一种具有PKCP-MixUp增强的多阶段深度学习框架用于多期对比增强CT中的儿童肝肿瘤诊断
A Multi-Stage Deep Learning Framework with PKCP-MixUp Augmentation for Pediatric Liver Tumor Diagnosis Using Multi-Phase Contrast-Enhanced CT
AI总结：
本研究提出了一种具有PKCP-MixUp增强的多阶段深度学习框架，用于多期对比增强CT中儿童肝肿瘤的非侵入性诊断。
摘要：
儿童肝肿瘤是儿科最常见的实体肿瘤之一，区分良性或恶性状态及病理分类对临床治疗至关重要。虽然病理检查是金标准，但侵入性活检有显著局限性：高度血管化的儿童肝脏和脆弱的肿瘤组织会增加出血等并发症的风险；此外，不配合的小儿需要麻醉活检，增加医疗费用或心理创伤。尽管已有很多努力将AI应用于临床环境，但大多数研究者忽视了其在儿童肝肿瘤中的重要性。为建立非侵入性检查流程，我们开发了一种多阶段深度学习（DL）框架，用于使用多期对比增强CT自动诊断儿童肝肿瘤。纳入了回顾性和前瞻性队列。我们建立了新的PKCP-MixUp数据增强方法以解决数据稀缺和类别不平衡问题。我们还训练了一个肿瘤检测模型以提取感兴趣区域（ROIs），然后设置了一个两阶段诊断流程，使用三个具有ROI遮蔽图像的backbone。我们的肿瘤检测模型已取得高性能（mAP=0.871），并且良性与恶性肿瘤之间的第一阶段分类模型表现出优异性能（AUC=0.989）。最终诊断模型也表现出鲁棒性，包括良性亚型分类（AUC=0.915）和恶性亚型分类（AUC=0.979）。我们还进行了多级比较分析，如数据和训练流程的消融研究，以及Shapley-Value和CAM可解释性分析。该框架填补了儿童特定DL诊断的空白，为CT期选择和模型设计提供了可操作见解，并为精确、可及的儿童肝肿瘤诊断铺平了道路。
英文摘要：
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.
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