探讨影像组学分析中多种标注的性能差异，为大规模医学图像分析中的肿瘤标注提供参考。对来自两个中心的342例接受直肠癌根治术的患者进行回顾性研究，分为训练组、内部组、验证和外部验证队列。进行了肿瘤 T 分期 (pT)、淋巴结转移 (pLNM) 和无病生存 (pDFS) 三个预测任务。使用 Lasso-Logistic 或 Lasso-Cox 构建十二个放射组学模型来评估和四种注释方法，沿肿瘤边界的 2D 详细注释 (2D)、沿肿瘤边界的 3D 详细注释 (3D)、2D 边界框 (2DBB) 和 3D 边界比较了 T2 加权图像上的框（3DBB）。放射组学模型用于建立纳入临床危险因素的组合模型。进行 DeLong 检验以使用受试者工作特征曲线比较模型的性能。对于放射组学模型，内部验证队列中的曲线下面积值范围为 0.627 (0.518-0.728) 至 0.811 (0.705-0.917)，外部验证队列中的值为 0.619 (0.469-0.754) 至 0.824 (0.689-0.918)。除了内部验证队列中 pLNM 的 3D 和 3DBB 模型 (p = 0.0188) 之外，大多数基于四个注释的放射组学模型没有显着差异。对于组合模型，只有 2D 模型与 pDFS 的 2DBB (p = 0.0372) 和 3D 模型 (p = 0.0380) 显着不同。Radiomics 和使用 2D 和边界框注释构建的组合模型显示出与使用 3D 和详细注释构建的组合模型相当的性能沿肿瘤边界进行直肠癌表征和预后预测。对于放射学图像的定量分析，选择2D最大肿瘤区域或边界框注释与3D整个肿瘤或沿肿瘤边界的详细注释一样具有代表性且易于操作。目前有缺乏关于放射组学中不同注释工作是否具有预测代表性的讨论。无论注释如何（2D、3D、详细或边界框），在放射组学和组合模型中均未观察到显着差异。优先选择更省时省力的 2D 最大面积边界框注释。© 2024。作者。

To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves.For radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS.Radiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction.For quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries.There is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.© 2024. The Author(s).