利用病理多样性单中心临床数据集提升儿童脑肿瘤分割效率
Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset
AI总结：
本研究利用病理多样性单中心临床数据集，通过3D nnU-Net模型提升儿童脑肿瘤分割效率，验证了深度学习在肿瘤分割中的可行性，尤其在T2H和WT分割上表现良好，但ET和CC分割仍需进一步优化。
摘要：
背景：脑肿瘤是儿童中最常见的实体恶性肿瘤，涵盖多样的组织学、分子亚型和影像学特征及预后。儿童脑肿瘤（PBTs），包括高级别和低级别胶质瘤（HGG，LGG）、髓母细胞瘤（MB）、室管膜瘤及其他罕见类型，给诊断和治疗带来了挑战。基于深度学习（DL）的分割技术提供了肿瘤勾勒的有前景工具，但其在异质性PBT亚型和MRI协议上的性能仍不确定。方法：回顾性单中心队列包括174名患有HGG、LGG、MB、室管膜瘤及其他罕见亚型的儿童患者。MRI序列包括T1、T1增强（T1-C）、T2和FLAIR。为四个肿瘤亚区域提供了手动标注：整个肿瘤（WT）、T2高信号（T2H）、增强肿瘤（ET）和囊性成分（CC）。一个3D nnU-Net模型被训练和测试（121/53分割），分割性能通过Dice相似性系数（DSC）评估，并与内和外评分者变异进行比较。结果：模型在WT和T2H上的表现稳健（平均DSC：0.85），与人类标注者变异（平均DSC：0.86）相当。ET分割中等准确（平均DSC：0.75），而CC表现较差。分割准确性因肿瘤类型、MRI序列组合和位置而异。值得注意的是，单独使用T1、T1-C和T2序列的结果几乎等同于完整协议。结论：DL在PBTs中是可行的，特别是对于T2H和WT。ET和CC分割仍存在挑战，突显了进一步改进的必要性。这些发现支持了协议简化和自动化以提高体积评估和优化儿童神经肿瘤学工作流程的潜力。
英文摘要：
Background Brain tumours are the most common solid malignancies in children, encompassing diverse histological, molecular subtypes and imaging features and outcomes. Paediatric brain tumours (PBTs), including high- and low-grade gliomas (HGG, LGG), medulloblastomas (MB), ependymomas, and rarer forms, pose diagnostic and therapeutic challenges. Deep learning (DL)-based segmentation offers promising tools for tumour delineation, yet its performance across heterogeneous PBT subtypes and MRI protocols remains uncertain. Methods A retrospective single-centre cohort of 174 paediatric patients with HGG, LGG, medulloblastomas (MB), ependymomas, and other rarer subtypes was used. MRI sequences included T1, T1 post-contrast (T1-C), T2, and FLAIR. Manual annotations were provided for four tumour subregions: whole tumour (WT), T2-hyperintensity (T2H), enhancing tumour (ET), and cystic component (CC). A 3D nnU-Net model was trained and tested (121/53 split), with segmentation performance assessed using the Dice similarity coefficient (DSC) and compared against intra- and inter-rater variability. Results The model achieved robust performance for WT and T2H (mean DSC: 0.85), comparable to human annotator variability (mean DSC: 0.86). ET segmentation was moderately accurate (mean DSC: 0.75), while CC performance was poor. Segmentation accuracy varied by tumour type, MRI sequence combination, and location. Notably, T1, T1-C, and T2 alone produced results nearly equivalent to the full protocol. Conclusions DL is feasible for PBTs, particularly for T2H and WT. Challenges remain for ET and CC segmentation, highlighting the need for further refinement. These findings support the potential for protocol simplification and automation to enhance volumetric assessment and streamline paediatric neuro-oncology workflows.
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