Pediatric liver tumors are one of the most common solid tumors in pediatrics, with differentiation of benign or malignant status and pathological classification critical for clinical treatment. While pathological examination is the gold standard, the invasive biopsy has notable limitations: the highly vascular pediatric liver and fragile tumor tissue raise complication risks such as bleeding; additionally, young children with poor compliance require anesthesia for biopsy, increasing medical costs or psychological trauma. Although many efforts have been made to utilize AI in clinical settings, most researchers have overlooked its importance in pediatric liver tumors. To establish a non-invasive examination procedure, we developed a multi-stage deep learning (DL) framework for automated pediatric liver tumor diagnosis using multi-phase contrast-enhanced CT. Two retrospective and prospective cohorts were enrolled. We established a novel PKCP-MixUp data augmentation method to address data scarcity and class imbalance. We also trained a tumor detection model to extract ROIs, and then set a two-stage diagnosis pipeline with three backbones with ROI-masked images. Our tumor detection model has achieved high performance (mAP=0.871), and the first stage classification model between benign and malignant tumors reached an excellent performance (AUC=0.989). Final diagnosis models also exhibited robustness, including benign subtype classification (AUC=0.915) and malignant subtype classification (AUC=0.979). We also conducted multi-level comparative analyses, such as ablation studies on data and training pipelines, as well as Shapley-Value and CAM interpretability analyses. This framework fills the pediatric-specific DL diagnostic gap, provides actionable insights for CT phase selection and model design, and paves the way for precise, accessible pediatric liver tumor diagnosis.

翻译：儿科肝肿瘤是儿科最常见的实体肿瘤之一，其良恶性鉴别与病理分型对临床治疗至关重要。虽然病理检查是金标准，但侵入性活检存在显著局限性：儿童肝脏血供丰富且肿瘤组织脆弱，易引发出血等并发症风险；此外，低龄患儿配合度差，活检需麻醉辅助，既增加医疗成本又可能造成心理创伤。尽管已有诸多研究致力于将人工智能应用于临床，但多数研究者忽视了其在儿科肝肿瘤领域的重要性。为建立无创检查方案，本研究开发了一种基于多期相增强CT的自动化儿科肝肿瘤多阶段深度学习诊断框架。研究纳入了回顾性与前瞻性两个队列。我们提出了一种新颖的PKCP-MixUp数据增强方法以应对数据稀缺与类别不平衡问题。同时训练了肿瘤检测模型用于提取感兴趣区域，并基于ROI掩膜图像构建了包含三种骨干网络的两阶段诊断流程。我们的肿瘤检测模型取得了优异性能（mAP=0.871），首阶段良恶性分类模型达到卓越水平（AUC=0.989）。最终诊断模型亦展现出稳健性能，包括良性亚型分类（AUC=0.915）与恶性亚型分类（AUC=0.979）。我们还开展了多层次的对比分析，如数据与训练流程的消融实验，以及沙普利值与类激活映射的可解释性分析。该框架填补了儿科特异性深度学习诊断的空白，为CT期相选择与模型设计提供了可行见解，并为实现精准、可及的儿科肝肿瘤诊断铺平了道路。