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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

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

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.

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Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Code availability

Not applicable.

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Funding

This work received funding from the National Natural Science Foundation of China (81871323 and 81801665); National Natural Science Foundation of Guangdong Province (2018B030311024); and Scientific Research Cultivation and Innovation Foundation of Jinan University (21620447). The funders had no role in study design, data collection and analysis, preparation of the manuscript, or decision to publish.

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Author notes

  1. Qiuying Chen and Lu Zhang have contributed equally to this work

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People’s Republic of China

    Qiuying Chen, Lu Zhang, Xiaokai Mo, Jingjing You, Luyan Chen, Jin Fang, Fei Wang, Zhe Jin, Bin Zhang & Shuixing Zhang

  2. Graduate College, Jinan University, Guangzhou, Guangdong, People’s Republic of China

    Qiuying Chen, Lu Zhang, Jingjing You, Luyan Chen, Zhe Jin, Bin Zhang & Shuixing Zhang

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  1. Qiuying Chen
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Contributions

QYC and LZ contributed to the conception and design of the study, the analysis and interpretation of data, and the work draft. XKM, FW, JF, and ZJ participated in the data extraction and analysis. JJY and LYC designed figures and tables. BZ and SXZ offered guidance in study design and revised the article critically for important intellectual content. All authors have read and approved the final version of the manuscript.

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Correspondence to Bin Zhang or Shuixing Zhang.

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259_2021_5509_MOESM2_ESM.tif

Supplementary Figure 1. Forest plot of pooled diagnostic performance of radiomic models in evaluating immunotherapy response in NSCLC patients. (a) Sensitivity; (b) Specificity; (c) Positive likelihood ratio; and (d) Negative likelihood ratio. Note: Diagnostic performance for each study is presented as a black dot, with the horizontal line indicating the 95% confidence interval. Pooled result for all studies is presented as a black diamond. (TIF 2896 kb)

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Chen, Q., Zhang, L., Mo, X. et al. 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. Eur J Nucl Med Mol Imaging 49, 345–360 (2021). https://doi.org/10.1007/s00259-021-05509-7

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