Marital Status and Prognostic Nomogram for Bladder Cancer With Distant Metastasis: A SEER-Based Study

Status quo and factors of depression and anxiety in patients with non-muscle invasive bladder cancer after plasma electrocision

BACKGROUND

Bladder cancer is a type of cancer with a high incidence in men. Plasma electrosurgery (PES) is often used in the treatment of bladder cancer. Postoperative complications often cause depression and anxiety in patients after surgery.

AIM

To investigate the current state of depression and anxiety after PES in patients with non-muscle-invasive bladder cancer and analyze the factors affecting them.

METHODS

A retrospective study was conducted to compare the baseline data of patients by collecting their medical history and grouping them according to their mental status into negative and normal groups. Logistic regression analysis was used to explore the risk factors affecting the occurrence of anxiety and depression after surgery in patients with bladder cancer.

RESULTS

Comparative analyses of baseline differences showed that the patients in the negative and normal groups differed in terms of their first surgery, economic status, educational level, and marital status. A logistic regression analysis showed that it affected the occurrence of anxiety in patients with bladder cancer, and the results showed that whether the risk factors were whether or not it was the first surgery, monthly income between 3000 and 3000-6000, secondary or junior high school education level, single, divorced, and widowed statuses.

CONCLUSION

The risk factors affecting the onset of anxiety and depression in bladder cancer patients after PES are the number of surgeries, economic status, level of education, and marital status. This study provides a reference for the clinical treatment and prognosis of bladder cancer patients in the future.

Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China

OBJECTIVE

We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results.

METHODS

We collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component-wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C-index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox-Snell residual plot. We also used time-dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model-agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model.

RESULTS

The final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS.

CONCLUSIONS

The ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

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.

Development and validation of a novel nomogram model for predicting the survival of patients with T2-4a, N0-x, M0 bladder cancer: a retrospective cohort study

OBJECTIVE

Recent developments in bladder cancer treatment strategies have significantly improved the prognosis of clinically curable muscle invasive bladder cancer (MIBC) patients. Here, the prognostic factors of T2-4a, N0-x, M0 MIBC patients were investigated using the Surveillance, Epidemiology, and End Results (SEER) database and a novel nomogram model was established for prognosis prediction.

METHODS

The data of 7,292 patients with T2-4a, N0-x, M0 MIBC were retrieved from the SEER database (2000-2020) and randomly classified into a training set (n = 5,106) and validation set (n = 2,188). Kaplan-Meier analysis was used to calculate cancer-specific survival (CSS) and overall survival (OS) rates of patients, and differences between survival curves were analyzed using the log-rank test. Cox regression analysis was used to screen and incorporate patient prognosis-affecting independent risk factors into the nomogram model. Consistency index (C-index) values and areas under the time-dependent receiver operating characteristic curve (AUC) were used to evaluate the discriminatory ability, and the calibration curve was used to assess the calibration of the model. Its predictive performance and American Joint Committee on Cancer (AJCC) stage were compared using decision curve analysis (DCA).

RESULTS

The 1-, 3-, and 5-year CSS and OS rates of patients with T2-4a, N0-x, M0 MIBC were 76.9%, 56.0%, and 49.9%, respectively, and 71.3%, 47.9%, and 39.5%, respectively. Cox regression analysis showed that age, marital status, race, pathological type, tumor size, AJCC stage, T stage, N stage, surgery of primary tumor, regional lymph node dissection, radiation, and chemotherapy were independent prognostic risk factors of both CSS and OS (P < 0.05). The C-index and AUC of the nomogram model constructed based on the training and validation sets were both > 0.7, and calibration curves for predicting the 1-, 3-, and 5-year survival were consistent with the ideal curve. The nomogram model showed a higher net benefit with DCA than AJCC stage analysis.

CONCLUSION

The nomogram model could accurately predict the prognosis of patients with T2-4a, N0-x, M0 MIBC. It may help clinicians perform personalized prognosis evaluations and formulate treatment plans.

Assessing the prognostic impact of prostatic urethra involvement and developing a nomogram for T1 stage bladder cancer

PURPOSE

To investigate prognostic values of prostatic urethra involvement (PUI) and construct a prognostic model that estimates the probability of cancer-specific survival for T1 bladder cancer patients.

METHOD AND MATERIALS

We investigated the national Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) to get patients diagnosed with T1 bladder cancer. An external validation cohort was obtained from the First Affiliated Hospital of Nanchang University. The Kaplan-Meier method with the log-rank test was applied to assess cancer-specific survival (CSS) and overall survival (OS). Moreover, the propensity score matching (PSM) and multivariable Cox proportional hazard model were performed. All patients were randomly divided into the development cohort and validation group at the ratio of 7:3. The performance of the model was internally validated by calibration curves and the concordance index (C-index).

