Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis - Ting Zheng; Xingxing Li; Li Zhou; Jianjiang Jin (World Journal of Surgical Oncology)
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.