External Validation of the International Prognosis Prediction Model of IgA Nephropathy

BACKGROUND

The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort.

METHODS

Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient's risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses.

RESULTS

The median follow-up duration within our cohort spanned 5.1 years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models.

CONCLUSIONS

Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility.

RMST-based multiple contrast tests in general factorial designs

Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area under the survival curve up to a prespecified time point and, thus, summarizes the survival curve into a meaningful estimand. For two-sample comparisons based on the RMST, previous research found the inflation of the type I error of the asymptotic test for small samples and, therefore, a two-sample permutation test has already been developed. The first goal of the present paper is to further extend the permutation test for general factorial designs and general contrast hypotheses by considering a Wald-type test statistic and its asymptotic behavior. Additionally, a groupwise bootstrap approach is considered. Moreover, when a global test detects a significant difference by comparing the RMSTs of more than two groups, it is of interest which specific RMST differences cause the result. However, global tests do not provide this information. Therefore, multiple tests for the RMST are developed in a second step to infer several null hypotheses simultaneously. Hereby, the asymptotically exact dependence structure between the local test statistics is incorporated to gain more power. Finally, the small sample performance of the proposed global and multiple testing procedures is analyzed in simulations and illustrated in a real data example.

A Nationwide Analysis of Head and Neck Fibromatoses

BACKGROUND

Head and neck fibromatoses (HNFs) are a rare, diverse group of soft tissue tumors characterized by an abnormal proliferation of fibroblasts. Available literature on these tumors is limited to case reports and small single-institutional studies.

OBJECTIVE

We aim to provide demographic, socioeconomic, tumor-related, and treatment characteristics of HNFs.

DESIGN

Retrospective cohort analysis using the National Cancer Database (NCDB).

METHODS

The NCDB was queried for fibromatosis-related histologic codes located within the head and neck region. Various factors were analyzed. Univariate and multivariate survival analyses were performed.

RESULTS

Between 2004 and 2016, 130 patients were included in the analyses. Average age was 57.4 years old with a predominance of White (83.6%) males (61.5%). Non-desmoid HNFs accounted for 60%-70% of the tumors. The salivary gland was the most common location (38.5%) and more than half of the tumors were high grade. The majority were treated surgically (90.8%) and 25% had positive margins. Mean and median overall survival (OS) were 98.9 and 135.4 months, respectively. Surgery is associated with better OS than nonsurgical alternatives. Addition of adjuvant treatments was not associated with differences in survival.

CONCLUSION

In the largest study to date, we describe demographic, socioeconomic, tumor-related, and treatment patterns of patients with this rare disease. These tumors are most frequently present in middle-aged males with high-grade histology. Most are treated surgically and positive surgical margins are common. Surgery has better OS than nonsurgical alternatives. While adjuvant radiation has become more common, we found no difference in survival compared to surgery alone.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:2228-2235, 2024.

Estimation of median survival time and its 95% confidence interval using SAS PROC LIFETEST

Estimation of median survival and its 95% confidence interval depends on the choice of the survival function, standard error, and a method for constructing the confidence interval. This paper outlines several available possibilities in SAS® (version 9.4) PROC LIFETEST and compares them on theoretical grounds and using simulated data, with criteria: ability to estimate the 95% CI, coverage probability, interval width, and appropriateness for practical use. Data are generated with varying hazard patterns, N, % censoring, and censoring patterns (early, uniform, late, last visit). LIFETEST was run using the Kaplan-Meier and Nelson-Aalen estimators and the transformations available (linear, log, logit, complementary log-log, and arcsine square root). Using the Kaplan-Meier estimator with the logarithmic transformation as well as with the logit leads to a high frequency of LIFETEST not being able to estimate the 95% CI. The combination of Kaplan-Meier with the linear transformation is associated with poor coverage achieved. For small samples, late/last visit censoring has a negative effect on being able to estimate the 95% CI. Heavy early censoring can lead to low coverage of the 95% CI of median survival for sample sizes up to and including N = 40. The two combinations that are optimal for being able to estimate the 95% CI and having adequate coverage are the Kaplan-Meier estimator with complementary log-log transformation, and the Nelson-Aalen estimator with linear transformation. The former fares best on the third criterion (smaller width) and is also the SAS® default and validates the choice of default.

Identification of Subtypes in Triple-negative Breast Cancer Based on Shared Genes Between Immunity and Cancer Stemness

The correlation between triple-negative breast cancer (TNBC) and genes related to immunity and cancer stemness, particularly shared genes, remains unclear. This study aimed to investigate the correlation of immunity and cancer stemness with the molecular subtyping and survival rates in TNBC using bioinformatics approaches. Differential gene analysis was conducted to identify TNBC-associated differentially expressed genes (DEGs). Cancer stem cell (CSC)-related genes were obtained using weighted gene coexpression network analysis. Immune-related gene sets were retrieved from the literature. Venn analysis was performed to identify the shared DEGs between immunity and cancer stemness in TNBC. Cluster analysis and survival analysis based on the expression of these genes were conducted to identify TNBC subtypes with significant survival differences. A total of 5259 TNBC-associated DEGs, 2214 CSC-related genes, 1793 immune-related genes, and 44 shared DEGs between immunity and cancer stemness were obtained. Among them, 3 shared DEGs were closely associated with TNBC survival rates ( P <0.05). Cluster and survival analyses revealed that among 3 subtypes, cluster2 exhibited the best survival rate, and cluster3 showed the worst survival rate ( P <0.05). Dendritic cells were highly infiltrated in cluster2, while plasma cells and resting mast cells were highly infiltrated in cluster3 ( P <0.05). Genes shared by immunity and cancer stemness were capable of classifying TNBC samples. TNBC patients of different subtypes exhibited significant differences in immune profiles, genetic mutations, and drug sensitivity. These findings could provide new insights into the pathogenesis of TNBC, the immune microenvironment, and the selection of therapeutic targets for drug treatment.

High-tissue FRMD6 expression predicts better outcomes among colorectal cancer patients

INTRODUCTION

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths. The hippo pathway works as a regulator of organ growth and is often a target for mutations in cancer. Ferm domain containing protein 6 (FRMD6) is an activator of the hippo pathway. This study aimed to explore the role of FRMD6 in CRC and to determine how well it works as a prognostic factor among CRC patients.

METHODS

The tumor expression of FRMD6 was evaluated using immunohistochemistry in 538 colorectal patients operated on at Helsinki University Hospital. We assessed FRMD6 expression with clinicopathological parameters and the impact of FRMD6 expression on survival.

RESULTS

Patients with a high FRMD6 expression exhibited a better prognosis (univariable hazard ratio [HR] 0.58, 95% confidence interval [CI] 0.41-0.81), with a 5-year disease-specific survival (DSS) of 66.3%. By contrast, patients with a low FRMD6 expression had a 5-year DSS of 52.8%. A high FRMD6 expression level served as an independent predictor for better survival in the Cox multivariable survival analysis (HR 0.53, 95% CI 0.33-0.86).

DISCUSSION

To our knowledge, this is the first study to show that a high FRMD6 expression is an independent marker for a better prognosis in CRC and could help determine the prognosis for CRC patients.