RESULTS

The PUI group had a lower survival rate of both CSS and overall survival OS before and after PSM when compared to non-involved patients (All P < 0.05). Multivariate analysis revealed a poor prognosis in the PUI group for cancer-specific mortality (CSM) and all-cause mortality (ACM) analyses before and after PSM (All P < 0.05). Seven variables, including age, surgery, radiotherapy, tumour size, PUI, and marital status, were incorporated in the final nomogram. The C-index in the development cohort was 0.715 (0.711-0.719), while it was 0.672 (0.667-0.677) in the validation group. Calibration plots for 3- and 5-year cancer-specific survival showed good concordance in the development and validation cohorts.

CONCLUSIONS

PUI was an independent risk factor of ACM and CSM in T1 bladder cancer patients. In addition, a highly discriminative and precise nomogram that predicted the individualized probability of cancer-specific survival for patients with T1 bladder cancer was constructed.

Risk factors for distant metastasis of Chondrosarcoma in the middle-aged and elderly people

Chondrosarcoma is the second most common primary bone malignancy with the highest incidence in middle-aged and elderly people, where distant metastasis (DM) still leads to poor prognosis. The purpose of this study was to construct a nomogram for studying the diagnosis of DM in middle-aged and elderly patients with chondrosarcoma. Data on chondrosarcoma patients aged ≥ 40 years diagnosed from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The data were divided into a training set and an internal validation set according to a 7:3 ratio, and the training set data were screened for independent risk factors for DM in chondrosarcoma patients using univariate and multivariate logistic regression analysis. The screened independent risk factors were then used to build a nomogram. In addition, data from 144 patients with chondrosarcoma aged ≥ 40 years diagnosed in a tertiary hospital in China from 2012 to 2021 were collected as the external validation set. The results were evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis in the training set, internal validation set, and external validation set. A total of 1462 middle-aged and elderly patients with chondrosarcoma were included, and 92 (6.29%) had DM at the time of diagnosis. Independent risk factors for DM in middle-aged and elderly patients with chondrosarcoma included being married (OR: 2.119, 95% CI: 1.094-4.105), histological type of dedifferentiated chondrosarcoma (OR: 1.290, 95% CI: 1.110-1.499), high-grade tumor (OR: 1.511, 95% CI: 1.079-2.115), T3 stage (OR: 4.184, 95% CI: 1.977- 8.858), and N1 staging (OR: 5.666, 95% CI: 1.964-16.342). The area under the receiver operating characteristic curve (AUC) was 0.857, 0.820, and 0.859 in the training set, internal validation set, and external validation set, respectively. The results of the calibration curve and decision curve analysis also confirmed that the established nomogram could accurately predict DM in middle-aged and elderly patients with chondrosarcoma. Married, histological type of dedifferentiated chondrosarcoma, high-grade tumor, T3 stage, and N1 stage are independent risk factors for DM in middle-aged and elderly chondrosarcoma patients, and clinicians should see more attention.

Development and validation of a nomogram to predict cancer-specific survival in nonsurgically treated elderly patients with prostate cancer

Prostate Cancer (PC) is the most common male nonskin tumour in the world, and most diagnosed patients are over 65 years old. The main treatment for PC includes surgical treatment and nonsurgical treatment. Currently, for nonsurgically treated elderly patients, few studies have evaluated their prognostic factors. Our aim was to construct a nomogram that could predict cancer-specific survival (CSS) in nonsurgically treated elderly PC patients to assess their prognosis-related independent risk factors. Patient information was obtained from the Surveillance, Epidemiology and End Results (SEER) database, and our target population was nonsurgically treated PC patients who were over 65 years old. Independent risk factors were determined using both univariate and multivariate Cox regression models. A nomogram was built using a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested using the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to examine the potential clinical value of this model. A total of 87,831 elderly PC patients with nonsurgical treatment in 2010-2018 were included in the study and were randomly assigned to the training set (N = 61,595) and the validation set (N = 26,236). Univariate and multivariate Cox regression model analyses showed that age, race, marital status, TNM stage, chemotherapy, radiotherapy modality, PSA and GS were independent risk factors for predicting CSS in nonsurgically treated elderly PC patients. The C-index of the training set and the validation set was 0.894 (95% CI 0.888-0.900) and 0.897 (95% CI 0.887-0.907), respectively, indicating the good discrimination ability of the nomogram. The AUC and the calibration curves also show good accuracy and discriminability. We developed a new nomogram to predict CSS in elderly PC patients with nonsurgical treatment. The model is internally validated with good accuracy and reliability, as well as potential clinical value, and can be used for clinical aid in decision-making.

Web-based nomograms for predicting overall survival and cancer-specific survival in retroperitoneal leiomyosarcoma: a population-based analysis

BACKGROUND

Retroperitoneal leiomyosarcoma is a type of carcinoma with low incidence and poor prognosis, and prognostic factors are currently unknown. Therefore, our study aimed to investigate the predictive factors of RPLMS and establish prognostic nomograms.

METHODS

Patients diagnosed with RPLMS between 2004 and 2017 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified by univariate and multivariate COX regression analyses and used to generate nomograms to predict overall survival (OS) and cancer-specific survival (CSS).

