Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China; Chinese PLA Medical School, Beijing, China.
Department of Interventional Ultrasound, First Medical Centre, Chinese PLA General Hospital, Beijing, China; Department of Ultrasound, First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Ultrasound Med Biol. 2023 Dec;49(12):2469-2475. doi: 10.1016/j.ultrasmedbio.2023.08.007. Epub 2023 Sep 24.
The purpose of the study was to develop and validate a radiomics model by using contrast-enhanced ultrasound (CEUS) data for pre-operative differential diagnosis of pancreatic cystic neoplasms (PCNs), especially pancreatic serous cystadenoma (SCA).
Patients with pathologically confirmed PCNs who underwent CEUS examination at Chinese PLA hospital from May 2015 to August 2022 were retrospectively collected. Radiomic features were extracted from the regions of interest, which were obtained based on CEUS images. A support vector machine algorithm was used to construct a radiomics model. Moreover, based on the CEUS image features, the CEUS and the combined models were constructed using logistic regression. The performance and clinical utility of the optimal model were evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity and decision curve analysis.
A total of 113 patients were randomly split into the training (n = 79) and test cohorts (n = 34). These patients were pathologically diagnosed with SCA, mucinous cystadenoma, intraductal papillary mucinous neoplasm and solid-pseudopapillary tumor. The radiomics model achieved an AUC of 0.875 and 0.862 in the training and test cohorts, respectively. The sensitivity and specificity of the radiomics model were 81.5% and 86.5% in the training cohort and 81.8% and 91.3% in the test cohort, respectively, which were higher than or comparable with that of the CEUS model and the combined model.
The radiomics model based on CEUS images had a favorable differential diagnostic performance in distinguishing SCA from other PCNs, which may be beneficial for the exploration of personalized management strategies.
本研究旨在开发和验证一种基于超声造影（CEUS）数据的影像组学模型，用于术前鉴别胰腺囊性肿瘤（PCN），尤其是胰腺浆液性囊腺瘤（SCA）。
回顾性收集 2015 年 5 月至 2022 年 8 月在中国人民解放军医院行 CEUS 检查并经病理证实为 PCN 的患者。从基于 CEUS 图像的感兴趣区提取影像组学特征。使用支持向量机算法构建影像组学模型。此外，基于 CEUS 图像特征，使用逻辑回归构建 CEUS 及联合模型。通过受试者工作特征曲线（ROC）下面积（AUC）、敏感性、特异性和决策曲线分析评估最佳模型的性能和临床实用性。
共 113 例患者被随机分为训练集（n=79）和测试集（n=34）。这些患者的病理诊断为 SCA、黏液性囊腺瘤、导管内乳头状黏液性肿瘤和实性假乳头状肿瘤。影像组学模型在训练集和测试集中的 AUC 分别为 0.875 和 0.862。在训练集中，影像组学模型的敏感性和特异性分别为 81.5%和 86.5%，在测试集中分别为 81.8%和 91.3%，高于或与 CEUS 模型和联合模型相当。
基于 CEUS 图像的影像组学模型在鉴别 SCA 与其他 PCN 方面具有良好的鉴别诊断性能，可能有助于探索个体化管理策略。
