Table 1_The value of CT texture analysis in predicting mitotic activity and morphological variants of adrenocortical carcinoma.docx

IntroductionAdrenocortical carcinoma presents significant diagnostic challenges due to its histological heterogeneity and variable clinical behavior. This study aimed to evaluate the diagnostic value of radiomic features in predicting mitotic activity (low/high-grade) and morphological variants (conventional, oncocytic, myxoid) of adrenocortical carcinoma.
Materials and methodsA retrospective analysis of 32 patients with histologically confirmed ACC (18 conventional, 9 oncocytic and 5 myxoid cases) was performed, with mitotic data available for 25 cases (13 low-grade and 12 high-grade cases). Radiomic features including Gray-Level Co-occurrence Matrix (GLCM), Run-Length (GLRLM), Size-Zone (GLSZM), Dependence (GLDM), Neighboring-Tone (NGTDM) and first order features were extracted from four-phase CT using PyRadiomics after manual 3D segmentation. Statistical analysis included Mann–Whitney U, Kruskal–Wallis tests, ROC curve (AUC, sensitivity, specificity) and PPV, NPV assessment.
ResultsOur analysis demonstrated statistically significant differences between tumor grades with firstorder_Skewness (AUC = 0.924, 95% CI: 0.819–0.986; p = 0.005) showing high predictive performance in the venous phase. Radiomic features did not show statistically significant differences between morphological variants of ACC after adjustment for multiple comparisons.
ConclusionOur results confirm the value of CT radiomics for preoperative stratification of ACC grade, but the question of differentiation of morphological variants remains unresolved and requires further validation in larger cohorts.

引言
肾上腺皮质癌（Adrenocortical carcinoma, ACC）因组织学异质性与临床行为多变性，面临显著的诊断挑战。本研究旨在评估放射组学特征在预测肾上腺皮质癌的有丝分裂活性（低/高级别）及形态学亚型（经典型、嗜酸细胞型、黏液型）中的诊断价值。

材料与方法
本研究对32例经组织学确诊的ACC患者进行回顾性分析，其中经典型18例、嗜酸细胞型9例、黏液型5例；25例具备有丝分裂相关数据（低级别13例，高级别12例）。采用PyRadiomics工具，在手动三维分割后，从四期CT影像中提取放射组学特征，包括灰度共生矩阵（Gray-Level Co-occurrence Matrix, GLCM）、灰度游程长度矩阵（Gray-Level Run-Length Matrix, GLRLM）、灰度大小区域矩阵（Gray-Level Size-Zone Matrix, GLSZM）、灰度依赖矩阵（Gray-Level Dependence Matrix, GLDM）、邻域灰度差分矩阵（Neighboring-Tone, NGTDM）以及一阶特征。统计学分析方法包括Mann–Whitney U检验、Kruskal–Wallis检验、ROC曲线（AUC、灵敏度、特异度）以及阳性预测值（PPV）、阴性预测值（NPV）评估。

结果
本研究分析显示，肿瘤不同级别间存在统计学显著性差异，其中静脉期的firstorder_Skewness（AUC=0.924，95%CI：0.819–0.986；p=0.005）表现出较高的预测性能。经多重比较校正后，放射组学特征在ACC不同形态学亚型间未呈现统计学显著性差异。

结论
本研究结果证实了CT放射组学在ACC术前分级分层中的应用价值，但ACC形态学亚型的鉴别问题仍未解决，需在更大队列中开展进一步验证。