Academic radiology · 2026
Clinical, CT Radiomic, and Pathomic Models for FOS Immunohistochemical Status Defined by the Training-Cohort Median in Ovarian Cancer.
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Qin Lan-Hui, Lu Yi-Xin, Liang Wenqing, Song Rui, Chen Pei-Yin, Xu Weihui, Zeng Guanzhen, Chen Jia, Zhao Yang, Long Liling
Аннотация
RATIONALE AND OBJECTIVES: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSION: 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.