• Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA.
• The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21205, USA.
• Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
• Johns Hopkins Physical Sciences Oncology Center, Baltimore, MD 21218, USA.
• Department of Radiology, Johns Hopkins University, Baltimore, MD 21218, USA.
• Department of Surgery, Johns Hopkins University, Baltimore, MD 21218, USA.
• Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.
• Intensive Care Unit, Howard University College of Medicine, Washington, DC 20059, USA.
• Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
• BC Cancer Research Institute, University of British Columbia, Vancouver, BC V5Z 1L3, Canada.

Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist's readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.