Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed tomography (CT) images, which is time-consuming and subjective. We used a semi-automatic deep learning (DL) method for segmentation of pNENs and verified its feasibility in radiomics analysis. This retrospective study included two datasets: Dataset 1, contrast-enhanced CT images (CECT) of 80 and 18 patients respectively collected from two centers; and Dataset 2, CECT of 56 and 16 patients respectively from two centers. A DL-based semi-automatic segmentation model was developed and validated with Dataset 1 and Dataset 2, and the segmentation results were used for radiomics analysis from which the performance was compared against that based on manual segmentation. The mean Dice similarity coefficient of the trained segmentation model was 81.8% and 74.8% for external validation with Dataset 1 and Dataset 2 respectively. Four classifiers frequently used in radiomics studies were trained and tested with leave-one-out cross-validation strategy. For pathological grading prediction with Dataset 1, the area under the receiver operating characteristic curve (AUC) with semi-automatic segmentation was up to 0.76 and 0.87 respectively for internal and external validation. For recurrence study with Dataset 2, the AUC with semi-automatic segmentation was up to 0.78. All these AUCs were not statistically significant from the corresponding results based on manual segmentation. Our study showed that DL-based semi-automatic segmentation is accurate and feasible for the radiomics analysis in pNENs.

中文翻译：

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基于 CT 中准确可行的深度学习的半自动分割，用于胰腺神经内分泌肿瘤的放射组学分析

当前胰腺神经内分泌肿瘤 (pNEN) 的临床实践或放射组学研究需要在计算机断层扫描 (CT) 图像中手动描绘病变，这既耗时又主观。我们使用半自动深度学习 (DL) 方法对 pNEN 进行分割，并验证了其在放射组学分析中的可行性。这项回顾性研究包括两个数据集：数据集 1，分别从两个中心收集的 80 和 18 名患者的对比增强 CT 图像 (CECT)；数据集 2，分别来自两个中心的 56 名和 16 名患者的 CECT。开发了基于深度学习的半自动分割模型，并使用数据集 1 和数据集 2 进行了验证，并将分割结果用于影像组学分析，并将其性能与基于手动分割的性能进行比较。对于数据集 1 和数据集 2 的外部验证，经过训练的分割模型的平均 Dice 相似系数分别为 81.8% 和 74.8%。放射组学研究中经常使用的四个分类器通过留一法交叉验证策略进行了训练和测试。对于数据集 1 的病理分级预测，采用半自动分割的受试者工作特征曲线 (AUC) 下面积分别高达 0.76 和 0.87，用于内部和外部验证。对于数据集 2 的递归研究，半自动分割的 AUC 高达 0.78。从基于手动分割的相应结果来看，所有这些 AUC 均无统计学意义。我们的研究表明，基于 DL 的半自动分割对于 pNEN 中的放射组学分析是准确且可行的。