Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients.

ABSTRACT:

Introduction

Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses.

Methods

We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the

SUBMITTER: Ho KMA 

PROVIDER: S-EPMC10602932 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature