# Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

## TL;DR

This study evaluates the use of radiomics features from PET and CT images to predict tumor recurrence and metastasis in head-and-neck cancer, aiming to improve personalized treatment strategies.

## Contribution

It introduces a comprehensive radiomics-based prediction model validated across multiple cohorts, enhancing risk assessment accuracy for clinical decision-making.

## Key findings

- Prediction models achieved AUC up to 0.86 for distant metastases.
- Radiomics features provided significant stratification of patient risk groups.
- Models demonstrated potential for personalized treatment planning.

## Abstract

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.