgesic use of weak opioids, lung metastasis, and previous chemotherapy. Compared to other traditional spinal metastases prognostic scoring systems, GSTSG shows the highest AUC for external validity in all follow-up times up to 24months.

A Kernel Attention-based Transformer Model for Survival Prediction of Heart Disease Patients.

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care.

Outcomes After Definitive Surgery for Spinal and Sacral Chordoma in 101 Patients Over 20 Years.

Spinal chordomas are primary bone tumors where surgery remains the primary treatment. However, their low incidence, lack of evidence, and late disease presentation make them challenging to manage. Here, we report the postoperative outcomes of a large cohort of patients after surgical resection, investigate predictors for overall survival (OS) and local recurrence-free survival (LRFS) times, and trend functional outcomes over multiple time periods. Retrospective review of all patients followed for spinal chordoma at a quaternary spinal oncology center from 2003 to 2023 was included. Data were collected regarding demographics, preoperative and perioperative management, and follow-up since initial definitive surgery. Primary outcomes were OS and LRFS, whereas secondary outcomes were functional deficits. One hundred one patients had an average follow-up of 6.0 ± 4.2 years. At the time of census, 25/101 (24.8%) had experienced a recurrence and 10/101 (9.9%) had died. After surgery, patients experienced a significant decrease in pain over time, but rates of sensory deficits, weakness, and bowel/bladder dysfunction remained static. Tumors ≥100 cm3 (hazard ratio (HR) = 5.89, 95% CI 1.72-20.18, P = .005) and mobile spine chordomas (HR = 7.73, 95% CI 2.09-28.59, P = .002) are related to worse LRFS, whereas having neoadjuvant radiotherapy is associated with improved LRFS (HR = 0.09, 95% CI 0.01-0.88, P = .038). On the other hand, being age ≥65 years was associated with decreased OS (HR = 16.70, 95% CI 1.54-181.28, P = .021). Surgeons must often weigh the pros and cons of en bloc resection and sacrificing important but affected native tissues. Our findings can provide a benchmark for counseling patients with spinal chordoma. Tumors ≥100 cm3 appear to have a 5.89-times higher risk of recurrence, mobile spine chordomas have a 7.73 times higher risk, and neoadjuvant radiotherapy confers an 11.1 times lower risk for local recurrence. Patients age ≥65 years at surgery have a 16.70 times higher risk of mortality than those <65 years.

Predictors of survival in immunotherapy-based treatments in advanced melanoma: a meta-analysis.

The introduction of immunotherapy-based strategies has significantly improved the prognosis for melanoma patients. Nevertheless, some patients still have dismal outcomes, emphasizing the significance of survival predictive indicators in immunotherapy-based approaches. We systematically searched randomized controlled clinical trials investigating dual immunotherapy or chemoimmunotherapy versus placebo or mono-immunotherapy or chemotherapy alone in advanced melanoma patients. R version 4.3.0. was employed to perform all analyses. A comprehensive analysis was conducted on a total of 13,809 patients with advanced melanoma from 19 randomized clinical trials. Immunotherapy-based strategies (alone or in combination) could significantly lengthen the overall survival(OS) and recurrence-free survival (RFS) compared with corresponding controls. Mono-immunotherapy improved RFS and OS in PD-L1 positive patients, in stage AJCC IIIC, and with 4 or morepositive lymph nodes, compared with chemotherapy. Combined immunotherapy statistically improved RFS and OS in those aged < 65, with an Eastern Cooperative Oncology Group (ECOG) status of 0, and LDH ≤ ULN at baseline compared with single treatment alone. Our findings indicated that certain clinicopathological and molecular features could assist in choosing appropriate melanoma patients for immune-based treatments.

MSFN: a multi-omics stacked fusion network for breast cancer survival prediction

Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge.Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multi-omics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction.Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.

Urine Output and Development of Acute Kidney Injury in Sepsis: A Multicenter Observational Study.

Acute kidney injury (AKI) is common in sepsis and a urine output <0.5 mL/kg/h associated with increased mortality is incorporated into AKI diagnosis. We aimed to identify the urine-output threshold associated with increased AKI incidence and hypothesized that a higher urine output than a specified threshold, which differs from the predominantly used 0.5 mL/kg/h threshold, would be associated with an increased AKI incidence. This was a post-hoc analysis of a nationwide prospective observational study. This study included adult patients newly diagnosed with sepsis and requiring intensive care. Urine output on the day of sepsis diagnosis was categorized as low, moderate, or high (<0.5, 0.5-1.0, and >1.0 mL/kg/h, respectively), and we compared AKI incidence, renal replacement therapy (RRT) requirement, and 28-day survival by category. Estimated probabilities for these outcomes were also compared after adjusting for patient background and hourly fluid administration. Among 172 eligible patients, AKI occurred in 46.3%, 48.3%, and 53.1% of those with high, moderate, and low urine output, respectively. The probability of AKI was lower in patients with high urine output than in those with low output (43.6% vs 56.5%; P = .028), whereas RRT requirement was lower in patients with high and moderate urine output (11.7% and 12.8% vs 49.1%; P < .001). Patients with low urine output demonstrated significantly lower survival (87.7% vs 82.8% and 67.8%; P = .018). Cubic spline curves for AKI, RRT, and survival prediction indicated different urine-output thresholds, including <1.2 to 1.3 mL/kg/h for AKI and <0.6 to 0.8 mL/kg/h for RRT and mortality risk. Urine output >1.0 mL/kg/h on the day of sepsis diagnosis was associated with lower AKI incidence. The urine-output threshold was higher for developing AKI than for RRT requirement or mortality.

Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression

Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression

Phase angle is a predictor of overall 5-year survival after head and neck cancer surgery

ObjectiveTo evaluate the ability of the variables Muscle Mass percentage (%MM), Body Fat percentage (%BF), Phase Angle (PhA), and Standardized Phase Angle (SPhA) to predict overall survival rates in individuals with surgically treated HNC. MethodsA prospective cohort study of surgically treated HNC patients was carried out between 2017 and 2022 at a cancer referral center in Feira de Santana, Bahia. All subjects were seen by an experienced dietitian the week before surgery. During this visit, patients had their sociodemographic and clinical data collected, as well as anthropometric measurements (weight and height) and BIA variables. In this study, postoperative overall 5-year survival was defined as the time in months between the date of surgery and the date of death, regardless of the cause of death. Patients were followed up after surgery until death. ResultsSeventy-eight older adult patients were consecutively included. Patients had a median age of 65.5-years, were mostly men (83.3%), and most had low education levels (62.3%) and low household income, ranging from zero (19.2%) to the minimum wage (60.2%). They were mostly alcohol drinkers (91%) and tobacco smokers (87.2%). Glottic cancer was diagnosed in 48.7% of patients, with 44.8% of cases with stage-IV disease. Forty (51.2%) deaths were recorded in the follow-up period, with a median survival time of 39 months. Variables %MM, %BF, and SPhA were not significantly different between groups (alive vs. dead patients). PhA was considered an independent predictor of overall survival, with an accuracy of 69% (95% CI 0.57‒0.80). When comparing survival curves, patients with a preoperative PhA < 6.8° were more than twice as likely to die during follow-up (HR = 2.38; p = 0.02; 95% CI 1.14‒4.97). ConclusionOut of the assessed BIA variables, preoperative PhA was considered a good predictor of overall survival after HNC surgery. Level of evidence3.

Preoperative Visceral-to-Subcutaneous Fat Ratio by Sex as a Predictor of Postoperative Survival in Patients With Gastric Cancer.

The sex-specific effect of the visceral-to-subcutaneous fat ratio (VSR) before gastrectomy on postoperative survival in patients with gastric cancer (GC) remains unclear. This study measured the preoperative VSR in patients with GC and analyzed its relationship with 5-year overall survival (OS) and relapse-free survival (RFS) by sex. This prospective study included 540 patients with GC undergoing gastrectomy. Preoperative visceral and subcutaneous fat volumes were measured using computed tomography, and the VSR was calculated. A cutoff value for the VSR was established using 5-year survival data, and its association with survival was analyzed using the Kaplan-Meier method, log-rank tests, and multivariate regression analysis. Among the 459 patients analyzed (300 males and 159 females), OS and RFS were significantly lower in the low-VSR group than in the high-VSR group in males (OS: 76.2% vs. 88.1%, p=0.01; RFS: 74.6% vs. 86.0%, p=0.02). In females, no difference in OS was observed between the groups, whereas the high-VSR group had significantly lower RFS than that of the low-VSR group (RFS: 74.7% vs. 88.9%, p=0.01). Multivariate analysis showed that a low VSR was an independent poor predictor of OS in males and a high VSR was an independent poor predictor of RFS in females. In patients with GC, the sex-dependent preoperative VSR was a potentially useful predictor of postoperative survival.

