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

ABSTRACT: Pathology images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology images is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology images. From the identified tumor regions, we extracted 22 well-defined shape and boundary 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 region 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.

SUBMITTER: Wang S

PROVIDER: S-EPMC6039531 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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

Wang Shidan S Chen Alyssa A Yang Lin L Cai Ling L Xie Yang Y Fujimoto Junya J Gazdar Adi A Xiao Guanghua G

Scientific reports 20180710 1

Pathology images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology images is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology images. From the identified tumor regions, we extracted 22 well-defined shape and boundary features and found that 15 of them were significantly associated with patient s ...[more]

PMID: 29991684

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Dataset Information

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

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

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