More than meets the eye: predicting adrenocortical carcinoma outcomes with pathomics

作者：Jianqiu Kong, Mingli Luo, Yi Huang, Ying Lin, Kaiwen Tan, Yitong Zou, Juanjuan Yong, Sha Fu, Shao‐Ling Zhang, Xinxiang Fan, Tianxin Lin · 发表于：European Journal of Endocrinology · 年份：2025 · DOI：10.1093/ejendo/lvae162 · 被引用次数：6 · 研究领域：Adrenal and Paraganglionic Tumors、Cardiac tumors and thrombi、Thyroid Cancer Diagnosis and Treatment

BACKGROUND: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with high recurrence rates and poor prognosis. Current prognostic models are inadequate, highlighting the need for innovative diagnostic tools. Pathomics, which utilizes computer algorithms to analyze whole-slide images, offers a promising approach to enhance prognostic models for ACC. METHODS: A retrospective cohort of 159 patients who underwent radical adrenalectomy between 2002 and 2019 was analyzed. Patients were divided into training (N = 111) and validation (N = 48) cohorts. Pathomics features were extracted using an unsupervised segmentation method. A pathomics signature (PSACC) was developed through LASSO-Cox regression, incorporating 5 specific pathomics features. RESULTS: The PSACC showed a strong correlation with ACC prognosis. In the training cohort, the hazard ratio was 3.380 (95% CI, 1.687-6.772, P < .001), and in the validation cohort, it was 3.904 (95% CI, 1.039-14.669, P < .001). A comprehensive nomogram integrating PSACC and M stage significantly outperformed the conventional clinicopathological model in prediction accuracy, with concordance indexes of 0.779 versus 0.668 in the training cohort (P = .002) and 0.752 versus 0.603 in the validation cohort (P = .003). CONCLUSIONS: The development of a pathomics-based nomogram for ACC presents a superior prognostic tool, enhancing personalized clinical decision making. This study highlights the potential of pathomics in refining prognostic...