Introduction:
The past few decades have seen
the rise of Human Papilloma Virus (HPV)-related Oropharyngeal Squamous
Cell Carcinoma (OPSCC). This shift in epidemiology has led to
exploration with de-escalation trials that affect our treatment of this
cancer. The optimal treatment for
T1-2, N0-N1 OPSCC is an ongoing
debate as the current understanding of disease processes and advancing
technologies are constantly changing.1-3
Current National Comprehensive
Cancer Network (NCCN) guidelines recommend single modality treatment
with either surgery or radiation for both HPV and non-HPV-related T1-2,
N0-N1 OPSCC. While we await the results of several ongoing clinical
trials, systematic and retrospective reviews suggest no difference in
survival outcomes for the treatment of early-stage OPSCC between either
modality.1,4
Given equivalent survival outcomes with either treatment modality in
this population, little work has been conducted looking at the influence
patient, socioeconomic, regional, or institutional factors have in
primary treatment modality for this category of OPSCC. This question is
ideally analyzed using large national data registries and a methodology
equipped to analyze multiple layers of influence.
Machine learning (ML) is a novel form of analysis that uses
sophisticated statistical theories to create a prediction
model.5,6 Many of the statistical principles vital to
the machine learning process are similar to traditional statistical
methodologies used in clinical medicine, but the primary objective of
machine learning is to predict an unknown component rather that
determine inferences.5,7,8 Machine learning excels in
its ability to analyze complicated interactions that exist between these
variables.56 There is growing interest in various
fields of medicine to use machine learning to improve upon current
methodologies.5-7
This study therefore seeks to utilize machine learning to create a
prediction model for the primary treatment modality of patients with
T1-2, N0-N1 OPSCC by examining patient, socioeconomic, regional, and
institutional factors in addition to tumor factors. In doing so, this
study will demonstrate how machine learning can be utilized to create
prediction models in a reproducible manner, and provide insight to the
variables that influence treatment patterns.