Patterns of Treatment and Outcomes in Older Men With Penile Cancer: A SEER Dataset Analysis

Immunotherapy in the Management of Penile Cancer-A Systematic Review

Penile cancer, though a rare malignancy, presents a significant challenge in the domain of male genitourinary oncology, particularly due to its limited treatment options and due to the profound physical and psychological impact on patients [...].

Management of Bulky Inguinal and Pelvic Lymph Nodes

Penile cancer with bulky inguinal metastasis has a high probability of harboring pathologically involved lymph nodes best managed in a multidisciplinary care setting. Appropriate staging with cross-sectional imaging and fine-needle aspirate cytology of suspicious nodes guide decision-making for the use of platinum-based neoadjuvant chemotherapy followed by inguinal lymph node dissection. Surgical resection plays an important diagnostic, therapeutic, and guiding role in disease management. Patients with adverse pathologic features, especially those with extranodal disease extension, may derive additional benefit from adjuvant radiotherapy.

Risk factors for distant metastasis and prognosis of the penile cancer with distant metastasis

Background

Penile cancer (PC) is a rare malignant tumor, whose distant metastasis (DM) is associated with the poorest outcomes. The risk factors associated with DM and prognosis of the PC with DM remain elusive. This study was aimed at investigating risk factors associated with DM and constructing prediction models of PC with DM.

Methods

This study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database over a period of 2000-2020, including clinical characteristics such as age, marital status, tumor size, Tumor Node Metastasis (TNM) staging, and treatment information. Utilizing univariate and multivariate logistic regression, alongside cox regression analysis, we identified independent risk factors for DM and prognosis in the total cases and the cases with DM. Nomograms were developed for predicting DM and prognosis in PC patients.

Results

Enrolling 1,488 cases, our study identified tumor size and N stage as independent predictors of DM. The predictive nomogram for DM achieved an area under the curve (AUC) of 0.904. Notably, the 1-, 3-, and 5-year cumulative survival rates for PC with DM were 35%, 17%, and 13%, respectively, with larger tumor size associated with prognosis of PC cases with DM. This study verified a correlation between advanced age and TNM stage, as well as chemotherapy with the poor PC prognosis. The nomogram yielded 0.72, 0.69 and 0.69, in predicting 1-, 3-, and 5-year overall survivals (OS), while 0.73, 0.70 and 0.69 in predicting 1-, 3-, 5-year cancer specific survivals (CSS), respectively.

Conclusions

This study investigated risk factors of PC with DM. Also, nomograms for predicting DM, OS and CSS of PC patients were developed.

Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinoma：A population-based retrospective study

Background

Penile cancer is a rare tumor and few studies have focused on the prognosis of M0 penile squamous cell carcinoma (PSCC). This retrospective study aimed to identify independent prognostic factors and construct predictive models for the overall survival (OS) and cancer-specific survival (CSS) of patients with M0 PSCC.

Methods

Data was extracted from the Surveillance, Epidemiology, and End Results database for patients diagnosed with malignant penile cancer. Eligible patients with M0 PSCC were selected according to predetermined inclusion and exclusion criteria. These patients were then divided into a training set, a validation set, and a test set. Univariate and multivariate COX regression analyses were initially performed to identify independent prognostic factors for OS and CSS in M0 PSCC patients. Subsequently, traditional and machine learning prognostic models, including random survival forest (RSF), COX, gradient boosting, and component-wise gradient boosting modelling, were constructed using the scikit-survival framework. The performance of each model was assessed by calculating time-dependent area under curve (AUC), C-index, and integrated Brier score (IBS), ultimately identifying the model with the highest performance. Finally, the Shapley additive explanation (SHAP) value, feature importance, and cumulative rates analyses were used to further estimate the selected model.

Results

A total of 2, 446 patients were included in our study. Cox regression analyses demonstrated that age, N stage, and tumor size were predictors of OS, while the N stage, tumor size, surgery, and residential area were predictors of CSS. The RSF and COX models had a higher time-independent AUC and C-index, and lower IBS value than other models in OS and CSS prediction. Feature importance analysis revealed the N stage as a common significant feature for predicting M0 PSCC patients' survival. The SHAP and cumulative rate analyses demonstrated that the selected models can effectively evaluate the prognosis of M0 PSCC patients.

Conclusion

In M0 PSCC patients, age, N stage, and tumor size were predictors of OS. In addition, the N stage, tumor size, surgery, and residential area were predictors of CSS. The machine learning-based RSF and COX models effectively predicted the prognosis of M0 PSCC patients.

Influence of Race/Ethnicity and Household Median Income on Penile Cancer Mortality

INTRODUCTION

Penile cancer, while relatively rare in developed nations, presents substantial disparities in outcomes among different demographic groups. Previous research has shown race/ethnicity and socioeconomic status, often proxied by household median income, to be critical determinants of health outcomes across various diseases.

OBJECTIVE

This study examines the association of race/ethnicity and household median income with survival among penile cancer patients in the United States.

METHODS

We utilized the Surveillance, Epidemiology, and End Results (SEER) Registry to identify patients with a primary diagnosis of penile malignancies from 2000 to 2019. Our primary outcome of interest was the hazard of death following a diagnosis of penile cancer. We utilized the Cox regression model to explore the association between race/ethnicity and median household income and how this influences survival among these patients. We adjusted for patients' characteristics, disease stage at presentation, and treatment modalities.

RESULT

Of the 6,520 penile cancer patients identified, 5,242 (80.4%) had primary malignancies. The distribution of patients was as follows: 64.1% non-Hispanic Whites, 8.9% non-Hispanic Blacks, 20.8% Hispanics, and 6.2% from other racial/ethnic groups. The median diagnosis age was 66 years (interquartile range: 56-74). Survival rates at 5, 10, and 15 years showed racial disparities: 76.4%, 72.5%, and 69.7% for non-Hispanic Whites; 70.6%, 64.1%, and 61.1% for non-Hispanic Blacks; and 70.5%, 67.4%, and 65.6% for Hispanics. Multivariate Cox regression revealed worst survival for Black (HR=1.40; 95% CI=1.08-1.81, p=0.01) and Hispanic patients (HR=1.24; 95% CI=1.01-1.52, p=0.04). No association was found between median household income and survival. Interaction analysis indicated that the poorest Black men had worse outcomes than the poorest Whites did (HR=2.08; 95% CI=1.27-3.41, p=0.003).

CONCLUSION

Survival rates for non-Hispanic Black and Hispanic patients are significantly lower than those for non-Hispanic Whites. Furthermore, survival is worse for low-income Black patients than their White counterparts in the same income bracket.