Identification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study

•An improved rank-based method for radiomic analysis has been proposed to develop a feature pair signatures to circumvent multicenter effects.•The novel proposed radiomic nomogram significantly improved the prognostic performance (concordance index) of clinicopathological factors.•Compared with the application of the quantitative signature, our novel rank-based signature can individually estimate the risk for LUAD patient.•The radiogenomic analyses link our rank-based signature with the tumour biological processes. Radiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD). Using computed tomography (CT) imaging of 234 stage I–IIIA LUAD patients derived from three public multicenter cohorts, we proposed a rank-based method that utilized the relative rank patterns of quantitative values between radiomic feature pairs within individual patients and established a feature pair signature for LUAD prognosis. We collected a new clinical cohort with 162 LUAD patients for independent validation. A rank-based radiomic signature, consisting of 12 feature pairs, was developed, and it could determine the mortality risk for an individual according to the rank patterns of 12 feature pairs within the patient’s CT imaging. The prognostic performance of the rank-based signature was effectively validated in the new clinical cohort (log-rank P = 0.0051, C-index = 0.73), whereas other signatures lost their prognostic ability across centers. The novel proposed radiomic nomogram significantly improved the prognostic performance of clinicopathological factors. The further radiogenomic analyses revealed the underlying biological characteristics (e.g., Stemness, Ferroptosis, ’ECM’) reflected by the rank-based radiomic signature. This multicenter study illustrates the accuracy and stability of the rank-based radiomic signature for LUAD prognosis, and demonstrates a unique advantage of clinical individualized application. The biological characteristics underlying the rank-based radiomic signature would accelerate its clinical application.