Competing-risks model for predicting the prognosis of penile cancer based on the SEER database

The hepatocellular carcinoma risk in patients with HBV-related cirrhosis: a competing risk nomogram based on a 4-year retrospective cohort study

Objective

The study aimed to build and validate a competitive risk nomogram to predict the cumulative incidence of hepatocellular carcinoma (HCC) for patients with hepatitis B virus (HBV)-related cirrhosis.

Methods

A total of 1401 HBV-related cirrhosis patients were retrospectively enrolled from January 1, 2011 to December 31, 2014. Application of 20 times imputation dealt with missing data using multiple imputation by chained equations (MICE). The patients were randomly divided into a training set (n = 1017) and a validation set (n = 384) at a ratio of 3:1. A prediction study was carried out using a competing risk model, where the event of interest was HCC and the competing events were death and liver transplantation, and subdistribution hazard ratios (sHRs) with 95% CIs were reported. The multivariate competing risk model was constructed and validated.

Results

There was a negligible difference between the original database and the 20 imputed datasets. At the end of follow-up, the median follow-up time was 69.9 months (interquartile range: 43.8-86.6). There were 31.5% (442/1401) of the patients who developed HCC, with a 5-year cumulative incidence of 22.9 (95%CI, 20.8%-25.2%). The univariate and multivariate competing risk regression and construction of the nomogram were performed in 20 imputed training datasets. Age, sex, antiviral therapy history, hepatitis B e antigen, alcohol drinking history, and alpha-fetoprotein levels were included in the nomogram. The area under receiver operating characteristic curve values at 12, 24, 36, 60, and 96 months were 0.68, 0.69, 0.70, 0.68, and 0.80, and the Brier scores were 0.30, 0.25, 0.23, 0.21, and 0.20 in the validation set. According to the cumulative incidence function, the nomogram effectively screened out high-risk HCC patients from low-risk patients in the presence of competing events (Fine-Gray test p < 0.001).

Conclusion

The competitive risk nomogram was allowed to be used for predicting HCC risk in individual patients with liver cirrhosis, taking into account both the association between risk factors and HCC and the modifying effect of competition events on this association.

Developing and validating a nomogram for penile cancer survival: A comprehensive study based on SEER and Chinese data

OBJECTIVE

The primary aim of this study was to create a nomogram for predicting survival outcomes in penile cancer patients, utilizing data from the Surveillance, Epidemiology, and End Results (SEER) and a Chinese organization.

METHODS

Our study involved a cohort of 5744 patients diagnosed with penile cancer from the SEER database, spanning from 2004 to 2019. In addition, 103 patients with penile cancer from Sun Yat-sen Memorial Hospital of Sun Yat-sen University were included during the same period. Based on the results of regression analysis, a nomogram is constructed and validated internally and externally. The predictive performance of the model was evaluated by concordance index (c-index), area under the curve, decision curve analysis, and calibration curve, in internal and external datasets. Finally, the prediction efficiency is compared with the TNM staging model.

RESULTS

A total of 3154 penile patients were randomly divided into the training group and the internal validation group at a ratio of 2:1. Nine independent risk factors were identified, including age, race, marital status, tumor grade, histology, TNM stage, and the surgical approach. Based on these factors, a nomogram was constructed to predict OS. The nomogram demonstrated relatively better consistency, predictive accuracy, and clinical relevance, with a c-index over 0.73 (in the training cohort, the validation cohort, and externally validation cohort.) These evaluation indexes are far better than the TNM staging system.

CONCLUSION

Penile cancer, often overlooked in research, has lacked detailed investigative focus and guidelines. This study stands as the first to validate penile cancer prognosis using extensive data from the SEER database, supplemented by data from our own institution. Our findings equip surgeons with an essential tool to predict the prognosis of penile cancer better suited than TNM, thereby enhancing clinical decision-making processes.

New findings in prognostic factor assessment for adenocarcinoma of transverse colon: a comparison study between competing-risk and COX regression analysis

Purpose

Competing-risk analysis was used to accurately assess prognostic factors for cancer-specific death in patients with adenocarcinoma of transverse colon (ATC), and the results were compared with those from a conventional Cox regression analysis.

Materials and Methods

Patients diagnosed with ATC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results database. The crude mortality rates of patients with ATC were calculated and their differences were tested using the Gray's test, respectively. In performing multivariate analysis, the Cox regression model and the subdistribution hazard function (SD) in competing risk analysis were utilized, respectively.

