Oral Dysfunction in Patients with Oral Cancer Could Occur Before Treatment and Require Early Nutritional Improvement: A Cross-Sectional Study

An excessive weight loss percentage over the two years before treatment is an independent prognostic factor for operated patients with advanced oral squamous cell carcinoma

The aim of this study was to assess the prognostic value of the weight loss percentage (WLP) over the 2 years pre-treatment for operated patients with advanced oral squamous cell carcinoma (OSCC). This cohort study included 506 operated patients who were diagnosed with advanced primary OSCC between October 2001 and March 2022, and who were followed up until July 2022. Fine-Gray models, marginal structural models with stabilized inverse probability of treatment weighting, and Cox proportional hazards models were utilized to evaluate the prognostic significance of pre-treatment WLP for disease-specific survival (DSS). The median follow-up time was 32.6 months (interquartile range 13.0-71.6 months). A high pre-treatment WLP (>9.23%) was significantly associated with worse DSS (multivariate Fine-Gray model: hazard ratio (HR) 2.04, 95% confidence interval (CI) 1.29-3.22, P = 0.002; multivariate Cox: HR 2.01, 95% CI 1.28-3.16, P = 0.002). In the weighted cohort, a similar association pattern was observed (marginal structural model: HR 2.26, 95% CI 1.28-3.98, P = 0.005; multivariate Cox: HR 2.28, 95% CI 1.38-3.76, P = 0.001). In subgroup analyses, high WLP could predict worse DSS among patients with buccal mucosa/other cancer sites (not including the oral tongue), moderate tumor differentiation, and larger cancer size (>1.8 cm) (all P < 0.05). Pre-treatment WLP over 2 years might be a useful tool to predict the prognosis of operated patients with advanced OSCC.

Automated evaluation of masseter muscle volume: deep learning prognostic approach in oral cancer

BACKGROUND

Sarcopenia has been identified as a potential negative prognostic factor in cancer patients. In this study, our objective was to investigate the relationship between the assessment method for sarcopenia using the masseter muscle volume measured on computed tomography (CT) images and the life expectancy of patients with oral cancer. We also developed a learning model using deep learning to automatically extract the masseter muscle volume and investigated its association with the life expectancy of oral cancer patients.

METHODS

To develop the learning model for masseter muscle volume, we used manually extracted data from CT images of 277 patients. We established the association between manually extracted masseter muscle volume and the life expectancy of oral cancer patients. Additionally, we compared the correlation between the groups of manual and automatic extraction in the masseter muscle volume learning model.

RESULTS

Our findings revealed a significant association between manually extracted masseter muscle volume on CT images and the life expectancy of patients with oral cancer. Notably, the manual and automatic extraction groups in the masseter muscle volume learning model showed a high correlation. Furthermore, the masseter muscle volume automatically extracted using the developed learning model exhibited a strong association with life expectancy.

CONCLUSIONS

The sarcopenia assessment method is useful for predicting the life expectancy of patients with oral cancer. In the future, it is crucial to validate and analyze various factors within the oral surgery field, extending beyond cancer patients.