RESULTS

646 eligible patients were randomly divided into training set (n = 323) and validation set (n = 323). Multivariate COX regression analysis indicated that the independent risk factors for OS and CSS were age, tumor size, grade, SEER stage, and surgery. In the nomogram of OS, the concordance indices (C-index) of the training and validation sets were 0.72 and 0.691, and in the nomogram of CSS, the C-indices of the training and validation sets were 0.737 and 0.737. Furthermore, calibration plots showed that the predicted results of the nomograms in the training and validation sets agree well with the actual observations.

CONCLUSION

Age, tumor size, grade, SEER stage, and surgery were independent prognostic factors for RPLMS. The nomograms developed and validated in this study can accurately predict the OS and CSS of patients, which could help clinicians make individualized survival predictions. Finally, we make the two nomograms into two web calculators for the convenience of clinicians.

Surveillance of prognostic risk factors in patients with SCCB using artificial intelligence: a retrospective study

Small cell carcinoma of the bladder (SCCB) is a rare urological tumor. The prognosis of SCCB is abysmal. Therefore, this study aimed to construct nomograms that predict overall survival (OS) and cancer-specific survival (CSS) in SCCB patients. Information on patients diagnosed with SCCB during 2004-2018 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models analyzed Independent risk factors affecting patients' OS and CSS. Nomograms predicting the OS and CSS were constructed based on the multivariate Cox regression model results. The calibration curve verified the accuracy and reliability of the nomograms, the concordance index (C-index), and the area under the curve (AUC). Decision curve analysis (DCA) assessed the potential clinical value. 975 patients were included in the training set (N = 687) and the validation set (N = 288). Multivariate COX regression models showed that age, marital status, AJCC stage, T stage, M stage, surgical approach, chemotherapy, tumor size, and lung metastasis were independent risk factors affecting the patients' OS. However, distant lymph node metastasis instead AJCC stage is the independent risk factor affecting the CSS in the patients. We successfully constructed nomograms that predict the OS and CSS for SCCB patients. The C index of the training set and the validation set of the OS were 0.747 (95% CI 0.725-0.769) and 0.765 (95% CI 0.736-0.794), respectively. The C index of the CSS were 0.749 (95% CI 0.710-0.773) and 0.786 (95% CI 0.755-0.817), respectively, indicating that the predictive models of the nomograms have excellent discriminative power. The calibration curve and the AUC also show good accuracy and discrimination of the nomograms. To sum up, We established nomograms to predict the OS and CSS of SCCB patients. The nomograms have undergone internal cross-validation and show good accuracy and reliability. The DCA shows that the nomograms have an excellent clinical value that can help doctors make clinical-assisted decision-making.

Prognostic factors and nomogram for the overall survival of bladder cancer bone metastasis: A SEER-based study

Bone metastasis has a poor prognosis in patients with bladder cancer (BC). This study aimed to construct a prognostic nomogram for predicting the overall survival of patients with bone-metastatic BC (BMBC). The Surveillance, Epidemiology, and End Results database was used to recruit patients with BMBC between 2010 and 2018. Univariate and multivariate analyses were performed to screen for prognostic factors and construct a nomogram. Harrell concordance index, receiver operating characteristic curve, and calibration curve were used to verify the prognostic nomograms. All statistical analyses and chart formation were performed using SPSS 23.0 and R software 4.1.2. A total of 1361 patients diagnosed with BMBC were identified in the Surveillance, Epidemiology, and End Results database. Six independent prognostic factors, including marital status, histological type, T stage, other metastases, surgery, and chemotherapy, were identified and included in the nomogram construction. Among them, chemotherapy contributed the most to the prognosis in the nomogram. The concordance index of the nomogram was 0.745 and 0.753 in the training and validation groups, respectively, and all values of the area under the curve were >0.77. The calibration curves showed perfect consistency between the observed and predicted survival rates. The prognostic nomogram developed in this study is expected to become an accurate and individualized tool for predicting overall survival in patients with BMBC and providing guidance for appropriate treatment or care.

Nomograms for predicting survival in patients with micropapillary bladder cancer: a real-world analysis based on the surveillance, epidemiology, and end results database and external validation in a tertiary center

BACKGROUND

The present study aimed to construct and validate nomograms that can be used to predict cancer-specific survival (CSS) and overall survival (OS) in patients with micropapillary bladder cancer.

METHODS

The data of 627 patients diagnosed with micropapillary bladder cancer between 2000 and 2018 were obtained from the surveillance, epidemiology, and end results database. Patients were randomly divided into the training and internal validation sets (7:3). The Cox proportional hazards regression model was applied to evaluate the association between variables and survival and then nomograms were constructed to predict the survival of an individual patient. The performance of nomograms was validated by using calibration curves, concordance index, receiver operating characteristic curves with the calculated area under the curve and decision curve analysis in the training and internal validation set. Data from 41 micropapillary bladder cancer patients at Qilu Hospital of Shandong University from 2000 to 2022 were collected for external validation.