Results

This study included 21,477 eligible patients. The SD model indicated that age, etc. are actual independent prognostic factors. In contrast to previous recognition, the results of the Cox regression showed false-positives for sex and Carcinoembryonic antigen, and underestimated point-estimates in the stage and American Joint Committee on Cancer stage due to competing events. A detailed comparison of treatment revealed that the larger surgical scopes were prognostic risk factors compared with the smaller scope of local tumor excision, partial colectomy, or segmental resection. Patients treated with external proton beam radiotherapy had an increased risk compared with those with no radiotherapy and internal radiotherapy.

Conclusions

After comparing the results of the two methods and mitigating the significant bias introduced by Cox regression, we found independent factors that really affect the prognosis of ATC. On the other hand, in terms of ATC, a larger surgical scope and external proton beam radiotherapy may not improve the long-term survival of patients. Therefore, when faced with ATC patients, these differences should be noted and treated differently from common colorectal cancer patients. Thus, clinicians are able to give more targeted treatment plans and prognostic assessments.

Development and validation of a predictive model for penile cancer based on the surveillance, epidemiology, and end results database and multi-center cases

PURPOSE

Penile cancer (PC) is a great impact on the quality of life and psychological status of patients. This study aimed to construct nomograms using data from the Surveillance, Epidemiology, and End Results (SEER) database to predict overall survival (OS) and cancer-specific survival (CSS) in patients with penile cancer (PC).

METHODS

Patients were divided into a training cohort (n = 634) and a validation cohort (n = 272) in a 7:3 ratio. Independent risk factors influencing the prognosis of PC were screened using univariate and multivariate Cox analyses, and models for predicting PC were developed. Data from 203 patients with PC in four tertiary hospitals in Gansu Province from 2012 to 2021 were externally validated.

RESULTS

Univariate analysis and multivariate analysis showed revealed that the OS-related factors were age, grade, T stage, N stage, M stage and tumor size (p < 0.05); the CSS-related factors were age, mode of surgery, T stage, N stage, M stage and tumor size (p < 0.05). The C-indices of the OS and CSS nomograms in the training cohort were 0.743 [95% confidence interval (CI) (0.714-0.772)] and 0.797 (0.762-0.832), respectively. The C-indices of the OS and CSS nomograms in the internal validation cohort were 0.735 (0.686-0.784) and 0.755 (0.688-0.822), respectively, and those in the external validation cohort were 0.801 (0.746-0.856) and 0.863 (0.812-0.914), respectively. Receiver operating characteristic (ROC) curves, calibration curves, and survival curves all demonstrated good predictive performance of the nomograms.

CONCLUSION

The nomograms for PC were developed using the SEER database. The accuracy and clinical usefulness of the model were validated through a combination of internal and external validations.

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

In the United States (US), the Surveillance, Epidemiology, and End Results (SEER) program is the only comprehensive source of population-based information that includes stage of cancer at the time of diagnosis and patient survival data. This program aims to provide a database about cancer incidence and survival for studies of surveillance and the development of analytical and methodological tools in the cancer field. Currently, the SEER program covers approximately half of the total cancer patients in the US. A growing number of clinical studies have applied the SEER database in various aspects. However, the intrinsic features of the SEER database, such as the huge data volume and complexity of data types, have hindered its application. In this review, we provided a systematic overview of the commonly used methodologies and study designs for retrospective epidemiological research in order to illustrate the application of the SEER database. Therefore, the goal of this review is to assist researchers in the selection of appropriate methods and study designs for enhancing the robustness and reliability of clinical studies by mining the SEER database.

Real-world treatment outcomes of patients with penile cancer in the Kyushu-Okinawa area of Japan in the pre-guideline era

OBJECTIVES

To understand the real-world outcomes for patients with penile cancer in the Kyushu-Okinawa area before the introduction of practice guidelines in Japan.

METHODS

We retrospectively collected medical information on patients with penile squamous cell carcinoma and penile intraepithelial neoplasia at 12 university hospitals and their affiliated hospitals in the Kyushu-Okinawa area from January 2009 to December 2020. Patients with unknown clinical stage were excluded. Patient background characteristics and survival, as well as pretreatment factors involved in survival, were investigated.