Conditional survival to assess prognosis in patients with chronic lymphocytic leukemia

Biomarkers in chronic lymphocytic leukemia (CLL) allow assessment of prognosis. However, the validity of current prognostic biomarkers based on a single assessment point remains unclear for patients who have survived one or more years. Conditional survival (CS) studies that address how prognosis may change over time, especially in prognostic subgroups, are still rare. We performed CS analyses to estimate 5-year survival in 1-year increments, stratified by baseline disease characteristics and known risk factors in two community-based cohorts of CLL patients (Freiburg University Hospital (n = 316) and Augsburg University Hospital (n = 564)) diagnosed between 1984 and 2021. We demonstrate that 5-year CS probability is stable (app. 75%) for the entire CLL patient cohort over 10 years. While age, sex, and stage have no significant impact on CS, patients with high-risk disease features such as non-mutated IGHV, deletion 17p, and high-risk CLL-IPI have a significantly worse prognosis at diagnosis, and 5-year CS steadily decreases with each additional year survived. Our results confirm that CLL patients have a stable survival probability with excess mortality and that the prognosis of high-risk CLL patients declines over time. We infer that CS-based prognostic information is relevant for disease management and counseling of CLL patients.

Delayed diagnostic interval and survival outcomes in pediatric leukemia: A single-center, retrospective study

OBJECTIVE

This study primarily focused on the diagnostic interval (DI), defined as the duration from the onset of leukemic symptoms to diagnosis. We investigated whether a prolonged DI is associated with the outcomes of pediatric leukemia.

METHODS

We retrospectively collected data of children with newly diagnosed pediatric leukemia at Okayama University Hospital from January 2007 to December 2022. Survival analyses were conducted using Kaplan-Meier methods, and an unadjusted analysis to compare differences in survival was performed using the log-rank test.

RESULTS

In total, 103 children with leukemia were included in the analysis. The median DI was 20 days (interquartile range, 9.5-33.5 days). A prolonged DI (≥30 days) demonstrated no association with either 5-year event-free survival (70.1% for <30 days and 68.3% for ≥30 days, p = .99, log-rank test) or overall survival (84.7% for <30 days and 89.4% for ≥30 days, p = .85, log-rank test).

CONCLUSIONS

A prolonged DI was not associated with the survival of children with leukemia. If a precise classification of leukemia biology is provided for pediatric patients, a prolonged DI may have little impact on the prognosis of these patients.

Longevity in South African Afrikaner cows as assessed through survival analysis

The Afrikaner breed of cattle is indigenous to South Africa and, due to their hardiness, was once the most popular breed amongst South African farmers, although in recent years their numbers have decreased. The goal of this study was to assess factors affecting length of productive life, defined as the interval between production of the first and last calf, in Afrikaner cattle using survival analysis. The data spanned 40 years with an observed measure of length of life for 29,379 cows from 374 herds. Relative to similar analyses, few (n = 2964; 8.4%) cows had records that were right censored. The median length of productive life of an Afrikaner cow was just less than 6 years. Cows that were younger at their first parturition had longer productive lives than those that were older at their first calving. Cows that were born in the period from December to February had shorter productive lives than those born between March and November. The estimated animal genetic variance of 0.266 resulted in a heritability estimate for length of productive life in Afrikaner cattle of 0.225. Thus, there appeared to be sufficient additive genetic variance in Afrikaner cattle to enable genetic improvement in their length of productive life.

Robust best linear weighted estimator with missing covariates in survival analysis

Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators, but in general are more robust against model misspecification. In this article, we propose a robust best linear weighted estimator for Cox regression with missing covariates. Our proposed estimator is the projection of the simple inverse probability weighted estimator onto the orthogonal complement of the score space based on a working regression model of the observed data. The efficiency gain is from the use of the association between the survival outcome variable and the available covariates, which is the working regression model. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a colorectal cancer study to assess the association of the microsatellite instability status with colorectal cancer-specific mortality.

Derivative survival analyses: Analysis methods to derive survival outcomes for the remainder patient cohort without individual patient data

It is not uncommon for industry-sponsored randomized controlled trials to publish survival curves/data for the overall patient cohort("A+B") and for a favorable subgroup ("A") pre-specified or post hoc, but not the survival curves/data for the remainder cohort("B"). Consequently, following regulatory approval of the intervention treatment for the overall patient population if the primary endpoint is met, it is common for cancer patients representing the remainder cohort (B) to be treated as per the results of the overall cohort (A+B). To overcome this important issue in clinical decision-making, this study aimed to identify methods to accurately derive the survival curves and/or hazard ratio (95% confidence interval) for the remainder cohort (B), utilizing published curves and hazard ratios (95% confidence intervals) of the overall (A+B) and favorable subgroup (A) cohorts. The analysis methods (method I and method II) presented here, termed "derivative survival analyses," enable accurate assessment of survival outcomes in the remainder cohort without individual patient data.

Survival analysis of Sudanese oral squamous cell carcinoma patients with field of cancerization

BACKGROUND

The late presentation and diagnosis of OSCC account for the large number of patients with the advanced form of the disease. In Sudan, cases with delayed presentation, particularly those with risk factors such as Toombak dipping and alcohol consumption, frequently present with extensive lesions and a wide area of Field cancerization which characterized by the presence of genetic and epigenetic changes in histologically normal-appearing tissues, and have increased risk for recurrent and second primary tumors. This necessitates more aggressive treatment and is usually associated with poorer outcomes. The present study aims to investigate the survival of oral squamous cell carcinoma patients with a wide field of cancerization.

METHODS

This prospective longitudinal study includes ninety-three oral cancer patients with extensive fields of cancerization who underwent surgical treatment at Khartoum Teaching Dental Hospital (KTDH) conducted from 2019 to 2023. These patients were regularly assessed for clinical changes such as recurrence, the development of second primary tumours, and overall survival over a period of one year.

RESULTS

Out of the 93 patients, 57 (61.3%) were males, and 36 (38.7%) were females. The majority of the patients (82%) had stage IV tumours, and 62.3% had nodal metastasis. Twenty-eight (30%) patients developed recurrences, and 14 (15%) developed second primary tumours. The overall one-year survival rate was 89%, and all deceased patients passed away within 12 months. The survival rate for patients with different types of recurrences varied, with patients who had regional, local, and locoregional recurrences having survival rates of 87%, 74%, and 72%, respectively. Patients who did not experience a recurrence had a one-year survival rate of 92%. Patients who developed second primary tumours had an 86% survival rate. The survival rates for OSCC patients at stages III, IVa, and IVb were 90%, 90%, and 71%, respectively.

CONCLUSION

In this study, 62% of patients had nodal metastasis, 30% developed recurrence, and 15% developed second primary tumours. The overall one-year survival rate was 89%, although the development of recurrences and second primary tumours had a negative impact on the survival rate.

Characterizing quantile-varying covariate effects under the accelerated failure time model

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia.

Have the recent advancements in cancer therapy and survival benefitted patients of all age groups across the Nordic countries? NORDCAN survival analyses 2002-2021

BACKGROUND

Since the early 2000s, overall and site-specific cancer survival have improved substantially in the Nordic countries. We evaluated whether the improvements have been similar across countries, major cancer types, and age groups.

