Predictive bite force modelling of head and neck oncology patients for clinical mandibular reconstruction applications

TL;DR

This study develops a predictive model for bite force in head and neck cancer patients to improve personalized reconstructive surgery planning.

Contribution

A novel predictive model for bite force using anatomical and physiological factors in oncology patients is developed and validated.

Findings

Key predictors of bite force include body weight, masseter muscle volume, bite region, and tooth mobility.

The model explains 77.7% of the variance in bite force measurements.

Tailoring reconstructive designs to individual bite forces can prevent stress shielding and improve outcomes.

Abstract

Since the introduction of individualized planning using 3D technology, the necessity for an individualized predictive bite force model has become increasingly critical for optimizing functional rehabilitation, and for tailoring surgical interventions to each head and neck oncology patient’s unique anatomical and biomechanical profile. Current predictive models often lack comprehensive predictors and robust statistical methodologies, limiting their clinical applicability. To overcome these challenges, a predictive model of head and neck oncology patients’ maximum voluntary bite force was developed by analyzing key anatomical and physiological factors.

Data were collected from 41 head and neck cancer patients, with 109 bite force observations analyzed using a linear mixed-effects model. The identified key predictors include body weight, superficial masseter muscle volume, bite force measurement region, and maxilla and/or mandibular teeth mobility. Superficial masseter muscle volume showed a positive association with bite force, while increased mobility of the teeth in the maxilla or mandible impacted bite force negatively.

The model demonstrated strong explanatory performance (conditional R² = 0.777).

This model enables patient-specific treatment planning and implant design for head and neck reconstructive surgery by implementing the bite forces in a finite element software used for computer aided designs. For instance, by tailoring mandibular reconstruction plates to individual bite forces using novel materials or designs, the model allows for a balance between sufficiently strong designs while preventing stress shielding seen with excessively strong designs. This study provides valuable insights into the multifactorial nature of bite force and its implications for patient care.

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