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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BMC
2025-05-01
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| Series: | World Journal of Surgical Oncology |
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| Online Access: | https://doi.org/10.1186/s12957-025-03847-6 |
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| author | Ting Zheng Xingxing Li Li Zhou Jianjiang Jin |
| author_facet | Ting Zheng Xingxing Li Li Zhou Jianjiang Jin |
| author_sort | Ting Zheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-54b1bfdee12c4136a2ce83e267555b8f |
| institution | OA Journals |
| issn | 1477-7819 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj-art-54b1bfdee12c4136a2ce83e267555b8f2025-08-20T01:53:19ZengBMCWorld Journal of Surgical Oncology1477-78192025-05-0123111610.1186/s12957-025-03847-6Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysisTing Zheng0Xingxing Li1Li Zhou2Jianjiang Jin3Department of Medical Oncology, The First People’s Hospital of Linping DistrictDepartment of Medical Oncology, The First People’s Hospital of Linping DistrictDepartment of Medical Oncology, The First People’s Hospital of Linping DistrictDepartment of Medical Oncology, The First People’s Hospital of Linping DistrictAbstract 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.https://doi.org/10.1186/s12957-025-03847-6Lung cancerPD-L1Machine learningMeta-analysisRadiomics |
| spellingShingle | Ting Zheng Xingxing Li Li Zhou Jianjiang Jin Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis World Journal of Surgical Oncology Lung cancer PD-L1 Machine learning Meta-analysis Radiomics |
| title | Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis |
| title_full | Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis |
| title_fullStr | Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis |
| title_full_unstemmed | Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis |
| title_short | Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis |
| title_sort | predictive value of machine learning for pd l1 expression in nsclc a systematic review and meta analysis |
| topic | Lung cancer PD-L1 Machine learning Meta-analysis Radiomics |
| url | https://doi.org/10.1186/s12957-025-03847-6 |
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