MATERIAL AND METHODS

Using population-based data from the five Nordic cancer registries recorded in the NORDCAN database, we included a cohort of 1,525,854 men and 1,378,470 women diagnosed with cancer (except non-melanoma skin cancer) during 2002-2021, and followed for death until 2021. We estimated 5-year relative survival (RS) in 5-year calendar periods, and percentage points (pp) differences in 5-year RS from 2002-2006 until 2017-2021. Separate analyses were performed for eight cancer sites (i.e. colorectum, pancreas, lung, breast, cervix uteri, kidney, prostate, and melanoma of skin).

RESULTS

Five-year RS improved across nearly all cancer sites in all countries (except Iceland), with absolute differences across age groups ranging from 1 to 21 pp (all cancer sites), 2 to 20 pp (colorectum), -1 to 36 pp (pancreas), 2 to 28 pp (lung), 0 to 9 pp (breast), -11 to 26 pp (cervix uteri), 2 to 44 pp (kidney), -2 to 23 pp (prostate) and -3 to 30 pp (skin melanoma). The oldest patients (80-89 years) exhibited lower survival across all countries and sites, although with varying improvements over time.

INTERPRETATION

Nordic cancer patients have generally experienced substantial improvements in cancer survival during the last two decades, including major cancer sites and age groups. Although survival has improved over time, older patients remain at a lower cancer survival compared to younger patients.

Lenvatinib Plus Pembrolizumab Versus Sunitinib in First-Line Treatment of Advanced Renal Cell Carcinoma: Final Prespecified Overall Survival Analysis of CLEAR, a Phase III Study

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.We present the final prespecified overall survival (OS) analysis of the open-label, phase III CLEAR study in treatment-naïve patients with advanced renal cell carcinoma (aRCC). With an additional follow-up of 23 months from the primary analysis, we report results from the lenvatinib plus pembrolizumab versus sunitinib comparison of CLEAR. Treatment-naïve patients with aRCC were randomly assigned to receive lenvatinib (20 mg orally once daily in 21-day cycles) plus pembrolizumab (200 mg intravenously once every 3 weeks) or sunitinib (50 mg orally once daily [4 weeks on/2 weeks off]). At this data cutoff date (July 31, 2022), the OS hazard ratio (HR) was 0.79 (95% CI, 0.63 to 0.99). The median OS (95% CI) was 53.7 months (95% CI, 48.7 to not estimable [NE]) with lenvatinib plus pembrolizumab versus 54.3 months (95% CI, 40.9 to NE) with sunitinib; 36-month OS rates (95% CI) were 66.4% (95% CI, 61.1 to 71.2) and 60.2% (95% CI, 54.6 to 65.2), respectively. The median progression-free survival (95% CI) was 23.9 months (95% CI, 20.8 to 27.7) with lenvatinib plus pembrolizumab and 9.2 months (95% CI, 6.0 to 11.0) with sunitinib (HR, 0.47 [95% CI, 0.38 to 0.57]). Objective response rate also favored the combination over sunitinib (71.3% v 36.7%; relative risk 1.94 [95% CI, 1.67 to 2.26]). Treatment-emergent adverse events occurred in >90% of patients who received either treatment. In conclusion, lenvatinib plus pembrolizumab achieved consistent, durable benefit with a manageable safety profile in treatment-naïve patients with aRCC.

Identification of Novel Molecular and Clinical Biomarkers of Survival in Glioblastoma Multiforme Patients: A Study Based on The Cancer Genome Atlas Data

Glioblastoma multiforme (GBM) is the most prevalent type of brain tumour; although advancements in treatment have been made, the median survival time for GBM patients has persisted at 15 months. This study is aimed at investigating the genetic alterations and clinical features of GBM patients to find predictors of survival. GBM patients' methylation and gene expression data along with clinical information from TCGA were retrieved. The most overrepresented pathways were identified independently for each omics dataset. From the genes found in at least 30% of these pathways, one gene that was identified in both sets was further examined using the Kaplan-Meier method for survival analysis. Additionally, three groups of patients who started radio and chemotherapy at different times were identified, and the influence of these variations in treatment modality on patient survival was evaluated. Four pathways that seemed to negatively impact survival and two with the opposite effect were identified. The methylation status of PRKCB was highlighted as a potential novel biomarker for patient survival. The study also found that treatment with chemotherapy prior to radiotherapy can have a significant impact on patient survival, which could lead to improvements in clinical management and therapeutic approaches for GBM patients.

Modeling time to death for under-five children in Malawi using 2015/16 Demographic and Health Survey: a survival analysis

BACKGROUND

Malawi has one of the highest under-five mortality rates in Sub Sahara Africa. Understanding the factors that contribute to child mortality in Malawi is crucial for the development and implementation of effective interventions to reduce child mortality. The aim of this study is to use survival analysis in modeling time to death for under-five children in Malawi. In turn, identify potential risk factors for child mortality and inform the development of interventions to reduce child mortality in the country.

METHOD

This study used data from all births that occurred in the five years leading up to the 2015/16 Malawi Demographic and Health Survey. The Frailty hazard model was applied to predict infant survival in Malawi. In this analysis, the outcome of interest was death and it had two possible outcomes: "dead" or "alive". Age at death was regarded as the survival time variable. Infants who were still alive at the time of the study as of the day of the interview were considered as censored observations in the analysis.

RESULTS

A total of 17,286 live births born during the 5 years preceding the survey were analysed. The study found that the risk of death was higher among children born to mothers aged 30-39 and 40 or older compared to teen mothers. Infants whose mothers attended fewer than four antenatal care visits were also found to be at a higher risk of death. On the other hand, the study found that using mosquito nets and early breastfeeding were associated with a lower risk of death, as were being male and coming from a wealthier household.

CONCLUSION

The study reveals a notable decline in infant mortality rates as under-five children age, underscoring the challenge of ensuring newborn survival. Factors such as maternal age, birth order, socioeconomic status, mosquito net usage, early breastfeeding initiation, geographic location, and child's sex are key predictors of under-five mortality. To address this, public health strategies should prioritize interventions targeting these predictors to reduce under-five mortality rates.

A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients

Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is ()often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined can help overcome these issues, as one provides the means of training models without exchanging sensitive data, while the other identifies the most informative features, reduces overfitting, and improves model interpretability. Our proposed FS pipeline based on FL principles targets data-driven radiomics FS in a multivariate survival study of non-small cell lung cancer patients. The pipeline was run across datasets from three institutions without patient-level data exchange. It includes two FS techniques, Correlation-based Feature Selection and LASSO regularization, and Cox Proportional-Hazard regression with Overall Survival as endpoint. Trained and validated on 828 patients overall, our pipeline yielded a radiomic signature comprising "intensity-based energy" and "mean discretised intensity". Validation resulted in a mean Harrell C-index of 0.59, showcasing fair efficacy in risk stratification. In conclusion, we suggest a distributed radiomics approach that incorporates preliminary feature selection to systematically decrease the feature set based on data-driven considerations. This aims to address dimensionality challenges beyond those associated with data constraints and interpretability concerns.

