Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis
Abstract 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 eff...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
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| Series: | World Journal of Surgical Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12957-025-03847-6 |
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| Summary: | Abstract 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. |
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| ISSN: | 1477-7819 |