RESULTS

A total of 196 patients were included. Patients with clinical stage 0, I, IIA, IIB, IIIA, IIIB and IV comprised 9.7, 26.0, 22.4, 2.6, 10.7, 14.3 and 14.3%, respectively. The median follow-up was 26 months, and the mean 5-year overall survival and cancer-specific survival rates were 74.3 and 79.8%, respectively. On univariate analysis, tumor diameter ≥ 30 mm, penile shaft tumor, Eastern Cooperative Oncology Group performance status ≥ 1, cT ≥ 3, cN ≥ 2 and cM1 were associated with significantly poorer cancer-specific survival. On multivariate analysis, pretreatment factors of cN ≥ 2 (hazard ratio, 32.5; 95% confidence interval, 5.08-208; P = 0.0002), Eastern Cooperative Oncology Group performance status ≥ 1 (4.42; 1.79-10.9; P = 0.0012) and cT ≥ 3 (3.34; 1.11-10.1; P = 0.0319) were identified as independent prognostic factors.

CONCLUSIONS

The study revealed basic data for future penile cancer treatment and research, including survival rates according to clinical stages, and identified cN ≥ 2, Eastern Cooperative Oncology Group performance status ≥ 1 and cT ≥ 3 at initial diagnosis as independent prognostic factors. Evidence for penile cancer in Japan is particularly scarce, and future large-scale prospective studies are warranted.

Utility of gamma-glutamyl transpeptidase to lymphocyte count ratio in predicting prognosis of patients with oral cancer: A prospective cohort study in Southeastern China

BACKGROUND

To assess the prognostic role of gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) and develop a prognostic nomogram for patients with oral cancer.

METHODS

A prospective cohort (n = 1011) was conducted during July 2002 to March 2021 in Southeastern China.

RESULTS

The median follow-up time was 3.5 years. Multivariate Cox regression (OS: HR = 1.51, 95% CI: 1.04, 2.18) and Fine-Gray model (DSS: HR = 1.68, 95% CI: 1.14, 2.49) both showed that high GLR could act as an indicator of poor prognosis. A nonlinear dose-response relationship was observed between continuous GLR and the risk of all-cause mortality (p for overall = 0.028, p for nonlinear = 0.048). Compare with TNM stage, time-dependent ROC curve proved that GLR-based nomogram model performs better in predicting prognosis (the area under curve for 1-, 3-, and 5-years mortality: 0.63, 0.65, and 0.64 vs. 0.76, 0.77, and 0.78, p < 0.001).

CONCLUSION

GLR might be a useful tool in predicting prognosis for patients with oral cancer.

Impact of marital status on overall survival in patients with early-stage hepatocellular carcinoma

The purpose of the present research was to assess the prognostic impact of marital status in hepatocellular carcinoma (HCC) patients with tumors ≤ 2 cm (stage Ia) based on the data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients who received a histopathologic HCC diagnosis between 2004 and 2016 were recruited. Overall survival (OS) was the major outcome measure. The Cox regression model and the Fine-Gray regression model were used for the purpose of comparing and examining the prognostic value of marital status for OS. The data for a total of 2446 stage Ia HCC patients were extracted from the database. The median overall survival time was 96.0 months, with 5-year and 10-year overall survival rates of 58.2% and 45.8%, respectively. In both the Fine-Gray regression model and Cox regression model, marital status [married vs. unmarried and others, both P < 0.001, hazard ratio (HR) = 1.389 for Cox and HR = 1.378 for Fine-Gray], age at diagnosis, tumor grade, and surgery at the primary site independently served as prognostic indicators associated with OS. In conclusion, positive marital status was independently associated with better OS for stage Ia HCC patients, and its prognostic influence should be validated in the near future.

Survival Comparison Between Squamous Cell Carcinoma and Adenocarcinoma for Radiotherapy-Treated Patients with Stage IIB-IVA Cervical Cancer

Objective

To compare the prognostic significance of adenocarcinoma (AC) with squamous cell carcinoma (SCC) on overall survival (OS) in patients with stage IIB-IVA cervical cancer (CC) treated by external beam radiotherapy (EBRT) and brachytherapy (BRT) with/without chemotherapy registered in the Surveillance, Epidemiology, and End Results database.