Conservative Versus Surgical Therapy in Patients With Infective Endocarditis and Surgical Indication-Meta-Analysis of Reconstructed Time-to-Event Data

BACKGROUND

Infective endocarditis represents a life-threatening disease with high mortality rates. A fraction of patients receives exclusively conservative antibiotic treatment due to their comorbidities and high operative risk, despite fulfilling criteria for surgical therapy. The aim of the present study is to compare outcomes in patients with infective endocarditis and indication for surgical therapy in those who underwent or did not undergo valve surgery.

METHODS AND RESULTS

Three databases were systematically assessed. A pooled analysis of Kaplan-Meier-derived reconstructed time-to-event data from studies with longer follow-up comparing conservative and surgical treatment was performed. A landmark analysis to further elucidate the effect of surgical intervention on mortality was carried out. Four studies with 3003 patients and median follow-up time of 7.6 months were included. Overall, patients with an indication for surgery who were surgically treated had a significantly lower risk of mortality compared with patients who received conservative treatment (hazard ratio [HR], 0.27 [95% CI, 0.24-0.31], P<0.001). The survival analysis in the first year showed superior survival for patients who underwent surgery when compared with those who did not at 1 month (87.6% versus 57.6%; HR, 0.31 [95% CI, 0.26-0.37], P<0.01), at 6 months (74.7% versus 34.6%) and at 12 months (73.3% versus 32.7%).

CONCLUSIONS

Based on the findings of this study-level meta-analysis, patients with infective endocarditis and formal indication for surgical intervention who underwent surgery are associated with a lower risk of short- and long-term mortality when compared with conservative treatment.

Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy

BACKGROUND AND PURPOSE

Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learning (ML)-based approaches, such as random survival forests (RSFs) and, more recently, deep learning (DL) have been proposed; however, these techniques are largely black-box in nature, limiting explainability. We compared CPH, RSF and DL to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients receiving radiotherapy using pre-treatment covariables. We employed explainable techniques to provide insights into the contribution of each covariable on OS prediction.

MATERIALS AND METHODS

The dataset contained 471 stage I-IV NSCLC patients treated with radiotherapy. We built CPH, RSF and DL OS prediction models using several baseline covariable combinations. 10-fold Monte-Carlo cross-validation was employed with a split of 70%:10%:20% for training, validation and testing, respectively. We primarily evaluated performance using the concordance index (C-index) and integrated Brier score (IBS). Local interpretable model-agnostic explanation (LIME) values, adapted for use in survival analysis, were computed for each model.

RESULTS

The DL method exhibited a significantly improved C-index of 0.670 compared to the CPH and a significantly improved IBS of 0.121 compared to the CPH and RSF approaches. LIME values suggested that, for the DL method, the three most important covariables in OS prediction were stage, administration of chemotherapy and oesophageal mean radiation dose.

CONCLUSION

We show that, using pre-treatment covariables, a DL approach demonstrates superior performance over CPH and RSF for OS prediction and use explainable techniques to provide transparency and interpretability.

To study the survival analysis and recurrence pattern in women treated for breast cancer: Retrospective study, a tertiary cancer center experience from Sub-Himalayan Region of India

BACKGROUND

Breast cancer is a leading health concern in India, comprising 25% of female cancers with significant mortality. This study was conducted at the Cancer Research Institute in the Northern Sub-Himalayan region of India from 2016 to 2021, evaluated 674 breast cancer cases to analyze factors that influence recurrence.

METHODOLOGY

Retrospective clinical audit assessing patients' survival outcomes using Kaplan-Meier curves and Cox proportional hazard regression. Factors including age, molecular subtype, TNM staging, and treatment modalities were evaluated.

RESULTS

Notable findings include a high occurrence of breast cancer in young patients (24.48% ≤ 40 years) and varying recurrence rates among molecular subtypes with human epidermal growth factor receptor 2 neu-enriched (25.24%) and triple‑negative breast cancer (22.58%) being the most common. Advanced T and N stages, neoadjuvant chemotherapy, and the number of nodes dissected showed significant associations with higher recurrence rates.

CONCLUSION

This study sheds light on survival and recurrence patterns in Northern Sub-Himalayan breast cancer patients, emphasizing the need for tailored treatment strategies, comprehensive follow-up care, with improved understanding of regional outcomes. These findings contribute valuable insights for optimizing patient care and improving survival rates in this region.

Epidemiologic Characteristics, Treatment Patterns, and Survival Analysis of Plasmablastic Lymphoma in the United States: A SEER and NCDB Analysis

BACKGROUND

Plasmablastic Lymphoma (PBL) is a rare aggressive B-cell lymphoma that primarily affects immunocompromised individuals, including those living with HIV. Historically, survival estimates are dismal and range from 8 to 15 months. We aimed to evaluate epidemiologic characteristics, treatment patterns and survival trends on a national scale.

PATIENTS AND METHODS

Patients diagnosed with PBL from 2010 to 2020 were identified in the National Cancer Database (NCDB) and in the Surveillance, Epidemiology, and End Results (SEER) program. Incidence rates were calculated using SEER. Demographic features, treatment characteristics, and overall survival (OS) were identified using the NCDB.

RESULTS

We identified 1153 patients in the SEER database and 1822 patients in the NCDB. The incidence of PBL is 0.07 cases per 100,000 US population per year. PBL is more common in males (77%), and white patients (77%), with 50% of cases in patients with HIV. Patients who were treated with multiagent chemotherapy had a median OS of 58.6 months. On multivariate Cox regression, we found that HIV status did not have a significant impact on OS. Factors associated with worse OS included advancing age and stage.

CONCLUSION

We present the largest study to date on PBL. Among treated patients, we described a median OS of 58.6 months, greatly improved from previously reported estimates. We found that HIV status did not have a significant impact on OS. While OS remains poor, therapeutic advances over the last decade are promising and highlight the need for continued clinical advances aimed at improving therapeutic options for this rare lymphoma.

Subsequent management and outcomes after first-line PARP inhibitors progression in ovarian cancer patients

OBJECTIVES

This retrospective study aims to evaluating the subsequent management and outcomes after first-line PARPi progression in Chinese ovarian cancer population.

METHODS

Clinical and pathologic variables, treatment modalities, and outcomes were assessed. We investigated the subsequent management and outcomes after first-line PARPi progression. The objective response rate (ORR) and disease control rate (DCR) parameters were evaluated to determine the response to subsequent chemotherapy. For the survival analyses, progression-free survival 1 (PFS1), PFS2, overall survival (OS) and PFS2 - PFS1 were analysed.

