DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma

Author:

Zhang Baoyi1,Li Chenyang23,Wu Jia4,Zhang Jianjun235,Cheng Chao678ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering Rice University Houston Texas USA

2. Genomic Medicine Department The University of Texas MD Anderson Cancer Center Houston Texas USA

3. Graduate School of Biomedical Sciences The University of Texas MD Anderson Cancer Center UTHealth Houston Houston Texas USA

4. Department of Imaging Physics, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center Houston Texas USA

5. Department of Thoracic/Head and Neck Medical Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA

6. Department of Medicine Baylor College of Medicine Houston Texas USA

7. Dan L Duncan Comprehensive Cancer Center Baylor College of Medicine Houston Texas USA

8. The Institute for Clinical and Translational Research Baylor College of Medicine Houston Texas USA

Abstract

AbstractLung cancer is the first leading cause of cancer‐related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image‐based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high‐quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image‐based prognostic model, termed Deep‐learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell‐level interpretation, which was more biologically relevant than previous region‐level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

Funder

Cancer Prevention and Research Institute of Texas

National Cancer Institute

Publisher

Wiley

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