RESULTS

Several independent risk factors were taken into the two nomograms (CSS and OS), including age, marital status, AJCC TMN stage, surgical approach, lymph node ratio, and tumor size while the OS nomogram additionally contained race. The concordance index of the training set, internal validation set, and external verification set were all over 0.7. The calibration curve indicated good consistence between the nomogram prediction and actual survival. Area under the curve and decision curve analysis results indicated great clinical usefulness of nomograms.

CONCLUSIONS

The nomograms predicting the survival outcome of patients with micropapillary bladder cancer would provide a valuable tool to help clinicians to evaluate the risk of patients and make individual treatment strategies.

Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database

BACKGROUND

Our study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients.

METHODS

A total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing. Nomograms were established using prognostic factors identified through Cox regression analyses and validated internally and externally. The concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discrimination and calibration of nomogram models, while decision curve analyses (DCA) and Kaplan-Meier (KM) curves were used to estimate the clinical applicability.

RESULTS

Marital status, tumor metastasis (brain, liver, and lung), primary site surgery, and chemotherapy were indicated as independent prognostic factors for OS and CSS. Calibration plots and the overall C-index showed a novel agreement between the observed and predicted outcomes. Nomograms revealed significant advantages in OS and CSS predictions. AUCs for internal and external validation were listed as follows: for OS, 3-month AUCs were 0.853 and 0.849; 6-month AUCs were 0.873 and 0.832; 12-month AUCs were 0.825 and 0.805; for CSS, 3-month AUCs were 0.849 and 0.847; 6-month AUCs were 0.870 and 0.824; 12-month AUCs were 0.815 and 0.797, respectively. DCA curves demonstrated good clinical benefit, and KM curves showed distinct stratification performance.

CONCLUSION

The nomograms as web-based tools were proved to be accurate, efficient, and clinically beneficial, which might help in patient management and clinical decision-making for BCBM patients.

Construction and validation of a prognostic nomogram in metastatic breast cancer patients of childbearing age: A study based on the SEER database and a Chinese cohort

Introduction

Cancer in patients of childbearing age continues to become increasingly common. The purpose of this study was to explore the impact of metastatic breast cancer (MBC) on overall survival (OS) and cancer-specifific survival (CSS) in patients of childbearing age and to construct prognostic nomograms to predict OS and CSS.

Methods

Data from MBC patients of childbearing age were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and the patients were randomly assigned into the training and validation cohorts. Univariate and multivariate Cox analyses were used to search for independent prognostic factors impacting OS and CSS, and these data were used to construct nomograms. The concordance index (C-index), area under the curve (AUC), and calibration curves were used to determine the predictive accuracy and discriminative ability of the nomograms. Additional data were obtained from patients at the Yunnan Cancer Hospital to further verify the accuracy of the nomograms.

Results

A total of 1,700 MBC patients of childbearing age were identifified from the SEER database, and an additional 92 eligible patients were enrolled at the Yunnan Cancer Hospital. Multivariate Cox analyses identifified 10 prognostic factors for OS and CSS that were used to construct the nomograms. The calibration curve for the probabilities of OS and CSS showed good agreement between nomogram prediction and clinical observations. The C-index of the nomogram for OS was 0.735 (95% CI = 0.725-0.744); the AUC at 3 years was 0.806 and 0.794 at 5 years.The nomogram predicted that the C-index of the CSS was 0.740 (95% CI = 0.730- 0.750); the AUC at 3 years was 0.811 and 0.789 at 5 years. The same results were observed in the validation cohort. Kaplan- Meier curves comparing the low-,medium-, and high-risk groups showed strong prediction results for the prognostic nomogram.

Conclusion

We identifified several independent prognostic factors and constructed nomograms to predict the OS and CSS for MBC patients of childbearing age.These prognostic models should be considered in clinical practice to individualize treatments for this group of patients.

Cancer-specific survival and metastasis in pancreatic mucinous cystadenocarcinoma: A SEER-based cohort study

Aims

This study aimed to investigate the prognostic value of clinical features for cancer-specific survival (CSS) and metastasis in patients with pancreatic mucinous cystadenocarcinoma (MCAC). We further constructed and validated an effective nomogram to predict CSS.

Methods

We screened patients diagnosed with pancreatic MCAC from Surveillance Epidemiology and End Results (SEER) database. Kaplan-Meier curves were used to determine the CSS time. Univariate and multivariate Cox and logistic regression analyses were conducted to identify the prognostic factors for CSS and metastasis. The nomogram was constructed to predict the prognosis of pancreatic MCAC based on the results from the multivariate analysis. We used the concordance index (C-index), the area under the curve (AUC), and the calibration plots to determine the predictive accuracy and discriminability of the nomogram.