RESULTS

A total of 124 patients received PARPi maintenance treatment after first-line chemotherapy during the study period in our center. 44 of them (35.5%) experienced a recurrence. The median duration of PARPi in these patients was 11.1 months (range: 1.2-75.1 months). A total of 40 patients (40/44, 90.9%) received subsequent chemotherapy with 35 (35/44, 79.5%) and 5 (5/44, 11.4%) patients received platinum-based and non-platinum-based chemotherapy in our center. 2 patients (4.5%) received target therapy and other 2 patients (4.5%) received best supportive care. 27.3% (12/44) patients received secondary cytoreduction surgery (SCS). After subsequent chemotherapy, 14 patients received PARPi retreatment as maintenance therapy. In patients who received platinum-based regimens (n = 35), 23 of 35 patients (65.7%) had complete/partial response (CR/PR), 8 of 35 (22.9%) had stable disease (SD), and 4 of 35 (12.1%) had progressive disease (PD). The ORR and DCR of patients who received subsequent chemotherapy was 65.7% and 88.6%, respectively. 15 patients (57.7%, 15/26) were reported to be platinum resistant with a platinum-free interval (PFI)  of < 6 months in patients whose platinum sensitivity of the second line platinum-based regimens was evaluable. Patients who received SCS after PARPi resistant associated with a borderline better PFS2 (median PFS2: 41.9 vs. 29.2 months, P = 0.051) and a non-significantly increased PFS2-PFS1 (median PFS2-PFS1: 12.2 vs. 9.8 months, P = 0.551). Patients with a PFI ≥ 12 months had a significantly better PFS2 (median PFS2: 37.0 vs. 25.3 months, P < 0.001) and a tendency towards a better PFS2-PFS1 than those with a PFI < 12 months (median PFS2-PFS1: 11.2 vs. 8.5 months, P = 0.334). A better PFS2 was observed in patients who received second PARPi maintenance therapy (median PFS2 of 35.4 vs. 28.8 months); however, the difference was not statistically significant (P = 0.200). A better PFS2-PFS1 was observed in patients who received second PARPi maintenance therapy (median PFS2-PFS1: 13.6 vs. 8.9 months, P = 0.002) than those without.

CONCLUSIONS

In summary, some degree of resistance to standard subsequent platinum and non-platinum chemotherapy is noted in the entire cohort. A trend towards higher benefit from subsequent chemotherapy after first-line PARP inhibitors progression was observed in the PFI ≥ 12 months subgroup than those with PFI < 12 months. PARPi retreatment as maintenance therapy and SCS can be offered to some patients with PARPi resistance.

PD-L1 (SP142) Expression in Primary and Recurrent/Metastatic Triple-Negative Breast Cancers and Its Clinicopathological Significance

PURPOSE

The programmed death-ligand 1 (PD-L1) SP142 assay identifies patients with triple-negative breast cancer (TNBC) who are most likely to respond to the anti-PD-L1 agent atezolizumab. We aimed to compare PD-L1 (SP142) expression between primary and recurrent/metastatic TNBCs and elucidate the clinicopathological features associated with its expression.

MATERIALS AND METHODS

Primary and recurrent/metastatic TNBCs tested with PD-L1 (SP142) were collected, and clinicopathological information of these cases was obtained through a review of slides and medical records.

RESULTS

PD-L1 (SP142) positivity was observed in 50.9% (144/283) of primary tumors and 37.8% (31/82) of recurrent/metastatic TNBCs with a significant difference. Recurrent or metastatic sites were associated with PD-L1 positivity, with high PD-L1 positivity in the lung, breast, and soft tissues, and low positivity in the bone, skin, liver, and brain. When comparing PD-L1 expression between primary and matched recurrent/metastatic TNBCs using 55 paired samples, 20 cases (36.4%) showed discordance; 10 cases revealed positive conversion, and another 10 cases revealed negative conversion during metastatic progression. In primary TNBCs, PD-L1 expression was associated with a higher histologic grade, lower T category, pushing border, and higher tumor-infiltrating lymphocyte infiltration. In survival analyses, PD-L1 positivity, especially high positivity, was found to be associated with favorable prognosis of patients.

CONCLUSION

PD-L1 (SP142) expression was lower in recurrent/metastatic TNBCs, and substantial cases showed discordance in its expression between primary and recurrent/metastatic sites, suggesting that multiple sites may need to be tested for PD-L1 (SP142) when considering atezolizumab therapy. PD-L1 (SP142)-positive TNBCs seems to be associated with favorable clinical outcomes.

Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions

Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.

Long-Term Survival Following Primary Graft Dysfunction Development in Lung Transplantation

INTRODUCTION

Primary graft dysfunction (PGD) is a known risk factor for early mortality following lung transplant (LT). However, the outcomes of patients who achieve long-term survival following index hospitalization are unknown. We aimed to determine the long-term association of PGD grade 3 (PGD3) in patients without in-hospital mortality.

METHODS

LT recipients were identified from the United Network for Organ Sharing Database. Patients were stratified based on the grade of PGD at 72 h (No PGD, Grade 1/2 or Grade 3). Groups were assessed with comparative statistics. Long-term survival was evaluated using Kaplan-Meier methods and a multivariable shared frailty model including recipient, donor, and transplant characteristics.

RESULTS

The PGD3 group had significantly increased length of stay, dialysis, and treated rejection post-transplant (P < 0.001). Unadjusted survival analysis revealed a significant difference in long-term survival (P < 0.001) between groups; however, following adjustment, PGD3 was not independently associated with long-term survival (hazard ratio: 0.972; 95% confidence interval: 0.862-1.096). Increased mortality was significantly associated with increased recipient age and treated rejection. Decreased mortality was significantly associated with no donor diabetes, bilateral LT as compared to single LT, transplant in 2015-2016 and 2017-2018, and no post-transplant dialysis.

CONCLUSIONS

While PGD3 remains a challenge post LT, PGD3 at 72 h is not independently associated with decreased long-term survival, while complications such as dialysis and rejection are, in patients who survive index hospitalization. Transplant providers should be aggressive in preventing further complications in recipients with severe PGD to minimize the negative association on long-term survival.

Adolescent Predictors of Firearm Suicide Over Four Decades of Life in U.S. Men

INTRODUCTION

There are meager individual-level data on long-term predictors of firearm suicide.

METHODS

This was an analysis of males (N=189,558) in the Project Talent cohort, a national probability sample of high school schools in 1960 when students completed a baseline Project Talent self-report inventory. Mortality follow-up was contingent on survival until 1979, the onset of the National Death Index when the cohort had a mean age of 35.7 years. Mortality follow-up continued until death or age 75 years, reached by all surviving members by 2018. Analyses were conducted in 2022, with the main outcome being firearm suicide deaths (n=479). Factor analyses of Project Talent items yielded three key factors: (1) interests in firearm-related professions (i.e., military service, police force), (2) interests in hunting or fishing and knowledge of long guns, and (3) stereotypic masculinity.

RESULTS

Survival analyses showed that long-term risk for firearm suicide was associated with 1-SD increases in firearm-related vocational interests in adolescence (adjusted hazard ratio [95% CI]=1.23 [1.09, 1.40]) and masculinity (adjusted hazard ratio [95% CI]=1.15 [1.04, 1.28]). Decreased long-term firearm suicide risk was associated with increased hunting interests and knowledge of long guns in adolescence (adjusted hazard ratio [95% CI]=0.86 [0.77, 0.96]) and competitive sports participation, an exploratory variable (adjusted hazard ratio [95% CI]=0.89 [0.80, 0.99]).

CONCLUSIONS

Prevention efforts are needed to lower long-term firearm suicide risk among adolescent males with high stereotypic masculinity and those interested in military or police service. Potential protective effects of competitive sports participation and socialization to long guns through hunting require further study.

Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry

BACKGROUND

In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework.

AIM

To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks.

METHODS

From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales).

