Risk-aware survival time prediction from whole slide pathological images

Zhixin Xu, Seohoon Lim, Hong Kyu Shin, Kwang Hyun Uhm, Yucheng Lu, Seung Won Jung, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Deep-learning-based survival prediction can assist doctors by providing additional information for diagnosis by estimating the risk or time of death. The former focuses on ranking deaths among patients based on the Cox model, whereas the latter directly predicts the survival time of each patient. However, it is observed that survival time prediction for the patients, particularly with close observation times, possibly has incorrect orders, leading to low prediction accuracy. Therefore, in this paper, we present a whole slide image (WSI)-based survival time prediction method that takes advantage of both the risk as well as time prediction. Specifically, we propose to combine these two approaches by extracting the risk prediction features and using them as guides for the survival time prediction. Considering the high resolution of WSIs, we extract tumor patches from WSIs using a pre-trained tumor classifier and apply the graph convolutional network to aggregate information across these patches effectively. Extensive experiments demonstrate that the proposed method significantly improves the time prediction accuracy when compared with direct prediction of the survival times without guidance and outperforms existing methods.

Original languageEnglish
Article number21948
JournalScientific reports
Issue number1
Publication statusPublished - 2022 Dec

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

ASJC Scopus subject areas

  • General


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