Benefit of postoperative radiotherapy in patients with oropharyngeal squamous cell carcinoma in human papillomavirus (HPV) era: A Surveillance, Epidemiology, and End Results (SEER) database analysis

Sex and human papillomavirus in oropharyngeal cancer: A systematic review and meta-analysis

INTRODUCTION

While the presence of human papillomavirus (HPV) is known to affect the outcomes of oropharyngeal squamous cell carcinoma (OPSCC), there is a significant gap in research regarding the potential sex-based differences. This systematic review-metanalysis (SR-MA) aims to evaluate if sex is a prognostic factor in HPV-associated OPSCC.

METHODS

A systematic review and meta-analysis was performed. COCHRANE Library, CINAHL, PubMed, and Scopus were searched for English-language articles from 1966 to October 2023. Studies with multivariable analysis of overall survival (OS) based on sex were included. Adjusted hazard ratios (aHRs) with a 95 % confidence interval (CI) were presented for the reported outcome. A meta-analysis of single means, proportions, and aHRs with a 95 % CI was conducted.

RESULTS

This SR-MA included 24 studies (n = 101,574). The proportion of female patients was 16.6 % [15.4 %-17.8 %]. A meta-analysis of all included studies with OS showed no significant difference in survival between male and female patients. In US-based studies, no significant difference in OS is observed between male and female patients. International studies reported a better OS for female patients (aHR = 0.68, 95 % CI, 0.48-0.95).

CONCLUSION

This meta-analysis suggests that sex does not represent a significant prognostic factor for patients affected by HPV associated OPSCC. When stratified by geographic location, findings suggests that female patients from the US with HPV OPSCC have similar OS than male patients but in international studies it suggests male patients have worse OS.

Prediction of survival in oropharyngeal squamous cell carcinoma using machine learning algorithms: A study based on the surveillance, epidemiology, and end results database

Background

We determined appropriate survival prediction machine learning models for patients with oropharyngeal squamous cell carcinoma (OPSCC) using the “Surveillance, Epidemiology, and End Results” (SEER) database.

Methods

In total, 4039 patients diagnosed with OPSCC between 2004 and 2016 were enrolled in this study. In particular, 13 variables were selected and analyzed: age, sex, tumor grade, tumor size, neck dissection, radiation therapy, cancer directed surgery, chemotherapy, T stage, N stage, M stage, clinical stage, and human papillomavirus (HPV) status. The T-, N-, and clinical staging were reconstructed based on the American Joint Committee on Cancer (AJCC) Staging Manual, 8th Edition. The patients were randomly assigned to a development or test dataset at a 7:3 ratio. The extremely randomized survival tree (EST), conditional survival forest (CSF), and DeepSurv models were used to predict the overall and disease-specific survival in patients with OPSCC. A 10-fold cross-validation on a development dataset was used to build the training and internal validation data for all models. We evaluated the predictive performance of each model using test datasets.

Results

A higher c-index value and lower integrated Brier score (IBS), root mean square error (RMSE), and mean absolute error (MAE) indicate a better performance from a machine learning model. The C-index was the highest for the DeepSurv model (0.77). The IBS was also the lowest in the DeepSurv model (0.08). However, the RMSE and RAE were the lowest for the CSF model.

Conclusions

We demonstrated various machine-learning-based survival prediction models. The CSF model showed a better performance in predicting the survival of patients with OPSCC in terms of the RMSE and RAE. In this context, machine learning models based on personalized survival predictions can be used to stratify various complex risk factors. This could help in designing personalized treatments and predicting prognoses for patients.