How to use the Surveillance, Epidemiology, and End Results (SEER) data: research design and methodology

Demographic and Treatment Analysis of Periosteal Osteosarcoma

BACKGROUND AND AIMS

Periosteal osteosarcoma (PO) is a rare bone cancer that makes up between 1% and 6% of osteosarcomas. No epidemiological survey of the United States has been conducted to study this disease, and most of the literature is limited to single-center analyses and case reports. We seek to perform the first such assessment.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) Program was queried for patients with primary PO (ICD-O-3 code 9193/3). Analysis of demographic, disease, and treatment variables was conducted via Fisher's exact test and 20-year cause-specific survival (20y CSS) was assessed via logrank analysis.

RESULTS

Fifty-four patients with PO were identified; median age was 20-24 years at diagnosis. Multivariate analysis demonstrated surgery provided 20y CSS benefit (hazard ratio [HR] = 0.08, p = 0.040) while chemotherapy (CTX) did not (p = 0.29); however, given the limited number of events (n = 11), recalculation of Cox regression for each variable demonstrated significance only with race (p = 0.026). Younger patients were more likely to be diagnosed with PO of the appendicular skeleton compared to the axial skeleton (p = 0.038). Mean survival time was greater among patients diagnosed with appendicular PO (16.0 years [14.2, 17.9]) compared to axial PO (10.9 years [9.5, 12.4]). Stage-stratified survival analysis demonstrated surgery alone was non-inferior to surgery with the addition of CTX (local disease: p = 0.37; regional disease: p = 0.85).

CONCLUSION

Axial PO is associated with decreased mean 20y CSS compared to appendicular PO, though in general, appendicular PO is more common than axial PO. In keeping with current literature, treatment of PO with CTX in addition to surgery should be reserved for high-risk patients, though its use in the treatment of PO is questionable.

Global burden and cross-country health inequalities of early-onset colorectal cancer and its risk factors from 1990 to 2021 and its projection until 2036

PURPOSE

To explore the worldwide, regional, and country-specific burden of early-onset colorectal cancer (EO-CRC) and identify its associated risk factors between 1990 and 2021, and to project its incidence and mortality rates for 2036.

METHODS

We acquired data on EO-CRC categorized by gender, socio-demographic index (SDI), and risk factors based on the Global Burden of Disease (GBD) Study 2021. Joinpoint regression analysis was utilized to explore the variation in disease burden. The autoregressive integrated moving average (ARIMA) model was performed to forecast the disease burden up to 2036.

RESULTS

Globally, the incidence rate, prevalence rate, mortality rate, and disability-adjusted life years (DALYs) rate of EO-CRC were estimated at 5.37 (95%UI: 4.91 to 5.86)/100,000, 34 (95%UI: 30.96 to 37.35)/100,000, 2.01 (95%UI: 1.84 to 2.19)/100,000, and 101.37 (95%: 92.85 to 110.18)/100,000 in 2021. The prevalence and incidence rates of EO-CRC showed an ascending trajectory, whilst the DALYs and mortality rates demonstrated a downward trajectory between 1990 to 2021. The high-middle SDI regions and East Asia exhibited the highest EO-CRC burden among the five SDI regions and 21 GBD regions respectively. A low-whole-grains diet was the chief risk factor contributing to EO-CRC. It was predicted that the age-standardized rate (ASR) of EO-CRC incidence would increase by 5.56%, while the ASR of mortality would decrease by 13.9% globally until 2036.

CONCLUSION

The current and future global burden of EO-CRC is heavy and varies significantly across different regions and countries.

A population-based study using nomograms to predict overall and cancer-specific survival in HPV-associated CSCC

Constructing and validating two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) in cutaneous squamous cell carcinoma (CSCC) correlated with human papillomavirus (HPV) infection was the main goal of this study. We constructed predictive models for OS and CSS incidence in HPV infection-associated CSCC using information from 2238 patients in the Surveillance, Epidemiology, and End Results (SEER) database and screened the variables by LASSO regression, Cox univariate regression, and Cox multifactorial regression models, which were calibrated and validated by internal and external cohorts. Finally, all patients were categorized into intermediate-risk, low-risk, and high-risk groups based on the optimal threshold calculated from the total score. Multivariate analysis showed that HPV infection status, marital status, tumor metastatic stage, surgical status, radiotherapy status, lymph node biopsy, local lymph node dissection, primary tumor status, and bone metastasis were risk factors for OS and CSS. The C index, the time-dependent area under the receiver-operating characteristic curve, and the column-line diagrams of the calibration plot were among the excellent-performance metrics that were effectively displayed. Moreover, the decision curve analysis of the two nomograms consistently revealed their favorable net benefits spanning 1, 2, and 3 years. In addition, the survival curves indicate that each of the two risk classification systems clearly differentiates high, medium, and low risk groups. These meticulously crafted nomograms stand poised to serve as indispensable instruments in clinical practice, empowering clinicians to adeptly communicate with patients regarding their prognostic outlook over the forthcoming 1, 2, and 3 years.