Methods

Data of eligible patients were extracted between 2004 and 2016. A univariate analysis was conducted using the cumulative incidence function (CIF) by considering competing events and compared using Gray’s test. The significant variables in univariate analysis were further evaluated in a multivariate analysis performed with the Fine-Gray regression model. Propensity score matching (PSM) analysis was also employed to reconfirm the results found in the present study.

Results

A total of 2,243 patients with SCC and 176 patients with AC were extracted from the database. The 5-year OS rates were 57.8% in the SCC group and 52.8% in the AC group. 149 patients died of causes other than CC—considered as competing events. Compared with the SCC group, patients diagnosed with AC had statistically significant worse 5-year OS rate before and after PSM. In the multivariate Fine-Gray regression model, the histological subtype of AC was proven as an independent prognostic factor associated with poorer OS before [hazard ratio (HR) = 1.340; 95% confidence interval (CI): 1.081-1.660; P = 0.007] and after [HR = 1.376; 95% CI: 1.107-1.711; P = 0.004] PSM.

Conclusions

The histological subtype of AC is significantly correlated with impaired OS as an independent prognostic variable in patients with stage IIB-IVA CC who received EBRT and BRT compared to patients with SCC. Future studies should incorporate effective and individualized treatment strategies into clinical decision-making to improve the unsatisfactory survival outcomes for patients with AC.

The implicated clinical factors for outcomes in 304 patients with salivary duct carcinoma: Multi-institutional retrospective analysis in Japan

Background

Salivary duct carcinoma (SDC) is a high‐grade salivary malignancy that frequently occurs as the carcinomatous component of carcinoma ex pleomorphic adenoma. We herein examined the clinical factors affecting outcomes in a large cohort of SDC.

Methods

We selected 304 SDC cases and investigated clinical characteristics and the factors affecting outcomes.

Results

The median age of the cases examined was 68 years, the most common primary site was the parotid gland (238 cases), and there was a male predominance (M/F = 5:1). Outcomes were significantly worse when the primary tumor site was the minor salivary glands (SG) than when it was the major SG. Outcomes were also significantly worse in pN(+) cases (161 cases) than in pN0 cases, particularly those with a metastatic lymph node number ≥11. The cumulative incidence of relapse and distant metastases was significantly higher in stage IV cases than in stage 0–III cases.

Conclusions

The absolute number of lymph node metastases, higher stages, and the minor SG as the primary tumor site were identified as factors affecting the outcome of SDC.

Sleep Duration and Frailty Risk among Older Adults: Evidence from a Retrospective, Population-Based Cohort Study

OBJECTIVES

Frailty and sleep duration complaints are both prevalent and often coexist among older adults. The purpose of this study was to examine the prospective association between sleep duration and frailty risk in a nationally representative cohort study.

DESIGN

Prospective cohort study, ten-year follow-up.

SETTING

Community-based setting in 23 provinces of China.

PARTICIPANTS

A total of 7623 older adults age 65 and over without frailty at baseline were included in the analysis.

MEASUREMENTS

The participants were divided into three groups according to self-reported sleep duration: short (≤6 hours per day), middle (>6 but <10 hours per day) and long (≥10 hours per day). Frailty was measured according to the accumulation of health deficits by the construction of a frailty index of 38 items with 0.25 as the cutoff. A Cox proportional hazard model, a competing risk model and a generalized estimating equation (GEE) model with multiple adjustments were performed to evaluate the association between sleep duration and frailty risk.

RESULTS

During a median follow-up period of 4.4 years (IQR 2.9-9.0), 2531 (33.2%) individuals developed frailty. Compared with participants with middle sleep duration, the risk of frailty was increased among participants with long sleep duration (HR 1.26, 95% CI 1.14-1.38) in the fully adjusted Cox proportional hazard model. However, short sleep duration was insignificantly associated with frailty risk. The competing risk model and the GEE model yielded similar results.

CONCLUSION

Long sleep duration is significantly associated with frailty incidence among older adults even after adjustment for confounding factors. This study provides reinforcing longitudinal evidence for the need to design sleep quality improvement interventions in health care programs to prevent frailty among older adults.

Data mining in clinical big data: the frequently used databases, steps, and methodological models

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis

OBJECTIVE

This study aimed to use a competing-risks model to establish a nomogram to accurately analyse the prognostic factors for upper tract urothelial carcinoma (UTUC) cancer-specific death (CSD).

