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 Garmire

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.

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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