Abstract

Background: Ki-67 is a pivotal biomarker of tumor proliferative activity in esophageal cancer, yet its clinical application is hindered by reliance on invasive biopsy. Radiomics offers a non-invasive alternative, but conventional methods may be confounded by inter-individual baseline variations. This exploratory study aims to develop a radiomics-based biomarker for predicting Ki-67 expression. Methods: This single-center retrospective study included 59 patients with esophageal cancer. Delta-radiomics features were derived from preoperative CT images by calculating the difference between radiomic features from the tumor and paired normal esophageal tissue. Feature selection (mRMR, k = 3) was nested within leave-one-out cross-validation (LOOCV) to prevent data leakage. A Random Forest model was compared with Logistic Regression and Support Vector Machine across three feature types, five Ki-67 thresholds, and clinical variables. SHAP analysis was used for interpretability. Results: The Random Forest model achieved an AUC of 0.643 (95% CI: 0.483–0.792). Delta radiomics outperformed esotarget (AUC = 0.546) and eso (AUC = 0.514) models. The combined model (AUC = 0.619) did not outperform delta radiomics alone. SHAP analysis identified GrayLevelVariance and SmallAreaEmphasis as the most influential features. Conclusions: This exploratory study demonstrates that delta radiomics provides moderate discriminatory performance for predicting Ki-67 expression. External validation in independent multi-center cohorts is required before clinical application.

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