DataSheet_1_A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study.docx

ObjectiveThis study aimed to investigate the application of modified region-of-interest (ROI) segmentation method in unenhanced computed tomography in the radiomics model of adrenal lipid-poor adenoma, and to evaluate the diagnostic performance using an external medical institution data set and select the best ROI segmentation method.
MethodsThe imaging data of 135 lipid-poor adenomas and 102 non-adenomas in medical institution A and 30 lipid-poor adenomas and 43 non-adenomas in medical institution B were retrospectively analyzed, and all cases were pathologically or clinically confirmed. The data of Institution A builds the model, and the data of Institution B verifies the diagnostic performance of the model. Semi-automated ROI segmentation of tumors was performed using uAI software, using maximum area single-slice method (MAX) and full-volume method (ALL), as well as modified single-slice method (MAX_E) and full-volume method (ALL_E) to segment tumors, respectively. The inter-rater correlation coefficients (ICC) was performed to assess the stability of the radiomics features of the four ROI segmentation methods. The area under the curve (AUC) and at least 95% specificity pAUC (Partial AUC) were used as measures of the diagnostic performance of the model.
ResultsA total of 104 unfiltered radiomics features were extracted using each of the four segmentation methods. In the ROC analysis of the radiomics model, the AUC value of the model constructed by MAX was 0.925, 0.919, and 0.898 on the training set, the internal validation set, and the external validation set, respectively, and the AUC value of MAX_E was 0.937, 0.931, and 0.906, respectively. The AUC value of ALL was 0.929, 0.929, and 0.918, and the AUC value of ALL_E was 0.942, 0.926, and 0.927, respectively. In all samples, the pAUCs of MAX, MAX_E, ALL, and ALL_E were 0.021, 0.025, 0.018, and 0.028, respectively.
ConclusionThe diagnostic performance of the radiomics model constructed based on the full-volume method was better than that of the model based on the single-slice method. The model constructed using the ALL_E method had a stronger generalization ability and the highest AUC and pAUC value.

本研究旨在探讨改良感兴趣区（region-of-interest, ROI）分割方法在肾上腺乏脂性腺瘤(adrenal lipid-poor adenoma)平扫计算机断层扫描（unenhanced computed tomography, CT）放射组学模型中的应用，并采用外部医疗机构数据集评估模型的诊断效能，筛选最优的ROI分割方法。
方法 本研究回顾性分析了A医疗机构的135例肾上腺乏脂性腺瘤、102例非腺瘤病例，以及B医疗机构的30例肾上腺乏脂性腺瘤、43例非腺瘤病例，所有病例均经病理或临床确诊。以A医疗机构数据集构建放射组学模型，B医疗机构数据集验证模型的诊断效能。采用uAI软件对肿瘤进行半自动ROI分割，分别使用最大面积单层法（MAX）、全容积法（ALL），以及改良单层法（MAX_E）和改良全容积法（ALL_E）完成肿瘤分割。通过组内相关系数（intraclass correlation coefficient, ICC）评估四种ROI分割方法提取的放射组学特征的稳定性；以曲线下面积（area under the curve, AUC）及至少95%特异性的部分曲线下面积（partial AUC, pAUC）作为模型诊断效能的评价指标。
结果 四种分割方法各提取得到104个未过滤的放射组学特征。在放射组学模型的ROC分析中，基于MAX构建的模型在训练集、内部验证集及外部验证集的AUC值分别为0.925、0.919及0.898；MAX_E构建的模型对应AUC值分别为0.937、0.931及0.906。ALL构建的模型AUC值分别为0.929、0.929及0.918，ALL_E构建的模型对应AUC值分别为0.942、0.926及0.927。在全部样本中，MAX、MAX_E、ALL及ALL_E的pAUC值分别为0.021、0.025、0.018及0.028。
结论 基于全容积法构建的放射组学模型诊断效能优于基于单层法的模型；采用ALL_E方法构建的模型泛化能力更强，且拥有最高的AUC及pAUC值。