RESULTS

For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models.

CONCLUSIONS

For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models.

HIGHLIGHTS

For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.

Atypical teratoid/rhabdoid tumor in adult: case series and an integrated survival analysis

BACKGROUND

Atypical teratoid/rhabdoid tumor (AT/RT) is well documented in children but less so in adults because of its rarity.

METHOD

We report a series of five cases, a literature review and quantitative analysis of the survival outcome of adult AT/RT patients.

RESULTS

Seventy-four patients including our five cases (male: female = 16: 58) were evaluated, whose median age was 32.5 years (18-80 years). The commonest location was the sellar region. Median overall survival (OS) was 12.5 months (0.5-204.00 months). Chemotherapy was associated with OS (HR: 0.349, 95%CI: 0.176-0.694, p = 0.003), while other factors did not influence OS. From Kaplan-Meier analysis, surgery combining postoperative chemotherapy and radiotherapy was associated with better prognosis (Log Rank: χ2 = 14.662, p = 0.001).

CONCLUSIONS

Adult AT/RT is commoner in females and tends to recur rapidly after surgical resection. Chemotherapy may provide a survival benefit. Surgery combined with postoperative chemotherapy and radiotherapy was associated with better prognosis for adult AT/RT patients, though the overall prognosis was still poor.

Kaplan-Meier Survival Analysis: Practical Insights for Clinicians

This article aims to provide a guide that will help healthcare professionals and clinical researchers from all fields that deal with Kaplan-Meier curves. Survival analysis methods are among the most frequently used in the medical sciences and in clinical research. Overall survival, progression free survival, time to recurrence, or any other clinically relevant parameter represented by a Kaplan-Meier curve will be discussed. We will present a practical and straightforward interpretation of these curves, setting aside intricate mathematical considerations. Our focus will be on essential concepts that interface with biological sciences and medicine in order to guarantee proficiency in one of the most popular yet frequently misunderstood methods in clinical research. Being familiar with these concepts is not only essential for designing new clinical studies but also for critically assessing and interpreting published data.

Analysis of patients with endometrial carcinoma using the ProMise classifier: a pilot study from India

BACKGROUND

Molecular subtyping of endometrial carcinomas (EC) has been shown to classify tumors into prognostically relevant groups. Characterizing EC with a limited number of markers viz., POLE mutations, p53 mutations, and MMR status, can provide valuable information.

DESIGN

Paraffin sections of a cohort of 48 EC from a tertiary care center were characterized for the above-mentioned molecular markers and analyzed in the context of survival.

METHODS

Formalin fixed paraffin embedded tissues from 48 EC were characterized for POLE mutations by Sanger sequencing (exons 9-14), for MMR (MLH1, MH2, MSH6) using immunohistochemistry (IHC) and copy number (high/low) using p53 IHC. Mutational status was integrated along with the clinicopathological details and survival analysis performed.

RESULTS

Eleven (22.9%) patients were MMR deficient, 3 (6.3%) had POLE mutation, while 2 (4.1%) had both POLE and P53 mutations (regarded as multiple classifiers). Twelve (25%) patients were found to have P53 mutations, while the remaining 20 (41.7%) had no specific molecular profile (NSMP). Median follow-up duration was 43.5 (2-62) months with 8 recurrences and 9 deaths. Tumors with POLE mutation had the most favorable prognosis followed by the NSMP and the MMR mutated group while the P53 and multiple classifier groups had the worst prognosis in terms of OS (Log-rank p: 0.006) and PFS (Log-rank p: 0.001).

CONCLUSION

The integration of molecular-clinicopathologic data for endometrial cancer classification, through cost-effective, clinically applicable assays appears to be a highly objective tool that can be adopted even in resource-limited settings. It has the potential to cause a shift in the paradigm of EC pathology and management practice.

The Influence of Routine Health care on Reoffending in a Sample of Gun Involved Youths

In the wake of heightened concerns about gun violence and its impacts on youth, "what works" in gun violence prevention remains a critical public health concern. Gun violence prevention in the U.S. is increasingly interdisciplinary, involving both the criminal legal system and the health care system in developing an evidence base for promising programs and policies. The current study contributes to the literature by examining recidivism outcomes (i.e., rearrest) for a cohort of n = 409 Indianapolis youth involved in gun violence who were court-ordered to complete a health education-based prevention program called Project Life. The youth in our sample were predominantly from marginalized communities, all had been charged with a gun-involved or violence offense, 96% were detained by the juvenile justice system for some time, and 64% received at least one routine well check within five years prior to Project Life. Survival analyses of merged juvenile court records and health records show that routine health care (i.e., well visits) and completing the Project Life program were protective against recidivism, whereas time spent in detention increased risk. The findings provide evidence for the value of interdisciplinary approaches that include the health system in disrupting cycles of gun violence, while reducing the carceral footprint on youth.

SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions

Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches, especially when data is sensitive, such as in medical applications. Federated learning is a possible solution, but requires fast communications and flawless security. Here, we propose SYNDSURV (SYNthetic Distributed SURVival), an alternative approach that simplifies the current state-of-the-art paradigm by allowing different centres to generate local simulated instances from real data and then gather them into a centralised hub, where an Artificial Intelligence (AI) model can learn in a standard way. The main advantage of this procedure is that it is model-agnostic, therefore prediction models can be directly applied in distributed applications without requiring particular adaptations as the current federated approaches do. To show the validity of our approach for medical applications, we tested it on a survival analysis task, offering a viable alternative to train AI models on distributed data. While federated learning has been mainly optimised for gradient-based approaches so far, our framework works with any predictive method, proving to be a comparable way of performing distributed learning without being too demanding towards each participating institute in terms of infrastructural requirements.

Married Status Affects Rates of Treatment and Mortality in Male and Female Renal Cell Carcinoma Patients Across all Stages

INTRODUCTION

The association between treatment rates and cancer specific mortality (CSM) according to married status in male and female clear cell renal cell carcinoma (ccRCC) patients across all stages is unknown.

PATIENT AND METHODS

Using the Surveillance, Epidemiology, and End Results database (2004-2020), ccRCC patients were stratified according to married status (married vs. unmarried). Logistic regression models addressed treatment rates; Cox regression models addressed CSM rates.

RESULTS

Of 98,142 patients, 43,999 (72%) males and 20,287 (55%) females were married. In stage-specific analyses, married status independently predicted higher nephrectomy rates in males and females (all P ≤ .03). In stage IV, married status predicted higher systemic therapy rate in males (P < .001), but not in females. In survival analyses, married males exhibited lower CSM rates relative to unmarried males (all P ≤ .02). Conversely, married females exhibited lower CSM rates only in stages I and III (all P ≤ .02), but not in stages II and IV. In subgroup analyses of T1aN0M0 patients, married status was associated with higher partial nephrectomy rates in both males and females (all P ≤ .005).

CONCLUSION

In ccRCC, married status invariably predicts higher rates of guideline recommended surgical management (nephrectomy and partial nephrectomy). Moreover, even after adjustment for treatment type, married status independently predicted lower CSM rates in males across all stages. However, the effect of married status in females is only operational in stages I and III. Lack of association between married status in stages II and IV may potentially be explained by stronger association with treatment assignment which reduces the residual effect on survival.

