The primary objective was to develop and externally validate a CT-only model for preoperative classification of FOS immunohistochemical status dichotomized at the training-cohort median H-score. Secondary analyses evaluated tissue-derived H&E pathomics and a combined model after tissue acquisition. Survival stratification was exploratory. This retrospective two-center study included 289 patients in training (n = 151), internal validation (n = 38), and external validation (n = 100) cohorts. The binary endpoint was an H-score greater than 15 versus 15 or less; the training-median cutoff was locked for validation. Venous-phase CT and H&E whole-slide images yielded 1834 radiomic and 682 pathomic features. Feature reduction used reproducibility and redundancy filters, group-difference testing, and five-fold cross-validated LASSO. XGBoost modeled relative FOS status. The final feature sets contained two radiomic and 23 pathomic features. External AUCs were 0.794, 0.827, 0.842, and 0.871 for clinical, radiomic, pathomic, and combined models. The combined-model AUC was numerically higher than the CT-only AUC. Inferential comparison was unavailable because aligned patient-level prediction vectors were not retained. Combined-model accuracy, sensitivity, specificity, and F1-score were 0.806, 0.846, 0.750, and 0.835. Rad-score and Path-score were associated with continuous FOS H-score (both P < 0.001) and worse OS (P = 0.044 and P = 0.012). The primary CT-only model provided a noninvasive preoperative estimate of the data-derived FOS endpoint. Tissue-dependent models did not replace direct FOS immunohistochemistry. The combined model's numerically higher AUC did not establish incremental discrimination.