Nomogram-based prognostic stratification for patients with large hepatocellular carcinoma: a population study of SEER database and a Chinese cohort

Background

Large hepatocellular carcinoma (HCC) with a diameter ≥5 cm remains a significant challenge of poor survival and raises the need for prognosis evaluation. This study aimed to develop and validate a nomogram-based prognostic stratification to assess overall survival (OS) of patients with large HCC.

Methods

Data of patients with large HCC were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database and our hospital, and were divided into the training cohort, internal validation cohort and external validation cohort. Cox analysis was performed to identify independent prognostic factors for the construction of nomogram in training cohort. The predictive ability of the nomogram was validated compared with the tumor node metastasis (TNM) classification staging system. Furthermore, prognostic stratification system based on nomogram was developed.

Results

Independent prognostic factors including histological grade, T stage, M stage, alpha fetoprotein (AFP), fibrosis score and surgery, were incorporated to construct nomogram. C-indexes of nomogram were 0.730, 0.726 and 0.724 in the training, internal and external validation cohorts, respectively. Importantly, nomogram harbored a superior discrimination and clinical benefit than the TNM staging system. Nomogram-based prognostic stratification divided patients into three groups: 345-414 (low-risk group), 415-460 (medium-risk group) and 461-513 (high-risk group). As shown in Kaplan-Meier curves, there were significant differences in OS among low-, medium- and high-risk groups (P<0.01).

Conclusions

Nomogram showed a superior prognostic predictive ability compared with the TNM staging system. The prognostic stratification serves as a valuable tool to assist clinicians on the selection of optimal treatment method and follow-up plan, particularly for the high-risk population.

Unveiling the Promise: Navigating Clinical Trials 1978-2024 for PDAC

Despite many decades of research, pancreatic ductal adenocarcinoma (PDAC) remains one of the most difficult cancers to diagnose and treat effectively. Although there have been improvements in the 5-year overall survival rate, it is still very low at 12.5%. The limited efficacy of current therapies, even when PDAC is detected early, underscores the aggressive nature of the disease and the urgent need for more effective treatments. Clinical management of PDAC still relies heavily on a limited repertoire of therapeutic interventions, highlighting a significant gap between research efforts and available treatments. Over 4300 clinical trials have been or are currently investigating different treatment modalities and diagnostic strategies for PDAC, including targeted therapies, immunotherapies, and precision medicine approaches. These trials aim to develop more effective treatments and improve early detection methods through advanced imaging techniques and blood-based biomarkers. This review seeks to categorize and analyze PDAC-related clinical trials across various dimensions to understand why so few chemotherapeutic options are available to patients despite the numerous trials being conducted. This review aims to provide a comprehensive and nuanced understanding of the landscape of PDAC-related clinical trials, with the overarching goal of identifying opportunities to accelerate progress in drug development and improve patient outcomes in the fight against this devastating disease.

Risk stratification model for foreseeing overall survival in Chinese patients with initially metastatic small-cell lung cancer

The study was outlined to develop and approve a nomogram and chance stratification demonstrate for foreseeing overall survival of Chinese patients with at first metastatic small-cell lung cancer (SCLC). We collected information from the Surveillance, Epidemiology, and End Results (SEER) database approximately Chinese SCLC patients with at first distant metastases between 2010 and 2015. Patients with incomplete data about the follow-up time or clinicopathological information were excluded. The included patients were randomized into the training and validation set. Univariate and multivariate Cox proportional hazard regression models were performed. By integrating the significant variables screened, a prescient nomogram and risk stratification model were developed. In addition, we collected 198 small-cell lung cancer patients with metastasis at diagnosis from the case database of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine as an external validation cohort. In all, 421 patients were screened from the SEER database. Multivariate examination showed that age (P = .049), sex (P = .001), grade (P = .008), chemotherapy (P = .001), liver metastasis (P = .001), and pleural invasion (P = .012) were independent prognostic factors. The C-indicator of the nomogram to anticipate overall survival was higher than that of the eighth edition of the American Joint Committee on Cancer Tumor Node Metastasis classification system (0.75 vs 0.543; P < .001). A risk stratification model was encouraged to be created to precisely classify patients into 2 prognostic bunches. The survival rates anticipated by the nomogram appeared to have critical qualifications from the Kaplan-Meier curves in the entire SEER cohort. Calibration curves and survival predictions also showed strong accuracy and consistency in the external validation cohort. The nomogram provided a clear prognostic superiority over the traditional Tumor Node Metastasis system. It could help clinicians make individual risk predictions for initially metastatic Chinese SCLC cancer patients and give necessary treatment recommendations.

