Full Text | PDF

Theranostics 2020; 10(25):11707-11718. doi:10.7150/thno.50565 This issue Cite

Research Paper

Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer

Nasha Zhang^1,2, Rachel Liang¹, Michael F. Gensheimer¹, Meiying Guo², Hui Zhu¹, Jinming Yu², Maximilian Diehn¹, Bill W Loo Jr¹, Ruijiang Li¹, Jia Wu^1,3✉

1. Department of Radiation Oncology, Stanford University School of Medicine, 1070 Arastradero Rd, Palo Alto, CA 94304
2. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
3. Imaging Physics, Thoracic/Head & Neck Medical Oncology (joint appointment), MD Anderson Cancer Center, 1400 Pressler St., Unit 1472, Houston, Texas 77030

✉ Corresponding author: Jia Wu, Assistant Professor, Imaging Physics, Thoracic/Head & Neck Medical Oncology (joint appointment), MD Anderson Cancer Center, 1400 Pressler St., Unit 1472, Houston, Texas 77030. Tel: (713) 563-2719; E-mail: JWu11org More

Abstract

Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC.

Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model.

Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUV_max value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics).

Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.

Keywords: locally advanced NSCLC, pre and mid-treatment PET, radiomics, imaging model, PFS

Citation styles

©2024 Ivyspring International Publisher. Terms of use