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Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome

Shidan Wang, Alyssa Chen, Lin Yang, Ling Cai, Yang Xie, Junya Fujimoto, Adi Gazdar, Guanghua Xiao
doi: https://doi.org/10.1101/274332
Shidan Wang
1Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
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Alyssa Chen
1Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
2Department of Computer Sciences, Massachusetts Institute of Technology
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Lin Yang
1Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
3Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
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Ling Cai
1Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
4Children’s Medical Center Research Institute at UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390 USA
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Yang Xie
1Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
5Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
6Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA
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Junya Fujimoto
7Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, University of Texas MD Anderson Cancer Center, Houston, TX
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Adi Gazdar
6Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA
7Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, University of Texas MD Anderson Cancer Center, Houston, TX
8Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
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Guanghua Xiao
1Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA
5Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
6Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA
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  • For correspondence: Xiao@utsouthwestern.edu
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ABSTRACT

Pathology slide images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology slides is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology slides. From the identified regions, we extracted 22 well-defined tumor shape features and found that 15 of them were significantly associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial. A tumor shape-based prognostic model was developed and validated in an independent patient cohort (n=389). The predicted high-risk group had significantly worse survival than the low-risk group (p value = 0.0029). Predicted risk group serves as an independent prognostic factor (high-risk vs. low-risk, hazard ratio = 2.25, 95% CI 1.34-3.77, p value = 0.0022) after adjusting for age, gender, smoking status, and stage. This study provides new insights into the relationship between tumor shape and patient prognosis.

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Posted March 02, 2018.
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Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome
Shidan Wang, Alyssa Chen, Lin Yang, Ling Cai, Yang Xie, Junya Fujimoto, Adi Gazdar, Guanghua Xiao
bioRxiv 274332; doi: https://doi.org/10.1101/274332
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Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome
Shidan Wang, Alyssa Chen, Lin Yang, Ling Cai, Yang Xie, Junya Fujimoto, Adi Gazdar, Guanghua Xiao
bioRxiv 274332; doi: https://doi.org/10.1101/274332

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