Identifying 18F-FDG PET-metabolic radiomic signature for lung adenocarcinoma prognosis via the leveraging of prognostic transcriptomic module

Jin Li; Yixin Liu; Wenlei Dong; Yang Zhou; Jingquan Wu; Kuan Luan; Lishuang Qi

doi:10.21037/qims-21-706

Identifying ¹⁸F-FDG PET-metabolic radiomic signature for lung adenocarcinoma prognosis via the leveraging of prognostic transcriptomic module

Quant Imaging Med Surg. 2022 Mar;12(3):1893-1908. doi: 10.21037/qims-21-706.

Authors

Jin Li^#¹, Yixin Liu^#^{1

2}, Wenlei Dong³, Yang Zhou^{1

3}, Jingquan Wu¹, Kuan Luan¹, Lishuang Qi⁴

Affiliations

¹ College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
² Basic Medicine College, Harbin Medical University, Harbin, China.
³ Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China.
⁴ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

^# Contributed equally.

Abstract

Background: Imaging with ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET), which identifies molecular and metabolic abnormalities within tumor cells, could support prognostic assessment of lung adenocarcinoma (LUAD). We aimed to develop a radiomic signature with the aid of a transcriptomic module for individualized clinical prognostic assessment of LUAD patients.

Methods: Using a gene expression profile consisting of 334 stage I-IIIA LUAD patients, prognostic-related gene coexpression modules were constructed via a weighted correlation network analysis algorithm. The robustness and prognostic performance of the coexpression modules were then tested across 2 gene expression datasets totaling 331 patients. Finally, using a discovery dataset with matched transcriptomic and ¹⁸F-FDG PET radiomic data of 15 patients and multiple linear regression analysis, we developed a PET-metabolic radiomic signature that had optimal correlation with the expression of a robust prognostic module.

Results: We selected a superior coexpression module for LUAD prognosis in which the genes were significantly enriched in important biological processes associated with tumors (e.g., cell cycle, DNA replication and p53 signaling pathway). The prognostic performance of the module for overall survival (OS) and recurrence-free survival (RFS) was validated in 2 independent gene expression datasets (log-rank P<0.05). Through the leveraging of this prognostic coexpression module, a radiomic signature consisting of 3 PET features associated with metabolic processes was developed in the discovery dataset. The radiomic signature was significantly associated with patients' OS and RFS in an independent PET dataset consisting of 72 LUAD patients (OS: log-rank P=0.0006; RFS: log-rank P=0.0013). Multivariate Cox analysis demonstrated that the radiomic signature was an independent prognostic factor for OS and RFS. Furthermore, the novel proposed radiomic nomograms for OS and RFS had significantly better performance (concordance indices) than did the clinicopathological nomograms.

Conclusions: The radiomic signature, which reflects biological processes in tumors (e.g., cell cycle and p53 signaling pathway), could noninvasively identify LUAD patients with poor prognosis who should receive postoperative adjuvant treatment. The signature is suitable for clinical application and could be robustly applied at an individual level across multicenter cohorts.

Keywords: Lung adenocarcinoma (LUAD); coexpression module; positron emission tomography; prognosis; radiomic signature.