Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors.Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland–Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort.Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai’s trace:p< 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set.Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.
背景与目的：鉴别儿童后颅窝肿瘤（如髓母细胞瘤、室管膜瘤和毛细胞星形细胞瘤）具有重要临床意义，因其治疗方案及预后存在显著差异。目前尚未有研究探讨扩散峰度成像在儿童后颅窝肿瘤鉴别诊断中的应用。相较于传统感兴趣区分析法，基于全肿瘤体积分割的扩散值测量可能提升扩散参数的可重复性。本研究旨在比较感兴趣区与全肿瘤体积分割法在扩散峰度成像参数测量中的可重复性，并评估扩散峰度成像鉴别儿童后颅窝肿瘤的准确性。 材料与方法：我们回顾性分析了34例后颅窝肿瘤患儿（男女均有，平均年龄7.48岁），所有患儿术前均在3特斯拉磁共振设备接受包括扩散峰度成像在内的检查。由两名神经放射科医师独立完成每例患者的全实体肿瘤分割、肿瘤最大径区域感兴趣区勾画及5毫米小感兴趣区标注。自动化分析流程包含观察者间变异度分析、统计学分析及机器学习分析。通过变异系数和Bland-Altman图评估观察者间变异度，采用多元方差分析评估扩散峰度成像参数鉴别肿瘤组织学类型的准确性。为平衡类别样本量，应用SMOTE算法生成均衡数据集。最终基于SMOTE数据集的所有扩散峰度成像参数，采用随机森林机器学习分类算法，按7:3比例划分训练集与测试集进行分析。 结果：肿瘤组织学类型包括髓母细胞瘤（15例）、毛细胞星形细胞瘤（14例）和室管膜瘤（5例）。全肿瘤体积分割法在所有扩散峰度成像参数中均表现出较感兴趣区法更低的测量变异度，故采用该法进行后续分析。扩散峰度成像参数能准确鉴别肿瘤亚型（Pillai轨迹检验：p<0.001）。SMOTE算法生成11个合成样本（10个室管膜瘤和1个毛细胞星形细胞瘤），最终获得包含45个样本（34个原始样本和11个合成样本）的均衡数据集。机器学习分析准确率达0.928，在测试集中仅误判1个病灶。 结论：全肿瘤体积分割法在所有扩散参数测量中均较感兴趣区法具有更优的可重复性。基于SMOTE生成数据集的机器学习分类算法能准确鉴别后颅窝肿瘤。结合扩散峰度成像参数的机器学习技术对儿童后颅窝肿瘤的鉴别诊断具有重要价值。
肿瘤(癌症)患者之家