CT, Digital Pathology, and CT–Pathology Fusion AI for Postoperative Recurrence Prediction in Clear Cell Renal Cell Carcinoma: A Systematic Review and Meta-Analysis: Aggregate Data and R Code

Aggregate study-level data and R analysis code for a systematic review and meta-analysis of CT-based, digital pathology-based, and CT–pathology fusion AI models for postoperative recurrence prediction in clear cell renal cell carcinoma. The review includes 23 reports, with 16 contributing to at least one quantitative analysis. The archive includes 14 original CSV files, standardized analysis inputs, study and dataset mappings, cohort-count documentation, numerical analysis scripts, reference results, a data guide, software versions, and checksums. The scripts cover sensitivity and specificity, reported discrimination, hazard ratios, exploratory modality comparisons, CT meta-regression and subgroups, and diagnostic checks. These are aggregate data extracted from published reports; individual patient data, images, trained models, literature PDFs, and figure-generation code are not included. The data and numerical analysis code are preserved from the previously shared source version; the README has been adapted for direct download from Zenodo.