DESIGN

Retrospective observational cohort study.

SETTING

The programme has yielded a database of all patients with cancer in 18 defined geographical regions of the USA.

PARTICIPANTS

We selected patients with UTUC from the latest edition of the Surveillance, Epidemiology, and End Results database from 1975 to 2016. After excluding patients with unknown histological grade, tumour size and lymph node status, 2576 patients were finally selected.

PRIMARY AND SECONDARY OUTCOME MEASURES

We used the Fine-Gray proportional subdistribution hazards model for multivariate analysis and compared the results with cause-specific hazards model. We finally constructed a nomogram for 3-year, 5-year and 8-year CSD rates and tested these rates in a validation cohort.

RESULTS

The proportional subdistribution hazards model showed that sex, tumour size, distant metastasis, surgery status, number of lymph nodes positive (LNP) and lymph nodes ratio (LNR) were independent prognostic factors for CSD. All significant factors associated with CSD were included in the nomogram. The 3-year, 5-year and 8-year concordance indexes were 0.719, 0.702 and 0.692 in the training cohort and 0.701, 0.675 and 0.668 in the validation cohort, respectively.

CONCLUSIONS

The competing-risks model showed that sex, tumour size, distant metastasis, surgery status, LNP and LNR were associated with CSD. The nomogram predicts the probability of CSD in patients with UTUC at 3, 5 and 8 years, which may help clinicians in predicting survival probabilities in individual patients.

Prognostic Values of Different Clinicopathological Factors and Predictive Models for Penile Carcinoma

Objective

To evaluate the prognostic factors of penile cancer and the utility of prognostic models.

Methods

We analyzed postoperatively collected data of 311 patients diagnosed with penile cancer. Survival analysis (Kaplan–Meier and cox regression methods) was performed on this cohort. The c-index was used to determine the predictive accuracies of potential prognostic factors. The accuracies of four prognostic models were also evaluated, which were AJCC prognostic stage group for three recent editions, and four nomograms constructed by the Surveillance, Epidemiology, and End Results program (SEER). Two novel nomograms using our data were created and AUC of 2-year survival were determined to compare existing and newly established models.

Results

Tumor site, T and N stages, nuclear grade and lymph vascular invasion (LVI) significantly influenced prognosis. The 8th T and N stages had better c-indexes than former editions, while no improvement was seen in the 8thAJCC stage group. 6th AJCC+grade nomogram had a higher c-index than other three nomograms (SEER+grade, 6th TNM+grade, and 6th T_1-3N_0-3+grade nomograms; c-index: 0.831 vs 0.738, 0.792 and 0.781). New nomogram 1 included the 8th T and N stages, tumor site, nuclear grade, and LVI, with a c-index of 0.870. Novel nomogram 2 replaced the T and N stages with the AJCC stage group, which had a lower c-index of 0.855. The order of prediction accuracy of 2-year survival in the old and new models is consistent with the c-index results.

Conclusion

Tumor site, stages, grade, and LVI play important roles in predicting survival of penile cancer. The 8th stages have better predictive accuracy than former editions. We proposed two models with better predictive accuracy than former models; specifically, nomogram 1 may be a more precise and convenient tool for predicting penile cancer outcomes.

Nomograms for Estimating Cause-Specific Death Rates of Patients With Inflammatory Breast Cancer: A Competing-Risks Analysis

Purpose:

Inflammatory breast cancer (IBC) is a rare, aggressive and special subtype of primary breast cancer. We aimed to establish competing-risks nomograms to predict the IBC-specific death (BCSD) and other-cause-specific death (OCSD) of IBC patients.

Methods:

We extracted data on primary IBC patients from the SEER (Surveillance, Epidemiology, and End Results) database by applying specific inclusion and exclusion criteria. Cumulative incidence function (CIF) was used to calculate the cumulative incidence rates and Gray’s test was used to evaluate the difference between groups. Fine-Gray proportional subdistribution hazard method was applied to identify the independent predictors. We then established nomograms to predict the 1-, 3-, and 5-year cumulative incidence rates of BCSD and OCSD based on the results. The calibration curves and concordance index (C-index) were adopted to validate the nomograms.