Long term clinical outcomes of cervical cancer patients who were recommended surgery but did not undergo it: A SEER database study

BACKGROUND

This study analyzed the long-term clinical outcomes of cervical cancer patients recommended surgery but who did not undergo it using the Surveillance, Epidemiology, and End Results (SEER) database. The aim was to identify the subgroups with comparable overall survival (OS) and cancer-specific survival (CSS) through stratified analysis.

METHODS

Cases of cervical cancer were retrieved from SEER database using SEER*Stat software. This included patients in the non-surgery group (recommended surgery but did not undergo it), and a reference surgery group. Propensity score matching balanced differences between the non-surgery and surgery groups. Stratified analysis and log-rank tests were used to identify subgroups within the non-surgery group with comparable OS and CSS to the surgery group.

RESULTS

A total of 30,807 cervical cancer patients were included in the OS and CSS analysis. In the matched cohort (n = 1278), patients in the non-surgery group had significantly lower 5-year CSS (63.2 % vs. 80.1 %, P < 0.001) and 5-year OS (59.0 % vs. 78.0 %, P < 0.001). However, within the matched cohort, there was no statistically significant difference in OS and CSS between the non-surgery and surgery groups in subgroups diagnosed during 2010-2014 (P = 0.064, P = 0.182), 2015-2020 (P = 0.122, P = 0.518), T2 stage (P = 0.139, P = 0.052), T3 stage (P = 0.502, P = 0.317), or with distant metastasis (M1) (P = 0.411, P = 0.520).

CONCLUSION

Patients in the non-surgery group generally exhibited lower long-term clinical outcomes compared to those in the surgery group. However, with advancements in non-surgical treatment techniques, particularly notable in patients with T2, T3, and M1 stages, these differences are gradually diminishing.

Epidemiology and risk factors for adult gliomas died by respiratory diseases during the COVID-19 pandemic: a population-based study

BACKGROUND

The research on epidemiology of gliomas died of respiratory diseases (RDs) is very scarce. The study aimed to explore the epidemiology and risk factors for adult gliomas death from respiratory diseases during the COVID-19 pandemic.

METHODS

Adult gliomas patients (age ≥ 18 years) diagnosed between 2020 and 2021 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching analysis was used to reduce confounding bias between gliomas died of respiratory diseases and died of gliomas directly. The Cox proportional hazards regression model and Kaplan-Meier (K-M) survival curves were used for survival analyses in the matched groups. Logistic regression analyses were conducted to identify risk factors for dying of respiratory diseases in the entire population.

RESULTS

Among 9315 eligible adult gliomas enrolled in the study, 39.4% died from gliomas, 1.0% from respiratory diseases, and 61.4% survived. Gliomas who died from respiratory diseases had a trend towards a higher risk of death (HR = 1.35, P = 0.031). Surgery did not increase the all-cause mortality risk (HR = 0.86, P = 0.327). The K-M survival curves suggested a worse prognosis for dying from respiratory diseases. Those who died from RDs had a shortened median survival (median 3 months) compared with those who died from gliomas directly (median 5 months). Multivariable logistic regression models indicated that those aged ≥ 65 years, with median household income < 75,000$/year, and not receiving surgery had a higher risk of dying from RDs.

CONCLUSIONS

RDs have become a crucial cause of death for gliomas. Those with advanced age and lower median household income have a higher risk of dying from respiratory diseases. Surgical treatment has been found to be safe for glioma patients and has been shown to reduce the risk of glioma patients dying from respiratory diseases. The study provides valuable insights for the perioperative management of gliomas patients in the post-pandemic era.

