Mao Hui-Min, Chen Kai-Ge, Zhu Bin, Guo Wan-Liang, Shi San-Li
Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025, China.
Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China.
BMC Med Imaging. 2025 Feb 5;25(1):40. doi: 10.1186/s12880-025-01579-3.
Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preoperative diagnosis of moderate-to-severe chronic cholangitis is essential for guiding treatment strategies and surgical planning. This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on contrast-enhanced CT images and clinical characteristics to preoperatively identify moderate-to-severe chronic cholangitis in children with PBM.
A total of 323 pediatric patients with PBM who underwent surgery were retrospectively enrolled from three centers, and divided into a training cohort (n = 153), an internal validation cohort (IVC, n = 67), and two external test cohorts (ETC1, n = 58; ETC2, n = 45). Chronic cholangitis severity was determined by postoperative pathology. Handcrafted radiomics features and deep learning (DL) radiomics features, extracted using transfer learning with the ResNet50 architecture, were obtained from portal venous-phase CT images. Multivariable logistic regression was used to establish the DLRN, integrating significant clinical factors with handcrafted and DL radiomics signatures. The diagnostic performances were evaluated in terms of discrimination, calibration, and clinical usefulness.
Biliary stones and peribiliary fluid collection were selected as important clinical factors. 5 handcrafted and 5 DL features were retained to build the two radiomics signatures, respectively. The integrated DLRN achieved satisfactory performance, achieving area under the curve (AUC) values of 0.913 (95% CI, 0.834-0.993), 0.916 (95% CI, 0.845-0.987), and 0.895 (95% CI, 0.801-0.989) in the IVC, and two ETCs, respectively. In comparison, the clinical model, handcrafted signature, and DL signature had AUC ranges of 0.654-0.705, 0.823-0.857, and 0.840-0.872 across the same cohorts. The DLRN outperformed single-modality clinical, handcrafted radiomics, and DL radiomics models, with all integrated discrimination improvement values > 0 and P < 0.05. The Hosmer-Lemeshow test and calibration curves showed good consistency of the DLRN (P > 0.05), and the decision curve analysis and clinical impact curve further confirmed its clinical utility.
The integrated DLRN can be a useful and non-invasive tool for preoperatively identifying moderate-to-severe chronic cholangitis in children with PBM, potentially enhancing clinical decision-making and personalized management strategies.
长期严重胆管炎可导致胆管粘连致密及胆管脆性增加，使胰胆管合流异常（PBM）患儿的手术操作复杂化并增加手术风险。因此，术前诊断中重度慢性胆管炎对于指导治疗策略和手术规划至关重要。本研究旨在基于增强CT图像和临床特征开发并验证一种深度学习影像组学列线图（DLRN），以术前识别PBM患儿的中重度慢性胆管炎。
回顾性纳入来自三个中心的323例接受手术的PBM患儿，分为训练队列（n = 153）、内部验证队列（IVC，n = 67）和两个外部测试队列（ETC1，n = 58；ETC2，n = 45）。慢性胆管炎严重程度由术后病理确定。从门静脉期CT图像中获取手工影像组学特征和使用ResNet50架构的迁移学习提取的深度学习（DL）影像组学特征。采用多变量逻辑回归建立DLRN，将重要临床因素与手工和DL影像组学特征相结合。从区分度、校准度和临床实用性方面评估诊断性能。
胆管结石和胆管周围积液被选为重要临床因素。分别保留5个手工特征和5个DL特征以构建两个影像组学特征。整合后的DLRN表现出令人满意的性能，在IVC和两个ETC中的曲线下面积（AUC）值分别为0.913（95%CI，0.834 - 0.993）、0.916（95%CI，）和0.895（95%CI，0.801 - 0.989）。相比之下，临床模型、手工特征和DL特征在相同队列中的AUC范围分别为0.654 - 0.705、0.823 - 0.857和0.840 - 0.872。DLRN优于单模态临床、手工影像组学和DL影像组学模型，所有整合区分度改善值均>0且P < 0.05。Hosmer-Lemeshow检验和校准曲线显示DLRN具有良好的一致性（P > 0.05），决策曲线分析和临床影响曲线进一步证实了其临床实用性。
整合后的DLRN可作为术前识别PBM患儿中重度慢性胆管炎的有用且非侵入性工具，可能会加强临床决策和个性化管理策略。
