Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis

Author:

Diao Songhui123ORCID,Chen Pingjun3ORCID,Showkatian Eman3,Bandyopadhyay Rukhmini3ORCID,Rojas Frank R.4,Zhu Bo5,Hong Lingzhi35ORCID,Aminu Muhammad3,Saad Maliazurina B.3,Salehjahromi Morteza3ORCID,Muneer Amgad3ORCID,Sujit Sheeba J.3ORCID,Behrens Carmen5,Gibbons Don L.5,Heymach John V.5,Kalhor Neda6,Wistuba Ignacio I.4,Solis Soto Luisa M.4ORCID,Zhang Jianjun57ORCID,Qin Wenjian1,Wu Jia35ORCID

Affiliation:

1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China

3. Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

4. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

5. Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

6. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

7. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

Abstract

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.

Funder

Shenzhen Science and Technology Program of China

National Natural Science Foundation of China

Youth Innovation Promotion Association CAS

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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