Abstract
Lung 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.
Original language | English (US) |
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Pages (from-to) | 2151-2161 |
Number of pages | 11 |
Journal | International journal of cancer |
Volume | 154 |
Issue number | 12 |
DOIs | |
State | Accepted/In press - 2024 |
Keywords
- H&E
- graph neural network
- lung adenocarcinoma
- prognosis
ASJC Scopus subject areas
- Oncology
- Cancer Research