Incidence and Dynamics of CRC Stage Migration: A Regional vs. a National Analysis

BACKGROUND/OBJECTIVES

Due to an increased rate of surveillance colonoscopy, we aim to determine the impact of stage migration on the incidence and overall survival (OS) of patients who underwent pathological staging of colorectal cancer (CRC) at our Health Network System.

METHODS

Two datasets were included: subjects from the tumor registry at a regional Comprehensive Cancer Center (n = 1385) and subjects from the Surveillance, Epidemiology, and End Results (SEER) national database (n = 202,391).

RESULTS

A significant increase in the diagnosis of CRC Stage 1 and 4 was observed, with a decrease in stage 2, and no change in Stage 3 in the National datasets (p < 0.01). There was an increase in Stage 4 CRC diagnosis, with a concurrent decrease in stage 2, and no changes in stages 1 and 3 in the regional dataset (p < 0.05). OS followed the expected and progressive decrease in OS by stage (from 1 to 4, p < 0.01).

CONCLUSIONS

The present findings confirmed CRC stage migration in our Health Network System, along with a national trend conducive to an increased OS for early CRC stages.

PCAF acetylates AIB1 to form a transcriptional coactivator complex to promote glycolysis in endometrial cancer

Introduction

Despite rapid advances in molecular biology, personalized molecular therapy remains a clinical challenge for endometrial cancer due to its complex and heterogeneous tumor microenvironment.Based on clinical findings, AIB1 is a marker molecule for poor prognosis in endometrial cancer and may serve as a potential therapeutic target. Moreover, it is well known that aerobic glycolysis plays an important role in tumour energy metabolism. It has been previously reported in various hormone-related tumour studies that AIB1 affects glycolysis and promotes tumour development. However, the link between AIB1 and aerobic glycolysis in estrogen-dependent endometrial cancer remains unclear.

Methods

We used two endometrial cancer cell lines to validate the high expression of target genes and the effect on the proliferative and invasive capacity of the tumours and verified the pattern of interactions and epigenetic modifications by CHIP and CO-IP techniques. Finally, the conclusions were validated on homozygous mice.

Results

In this study, we investigated the transcriptional co-activation functions of AIB1, including its acetylation by PCAF, binding to the c-myc transcription factor, and recruitment of glycolysis-related gene promoters.

Discussion

Our findings provide new clues that perturbation of normal homeostatic levels of AIB1 is linked with endometrial cancer. These findings suggest that targeting AIB1-mediated regulation of aerobic glycolysis may offer a novel therapeutic approach for endometrial cancer with high AIB1 expression, opening new avenues for personalized diagnostics and treatment strategies in this disease.

Delirium Mediated the Association Between a History of Falls and Short-Term Mortality Risk in Critically Ill Ischemic Stroke Patients

Patients with ischemic stroke have an increased propensity to fall, resulting in significant physical and psychological distress. This study examined the association between falls in the 3 months prior to intensive care unit (ICU) admission and mortality within 28 days among 2950 adult ICU patients diagnosed with ischemic stroke from 2008 to 2019, focusing on the potential mediating role of delirium. The primary outcomes were short-term mortality (28, 60, and 90 days) and the risk of delirium. Each patient was followed for at least 1 year. Delirium was primarily assessed using the Confusion Assessment Method for the ICU and by reviewing nursing notes. Group differences between patients with and without a history of falls were compared using the Wilcoxon rank-sum test or the chi-squared test. Cox proportional risk or logistic regression models were used to explore the association between fall history and outcomes, and causal mediation analysis was performed. Results showed that patients with a recent fall history had a significantly increased risk of 28-day (hazard ratio [HR]: 1.62, 95% confidence interval [CI]: 1.35-1.94), 60-day (HR: 1.67, 95% CI: 1.42-1.98), and 90-day mortality (HR: 1.66, 95% CI: 1.41-1.95), as well as an increased risk of delirium (odds ratio: 2.00, 95% CI: 1.66-2.42). Delirium significantly mediated the association between fall history and 28-day mortality (total effect: HR: 1.77, 95% CI: 1.45-2.16; natural indirect effect: HR: 1.12, 95% CI: 1.05-1.21; proportion mediated: 24.6%). These findings suggest that ischemic stroke patients with a recent fall have an increased risk of short-term mortality, partly mediated by delirium. Strategies aimed at preventing delirium may potentially improve prognosis in this patient population.

