Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China.
Pediatric Surgery, Children's Hospital of Soochow University, Suzhou 215025, China.
Br J Radiol. 2024 May 7;97(1157):1029-1037. doi: 10.1093/bjr/tqae054.
Since neither abdominal pain nor pancreatic enzyme elevation is specific for acute pancreatitis (AP), the diagnosis of AP in patients with pancreaticobiliary maljunction (PBM) may be challenging when the pancreas appears normal or nonobvious on CT. This study aimed to develop a quantitative radiomics-based nomogram of pancreatic CT for identifying AP in children with PBM who have nonobvious findings on CT.
PBM patients with a diagnosis of AP evaluated at the Children's Hospital of Soochow University from June 2015 to October 2022 were retrospectively reviewed. The radiological features and clinical factors associated with AP were evaluated. Based on the selected variables, multivariate logistic regression was used to construct clinical, radiomics, and combined models.
Two clinical parameters and 6 radiomics characteristics were chosen based on their significant association with AP, as demonstrated in the training (area under curve [AUC]: 0.767, 0.892) and validation (AUC: 0.757, 0.836) datasets. The radiomics-clinical nomogram demonstrated superior performance in both the training (AUC, 0.938) and validation (AUC, 0.864) datasets, exhibiting satisfactory calibration (P > .05).
Our radiomics-based nomogram is an accurate, noninvasive diagnostic technique that can identify AP in children with PBM even when CT presentation is not obvious.
This study extracted imaging features of nonobvious pancreatitis. Then it developed and evaluated a combined model with these features.
由于腹痛和胰腺酶升高均不是急性胰腺炎(AP)的特异性表现,因此当 CT 示胰腺正常或无明显异常时,胰胆管合流异常(PBM)患者的 AP 诊断可能具有挑战性。本研究旨在建立一种基于胰腺 CT 定量放射组学的列线图,以识别 CT 示胰腺无明显异常的 PBM 患儿中的 AP。
回顾性分析 2015 年 6 月至 2022 年 10 月在苏州大学附属儿童医院诊断为 AP 的 PBM 患者。评估与 AP 相关的影像学特征和临床因素。基于选定的变量,采用多变量逻辑回归构建临床、放射组学和联合模型。
根据与 AP 的显著相关性,从训练(AUC:0.767、0.892)和验证(AUC:0.757、0.836)数据集选择了两个临床参数和 6 个放射组学特征。放射组学-临床列线图在训练(AUC:0.938)和验证(AUC:0.864)数据集均表现出优异的性能,且校准良好(P > .05)。
我们的基于放射组学的列线图是一种准确、无创的诊断技术,即使 CT 表现不明显,也可用于识别 PBM 患儿中的 AP。
本研究提取了不明显胰腺炎的影像学特征。然后,它开发并评估了一个包含这些特征的联合模型。