Results

Multivariate Cox analysis revealed that age, primary site, grade, and radiotherapy were independent prognostic factors associated with CSS. Multivariate logistic regression analysis revealed that surgery and grade were independent risk factors associated with metastasis. The independent risk factors were included to construct a prognosis prediction model for predicting CSS in patients with pancreatic MCAC. The concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots of the training cohort and the validation cohort showed that the nomogram had an acceptable predictive performance.

Conclusion

We established a nomogram that could determine the 3- and 5-year CSS, which could evaluate individual clinical outcomes and provide individualized clinical decisions.

A novel nomogram and risk classification system predicting the overall survival of patients with papillary renal cell carcinoma after nephrectomy: A population-based study

Background

Papillary renal cell carcinoma (pRCC) is the largest histologic subtype of non-clear-cell RCC. To date, there is no reliable nomogram to predict the prognosis of patients with pRCC after nephrectomy. We aimed to first establish an effective nomogram to predict the overall survival (OS) of patients with pRCC after nephrectomy.

Methods

A total of 3,528 eligible patients with pRCC after nephrectomy were identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The patients were randomized into the training cohort (n = 2,472) and the validation cohort (n = 1,056) at a 7:3 ratio. In total, 122 real-world samples from our institute (titled the AHMU-pRCC cohort) were used as the external validation cohort. Univariate and subsequent multivariate Cox regression analyses were conducted to identify OS-related prognostic factors, which were further used to establish a prognostic nomogram for predicting 1-, 3-, and 5-year OS probabilities. The performance of the nomogram was evaluated by using the concordance index (C-index), receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA).

Results

Multivariate Cox analysis showed that age, race, marital status, TNM stage, tumor size, and surgery were significant OS-related prognostic factors. A prognostic model consisting of these clinical parameters was developed and virtualized by a nomogram. High C-index and area under the ROC curve (AUC) values of the nomogram at 1, 3, and 5 years were found in the training, validation, and AHMU-pRCC cohorts. The calibration plot and DCA also showed that the nomogram had a satisfactory clinical application value. A risk classification system was established to risk-stratify patients with pRCC.

Conclusion

Based on a large cohort from the public SEER database, a reliable nomogram predicting the OS of patients with pRCC after nephrectomy was constructed, which could optimize the survival assessment and clinical treatment.

Relationship between metastasis and second primary cancers in women with breast cancer

Background

Breast cancer (BC) survivors have an increased risk of developing second primary cancers (SPCs); however, it is still unclear if metastasis is a risk factor for developing SPCs. Usually, long-term cancer survivors face an increased risk of developing SPCs; however, less attention has been paid to SPCs in patients with metastatic cancer as the survival outcomes of the patients are greatly reduced.

Methods

A total of 17,077 American women diagnosed with breast cancer between 2010 and 2018 were identified from Surveillance, Epidemiology, and End Results (SEER) database and were included in the study. The clinical characteristics, standardized incidence ratio (SIR), standardized mortality ratio (SMR), and patterns of SPCs in BC patients with no metastasis, regional lymph node metastasis, and distant metastasis were investigated. Kaplan-Meier method was used to compare the prognosis of BC patients after developing SPCs with different metastatic status. XGBoost, a high-precision machine learning algorithm, was used to create a prediction model to estimate the prognosis of metastatic breast cancer (MBC) patients with SPCs.

Results

The results reveal that the SIR (1.01; 95% CI, 0.99–1.03, p>0.05) of SPCs in non-metastasis breast cancer (NMBC) patients was similar to the general population. Further, patients with regional lymph node metastasis showed an 8% increased risk of SPCs (SIR=1.08, 95%CI, 1.05–1.11, p<0.05), and patients with distant metastasis had a 26% increased risk of SPCs (SIR=1.26, 95%CI, 1.16–1.37, p<0.05). The SIR of SPCs in all patients below the age of 40 was the highest, which decreased with age. Patients with poorly differentiated cancers, large tumor size, and late N stage had an increased risk of SPCs. However, an increase in SIR of SPCs was observed in distant MBC patients, even at the early T1 (SIR=1.60, 95% CI, 1.22–1.98, p<0.05) and N1 (SIR=1.27, 95% CI, 1.10–1.44, p<0.05) stage. An increase in the SIR of SPCs was observed in patients with triple-negative BC, and the SIR of SPC increased with metastasis development in BC patients with luminal A subtype. The peak of SPCs risk occurrence was earlier in MBC patients (4-6 months and 10 months) compared to NMBC patients (12 months). The effect of metastasis on the prognosis of SPCs patients was dependent on the type of SPCs. Meanwhile, the XGBoost model was created to predict the 3-year (AUC=0.873) and 5-year survival (AUC=0.918) of SPCs in MBC patients.

Conclusions

Our study provides novel insight into the impact of metastasis on SPCs in BC patients. Metastasis could promote the second primary tumorigenesis which further increased cancer-related deaths. Therefore, more attention should be paid to the occurrence of SPCs in MBC patients.