Results:

We enrolled 1699 eligible IBC patients eventually. In general, the 1-, 3-, and 5-years cumulative incidence rates of BCSD were 15.3%, 41.0%, and 50.7%, respectively, while those of OCSD were 3.0%, 5.1%, and 7.4%. The following 9 variables were independent predictive factors for BCSD: race, lymph node ratio (LNR), AJCC M stage, histological grade, ER (estrogen receptor) status, PR (progesterone receptor) status, HER-2 (human epidermal growth factor-like receptor 2) status, surgery status, and radiotherapy status. Meanwhile, age, ER, PR and chemotherapy status could predict OCSD independently. These factors were integrated for the construction of the competing-risks nomograms. The results of calibration curves and C-indexes indicated the nomograms had good performance.

Conclusions:

Based on the SEER database, we established the first competing-risks nomograms to predict BCSD and OCSD of IBC patients. The good performance indicated that they could be incorporated in clinical practice to provide references for clinicians to make individualized treatment strategies.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis

INTRODUCTION

Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.

METHODS

Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.

RESULTS

We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability.

CONCLUSION

We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Incidence trends and survival outcomes of penile squamous cell carcinoma: evidence from the Surveillance, Epidemiology and End Results population-based data

Background

To provide the latest incidence trends and explore survival outcomes of penile squamous cell carcinoma (PSCC) patients with or without a previous primary malignancy.

Methods

Patients diagnosed with PSCC between 1975 and 2016 in the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively included. Then, we calculated the age-adjusted incidence rates (IRs) and annual percentage changes (APCs). Multivariate Cox analysis and Kaplan-Meier (KM) survival curves were conducted to investigate prognostic variables for cancer-specific survival (CSS).

Results

A total of 6,122 PSCC patients were enrolled, 1,137 of whom had a prior malignancy. The age-adjusted IR for the general population in men declined before 1987, fluctuated slightly between 1987 and 1997, and showed an upward trend after 1997, which was basically consistent with that in patients without a previous primary malignancy. The incidence trend of PSCC in the general population was similar with that in those without a previous malignancy. However, the IRs of PSCC in men with a previous malignancy have been increasing since 1975 regardless of race. Furthermore, age at diagnosis, pathological grade, extent of disease, marital status, the administration of surgery and presence of previous primary malignancy were identified to be significantly related to CSS.

Conclusions

The IRs of PSCC have been increasing in recent years. Several independent prognostic factors for CSS were identified, allowing surgeons to assess the individualized risk in advance.

A competing risk nomogram for predicting cancer-specific death of patients with buccal mucosa cancer

OBJECTIVES

Our aim was to develop and validate a competing risk nomogram to determine the probability of cancer-specific death in buccal mucosa cancer (BMC) patients.

MATERIALS AND METHODS

We examined the records of BMC patients in the Surveillance, Epidemiology, and End Results (SEER) Program and First Affiliated Hospital of Nanchang University (China). We adopted the cumulative incidence function and Fine-Gray proportional hazards model based on univariate and multivariate analyses by R-software to identify the risk factors associated with cancer-specific death. Subsequently, a nomogram was developed and validated to predict the 3- and 5-year probability of cancer-specific death.

RESULTS

In 1,286 BMC patients identified from SEER database, cumulative incidences of cancer-specific death after diagnosis were 33.4% and 35.5% for 3 and 5 years, respectively. In the training cohort (n = 902) from SEER database, the Fine-Gray model indicated that age, Tumor Node Metastasis (TNM) stages, grade, surgery, and histological type were independent risk factors associated with cancer-specific death, based on which a prognostic nomogram was developed. In the internal validation cohort from SEER database (n = 384) and the external validation cohort from our medical center (n = 174), the nomogram was well calibrated and showed remarkable prediction performance.

CONCLUSION

The nomogram created herein may prove to be a good assistant tool for assessing the prognosis of BMC patients.

Nomograms to predict overall and cancer-specific survival in patients with penile cancer

BACKGROUND

To develop and validate prognostic nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with penile cancer (PC).

METHODS

Based on the Surveillance, Epidemiology, and End Result (SEER) database, patients diagnosed with PC from 2010 to 2015 were enrolled in this study. For each patient, clinical characteristics and survival results were respectively collected. With the method of random-number generation, included patients were divided into the training cohort and the validation group. Subsequently, nomograms were constructed to predict 3- and 5-year OS and CSS based on the results of multivariate analyses. Kaplan-Meier (KM) method and the log-rank test were used to estimate survival curves of each variables. Finally, the calibration plots, concordance index (C-index), area under the receiver operating characteristic (ROC) curves were used to evaluate nomograms performance.

