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Background <p>As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there is a lack of systematic evidence on the effectiveness of ML.</p> Methods <p>We conducted a thorough search across Embase, PubMed, the Cochrane Library, and Web of Science from inception to December 14th, 2023.A systematic review and meta-analysis was conducted to assess the value of ML for predicting PD-L1 expression in NSCLC.</p> Results <p>Totally 30 studies with 12,898 NSCLC patients were included. The thresholds of PD-L1 expression level were &lt; 1%, 1–49%, and ≥ 50%. In the validation set, in the binary classification for PD-L1 ≥ 1%, the pooled C-index was 0.646 (95%CI: 0.587–0.705), 0.799 (95%CI: 0.782–0.817), 0.806 (95%CI: 0.753–0.858), and 0.800 (95%CI: 0.717–0.883), respectively, for the clinical feature-, radiomics-, radiomics + clinical feature-, and pathomics-based ML models; in the binary classification for PD-L1 ≥ 50%, the pooled C-index was 0.649 (95%CI: 0.553–0.744), 0.771 (95%CI: 0.728–0.814), and 0.826 (95%CI: 0.783–0.869), respectively, for the clinical feature-, radiomics-, and radiomics + clinical feature-based ML models.</p> Conclusions <p>At present, radiomics- or pathomics-based ML methods are applied for the prediction of PD-L1 expression in NSCLC, which both achieve satisfactory accuracy. In particular, the radiomics-based ML method seems to have wider clinical applicability as a non-invasive diagnostic tool. Both radiomics and pathomics serve as processing methods for medical images. In the future, we expect to develop medical image-based DL methods for intelligently predicting PD-L1 expression.</p>

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Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月22日

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  • Ting Zheng,
  • Xingxing Li,
  • Li Zhou,
  • Jianjiang Jin

摘要

Background

As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there is a lack of systematic evidence on the effectiveness of ML.

Methods

We conducted a thorough search across Embase, PubMed, the Cochrane Library, and Web of Science from inception to December 14th, 2023.A systematic review and meta-analysis was conducted to assess the value of ML for predicting PD-L1 expression in NSCLC.

Results

Totally 30 studies with 12,898 NSCLC patients were included. The thresholds of PD-L1 expression level were < 1%, 1–49%, and ≥ 50%. In the validation set, in the binary classification for PD-L1 ≥ 1%, the pooled C-index was 0.646 (95%CI: 0.587–0.705), 0.799 (95%CI: 0.782–0.817), 0.806 (95%CI: 0.753–0.858), and 0.800 (95%CI: 0.717–0.883), respectively, for the clinical feature-, radiomics-, radiomics + clinical feature-, and pathomics-based ML models; in the binary classification for PD-L1 ≥ 50%, the pooled C-index was 0.649 (95%CI: 0.553–0.744), 0.771 (95%CI: 0.728–0.814), and 0.826 (95%CI: 0.783–0.869), respectively, for the clinical feature-, radiomics-, and radiomics + clinical feature-based ML models.

Conclusions

At present, radiomics- or pathomics-based ML methods are applied for the prediction of PD-L1 expression in NSCLC, which both achieve satisfactory accuracy. In particular, the radiomics-based ML method seems to have wider clinical applicability as a non-invasive diagnostic tool. Both radiomics and pathomics serve as processing methods for medical images. In the future, we expect to develop medical image-based DL methods for intelligently predicting PD-L1 expression.