Competitive Risk Model for Specific Mortality Prediction in Patients with Bladder Cancer: A Population-Based Cohort Study with Machine Learning

Background

Noncancer death accounts for a high proportion of all patients with bladder cancer, while these patients are often excluded from the survival analysis, which increases the selection bias of the study subjects in the prediction model.

Methods

Clinicopathological information of bladder cancer patients was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, and the patients were categorized at random into the training and validation cohorts. The random forest method was used to calculate the importance of clinical variables in the training cohort. Multivariate and univariate analyses were undertaken to assess the risk indicators, and the prediction nomogram based on the competitive risk model was constructed. The model's performance was evaluated utilizing the calibration curve, consistency index (C index), and the area under the receiver operator characteristic curve (AUC).

Results

In total, we enrolled 39285 bladder cancer patients in the study (27500 patients were allotted to the training cohort, whereas 11785 were allotted to the validation cohort). A competitive risk model was constructed to predict bladder cancer-specific mortality. The overall C index of patients in the training cohort was 0.876, and the AUC values were 0.891, 0.871, and 0.853, correspondingly, for 1-, 3-, and 5-year cancer-specific mortality. On the other hand, the overall C index of patients in the validation cohort was 0.877, and the AUC values were 0.894, 0.870, and 0.847 for 1-, 3-, and 5-year correspondingly, suggesting a remarkable predictive performance of the model.

Conclusions

The competitive risk model proved to be of great accuracy and reliability and could help clinical decision-makers improve their management and approaches for managing bladder cancer patients.

Effects of marital status on survival of retroperitoneal liposarcomas stratified by age and sex: A population-based study

BACKGROUND

Previous studies have shown that marital status is associated with survival in patients with a variety of cancer types, including lung cancer, prostate cancer, and bladder cancer. However, to date, the impact of marital status on the survival of patients with retroperitoneal liposarcomas (RPLs) has not been established.

METHODS

A total of 1211 eligible patients diagnosed with RPLs were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The relationships between marital status and survival in patients with RPLs were assessed. Patients were stratified by age to determine whether an association exists between marital status and age. We also probed the association between marital status and survival in males and females.

RESULTS

Our findings suggest that divorced, separated, or widowed patients have more advanced cancer stages, and more of these patients do not undergo surgery. Meanwhile, divorced, separated, or widowed patients have worse survival outcomes than married patients (overall survival (OS): HR = 1.66 (95% CI, 1.12, 2.46)); cancer-specific survival (CSS): HR = 1.90 (95% CI, 1.13, 3.19)). OS does not differ between single patients and married patients (HR = 1.21 [95% CI, 0.81, 1.81]) or CSS (HR = 1.36 [95% CI, 0.80, 2.29]). In addition, these results demonstrate that being divorced, separated, or widowed can play a significant detrimental role in mortality in older and female patients.

CONCLUSION

Married patients have earlier disease stages at diagnosis and better survival outcomes than divorced, separated, or widowed patients with RPLs. In addition, this effect is especially pronounced in older people and females.

Effects of treatments on gender differences in patients with localized muscle-invasive bladder cancer

PURPOSE

To explore the gender differences in survival under different treatments in localized muscle-invasive bladder cancer (MIBC), and to find clinical strategies to improve the poor prognosis of female with bladder cancer (BC).

METHODS

Patients with localized MIBC were collected in the SEER database from 2010 to 2016 to analyze the gender differences in clinical characteristics. Propensity score matching was used to balance the effects of confounding factors. Kaplan-Meier method and Cox proportional hazards regression model were performed to compare the overall survival (OS) and cancer-specific survival (CSS) of patients between different treatment subgroups.

RESULTS

The entire cohort included 13,272 T2N0M0 MIBC patients, with a male-to-female incidence of 3:1. Compared with male patients, females had a higher age of onset and more blacks. There were more female patients undergoing bladder-sparing surgery (BSS) alone, and the OS and CSS were worse than those in males. The gender difference showed statistical significance in the BSS group, but not in the radical cystectomy (RC) group.

CONCLUSION

The survival of localized MIBC patients can be affected by treatments. Multi-modality treatment and RC may improve the survival prognosis of female patients.

Development of a prognostic nomogram for metastatic pancreatic ductal adenocarcinoma integrating marital status

Previous studies have shown that marital status can affect the overall survival (OS) of cancer patients yet its role in metastatic pancreatic ductal adenocarcinoma (mPDAC) remains unclear. This study aimed to explore the impact of marital status on the OS of mPDAC patients and to construct a prognostic nomogram to predict OS outcomes. Data from patients diagnosed with mPDAC were obtained from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. The patients were randomized into primary and validation cohorts. Kaplan-Meier survival analysis was performed to compare differences in survival depending on marital status. Univariate and multivariate analyses were conducted to identify independent prognostic factors and a nomogram was established based using Cox regression analyses. Validation of the prognostic nomogram was evaluated with a calibration curve and concordance index (C-index). Our data showed significant differences in the OS of mPDAC patients with different marital status by Kaplan-Meier analysis (P < 0.05). Univariate and multivariate analyses confirmed that marital status was an independent OS-related factor in mPDAC patients. Based on the multivariate models of the primary cohort, a nomogram was developed that combined marital status, age, grade, tumor size, surgery of primary site, surgery of lymph node and metastatic. The nomogram showed that marital status had a moderate influence on predicting the OS of mPDAC patients. Moreover, the internally and externally validated C-indexes were 0.633 and 0.619, respectively. A calibration curve confirmed favorable consistency between the observed and predicted outcomes. Marital status was identified as an independent prognostic factor for OS of mPDAC patients and is a reliable and valid parameter to predict the survival of patients with mPDAC. This prognostic model has value and may be integrated as a tool to inform decision-making in the clinic.

