Functional-structural sub-region graph convolutional network (FSGCN): Application to the prognosis of head and neck cancer with PET/CT imaging. (March 2023)

• Record Type: Journal Article
• Authors: Lv, Wenbing; Zhou, Zidong; Peng, Junyi; Peng, Lihong; Lin, Guoyu; Wu, Huiqin; Xu, Hui; Lu, Lijun
• Is Part Of: Computer methods and programs in biomedicine. Volume 230(2023)
• Publication Date: 2023-03
• Subjects: Graph convolutional network -- PET/CT -- Sub-region -- Head and neck cancer -- Prognosis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107341
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Propose a model based on graph neural networks for accurate risk stratification of HNC. Cluster the tumor into multiple sub-regions, and characterize their functional-structural interplay/interaction in PET/CT image by using a graph in non-Euclidean space. Validate the model on a large multi-center dataset to demonstrate its capability and generalization of prognosis prediction. Abstract: Background and objective: Accurate risk stratification is crucial for enabling personalized treatment for head and neck cancer (HNC). Current PET/CT image-based prognostic methods include radiomics analysis and convolutional neural network (CNN), while extracting radiomics or deep features in grid Euclidean space has inherent limitations for risk stratification. Here, we propose a functional-structural sub-region graph convolutional network (FSGCN) for accurate risk stratification of HNC. Methods: This study collected 642 patients from 8 different centers in The Cancer Imaging Archive (TCIA), 507 patients from 5 centers were used for training, and 135 patients from 3 centers were used for testing. The tumor was first clustered into multiple sub-regions by using PET and CT voxel information, and radiomics features were extracted from each sub-region to characterize its functional and structural information, a graph was then constructed to format the relationship/difference among different sub-regions in non-Euclidean space for each patient, followed by a residual gated graph convolutional network, the prognostic score was finally generated to predict the progression-free survival (PFS). Results: In the testing cohort, compared with radiomics or FSGCN or clinical model alone, the model PETCT Fea _CT ROI + Cli that integrates FSGCN prognostic score and clinical parameter achieved the highest C-index and AUC of 0.767 (95% CI: 0.759–0.774) and 0.781 (95% CI: 0.774–0.788), respectively for PFS prediction. Besides, it also showed good prognostic performance on the secondary endpoints OS, RFS, and MFS in the testing cohort, with C-index of 0.786 (95% CI: 0.778–0.795), 0.775 (95% CI: 0.767–0.782) and 0.781 (95% CI: 0.772–0.789), respectively. Conclusions: The proposed FSGCN can better capture the metabolic or anatomic difference/interaction among sub-regions of the whole tumor imaged with PET/CT. Extensive multi-center experiments demonstrated its capability and generalization of prognosis prediction in HNC over conventional radiomics analysis.