European journal of radiology · 2026
Interpretable machine learning and deep learning model for discriminating pheochromocytoma from adrenocortical adenoma based on CT: A multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Sun Hao, Yu Mulin, Zhou Yuhan, An Ziyue, Guo Xiaoxu, Zhang Shenquan, Lei Limin, Liu Yuanlong, Liang Fangcheng, Sun Bowen, Zhou Pengcheng, Han Suxia
Аннотация
BACKGROUND: Differentiating pheochromocytoma (PHEO) from adrenocortical adenoma (ACA) is vital to avoid intraoperative hypertensive crises or redundant surgeries.
PURPOSE: To identify the optimal diagnostic strategy by benchmarking various machine learning (ML) and deep learning (DL) architectures for the preoperative differentiation of PHEO from ACA.
METHODS: We retrospectively enrolled 401 patients from two centers. Center 1 (n = 331) was divided into training (n = 232), validation (n = 49), and internal test (n = 50) sets; Center 2 (n = 70) served as an external validation cohort. From dual-phase CT, 1,781 radiomics features were extracted. We benchmarked eight ML classifiers against a SqueezeNet-based DL model. Performance was quantified using AUC and decision curve analysis. Shapley Additive Explanations (SHAP) were applied to interpret the best-performing model and construct a clinical nomogram.
RESULTS: The Logistic Regression (LR)-based integrated model outperformed other classifiers and the DL baseline (AUC: 0.850, 95% CI: 0.838-0.869)), achieving an AUC of 0.917 (95% CI: 0.820-0.990) with an accuracy of 86.4% in the internal test set and 0.909 (95% CI: 0.7995-1.000) in external validation. Subgroup analysis showed high diagnostic stability, with an AUC of 0.933 (95% CI: 0.821-1.000) for tumors ≥ 4 cm. SHAP analysis identified dependence non-uniformity as a key diagnostic driver. The resulting nomogram provided personalized, transparent risk estimation.
CONCLUSION: The SHAP-interpreted integrated LR model is a robust, non-invasive tool for distinguishing PHEO from ACA. By combining radiomic and clinical data, this transparent framework supports precise surgical planning and individualized management.
PURPOSE: To identify the optimal diagnostic strategy by benchmarking various machine learning (ML) and deep learning (DL) architectures for the preoperative differentiation of PHEO from ACA.
METHODS: We retrospectively enrolled 401 patients from two centers. Center 1 (n = 331) was divided into training (n = 232), validation (n = 49), and internal test (n = 50) sets; Center 2 (n = 70) served as an external validation cohort. From dual-phase CT, 1,781 radiomics features were extracted. We benchmarked eight ML classifiers against a SqueezeNet-based DL model. Performance was quantified using AUC and decision curve analysis. Shapley Additive Explanations (SHAP) were applied to interpret the best-performing model and construct a clinical nomogram.
RESULTS: The Logistic Regression (LR)-based integrated model outperformed other classifiers and the DL baseline (AUC: 0.850, 95% CI: 0.838-0.869)), achieving an AUC of 0.917 (95% CI: 0.820-0.990) with an accuracy of 86.4% in the internal test set and 0.909 (95% CI: 0.7995-1.000) in external validation. Subgroup analysis showed high diagnostic stability, with an AUC of 0.933 (95% CI: 0.821-1.000) for tumors ≥ 4 cm. SHAP analysis identified dependence non-uniformity as a key diagnostic driver. The resulting nomogram provided personalized, transparent risk estimation.
CONCLUSION: The SHAP-interpreted integrated LR model is a robust, non-invasive tool for distinguishing PHEO from ACA. By combining radiomic and clinical data, this transparent framework supports precise surgical planning and individualized management.