Spatial Radiomic Graphs for Outcome Prediction in Radiation Therapy-treated Head and Neck Squamous Cell Carcinoma Using Pretreatment CT

Abstract

Purpose To develop a radiomic graph framework, RadGraph, for spatial analysis of pretreatment CT images to improve prediction of local-regional recurrence (LR) and distant metastasis (DM) in head and neck squamous cell carcinoma (HNSCC). Materials and Methods This retrospective study included four public pre-radiotherapy treatment CT datasets of patients with HNSCC obtained from The Cancer Imaging Archive (images collected between 2003 and 2018). Computational graphs and graph attention deep learning methods were leveraged to holistically model multiple regions in the head and neck anatomy. Clinical features, including age, sex, and human papillomavirus infection status, were collected for a baseline model. Model performance in predicting LR and DM was evaluated via area under the receiver operating characteristic curve (AUC) and qualitative interpretation of model attention. Results A total of 3434 patients (61 years ± 11 [SD], 2774 male) were divided into training (n = 1576), validation (n = 379), and testing (n = 1479) datasets. RadGraph achieved AUCs of up to 0.83 and 0.90 for LR and DM prediction, respectively. RadGraph showed higher performance compared with the clinical baseline (AUCs up to 0.73 for LR prediction and 0.83 for DM prediction) and previously published approaches (AUCs up to 0.81 for LR prediction and 0.87 for DM prediction). Graph attention atlases enabled visualization of regions coinciding with cervical lymph node chains as important for outcome prediction. Conclusion RadGraph leveraged information from tumor and nontumor regions to effectively predict LR and DM in a large multi-institutional dataset of patients with radiation therapy-treated HNSCC. Graph attention atlases enabled interpretation of model predictions. Keywords: CT, Informatics, Neural Networks, Radiation Therapy, Head/Neck, Computer Applications-General (Informatics), Tumor Response, Head and Neck Squamous Cell Carcinoma, Locoregional Recurrence, Radiotherapy, Deep Learning, Radiomics Supplemental material is available for this article. © RSNA, 2025.

Keywords: CT; Computer Applications–General (Informatics); Deep Learning; Head and Neck Squamous Cell Carcinoma; Head/Neck; Informatics; Locoregional Recurrence; Neural Networks; Radiation Therapy; Radiomics; Radiotherapy; Tumor Response.

Graphical abstract

Figure 1:

Patient inclusion and exclusion criteria. Flowchart shows the patient inclusion data for the four TCIA datasets studied, as well as the composition of the training, validation, and testing datasets studied in this work. GTV = gross target volume, HNPET = Head-Neck-PET-CT dataset, HN1 = Head-Neck-Radiomics-HN1 dataset, MDACC = MD Anderson Cancer Center head and neck squamous cell carcinoma dataset, RADCURE = Computed Tomography Images from Large Head and Neck Cohort dataset, TCIA = The Cancer Imaging Archive.

Figure 2:

Study overview shows the radiomic graph framework (RadGraph), as well as its potential place in the current radiation treatment pipeline. RT = radiation therapy, 3D = three-dimensional.

Figure 3:

Model performance on outcome prediction tasks. Graphs show AUCs for RadGraph and baseline model predictions of LR (top) and DM (bottom) across the four datasets studied. Bars outlined in dashed red lines indicate the highest performance achieved on each dataset. AUC = area under the receiver operating characteristic curve, CNN = convolutional neural network, DM = distant metastasis, HNPET = Head-Neck-PET-CT dataset, HN1 = Head-Neck-Radiomics dataset, LR = local-regional recurrence, MDACC = MD Anderson Cancer Center Head and Neck Squamous Cell Carcinoma dataset, RADCURE = Computed Tomography Images from Head and Neck Cohort dataset, RadGraph = radiomic graph framework.

Figure 4:

Graph attention atlases created via coregistration of patient CT images and attention maps from the HN1 dataset for oropharyngeal and laryngeal HNSCC tumors. Images in a 56-year-old male patient are shown. Top: Atlases for attention values from models predict local-regional recurrence. Bottom: Atlases for models predict distant metastasis. HN1 = Head-Neck-Radiomics-HN1 dataset, HNSCC = head and neck squamous cell carcinoma.

Figure 5:

Radiomic attention to GTVn. Bar graphs show model attention values to GTVn regions from the HNPET dataset for models predicting local-regional recurrence (top) and distant metastasis (bottom). Model attention is calculated directly from the final GAT layer and discretized into low, medium, and high values. GAT = graph attention network, GTVn = lymph node gross target volume, HNPET = Head-Neck-PET-CT dataset.