Medulloblastoma and pilocytic astrocytoma are the two most common pediatric brain tumors with overlapping imaging features. In this proof-of-concept study, we investigated using a deep learning classifier trained on a multicenter data set to differentiate these tumor types. We developed a patch-based 3D-DenseNet classifier, utilizing automated tumor segmentation. Given the heterogeneity of imaging data (and available sequences), we used all individually available preoperative imaging sequences to make the model robust to varying input. We compared the classifier to diagnostic assessments by five readers with varying experience in pediatric brain tumors. Overall, we included 195 preoperative MRIs from children with medulloblastoma (n= 69) or pilocytic astrocytoma (n= 126) across six university hospitals. In the 64-patient test set, the DenseNet classifier achieved a high AUC of 0.986, correctly predicting 62/64 (97%) diagnoses. It misclassified one case of each tumor type. Human reader accuracy ranged from 100% (expert neuroradiologist) to 80% (resident). The classifier performed significantly better than relatively inexperienced readers (p< 0.05) and was on par with pediatric neuro-oncology experts. Our proof-of-concept study demonstrates a deep learning model based on automated tumor segmentation that can reliably preoperatively differentiate between medulloblastoma and pilocytic astrocytoma, even in heterogeneous data.
髓母细胞瘤和毛细胞型星形细胞瘤是两种最常见的儿童脑肿瘤，其影像学特征存在重叠。在这项概念验证研究中，我们探讨了利用基于多中心数据集训练的深度学习分类器来区分这两种肿瘤类型。我们开发了一种基于图像块的3D-DenseNet分类器，并采用自动化肿瘤分割技术。鉴于影像数据的异质性（及可用序列差异），我们使用了所有可用的个体术前影像序列，使模型对不同输入具有鲁棒性。我们将该分类器与五位对儿童脑肿瘤经验各异的诊断医师的评估结果进行比较。研究共纳入来自六所大学医院的195例儿童术前磁共振影像，包括髓母细胞瘤（69例）和毛细胞型星形细胞瘤（126例）。在包含64名患者的测试集中，DenseNet分类器取得了0.986的高曲线下面积，正确预测了62/64例（97%）诊断，仅对两种肿瘤各误判一例。人类医师的诊断准确率介于100%（神经放射学专家）至80%（住院医师）之间。该分类器表现显著优于经验相对不足的医师（p<0.05），并与儿童神经肿瘤学专家水平相当。我们的概念验证研究表明，这种基于自动化肿瘤分割的深度学习模型能够可靠地在术前区分髓母细胞瘤和毛细胞型星形细胞瘤，即使在异质性数据中也能保持稳定性能。
肿瘤(癌症)患者之家