Extended excess hazard models for spatially dependent survival data

Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated with cancer in the absence of information about the cause of death. Recent data linkage developments have allowed for incorporating the place of residence into the population cancer databases; however, modeling this spatial information has received little attention in the relative survival setting. We propose a flexible parametric class of spatial excess hazard models (along with inference tools), named "Relative Survival Spatial General Hazard," that allows for the inclusion of fixed and spatial effects in both time-level and hazard-level components. We illustrate the performance of the proposed model using an extensive simulation study, and provide guidelines about the interplay of sample size, censoring, and model misspecification. We present a case study using real data from colon cancer patients in England. This case study illustrates how a spatial model can be used to identify geographical areas with low cancer survival, as well as how to summarize such a model through marginal survival quantities and spatial effects.

Clinical prognostic factors predicting survival of motor neuron disease patients with gastrostomy: A retrospective analysis

INTRODUCTION/AIMS

Enteral feeding via gastrostomy is a key intervention to prevent significant weight loss in Motor Neuron Disease (MND). The aim of this study was to explore demographic, clinical, and nutritional factors associated with survival time in MND patients with gastrostomy.

METHODS

The retrospective study analyzed 94 MND patients (n = 58 bulbar-onset and n = 36 limb-onset) who underwent gastrostomy between 2015 and 2021. The primary outcome was the survival time from gastrostomy insertion to death. Independent variables of interest explored were: age at gastrostomy insertion, disease onset type, known genetic cause, use of riluzole, non-invasive ventilation (NIV) use, forced vital capacity prior to gastrostomy, weight loss from diagnosis to gastrostomy insertion, and body mass index (BMI) at the time of gastrostomy insertion.

RESULTS

The median survival time from gastrostomy to death was 357 days (± 29.3, 95%CI: 299.5, 414.5). Kaplan-Meier method and log-rank test revealed patients with lower body mass index <18.5 kg/m2 at the time of gastrostomy insertion (p = .023) had shorter survival. Cox proportional hazards model with multivariable adjustment revealed that older age (p = .008), and greater weight loss from diagnosis to gastrostomy (p = .003) were associated with shorter survival time post gastrostomy. Limb onset (p = .023), NIV use not being required (p = .008) and daily NIV use when required and tolerated (p = .033) were associated with longer survival.

DISCUSSION

Preventing or minimizing weight loss from MND diagnosis and encouraging NIV use when clinically indicated are modifiable factors that may prolong the survival of MND patients with gastrostomy.

Overall Survival of Metastatic Prostate Cancer Patients According to Location of Visceral Metastatic Sites

INTRODUCTION

It is unknown whether specific locations of visceral metastatic sites affect overall survival (OS) of metastatic prostate cancer (mPCa) patients. We tested the association between specific locations of visceral metastatic sites and OS in mPCa patients.

MATERIALS AND METHODS

Within Surveillance, Epidemiology and End Results database (2010-2016), survival analyses relied on specific locations of visceral metastases: lung only vs. liver only vs. brain only vs. ≥2 visceral sites. Kaplan-Meier plots and Cox regression models were fitted.

RESULTS

Of 1827 patients, 1044 (57%) harbored lung only visceral metastases vs. 457 (25%) liver only vs. 131 (7%) brain only vs. 195 (11%) ≥2 visceral sites. Median OS was 22 months in all patients vs. 33 months in lung only vs. 15 months in liver only vs. 16 months in brain only vs. 15 months in patients with ≥2 visceral sites. Highest OS was recorded in lung only visceral metastases patients, especially when concomitant nonvisceral metastases were located in lymph nodes only (median OS 57 months) vs. bone only (26 months) vs. lymph nodes and bone (28 months). Liver only, brain only or ≥2 visceral sites exhibited poor OS, regardless of concomitant nonvisceral metastases type (median OS from 13 to 19 months).

CONCLUSION

In mPCa patients, lung only visceral metastases, especially when associated with lymph node only nonvisceral metastases, portend the best prognosis. Conversely, visceral metastatic sites other than lung portend poor prognosis, regardless of concomitant nonvisceral metastases type.

Equal censoring but still informative: When the reasons for censoring differ between treatment arms

In randomized controlled trials, informative censoring has been described as a potential bias, mainly affecting time-to-event composite endpoints, like progression-free survival (PFS). It is usually suspected in the presence of unequal attrition rates between arms. Early censoring occurs for different reasons: patients may withdraw from a trial because of toxicity, or because of disappointment with their allocation arm. If censoring is more frequent in one arm due to increased toxicity, this removes the frailest individuals and introduces a bias favoring this arm. Conversely, patients who withdraw because of disappointment of their allocation arm may be more affluent and healthy patients, who will seek treatment options outside the protocol. In trials with one treatment arm presenting higher toxicity rates, and the other arm potentially leading to patient disappointment, censoring can occur for different reasons in each arm however with the same rates. We modeled this hypothesis in a randomized controlled trial where modifying only 15% of censored patients' fate in each arm at early time-points made the PFS gain fade. Equal censoring but for different reasons is a hitherto unexplored form of informative censoring with potentially large implications across the cancer clinical trials landscape.

On variable selection in a semiparametric AFT mixture cure model

In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in survival analysis. In the presence of cure fraction, the mixture cure model is popular, since it allows to model probability to be cured (called the incidence) and the survival function of the uncured individuals (called the latency). In this paper, we develop a variable selection procedure for the incidence and latency parts of a mixture cure model, consisting of a logistic model for the incidence and a semiparametric accelerated failure time model for the latency. We use a penalized likelihood approach, based on adaptive LASSO penalties for each part of the model, and we consider two algorithms for optimizing the criterion function. Extensive simulations are carried out to assess the accuracy of the proposed selection procedure. Finally, we employ the proposed method to a real dataset regarding heart failure patients with left ventricular systolic dysfunction.

Physician frontline treatment preferences for stage III/IV classic Hodgkin lymphoma: the real-world US CONNECT study

Aim: To understand US physicians' frontline (1L) treatment preferences/decision-making for stage III/IV classic Hodgkin lymphoma (cHL). Materials & methods: Medical oncologists and/or hematologists (≥2 years' practice experience) who treat adults with stage III/IV cHL were surveyed online (October-November 2020). Results: Participants (n = 301) most commonly considered trial efficacy/safety data and national guidelines when selecting 1L cHL treatments. Most physicians (91%) rated overall survival (OS) as the most essential attribute when selecting 1L treatment. Variability was seen among regimen selection for hypothetical newly diagnosed patients, with OS cited as the most common reason for regimen selection. Conclusion: While treatment selection varied based on patient characteristics, US physicians consistently cited OS as the top factor considered when selecting a 1L treatment for cHL.

