Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

Abstract

PURPOSE:

Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC.

METHODS:

We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed.

RESULTS:

Fifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66-25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61-2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69-3.38, p < 0.001).

CONCLUSIONS:

Radiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.

• Authors: Qiuying Chen, Lu Zhang, Xiaokai Mo, Jingjing You, Luyan Chen, Jin Fang, Fei Wang, Zhe Jin, Bin Zhang, Shuixing Zhang
• Journal: European journal of nuclear medicine and molecular imaging (Eur J Nucl Med Mol Imaging) Vol. 49 Issue 1 Pg. 345-360 (12 2021) ISSN: 1619-7089 [Electronic] Germany
• PMID: 34402924 (Publication Type: Journal Article, Meta-Analysis, Review, Systematic Review)
• Copyright: © 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Topics

• Carcinoma, Non-Small-Cell Lung (diagnostic imaging, therapy)
• Diagnostic Imaging
• Humans
• Immunotherapy
• Lung Neoplasms (diagnostic imaging, therapy)