Impact of tumor multiplicity on the prognosis of patients with primary renal cell carcinoma: a SEER database analysis

To compare clinical characteristics and survival outcomes of patients with multiple renal cell carcinoma versus single renal cell carcinoma. Develop a prognostic model for predicting prognosis in patients with multiple tumors and analyze prognostic factors. Patients with primary multiple renal cell carcinoma were selected from the Surveillance, Epidemiology, and End Results database (2004-2015). They were divided into single-tumor and multiple-tumor groups. Survival analysis was conducted using the Kaplan-Meier method and log-rank test. A Cox regression model was used to identify potential prognostic factors. A total of 19,489 renal cell carcinoma cases were included, with 947 in the multiple-tumor group and 18,542 in the single-tumor group. The multiple-tumor group had lower cancer-specific survival (P = 0.03, HR = 1.431). Cox regression identified risk factors for the multiple-tumor group including number of tumors, gender, combined summary stage, T stage, N stage, tumor size, and type of surgery. The predicted probabilities showed acceptable agreement with the actual observations at 3-, 5-, and 8-years area under the curve values in both the training and validation cohorts (0.831 vs. 0.605; 0.775 vs. 0.672; and 0.797 vs. 0.699, respectively). Compared with single renal cell carcinoma, multiple renal cell carcinoma is associated with decreased cancer-specific survival. Additionally, we identified several prognostic factors including the number of tumors, T stage, tumor size, and type of surgery. These findings offer valuable insights for selecting appropriate treatment strategies for patients diagnosed with multiple renal cell carcinomas.

Did the Coronavirus Disease 2019 (COVID-19) Pandemic Cause a Delay in the Diagnosis of Breast Cancer Patients?

Introduction The coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, in December 2019, quickly hit the world in just one month, causing a global public health emergency. We aimed to investigate whether the COVID-19 pandemic caused a delay in the hospital admissions of breast cancer patients and diagnosis of breast cancer, thus increasing the tumor size and the stage of the disease. Materials and methods Included in the study were patients who underwent breast cancer surgery between 01/03/2019 and 01/03/2020 (pre-COVID-19, first period) and between 01/03/2020 and 01/03/2021 (post-COVID-19, second period). Three hundred and seventy patients with enough details were included, and details were analyzed retrospectively. Tumor characteristics of pre-COVID-19 breast cancer patients were compared with the tumor characteristics of post-COVID-19 breast cancer patients. Demographics, preoperative diagnosis, tumor properties, surgical procedure (breast-conserving surgery, modified radical mastectomy, simple mastectomy, skin-sparing mastectomy), tumor size, total lymph node number, metastatic lymph node number, locally advanced disease, metastatic disease, and neoadjuvant therapy were evaluated. Results The mean tumor size increased significantly in the post-COVID-19 primary surgery group (p=0.005). There is no significant relationship between the pre-COVID-19 and post-COVID-19 period and pT in the neoadjuvant received group (p>0.05). The presence of pT2+pT3+pT4 was statistically significantly higher in the post-COVID-19 primary surgery group (p=0.001). The mean value of metastatic lymph nodes dissected between pre-COVID-19 and post-COVID-19 primary surgery groups increased significantly (p=0.010). Pericapsular extension was higher in the post-primary surgery group (p=0.002). Conclusion During the COVID-19 outbreak, breast cancer patients have difficulty accessing healthcare services and hesitate to apply to hospitals to fear contracting the COVID-19 disease. This situation has led to delays in diagnosing breast cancer patients, increased tumor size and pT grade, increased number of metastatic lymph nodes, pericapsular extension, and the resulting disease often appearing in advanced sizes and stages.

Historical cosmetic talc consumption and incidence of mesothelioma in the United States

Jointpoint Regression Software from the National Cancer Institute was used to model age-adjusted male and female pleural and peritoneal mesothelioma rates in the surveillance, epidemiology, and end results (SEER) 8, SEER 12, and SEER 22 cancer registries. Linear mixed models were then used to determine if there was a statistical association between U.S. cosmetic talc consumption and the 30-year lagged age-adjusted mesothelioma rates (1) over the reporting period for each registry and (2) for the periods of time identified by the jointpoint model where changes in the rate of mesothelioma occurred. Regardless of the SEER registry used, from the early-1980s through 2020, rates of peritoneal mesothelioma have remained steady or declined. Female pleural mesothelioma rates were unchanged from the early-1980s until 2017 when rates declined, while male rates peaked in the early 1990s and have since declined. Cosmetic talc consumption was not statistically associated with an increased rate of pleural or peritoneal mesothelioma in males or females, suggesting that the use of cosmetic talc products is not associated with the development of mesothelioma.

Construction and validation of a nomogram for predicting the prognosis of breast cancer patients who received adjuvant therapy: an analysis based on the SEER database

Background

Breast cancer is the most common malignant tumor in women globally. Despite advances in primary treatment, the role of adjuvant therapy in reducing recurrence and improving survival is critical; however, there is a notable lack of tailored prognostic models for patients receiving adjuvant therapy. This study used the Surveillance, Epidemiology, and End Results (SEER) database to develop a prognostic nomogram for breast cancer patients receiving adjuvant therapy.

Methods

The data of breast cancer patients who received adjuvant therapy after surgery in 2014-2015 were extracted from the SEER database. Univariate Cox regression identified significant prognostic variables that were further refined by least absolute shrinkage and selection operator (LASSO) regression and cross-validation analyses. These variables were incorporated into a multivariate Cox regression analysis to establish the predictive model. This model was visualized and validated using various statistical measures.

Results

A total of 54,960 patients were included in the study, with 38,472 in the training set and 16,488 in the validation set. Age, sex, race, marital status, grade, tumor (T) stage, lymph node (N) stage, subtype, and radiotherapy were found to be significant independent risk factors of 1-, 3-, and 5-year overall survival (OS). The receiver operating characteristic curve area for 1-, 3-, and 5-year OS was >0.76 in both sets. The consistency index values were 0.768 and 0.763 for the training and validation sets, respectively. The calibration curves showed good fit, and the nomogram exhibited substantial clinical utility.

Conclusions

Incorporating various significant factors, the constructed nomogram was able to effectively predict the prognosis of breast cancer patients who received adjuvant therapy. This nomogram extends understandings of complex prognosis scenarios. In addition, it could enhance personalized treatment plans and assist in patient counseling.

Risk factors, prognostic factors, and nomograms for synchronous brain metastases of solid tumors: a population-based study

In previous literatures, we found that similar studies on the short-term prognosis of synchronous brain metastases (S-BM) from other systems are rare. Our aim was to evaluate the early mortality rate of patients with S-BM from the Surveillance, Epidemiology, and End Result (SEER) database and explore the risk factors for early mortality (≤ 1 year). We used Kaplan-Meier (KM) curves to evaluate early mortality in patients with S-BM from the SEER database. Logistic regression analyses were used to identify significant independent prognostic factors in patients with a follow-up time > 12 months. And the meaningful factors were used to construct a nomogram of overall early death. The receiver operating characteristic (ROC) curve was used to test the predictive ability of the model, while the decision curve analysis (DCA) curve was used to validate the clinical application ability of the model. A total of 47,284 patients were used for univariate and multivariate logistic regression analysis to screen variables to constructing a nomogram. In the all-cause early mortality specific model, the area under the ROC (AUC) curve of the training set was 0.764 (95% confidence interval (CI): 0.758-0.769), and the AUC of the validation set was 0.761 (95% CI: 0.752-0.770). The DCA calibration curves of the training set and validation set indicate that the 1-year early mortality rate predicted by this model is consistent with the actual situation. We found that the 1-year early mortality rate was 76.4%. We constructed a validated nomogram using these covariates to effectively predict 1-year early mortality in patients with S-BM. This nomogram can help clinical workers screen high-risk patients to develop more reasonable treatment plans.

Novel decision tree models predict the overall survival of patients with submandibular gland cancer

BACKGROUND

While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months.

MATERIALS AND METHODS

We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated.

RESULTS

A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points.

CONCLUSIONS

Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Epidemiology of Adenosquamous Carcinomas

Background

Adenosquamous carcinomas (ASCs) are a very rare histology containing cancer cells with both glandular-like (adeno) and squamous cell histologies, comprising typically a fraction of a percent of all solid tumors. The bulk of the literature on ASCs is comprised of case reports and small series, with the general finding that ASCs tend to have worse outcomes than either of their parent histologies. However, there is a lack of pan site-comparative studies in the literature that compare ASC clinicodemographic and survival outcomes with those of conventional adenocarcinomas (ACs) and squamous cell carcinomas (SCCs).

Methods

In this study, we summarize these outcomes in eight primary sites, comprising 92.7% of all ASC cases diagnosed from 1975 to 2020 in the Surveillance, Epidemiology, and End Results (SEER) database.

