Development and Validation of Prognostic Nomograms to Predict Overall and Cancer-Specific Survival for Patients with Adenocarcinoma of the Urinary Bladder: A Population-Based Study

Squamous cell carcinoma of the bladder: Demographics and outcomes associated with surgery and radiotherapy

BACKGROUND AND OBJECTIVES

Squamous cell carcinoma of the bladder (SCCB) is a rare disease composing 2%-5% of all bladder cancers with no consensus regarding treatment. The present study aims to analyze the outcomes of established treatments, namely chemotherapy, radiation, and surgery, to guide clinical decision-making for patients with non-schistosomal SCCB.

METHODS

Patients with bladder SCC diagnosed between 2000 and 2018 were reviewed utilizing data from the Surveillance, Epidemiology, and End Results Registry (SEER) program.

RESULTS

A total of 5653 patients with SCCB were identified; median survival was 13 months and was significantly decreased in patients treated with chemotherapy or radiation (median survival of 9 or 12 months, respectively). Patients treated with both surgery and radiotherapy saw a decreased 5 year overall survival (OS) of 14%, compared to 35% for those treated with surgery alone (p < 0.01). Furthermore, patients treated with surgery, chemotherapy, and radiotherapy saw a decreased 5 year OS of 20%, compared with 25% for those that received surgery and chemotherapy only (p < 0.01). Finally, surgical intervention provided an increased 5 year OS for patients with locoregional disease only; those with distant disease saw no increase in 5 year OS (p < 0.01).

CONCLUSIONS

Based on this study's analysis, radical surgery may be the most effective treatment for this disease.

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.

Development of a Pocket Nomogram to Predict Cancer and Disease Specific Survival After Radical Cystectomy For Bladder Cancer: The CRAB Nomogram

OBJECTIVES

To develop an easy tool to predict cancer specific (CSS) and disease-free survival (DFS) in patients with bladder cancer treated with radical cystectomy.

METHODS

Data from a consecutive series of 2395 patients with primitive or progression to muscle invasive bladder cancer (MIBC) undergone to radical cystectomy and lymph nodes dissection in 5 centers were evaluated. Using Cox proportional hazards analyses, the Cancer of the bladder risk assessment (CRAB) nomogram was generated. Accuracy of the nomogram was evaluated by Harrell's C test. Internal validation of the model was performed using 200 bootstraps.

RESULTS

Median age was 66 (IQR 58/73) years; 612/2395 (26%) patients presented an advanced pathological stage (≥pT3a); 478/2395 (20%) presented positive lymph nodes. Overall, 729/2395 (30%) presented local or distant recurrence with a median DFS of 42 (IQR 14/89) months. Overall, 642/2395 (27%) died of bladder cancer with a median follow up of 48 (IQR 22/92) months. On univariate Cox proportional hazards analyses, age, stage, and lymph nodes density were a significant predictor of 3 and5 years CSS and DFS. Accuracy of the CRAB nomogram was 0.73 and 0.71 respectively.

CONCLUSION

CRAB nomogram can be a practical and easily applicable tool that may help urologists to classify the long-term CSS and DFS of patients treated with radical cystectomy and to predict the oncological outcome.

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.

Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients

Background

Most patients with advanced non-small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan. We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients. This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients.

Methods

Multiple machine-learning models were used in a retrospective cohort of 6586 patients. First, we modeled and validated a nomogram to predict the overall survival of NSCLC patients. Subsequently, five machine-learning models (logistic regression, random forest, XGBoost, decision tree, and light gradient boosting machine) were used to predict survival status. Next, we evaluated the performance of the models. Finally, the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure: time-dependent prediction accuracy.

Results

Among the five machine-learning models, the accuracy of random forest model outperformed the others. Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months, the prediction accuracies of both the nomogram and machine-learning models changed as time varied. The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month, and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month.

Conclusions

Overall, the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period. Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies, the conventional nomogram was competitive. In real clinical applications, a comprehensive model that combines these two methods may demonstrate superior capabilities.