Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma

Figure 3 Kaplan-Meier curves for relapse-free survival. A: Comparison of relapse-free survival (RFS) based on pre-treatment gray-level co-occurrence matrix entropy (b = 1000 second/mm²). The high entropy group (n = 36) showed significantly better RFS than the low entropy group (n = 34) (5-year RFS: 72.9% vs 18.0%, P < 0.001); B: Comparison of RFS based on actual pathological complete response (pCR) status. The real pCR group (n = 15) had significantly better RFS than the real non-pCR group (n = 55) (5-year RFS: 83.9% vs 33.9%, P = 0.009); C: Comparison of RFS based on the artificial intelligence model’s prediction. The predicted pCR-positive group (n = 24) demonstrated significantly better RFS than the predicted pCR-negative group (n = 46) (5-year RFS: 73.1% vs 32.1%, P = 0.007). RFS: Relapse-free survival; pCR: Pathological complete response.