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

Wenbing Lv; Zidong Zhou; Junyi Peng; Lihong Peng; Guoyu Lin; Huiqin Wu; Hui Xu; Lijun Lu

doi:10.1016/j.cmpb.2023.107341

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

Comput Methods Programs Biomed. 2023 Mar:230:107341. doi: 10.1016/j.cmpb.2023.107341. Epub 2023 Jan 7.

Authors

Wenbing Lv¹, Zidong Zhou², Junyi Peng², Lihong Peng², Guoyu Lin², Huiqin Wu², Hui Xu², Lijun Lu³

Affiliations

¹ School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, China.
² School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
³ School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Pazhou Lab, Guangzhou 510515, China. Electronic address: ljlubme@gmail.com.

PMID: 36682111
DOI: 10.1016/j.cmpb.2023.107341

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.

Keywords: Graph convolutional network; Head and neck cancer; PET/CT; Prognosis; Sub-region.

MeSH terms

Head and Neck Neoplasms* / diagnostic imaging
Humans
Neural Networks, Computer
Positron Emission Tomography Computed Tomography* / methods
Prognosis