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