Systemic Immune-Inflammation Index in Patients Treated With First-Line Immune Combinations for Metastatic Renal Cell Carcinoma: Insights From the ARON-1 Study

BACKGROUND

Systemic treatment with immune combinations is the gold standard for metastatic renal cell carcinoma (mRCC) worldwide. The systemic immune-inflammation index (SII) is a prognostic marker for several types of malignant neoplasms, including mRCC, in the era of tyrosine kinase inhibitor (TKI) treatment. Data regarding the prognostic value of the SII in patients with mRCC treated with immunotherapy are scarce and controversial.  METHODS: We retrospectively collected the data of patients with mRCC from 56 centers in 18 countries. SII (Platelet × Neutrophil/Lymphocyte count) was calculated prior to the first systemic treatment and cut-off was defined by a survival receiver operating characteristic (ROC) analysis. The primary objective of our retrospective study was to assess the outcomes of patients treated with first-line immunotherapy.  RESULTS: Data from 1034 mRCC patients was collected and included in this analysis. The SII cut-off value was 1265. After a follow-up of 26.7 months, and the overall survival (OS) and progression-free survival (PFS) were 39.8 and 15.7 months, respectively. According to SII (low vs. high), patients with low-SII had longer OS (55.7 vs. 22.2 months, P < .001), better PFS (20.8 vs. 8.5 months, P < .001), and higher overall response rate (52 vs. 37%, P = .033).

CONCLUSION

A high SII is associated with poor oncological outcomes in patients with mRCC. SII could be an easily accessible prognostic indicator for use in clinical practice.

Statistical primer: individual patient data meta-analysis and meta-analytic approaches in case of non-proportional hazards

Individual patient data (IPD) meta-analyses build upon traditional (aggregate data) meta-analyses by collecting IPD from the individual studies rather than using aggregated summary data. Although both traditional and IPD meta-analyses produce a summary effect estimate, IPD meta-analyses allow for the analysis of data to be performed as a single dataset. This allows for standardization of exposure, outcomes, and analytic methods across individual studies. IPD meta-analyses also allow the utilization of statistical methods typically used in cohort studies, such as multivariable regression, survival analysis, propensity score matching, uniform subgroup and sensitivity analyses, better management of missing data, and incorporation of unpublished data. However, they are more time-intensive, costly, and subject to participation bias. A separate issue relates to the meta-analytic challenges when the proportional hazards assumption is violated. In these instances, alternative methods of reporting time-to-event estimates, such as restricted mean survival time should be used. This statistical primer summarizes key concepts in both scenarios and provides pertinent examples.

Investigating diagnostic potential of long non-coding RNAs in head and neck squamous cell carcinoma using TCGA database and clinical specimens

Head and neck squamous cell carcinoma (HNSCC) is a prevalent and prognostically challenging cancer worldwide. The role of long non-coding RNAs (lncRNAs) in cancer regulation is progressively being understood. This study aims to identify lncRNAs with diagnostic potential as biomarkers for HNSCC. Statistical analysis was performed on expression data from the Cancer Genome Atlas (TCGA) database to identify potential lncRNAs associated with HNSCC. Four selected lncRNAs were validated using real-time quantitative reverse transcription polymerase chain reaction and correlated with clinical factors. Functional roles were further investigated. A total of 488 differentially expressed lncRNAs were identified in TCGA-HNSC. After rigorous evaluation based on p-values, survival analysis, and ROC analysis, 24 lncRNAs were prioritized for additional investigation. LINC00460, LINC00941, CTC-241F20.4, and RP11-357H14.17 were established as candidate diagnostic biomarkers. These lncRNAs exhibited elevated expression in HNSCC tissues and were associated with poor prognosis. Combining them showed high diagnostic accuracy. Notably, LINC00460 and CTC-241F20.4 demonstrated a significant elevation in the advanced stages of HNSCC. We constructed an lncRNA-mRNA regulatory network, and the array of significant regulatory pathways identified included focal adhesion, regulation of epithelial cell migration, and others. Additionally, these lncRNAs were found to influence immune responses by modulating immune cell infiltration in the HNSCC microenvironment. Our research indicates that LINC00460, LINC00941, RP11-357H14.17, and CTC-241F20.4 may have diagnostic and prognostic importance in HNSCC. Furthermore, we have gained insights into their potential functional roles, particularly about immune responses and interactions in the microenvironment.

Deep partially linear cox model for current status data

Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained by using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is $\sqrt{n}$-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to real-world data on news popularity.

Clinicopathologic analysis of nodal T-follicular helper cell lymphomas, a multicenter retrospective study from China

Background

Nodal T-follicular helper cell lymphomas (nTFHLs) represent a new family of peripheral T-cell lymphomas (PTCLs), and comparative studies of their constituents are rare.

Methods

This study retrospectively enrolled 10 patients with nTFHL-F and 30 patients with nTFHL-NOS diagnosed between December 2017 and October 2023 at six large comprehensive tertiary hospitals; 188 patients with nTFHL-AI were diagnosed during the same period at the First Affiliated Hospital of Zhengzhou University for comparison.

Results

Compared with nTFHL-AI, nTFHL-NOS patients exhibited better clinical manifestations, lower TFH expression levels, and a lower Ki-67 index. However, no differences in clinicopathological features were observed between nTFHL-F and nTFHL-AI patients as well as nTFHL-NOS patients. According to the survival analysis, the median OS for patients with nTFHL-NOS, nTFHL-AI, and nTFHL-F were 14.2 months, 10 months, and 5 months, respectively, whereas the median TTP were 14 months, 5 months, and 3 months, respectively. Statistical analysis revealed differences in TTP among the three subtypes(P=0.0173). Among the population of patients receiving CHOP-like induction therapy, there were significant differences in the OS and TTP among the nTFHL-NOS, nTFHL-AI, and nTFHL-F patients (P=0.0134, P=0.0205). Both the GDPT and C-PET regimens significantly improved the ORR, OS, and PFS in nTFHL patients.

Conclusion

There are significant differences in the clinical manifestations, pathology, and survival outcomes among the three subtypes of nTFHLs. However, further research with a larger sample size, and involving clinical pathology and molecular genetics is needed to determine the distinctive biological characteristics of these tumors.

Synchronous versus metachronous spinal metastasis: a comparative study of survival outcomes following neurosurgical treatment

PURPOSE

Patients with spinal metastases (SM) from solid neoplasms typically exhibit progression to an advanced cancer stage. Such metastases can either develop concurrently with an existing cancer diagnosis (termed metachronous SM) or emerge as the initial indication of an undiagnosed malignancy (referred to as synchronous SM). The present study investigates the prognostic implications of synchronous compared to metachronous SM following surgical resection.

METHODS

From 2015 to 2020, a total of 211 individuals underwent surgical intervention for SM at our neuro-oncology facility. We conducted a survival analysis starting from the date of the neurosurgical procedure, comparing those diagnosed with synchronous SM against those with metachronous SM.

RESULTS

The predominant primary tumor types included lung cancer (23%), prostate cancer (21%), and breast cancer (11.3%). Of the participants, 97 (46%) had synchronous SM, while 114 (54%) had metachronous SM. The median overall survival post-surgery for those with synchronous SM was 13.5 months (95% confidence interval (CI) 6.1-15.8) compared to 13 months (95% CI 7.7-14.2) for those with metachronous SM (p = 0.74).

CONCLUSIONS

Our findings suggest that the timing of SM diagnosis (synchronous versus metachronous) does not significantly affect survival outcomes following neurosurgical treatment for SM. These results support the consideration of neurosurgical procedures regardless of the temporal pattern of SM manifestation.