Predicting HPV status in oropharyngeal carcinoma using multiparametric MRI and clinical variables

Predicting HPV status in oropharyngeal carcinoma using multiparametric MRI and clinical variables

Chen, Linda (TU Delft Mechanical, Maritime and Materials Engineering)

Petit, Steven (mentor)
Capala, Marta (mentor)
Lauwers, Iris (mentor)
Habraken, Steven (graduation committee)
Veenland, J.F. (graduation committee)

Delft University of Technology
Universiteit Leiden
Erasmus Universiteit Rotterdam

Technical Medicine

2023-08-23

Background: Histopathological examination in the diagnostic workflow of oropharyngeal squamous cell carcinoma (OPSCC) is essential. We aimed to develop a machine learning pipeline to predict human papillomavirus (HPV) status in OPSCC patients based on clinical variables and multiparametric magnetic resonance imaging (MRI).
Methods: In a dataset of OPSCC patients (n=59), we extracted features from three categories: clinical variables; histogram parameters from diffusion weighted imaging (DWI)-MRI model; radiomics based on T2-weighted and DWI-MRI. We performed ten-times repeated stratified five-fold cross-validation and divided each outer training set (80%) into an inner training set (80% of outer training set) and validation set (20% of outer training set) using five-fold stratified cross-validation. We performed three types of feature selection methods (LASSO, statistical analysis and manual selection), tuned and trained seven classifiers (logistic regression, k-nearest-neighbours, naive bayes, random forest, support vector machine, XGBoost and LightGBM) to find the optimal combination of features, hyperparameters and classifier on each inner training set. We ensembled the inner fold models to fit on the outer training set and tested on the outer test set (20%). We constructed additional models with subsets of the features.
Results: the combined model area under the curve was 0.793±0.136. Models including clinical features outperformed models without clinical features (p<0.001). Features from all feature categories were selected for the combined model.
Conclusion: we were able to predict HPV status in OPSCC patients using multiparametric MRI and clinical variables with reasonable accuracy, though retraining and validating on larger, external datasets is needed before implementation in clinic.

machine learning
radiomics
Magnetic Resonance Imaging
head and neck cancer
diffusion weighted imaging
human papillomavirus
oropharyngeal squamous cell carcinoma

http://resolver.tudelft.nl/uuid:f5a75444-0c9a-4982-ad12-0c4d618610ce

Student theses

master thesis

© 2023 Linda Chen