RESULTS

Totally, 1,418 patients were eventually enrolled in the study, including 994 patients in the training cohort and 424 patients in the validation cohort. No significant difference was detected in the baseline characteristics between two cohorts. According to results of the uni- and multivariate analysis in the training cohort, 7 factors (including age, race, T stage, N stage, M stage, histology codes, and the use of surgery) for OS and 7 factors (including race, T stage, N stage, M stage, histology codes, the use of surgery and lymph node removal) for CSS were selected for constructing the nomograms. The C-indices for OS and CSS were 0.755 and 0.805 in the training cohort and 0.711, 0.737 in the validation cohort. In addition, the 3- and 5-year area under the ROC curve (AUC)s for OS were 0.792 and 0.771 in the training cohort, and 0.687 and 0.695 in the validation group. When it came to CSS, it was 0.83 and 0.826 in the training cohort and 0.758 and 0.746 in the validation cohort. Lastly, the calibration curves indicated a good consistency between the actual survival and the predictive survival.

CONCLUSIONS

We firstly established survival models to predict OS and CSS in PC patients with good predictive ability. Further studies are needed to validate our results before clinical application in the future.

TGF-β and microRNA Interplay in Genitourinary Cancers

Genitourinary cancers (GCs) include a large group of different types of tumors localizing to the kidney, bladder, prostate, testis, and penis. Despite highly divergent molecular patterns, most GCs share commonly disturbed signaling pathways that involve the activity of TGF-β (transforming growth factor beta). TGF-β is a pleiotropic cytokine that regulates key cancer-related molecular and cellular processes, including proliferation, migration, invasion, apoptosis, and chemoresistance. The understanding of the mechanisms of TGF-β actions in cancer is hindered by the "TGF-β paradox" in which early stages of cancerogenic process are suppressed by TGF-β while advanced stages are stimulated by its activity. A growing body of evidence suggests that these paradoxical TGF-β actions could result from the interplay with microRNAs: Short, non-coding RNAs that regulate gene expression by binding to target transcripts and inducing mRNA degradation or inhibition of translation. Here, we discuss the current knowledge of TGF-β signaling in GCs. Importantly, TGF-β signaling and microRNA-mediated regulation of gene expression often act in complicated feedback circuits that involve other crucial regulators of cancer progression (e.g., androgen receptor). Furthermore, recently published in vitro and in vivo studies clearly indicate that the interplay between microRNAs and the TGF-β signaling pathway offers new potential treatment options for GC patients.

Competing-risks model for predicting the prognosis of penile cancer based on the SEER database

Objectives

This study performed a competing‐risks analysis using data from the SEER database on penile cancer patients with the aim of identifying more accurate prognostic factors.

Methods

Data on patients with penile cancer were extracted from the SEER database. A univariate analysis used the cumulative incidence function and Gray's test, while multivariate analysis was performed using the Fine‐Gray model. Cumulative hazards were compared with a competing‐risks model constructed using Kaplan‐Meier estimation.

Results

The multivariate Fine‐Gray analysis indicated that being black (HR = 1.51, 95%CI: 1.10‐2.07, P = .01), AJCC stage II (HR = 1.94, 95%CI: 1.36‐2.77, P < .001), AJCC stage III (HR = 1.98, 95%CI: 1.34‐2.91, P < .001), tumor size > 5 cm (HR = 2.23, 95%CI: 1.33‐3.72, P < .05), and TNM stages N1 (HR = 2.49, 95%CI: 1.71‐3.61, P < .001), N2 (HR = 3.25, 95%CI: 2.18‐4.84, P < .001), N3 (HR = 5.05, 95%CI: 2.69‐9.50, P < .001), and M1 (HR = 2.21, 95%CI: 1.28‐3.84, P < .05) were statistically significant. The results obtained using multivariate Cox regression were different, while Kaplan‐Meier curve analysis led to an overestimation of the cumulative risk of the patient.

Conclusions

This study established a competing‐risks analysis model for the first time based on the SEER database for the risk assessment of penile cancer patients. The results may help clinicians to better understand penile cancer and provide these patients with more appropriate support.