Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.

2026-07
European respiratory review : an official journal of the European Respiratory Society 35(181)
- Peng Zhang
- Biao Zhang
- Nian Liu
- Delong Huang
- Haoshu Zhong
- Junhao Wu
- Yiren Wang
- Qiang Ren

PubMed: 42419776
DOI: 10.1183/16000617.0299-2025

Study Design

Type: Meta-Analysis
Sample size: n = 35
Methods: search of medical databases, screened studies, feature extraction, data extraction, analysis using R software, forest plots, subgroup analyses based on imaging modality and algorithm model
Funding: Unclear

Background

Since 1992, the incidence of lung cancer has shown an overall downward trend, with survival rates significantly improving in recent years due to the application of immune checkpoint inhibitors (ICIs). Assessing programmed death ligand 1 (PD-L1) expression status in lung cancer patients is crucial for predicting the efficacy of ICIs and formulating treatment plans. Therefore, there is an urgent need to assess PD-L1 expression status in lung cancer patients, particularly those with nonsmall cell lung cancer (NSCLC). While numerous studies have demonstrated promising results using artificial intelligence (AI) models based on radiomics, current understanding in this area remains incomplete.

Method

We conducted a search of medical databases and screened studies that meet the criteria. We performed feature extraction and data extraction from these studies, analysed them using R software, and generated forest plots. We conducted subgroup analyses based on imaging modality and algorithm model.

Results

A total of 35 studies were included in the analysis. For the Tumor Proportion Score (TPS)1 group, the pooled area under the curve (AUC) for the validation set was 0.782. For the TPS50 group, the pooled AUC for the validation set was 0.798. Subgroup analysis revealed that machine learning performed better when predicting TPS50, while deep learning was superior for predicting TPS1. When predicting TPS, prediction models constructed using 2-fluoro-2-deoxy-d-glucose (¹⁸F) positron emission tomography/computed tomography (CT) and contrast-enhanced CT outperformed those based on noncontrast CT. Conversely, when predicting a TPS, the noncontrast-CT-based prediction model significantly outperformed the other two groups.

Conclusions

Imaging modality and algorithm model are key factors influencing AI prediction models for PD-L1 expression in NSCLC patients.