开发对比增强 CT 上实体肾肿瘤的自动分割模型，并以相关置信度可视化分割，以促进临床适用性。训练数据集包括来自两个三级中心的实体肾肿瘤患者，他们接受手术切除并在皮质髓质或肾源性部位接受 CT造影剂（CM）阶段。对所有轴向 CT 切片进行手动肿瘤分割，作为自动分割的参考标准。对公开的 KiTS 2019 数据集进行了独立测试。神经网络集成（ENN、DeepLabV3）用于自动肾肿瘤分割，其性能通过 DICE 评分进行量化。 ENN 平均前景熵测量分割置信度（二元：成功分割，DICE 评分 > 0.8 与不充分分割 ≤ 0.8）。N = 639/n = 210 名患者包含在训练和独立测试数据集中。数据集在年龄和性别方面具有可比性（p > 0.05），而训练数据集中的肾肿瘤更大且更常见为良性（p < 0.01）。在内部测试数据集中，ENN模型得出的中位DICE评分= 0.84（IQR：0.62-0.97，皮质髓质）和0.86（IQR：0.77-0.96，肾源性CM期），分割置信度AUC = 0.89（灵敏度=特异性=0.77）。在独立测试数据集中，ENN模型取得中位DICE评分=0.84（IQR：0.71-0.97，皮质髓质CM期）；分割置信度准确度 = 0.84（灵敏度 = 0.86，特异性 = 0.81）。 ENN 分割通过叠加在临床 CT 图像上的颜色编码体素肿瘤概率和阈值进行可视化。基于 ENN 的肾肿瘤分割在外部测试数据中表现稳健，可能有助于肾肿瘤分类和治疗计划。神经网络 (ENN) 模型的集成可以在常规 CT 上自动分割肾肿瘤，从而实现下游图像分析和治疗计划并使其标准化。在图像上提供置信度测量和分割叠加可以降低临床 ENN 实施的阈值。神经网络集成 (ENN) 分割通过颜色编码的体素肿瘤概率和阈值进行可视化。新奥在内部测试和独立的外部测试数据集中提供了很高的分割精度。 ENN 模型提供了分割置信度的度量，可以有效区分成功的分割和不充分的分割。© 2024。作者。

To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62-0.97, corticomedullary) and 0.86 (IQR: 0.77-0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71-0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds. ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset. ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.© 2024. The Author(s).