DeepAI AI Chat
Log In Sign Up

Lymph Node Graph Neural Networks for Cancer Metastasis Prediction

by   Michal Kazmierski, et al.

Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology. Machine learning (ML) offers a promising way to exploit rich multi-modal data, including clinical information and imaging to learn predictors of disease trajectory and help inform clinical decision making. In this paper, we present a novel graph-based approach to incorporate imaging characteristics of existing cancer spread to local lymph nodes (LNs) as well as their connectivity patterns in a prognostic ML model. We trained an edge-gated Graph Convolutional Network (Gated-GCN) to accurately predict the risk of distant metastasis (DM) by propagating information across the LN graph with the aid of soft edge attention mechanism. In a cohort of 1570 head and neck cancer patients, the Gated-GCN achieves AUROC of 0.757 for 2-year DM classification and C-index of 0.725 for lifetime DM risk prediction, outperforming current prognostic factors as well as previous approaches based on aggregated LN features. We also explored the importance of graph structure and individual lymph nodes through ablation experiments and interpretability studies, highlighting the importance of considering individual LN characteristics as well as the relationships between regions of cancer spread.


page 2

page 6


Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks

Accurate survival prediction is crucial for development of precision can...

Predicting erectile dysfunction after treatment for localized prostate cancer

While the 10-year survival rate for localized prostate cancer patients i...

A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

Accurate prognosis for an individual patient is a key component of preci...

IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction

Interpretability in Graph Convolutional Networks (GCNs) has been explore...

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease...

AMINN: Autoencoder-based Multiple Instance Neural Network for Outcome Prediction of Multifocal Liver Metastases

Colorectal cancer is one of the most common and lethal cancers and color...