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

TLDR

Radiomics extracts high‑dimensional, mineable data from medical images and is viewed as a key prognostic tool for assessing cancer risk and intratumour heterogeneity. In this study, 1,615 radiomic features from pre‑treatment FDG‑PET and CT scans of 300 head‑and‑neck cancer patients were used to build random‑forest models combining radiomic and clinical variables, trained on two of four cohorts with imbalance‑adjustment strategies. Validation on the remaining two cohorts produced AUCs of 0.69 for locoregional recurrence and 0.86 for distant metastasis, and Kaplan‑Meier analysis demonstrated that radiomics can stratify patients into risk groups, potentially guiding personalized chemo‑radiation.

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.