Results

Lung ASCs comprise 51.5% of all ASC cases, accounting for 1.1% of all lung cancer cases, followed by uterine/cervical cancers at 29.7% of all ASC cases, translating into 1.8% of all cancers in this site. In descending order, the remaining 20% of ASCs arise in pancreatic, oral cavity, biliary, esophageal, colorectal, and gastric sites, comprising between 0.1% and 0.7% of all cancers in these sites. Apart from pancreatic and oral cavity cancers, ASC tumors tended to favor higher rates of regional or distant disease at presentation with poor tumor differentiation compared to either AC or SCC histologies. After multivariable analysis, adjusting for age, sex, detection stage, grade differentiation, surgery, chemotherapy, and radiotherapy, except for oral cavity cancers, ASCs tended to have worse overall survivals compared to ACs (hazard ratios: 1.1 - 1.6) and SCC (1.0 - 1.3), with colorectal ASCs having the worse overall survival compared to colorectal ACs, with a hazard ratio of 1.4 (95% confidence interval: 1.3 - 1.6).

Conclusions

Overall, these results suggest that ASC outcomes are site specific, and in general, tend to have worse outcomes than nonvariant ACs and SCCs even after correction for common clinical and epidemiological factors. These cancers have a poorly understood but unique tumor biology that warrants further characterization.

A prognostic nomogram for the cancer-specific survival rate of choroidal melanoma using the Surveillance, Epidemiology, and End Results database

Objective

This study was conducted to develop a comprehensive nomogram for individuals with choroidal melanoma (CM) to determine their cancer-specific survival (CSS).

Methods

Data of individuals with CM, diagnosed between 2004 and 2015, were accessed at the Surveillance, Epidemiology, and End Results (SEER) database. The selected individuals were randomly categorized into a training and validation cohort. Multivariate Cox regression analysis was applied to screen the relevant variables. Followed by the development of a nomogram based on independent variables. Ultimately, the net reclassification index (NRI), concordance index (C-index), calibration charts, integrated discrimination improvement (IDI), receiver operating characteristic curves (ROC), area under the curve (AUC), and decision-curve analysis (DCA), were utilized to evaluate the discrimination, accuracy, and effectiveness of the model.

Results

This study enrolled 3,782 patients. Seven independent factors linked to prognosis were screened via multivariate Cox regression analysis, encompassing age at diagnosis; race; AJCC (American Joint Committee on Cancer) stage; histologic type; and therapy method of radiotherapy, surgery, and chemotherapy. The respective C-indexes of the training and validation cohorts were 0.709 and 0.726, indicative of the excellent accuracy of the nomogram. Furthermore, the AUCs of the training and validation cohorts across 3, 5, and 8 years were 0.767, 0.744, and 0.722 as well as 0.772, 0.770, and 0.753, respectively. Evident of the superiority of the established nomogram over the AJCC staging, both the NRI and IDI values exhibited improvement. The favorable clinical impact and good performance of the nomogram were evident via decision curve analyses (DCAs) and calibration plots, respectively.

Conclusion

This research dealt with establishing and validating a nomogram as a prognostic tool for assessing the prognosis of adult patients with CM utilizing the SEER database. A comprehensive assessment of the nomogram via diverse variables demonstrated its accuracy in predicting the CSS probabilities of CM patients across 3, 5, and 8 years in clinical settings. Notably, its performance surpassed that of the AJCC staging system.

The hospital frailty risk score effectively predicts adverse outcomes in patients with atrial fibrillation in the intensive care unit

Background

Atrial fibrillation (AF) and frailty are significant global public health problems associated with advancing age. However, the relationship between frailty and older patients with AF in the intensive care unit (ICU) has not been thoroughly investigated. This study aimed to investigate whether the hospital frailty risk score (HFRS) is associated with adverse outcomes in older patients with AF in the ICU.

Methods

This was the first retrospective analysis of older patients with AF admitted to the ICU between 2008 and 2019 at a tertiary academic medical center in Boston. The HFRS was used to measure frailty severity. The outcomes of interest were in-hospital and 30-day mortality and the incidence of sepsis and ischemic stroke.

Results

There were 7,792 participants aged approximately 80 years, almost half (44.9%) of whom were female. Among this group, 2,876 individuals were identified as non-frail, while 4,916 were classified as frail. The analysis revealed a significantly greater incidence of in-hospital (18.8% compared to 7.6%) and 30-day mortality (24.5% versus 12.3%) in the frail group. After accounting for potential confounding factors, a multivariate Cox proportional hazards regression analysis revealed that frail participants had a 1.56-fold greater risk of mortality within 30 days (95% CI = 1.38-1.76, p < 0.001).