Development and Validation of a Prognostic Nomogram for Predicting Overall Survival for T1 High-Grade Patients After Radical Cystectomy: A Study Based on SEER

Objective

To construct a prognostic model that estimates the probability of overall survival for T1 high-grade bladder cancer patients after radical cystectomy.

Patients and Methods

We enrolled 801 patients diagnosed with T1 high grade and received radical cystectomy from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2015). All patients were randomly divided into the development group (n = 561) and validation group (n = 240) with the ratio of 7:3. Cox proportional hazards regression analyses were used to filter variables and the Kaplan–Meier method to evaluate survival outcomes. The results of sensitivity analysis determined the variables in the final model. The performance of the model was internally validated by calibration curves, the receiver operating characteristic (ROC) curves, and the concordance index (C-index).

Results

The mean survival months were 56.086 in the development group and 58.21 in the validation group. Six variables including age, marital status, tumour size, tumour sites, region nodes examined, and N stage were incorporated in the final nomogram. The accuracy of the nomogram for prediction of overall survival was estimated by C-index (0.732; 0.712–0.752) and AUC (0.771 for 3-year; 0.766 for 5-year) in the development group. In the validation group, the C-index of the nomogram was 0.752 (0.723–0.781), and AUC was 0.761 for 3-year as well as 0.793 for 5-year. These results all showed better performance than the AJCC stage. Calibration plots for 3- and 5-year overall survival presented good concordance in both the development and validation group.

Conclusion

We have established a prognostic nomogram that provides a more accurate and relevant individualized probability of overall survival for patients with T1HG bladder transitional cell carcinoma after radical cystectomy. It can contribute to improving patient counselling and treatment selection.

Nomogram for predicting the overall survival of patients with early-onset prostate cancer: A population-based retrospective study

Background

The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients.

Methods

A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS.

Conclusions

We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes.

A Novel Machine Learning Algorithm Combined With Multivariate Analysis for the Prognosis of Renal Collecting Duct Carcinoma

Objectives

To investigate the clinical and non-clinical characteristics that may affect the prognosis of patients with renal collecting duct carcinoma (CDC) and to develop an accurate prognostic model for this disease.

Methods

The characteristics of 215 CDC patients were obtained from the U.S. National Cancer Institute’s surveillance, epidemiology and end results database from 2004 to 2016. Univariate Cox proportional hazard model and Kaplan-Meier analysis were used to compare the impact of different factors on overall survival (OS). 10 variables were included to establish a machine learning (ML) model. Model performance was evaluated by the receiver operating characteristic curves (ROC) and calibration plots for predictive accuracy and decision curve analysis (DCA) were obtained to estimate its clinical benefits.

Results

The median follow-up and survival time was 16 months during which 164 (76.3%) patients died. 4.2, 32.1, 50.7 and 13.0% of patients were histological grade I, II, III, and IV, respectively. At diagnosis up to 61.9% of patients presented with a pT3 stage or higher tumor, and 36.7% of CDC patients had metastatic disease. 10 most clinical and non-clinical factors including M stage, tumor size, T stage, histological grade, N stage, radiotherapy, chemotherapy, age at diagnosis, surgery and the geographical region where the care delivered was either purchased or referred and these were allocated 95, 82, 78, 72, 49, 38, 36, 35, 28 and 21 points, respectively. The points were calculated by the XGBoost according to their importance. The XGBoost models showed the best predictive performance compared with other algorithms. DCA showed our models could be used to support clinical decisions in 1-3-year OS models.

Conclusions

Our ML models had the highest predictive accuracy and net benefits, which may potentially help clinicians to make clinical decisions and follow-up strategies for patients with CDC. Larger studies are needed to better understand this aggressive tumor.

Novel Prediction Models for Patients With Oral Squamous Cell Carcinoma at Different Anatomical Sites

PURPOSE

The individualized prediction of oral cavity squamous cell carcinoma (OC-SCC) is essential and should be as comprehensive as possible. The aim of this study was to identify new risk factors and develop nomograms comparing all anatomic sites of the oral cavity.

