Two-stage Neural-network based Prognosis Models using Pathological Image and Transcriptomic Data: An Application in Hepatocellular Carcinoma Patient Survival Prediction

Zhucheng Zhan, Noshad Hosseni, Olivier Poirion, Maria Westerhoff, Eun-Young Choi, Travers Ching, Lana X. Garmire

2020

Abstract

Pathological images are easily accessible data type with potential as prognostic biomarkers. Here we extend Cox-nnet, a neural network based prognosis method previously used for transcriptomics data, to predict patient survival using hepatocellular carcinoma (HCC) pathological images. Cox-nnet based imaging predictions are more robust and accurate than Cox proportional hazards model. Moreover, using a novel two-stage Cox-nnet complex model, we are able to combine histopathology image and transcriptomics RNA-Seq data to make impressively accurate prognosis predictions, with C-index close to 0.90 and log-ranked p-value of 4e-21 in the testing dataset. This work provides a new, biologically relevant and relatively interpretable solution to the challenge of integrating multi-modal and multiple types of data, particularly for survival prediction.

Paper Citation

in Harvard Style

Zhan Z., Hosseni N., Poirion O., Westerhoff M., Choi E., Ching T. and Garmire L. (2020). Two-stage Neural-network based Prognosis Models using Pathological Image and Transcriptomic Data: An Application in Hepatocellular Carcinoma Patient Survival Prediction. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C, ISBN 978-989-758-398-8, pages 296-301. DOI: 10.5220/0009381002960301

in Bibtex Style

@conference{c2c20,
author={Zhucheng Zhan and Noshad Hosseni and Olivier Poirion and Maria Westerhoff and Eun-Young Choi and Travers Ching and Lana Garmire},
title={Two-stage Neural-network based Prognosis Models using Pathological Image and Transcriptomic Data: An Application in Hepatocellular Carcinoma Patient Survival Prediction},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C,},
year={2020},
pages={296-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009381002960301},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C,
TI - Two-stage Neural-network based Prognosis Models using Pathological Image and Transcriptomic Data: An Application in Hepatocellular Carcinoma Patient Survival Prediction
SN - 978-989-758-398-8
AU - Zhan Z.
AU - Hosseni N.
AU - Poirion O.
AU - Westerhoff M.
AU - Choi E.
AU - Ching T.
AU - Garmire L.
PY - 2020
SP - 296
EP - 301
DO - 10.5220/0009381002960301