Abstract: Whole slide images(WSIs) of lung cancer play a pivotal role in prognostic diagnosis.However,survival analysis for lung cancer without pixel-level annotations still encounters numerous challenges.Existing methods often overlook information from clinical feature modalities,spatial information of patches,and the heterogeneity between WSIs and natural images.To address these hurdles,a position-aware based multi-modality lung cancer survival prediction method PSMMSurv is proposed.This approach effectively leverages whole slide images and clinical features through multi-modality fusion and multi-task learning.Furthermore,the proposed whole slide image feature learning network achieves position awareness by interacting with information from adjacent locations.Moreover,data heterogeneity issues are overcome through self-supervised learning.Experimental results on a large lung cancer dataset demonstrate that the proposed method surpasses existing approaches in terms of the C-index metric,enabling more accurate prediction of lung cancer patients’ survival outcomes and providing reliable support for better lung cancer prognosis.

Key words: Survival analysis, Whole slide images, Multi-modality fusion, Self-supervised learning, Multi-scale fusion, Multi-task learning