Conclusions

Frailty is an independent risk factor for adverse outcomes in older patients with AF admitted to the ICU. Therefore, prioritizing frailty assessment and implementing specific intervention strategies to improve prognostic outcomes are recommended.

Individualized survival prediction and surgery recommendation for patients with glioblastoma

Background

There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients.

Aim

This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection.

Methods

We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation.

Results

The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group.

Conclusion

The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Charting the Course: Insights into Neuroendocrine Tumor Dynamics in the United States

OBJECTIVE

To explore changing trends and characteristics in neuroendocrine tumors (NETs) epidemiology, focusing on demographics, clinical aspects, and survival, including the impact of social determinants of health (SDOH) on outcomes.

BACKGROUND

The escalating incidence and prevalence of NETs underscore the pressing need for updated epidemiologic data to reveal the evolving landscape of this condition. Access to current information is imperative for informing clinical strategies and public health initiatives targeting NETs.

METHODS

A retrospective, population-based study analyzed NET patient data from 1975 to 2020, using the Surveillance, Epidemiology, and End Results (SEER 8, 12, 18) program. We calculated annual age-adjusted incidence, prevalence, and 5-year overall survival (OS) rates. Survival trends from 2000 to 2019 were examined, employing the Fine-Gray model to evaluate cancer-specific mortality.

RESULTS

NETs' age-adjusted incidence rate quadrupled from 1.5 per 100,000 in 1975 to 6.0 per 100,000 in 2020. A decline in incidence occurred from 6.8 per 100,000 in 2019 to 6.0 per 100,000 in 2020. All-cause survival multivariable analysis demonstrated high grade (HR: 2.95, 95% CI: 2.63-3.09, P<0.001), single patients (HR: 1.49, 95% CI: 1.45-1.54, P<0.001), and Black patients (HR: 1.17, 95% CI:1.13-1.22, P<0.001) all had worse survival than their controls.

CONCLUSION

In conclusion, our study shows a steady increase in NETs incidence until 2019, with a decline in 2020. Understanding the reasons behind this trend is vital for improved management and public health planning. Further research should focus on the factors driving these changes to enhance our understanding of NET epidemiology.

Nomogram model of survival prediction for nasopharyngeal carcinoma with lung metastasis: developed from the SEER database and validated externally

Objective

Distant metastasis occurs in some patients at the first diagnosis of nasopharyngeal carcinoma (NPC), the prognosis is poor, and there are significant individual differences. This study established a nomogram model of lung metastasis of NPC as a supplement to TNM staging.

Methods

The training cohort is used to build the nomogram model, and the validation cohort is used to evaluate the model. The training cohort of 177 patients is from the Surveillance, Epidemiology, and End Results (SEER) database. Factors affecting overall survival (OS) in patients with lung metastasis of NPC analysis by Cox regression analysis and then a nomogram were established. 122 patients from the Affiliated Tumor Hospital of Guangxi Medical University were selected as the external validation cohort. The concordance index (C-index), the area under the curve (AUC), and the calibration curve were used to assess the accuracy of the nomogram and used the decision curve analysis (DCA) curve to measure the clinical benefit capacity of the model. The patients were separated into two groups with different risks, and the "Kaplan-Meier (KM)" survival analysis was used to evaluate the differentiation ability of the model.

Results

Age, T-stage, radiation, chemotherapy, and brain metastases can affect the OS in NPC with lung metastasis. A nomogram was developed according to the above five factors. The C-index of the training cohort and the validation cohort were 0.726 (95% CI: 0.692-0.760) and 0.762 (95% CI: 0.733-0.791). The AUC of the nomogram was better than that of the TNM staging. In the training cohort, the nomogram predicted OS AUC values of 0.767, 0.746, and 0.750 at 1, 2, and 3 years, TNM stage of 0.574, 0.596, and 0.640. In the validation cohort, nomogram predictions of OS AUC values of 0.817, 0.857, and 0.791 for 1, 2, and 3 years, TNM stage of 0.575, 0.612, and 0.663. DCA curves suggest that nomogram have better clinical net benefits than TNM staging. The KM survival analysis shows that the nomogram has a reasonable risk stratification ability.

Conclusion

This study successfully established a nomogram model of NPC lung metastasis, which can be used as a supplement to TNM staging and provide reference for clinicians.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.