Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

Radiomics analyses provide a promising avenue for enabling personalized radiotherapy. Most frequently, prognostic radiomics models are based on features extracted from medical images that are acquired before treatment. Here, we investigate whether combining data from multiple timepoints during treatment and additionally from multiple imaging modalities can improve the predictive ability of radiomics models.
We extracted radiomics features from computed tomography (CT) images acquired before treatment as well as two and three weeks after the start of radiochemotherapy for 55 patients with locally advanced head and neck squamous cell carcinoma (HNSCC). Additionally, we obtained features from FDG-PET images taken before treatment and three weeks after start of therapy. Cox proportional hazards models were then built based on features of the different image modalities, treatment timepoints and combinations thereof using two different feature selection methods in a five-fold cross-validation approach. Based on the cross-validation results, feature signatures were derived and their performance was independently validated. Discrimination regarding loco-regional control was assessed by the concordance index (C-index) and log-rank tests were performed to assess risk stratification.
The best prognostic performance was obtained for timepoints during treatment for all modalities. Overall, CT was the best discriminating modality with an independent validation C-index of 0.78 for week two and week two and three combined. However, none of these models achieved a statistically significant patient stratification. Models based on FDG features from week three provided both, satisfactory discrimination (C-index=0.61 and 0.64) and a statistically significant stratification (p=0.044 and p<0.001) but produced highly imbalanced risk groups.
After independent validation on larger data sets, the value of (multimodal) radiomics models combining several imaging timepoints should be prospectively assessed for personalized treatment strategies.