Preoperative human epidermal growth factor receptor 2 assessment in gastric cancer using radiomics and machine learning: A diagnostic meta-analysis

Background/Objectives: Human epidermal growth factor receptor 2 (HER2) is an important biomarker in gastric cancer (GC). Histologic assessment may be limited by tissue sampling and spatial heterogeneity. We evaluated radiomics models for preoperative HER2 classification. Methods: PubMed, the Cochrane Library, Embase, and Web of Science were searched from inception to 7 November 2024 and updated through 10 August 2026. A Study_ID–Report_ID–Cohort_ID index identified overlaps. AUROC (C-statistic) values were pooled on the logit scale using random-effects models. Patient-level diagnostic accuracy was pooled using bivariate random-effects models only for directly reported 2 × 2 tables or unique integer reconstructions. Quality and reporting were assessed using RQS, PROBAST+AI, and TRIPOD+AI. Results: Twenty-three eligible reports represented 20 independent studies and three overlapping companion reports. The modeling population comprised 6,734 patients, including 1,484 with HER2-positive tumors. Pooled AUROC was 0.812 (95% CI, 0.763–0.853; I² = 78.9%) in training cohorts and 0.821 (95% CI, 0.772–0.861; I² = 46.3%) in validation cohorts. For validation CECT models, AUROC was 0.824 (95% CI, 0.772–0.866; I² = 55.5%). In the diagnostic accuracy meta-analysis, pooled sensitivity and specificity were 0.718 and 0.764 in five training cohorts and 0.714 and 0.827 in four validation cohorts. Conclusions: Radiomics models provided preoperative discrimination of HER2 status. CECT has the largest evidence base and may serve as a research adjunct to histologic testing. PET/CT, DECT, and deep learning require further prospective multicenter validation, with standardized imaging and complete reporting of calibration, decision curves, and clinical utility.