Comprehensive Assessment of Postoperative Recurrence and Survival in Patients with Cervical Cancer

BackgroundThe prediction of postoperative recurrence and survival in cervical cancer patients has been a major clinical challenge. The combination of clinical parameters, inflammatory markers, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and MRI-derived radiomics is expected to support the prediction of recurrence-free survival (RFS), disease-free survival (DFS), tumor-specific survival (CSS), and overall survival (OS) of cervical cancer patients after surgery. MethodsA retrospective analysis of 181 cervical cancer patients with continuous follow-up was completed. The parameters of IVIM-DWI and radiomics were measured, analyzed, and screened. The LASSO regularization was used to calculate the radiomics score (Rad-score). Multivariate Cox regression analysis was used to construct nomogram models for predicting postoperative RFS, DFS, CSS, and OS in cervical cancer patients, with internal and external validation. ResultsClinical stage, parametrial infiltration, internal irradiation, D-value, and Rad-score were independent prognostic factors for RFS; Squamous cell carcinoma antigen, internal irradiation, D-value, f-value and Rad-score were independent prognostic factors for DFS; Maximum tumor diameter, lymph node metastasis, platelets, D-value and Rad-score were independent prognostic factors for CSS; Lymph node metastasis, systemic inflammation response index, D-value and Rad-score were independent prognostic factors for OS. The AUCs of each model predicting RFS, DFS, CSS, and OS at 1, 3, and 5 years were 0.985, 0.929, 0.910 and 0.833, 0.818, 0.816 and 0.832, 0.863, 0.891 and 0.804, 0.812, 0.870, respectively. ConclusionsNomograms based on clinical and imaging parameters showed high clinical value in predicting postoperative RFS, DFS, CSS, and OS of cervical cancer patients and can be used as prognostic markers.

Prescription Medications and Overall Survival in Metastatic Hormone Sensitive Prostate Cancer.

With new therapies for metastatic prostate cancer, patients are living longer, increasing the need for better understanding of the impact of comorbid disease. Prescription medications may risk-stratify patients independent of established methods, such as the Charlson Comorbidity Index (CCI) and guide treatment selection. In a nationwide retrospective study of US Veterans, we used multivariable logistic regression and Cox proportional hazard modeling to evaluate the association between number and class of prescription medications and overall survival (OS) with age, race, body-mass index, prostate specific antigen (PSA), and Charlson comorbidities as covariates in veterans treated for de novo metastatic hormone sensitive prostate cancer (mHSPC) between 2010-2021. Among 8,434 Veterans, a median of nine medications and five medication classes were filled in the year prior to initial treatment with abiraterone or enzalutamide for mHSPC. Veterans on 1-4 medications had an average survival of 38 months compared to 5-9 medicines (33 months), 10-14 medicines (27 months), and 15+ medicines (22 months) (p<0.001). After adjusting for age, race, body mass index (BMI), PSA, CCI, and year of diagnosis, both the number of medications and medication classes were associated with increased mortality. The adjusted hazard ratio (aHR) [95% confidence interval (CI)] was 1.03 (1.02-1.03) for the number of medications and 1.05 (1.04-1.07) for medication classes. Medications within ATC B (blood/blood forming organs), ATC C (cardiovascular), and ATC N (nervous) were associated with worse OS, with aHRs of 1.14 (1.07, 1.21), 1.14 (1.06, 1.22), and 1.12 (1.06, 1.19), respectively. The number and class of medications were independently associated with overall survival in patients undergoing treatment for mHSPC. With new therapies for advanced prostate cancer, patients are living longer, highlighting the need for a better understanding of the impact of comorbid diseases. Simple methods to assess disease burden and prognosticate survival have the potential to guide treatment decisions.

Assessment of prognostic factors in patients with primary ocular adnexal lymphoma when considering competing risk elements.

Accurate prognostic factors for primary ocular adnexal lymphoma (POAL) are scarce. Survival models and prognostic factors derived without considering competing risk factors suffer from major statistical errors. This study aimed to accurately assess prognostic factors in POAL patients using competing risk models, and compare this to the traditional COX proportional hazards model. This retrospective study utilised data from the Surveillance, Epidemiology, and End Results (SEER) program 2010-2015 and included patients with B-cell POAL. The cumulative incidence function and Gray's test for cause-specific survival were calculated as univariate analysis. The competing risk models were a Fine-Gray subdistribution hazard model and a cause-specific model, and a traditional COX model was employed as a multivariate analysis. This study enrolled 846 eligible patients with POAL: 60 patients (7.09%) died from POAL and 123 patients (14.54%) died from other causes. Multivariate competing risk models indicated that age, laterality, histology subtype, the 7th edition of American Joint Committee on Cancer stage T, and radiotherapy were independent predictors for cause-specific survival of patients with POAL. There was high consistency between the two competing risk models. The COX model made several misestimations on the statistical significance and hazard ratios of prognostic factors. This study established competing risk models as a method to assess POAL prognostic factors, which was more accurate than traditional methods that do not consider competing risk elements.

Somatic Mutation Profile as a Predictor of Treatment Response and Survival in Unresectable Pancreatic Ductal Adenocarcinoma Treated with FOLFIRINOX and Gemcitabine Nab-Paclitaxel

Somatic Mutation Profile as a Predictor of Treatment Response and Survival in Unresectable Pancreatic Ductal Adenocarcinoma Treated with FOLFIRINOX and Gemcitabine Nab-Paclitaxel