Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

Jordan C Gainey; Yusen He; Robert Zhu; Stephen S Baek; Xiaodong Wu; John M Buatti; Bryan G Allen; Brian J Smith; Yusung Kim

doi:10.3389/fonc.2023.868471

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

Front Oncol. 2023 Apr 4:13:868471. doi: 10.3389/fonc.2023.868471. eCollection 2023.

Authors

Jordan C Gainey¹, Yusen He², Robert Zhu¹, Stephen S Baek³, Xiaodong Wu¹, John M Buatti¹, Bryan G Allen¹, Brian J Smith¹, Yusung Kim⁴

Affiliations

¹ Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States.
² Department of Data Science, Grinnell College, Grinnell, IA, United States.
³ Department of Data Science, University of Virginia, Charlottesville, VA, United States.
⁴ Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, United States.

Abstract

Purpose: The study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).

Methods: The DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.

Results: There was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and 'dead' group in their Kaplan-Meyer curves (p = 0.019).

Conclusion: Deep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.

Summary: While current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.

Keywords: RECIST (response evaluation criteria in solid tumors); deep learning; lung cancer; non-small cell lung cancer; prognostication.

Grants and funding

P30 CA086862/CA/NCI NIH HHS/United States