直肠癌表型和预后预测中不同放射组学注释方法的比较：一项双中心研究

探讨多种注释在放射组学分析中的性能差异，为大规模医疗影像分析中的肿瘤注释提供参考。回顾性分析了两中心共342例接受直肠癌根治术的患者，分为训练组、内部验证组和外部验证组。进行肿瘤T分期（pT）、淋巴结转移（pLNM）和无病生存期（pDFS）三项预测任务。采用Lasso-Logistic或Lasso-Cox构建了12个放射组学模型，比较了四种注释方法：沿肿瘤边界的二维详细注释（2D）、沿肿瘤边界的三维详细注释（3D）、二维边界框（2DBB）和三维边界框（3DBB）在T2加权像上的表现。模型还结合临床风险因素建立联合模型，利用DeLong检验比较模型的受试者工作特征曲线（ROC）性能。放射组学模型的曲线下面积（AUC）在内部验证组中范围为0.627（0.518-0.728）至0.811（0.705-0.917），在外部验证组中范围为0.619（0.469-0.754）至0.824（0.689-0.918）。大部分模型在四种注释方法之间无显著差异，除在内部验证组中pLNM的3D与3DBB模型（p=0.0188）存在差异。联合模型中，只有2D模型在pDFS上显著优于2DBB（p=0.0372）和3D模型（p=0.0380）。采用2D最大肿瘤面积或边界框注释的放射组学及联合模型表现与沿肿瘤边界的3D详细注释模型相当，适用于直肠癌的表型识别和预后预测。对于放射影像的定量分析，选择二维最大肿瘤面积或边界框注释既具有代表性，又操作简便，等同于三维全肿瘤或沿边界的详细注释。目前尚缺乏关于不同注释在放射组学中的预测代表性讨论。无论是2D、3D、详细还是边界框注释，模型性能无显著差异，优先选择节省时间和精力的2D最大面积边界框注释。

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.