MATERIALS AND METHODS

We performed a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER) database. All patients with OC-SCC diagnosed from 2004 to 2015 were selected and divided into the training cohort and the validation cohort. Age, gender, race, marital status, primary site, tumor grade, American Joint Committee on Cancer (AJCC) stage, TNM stage, surgical treatment, radiotherapy and chemotherapy were identified as predictor variables. The overall survival (OS) and disease specific survival (DSS) were identified as outcome variables. Kaplan-Meier method with log-rank test, univariate and multivariate cox regression analysis were performed. Independent prognostic factors were used to develop 3- and 5-year nomograms. Hazard ratio (HR) and corresponding 95% confidence interval (CI) showed the influence of each factor on OS or DSS. Concordance indexes (C-indexes) and calibration curves verified the nomograms internally and externally.

RESULTS

A total of 12,346 patients were included. Marital status and chemotherapy were independent prognostic factors (P < .05). Tumors occurring on the cheek mucosa had the highest risk in OS (HR, 2.0, 95% CI, 1.7-2.3) and DSS (HR, 4.7, 95% CI, 3.6-6.0), while tumors occurring on the lip had the lowest risk in OS (HR, 1.0) and DSS (HR,1.0). The C-indexes for OS in the training and validation sets were 0.767 and 0.770, respectively, and for DSS were 0.800 and 0.799, respectively.

CONCLUSION

Marital status and chemotherapy independently affect OC-SCC patients' survival. The prognosis is least favorable for tumors occurring on the cheek mucosa and most favorable for tumors occurring on the lip.

Development and Validation of a Prognostic Model for Predicting Overall Survival in Patients With Bladder Cancer: A SEER-Based Study

Objective

To establish a prognostic model for Bladder cancer (BLCA) based on demographic information, the American Joint Commission on Cancer (AJCC) 7th staging system, and additional treatment using the surveillance, epidemiology, and end results (SEER) database.

Methods

Cases with BLCA diagnosed from 2010–2015 were collected from the SEER database, while patient records with incomplete information on pre-specified variables were excluded. All eligible cases were included in the full analysis set, which was then split into training set and test set with a 1:1 ratio. Univariate and multivariate Cox regression analyses were conducted to identify prognostic factors for overall survival (OS) in BLCA patients. With selected independent prognosticators, a nomogram was mapped to predict OS for BLCA. The nomogram was evaluated using receiver operating characteristic (ROC) analysis and calibration plot in both the training and test sets. The area under curve [AUC] of the nomogram was calculated and compared with clinicopathological indicators using the full analysis set. Statistical analyses were conducted using the R software, where P-value <0.05 was considered significant.

Results

The results indicated that age, race, sex, marital status, histology, tumor-node-metastasis (TNM) stages based on the AJCC 7th edition, and additional chemotherapy were independent prognostic factors for OS in patients with BLCA. Patients receiving chemotherapy tend to have better survival outcomes than those without. The proposed nomogram showed decent classification (AUCs >0.8) and prediction accuracy in both the training and test sets. Additionally, the AUC of the nomogram was observed to be better than that of conventional clinical indicators.

Conclusions

The proposed nomogram incorporated independent prognostic factors including age, race, sex, marital status, histology, tumor-node-metastasis (TNM) stages, and additional chemotherapy. Patients with BLCA benefit from chemotherapy on overall survival. The nomogram-based prognostic model could predict overall survival for patients with BLCA with accurate stratification, which is superior to clinicopathological factors.

Marital Status Independently Predicts Glioma Patient Mortality: A Surveillance, Epidemiology, and End Results (SEER) Analysis

OBJECTIVE

To examine the impact of marital status on the mortality of patients with primary malignant brain tumors excluding bias from basic characteristics and treatment.

METHODS

We used the Surveillance, Epidemiology, and End Results program to identify 81,277 patients diagnosed from 2000 through 2016 with the most common primary malignant brain tumors, including glioma, ependymoma, and medulloblastoma. To avoid bias, we used the propensity score matching method to match 44,854 patients with complete clinical and follow-up information. Then, we used Cox regression and Kaplan-Meier survival analysis to investigate the impact of marital status on cancer patient mortality.

RESULTS

Married patients were more likely to receive surgery and adjuvant chemo- or radiotherapy than single and divorced, separated, and widowed (DSW) patients (all P < 0.001). Married patients with high grade glioma were more likely to survive longer and less likely to die of their malignance compared with single (adjusted odds ratio [OR] 1.120; 95% confidence interval [CI], 1.069 to 1.174; P < 0.001; OR 1.078; 95% CI, 1.025 to 1.133; P = 0.003; respectively), and DSW patients (OR 1.117; 95% CI, 1.074 to 1.161; P <0.001; OR 1.090; 95% CI, 1.046 to 1.136; P<0.001; respectively) (all adjusted to the married group). Similar results were identified in patients with low-grade glioma but not ependymoma and medulloblastoma.

CONCLUSIONS

Even after adjusting for known confounders, married patients with high-grade glioma and low-grade glioma are at higher possibility to have a better outcome. This study highlights the potential significance that intimate support from spouse can improve glioma patient survival.