Predicting recurrence of PMP arising from LAMN using Machine Learning
Research type
Research Study
Full title
Prediction, modelling and understanding the risks of recurrence of Pseudomyxoma peritonei arising from appendiceal epithelial mucinous neoplasms after Cytoreductive surgery and Hyperthermic intraperitoneal chemotherapy using Machine learning.
IRAS ID
342832
Contact name
Thomas D Cecil
Contact email
Sponsor organisation
Hampshire Hospitals NHS foundation Trust
Duration of Study in the UK
1 years, 10 months, 29 days
Research summary
Pseudomyxoma Peritonei (PMP) is a rare cancer of the abdomen originating from the appendix. Despite advances in treatment, the recurrence rate is very high. It is not yet understood what the risk factors are for this recurrence rate. Recent evidence suggests that modern Machine Learning (ML) techniques will better predict risk factors than traditional methods.
This study therefore looks to develop a trustworthy and explainable model to predict recurrence and survival rates of PMP in patients who have undergone complete cytoreduction (the most common form of treatment). We will do this through three stages:
Large datasets from Hampshire Hospitals, using over 30 variables, will be analysed by the University of Southampton to create a model framework
A focus group of PMP patients will be consulted on attitudes and acceptability of using AI for guidance on treatment
Clinician interviews will be conducting to ascertain attitudes and acceptability of using AI for guidance on treatment amongst staff
Results from points 2 and 3 will be fed into the model to facilitate trust in this AI solution.REC name
Yorkshire & The Humber - Bradford Leeds Research Ethics Committee
REC reference
25/YH/0049
Date of REC Opinion
19 Mar 2025
REC opinion
Further Information Favourable Opinion