Molecular subgrouping of medulloblastoma based on few-shot learning of multitasking using conventional MR images: a retrospective multicenter study
Molecular subgrouping of medulloblastoma based on few-shot learning of multitasking using conventional MR images: a retrospective multicenter study
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基于使用传统 MR 图像进行多任务处理的少样本学习的髓母细胞瘤分子亚群：一项回顾性多中心研究
DOI：
10.1093/noajnl/vdaa079
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发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Zhou, Liangfu
中科院分区：
文献类型：
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作者：
Chen, Xi;Fan, Zhen;Zhou, Liangfu
背景。髓母细胞瘤分子亚型（WNT）、声刺猬（SHH）、第 3 组和第 4 组的确定对于预后和风险适应性治疗策略非常重要。由于髓母细胞瘤的罕见疾病特征，我们为少样本场景设计了独特的多任务框架，以实现高精度的无创分子亚组。方法。我们引入了一种基于掩模区域卷积神经网络（Mask-RCNN）的多任务技术。多任务技术通过有效利用基因分型、肿瘤掩模、预后等综合信息，一方面实现了多用途建模，另一方面也提高了分子亚群分型的准确性。回顾性研究收集了 4 家医院 8 年期间的 113 例髓母细胞瘤病例，分为 2 家医院的 3 倍交叉验证队列（N = 74）和另外 2 家医院的独立测试队列（N = 39）。设计了不同辅助任务的比较实验，以说明多任务处理在分子亚群中的效果。结果。与单任务框架相比，结合 3 个任务的多任务框架将交叉验证中的分子亚组平均准确度从 0.84 提高到 0.93，将独立测试中的分子亚组平均准确度从 0.79 提高到 0.85。分子亚组的接受者操作特征曲线下的平均面积 (AUC) 在交叉验证中为 0.97，在独立测试中为 0.92。交叉验证中预测的平均 AUC 也达到 0.88，独立测试中达到 0.79。两个队列的肿瘤分割结果的 Dice 系数均达到 0.90。结论。多任务 Mask-RCNN 是一种有效的髓母细胞瘤分子亚群和预测方法，在少样本学习中具有高精度。
Background. The determination of molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4-of medulloblastomas is very important for prognostication and risk-adaptive treatment strategies. Due to the rare disease characteristics of medulloblastoma, we designed a unique multitask framework for the few-shot scenario to achieve noninvasive molecular subgrouping with high accuracy. Methods. We introduced a multitask technique based on mask regional convolutional neural network (Mask-RCNN). By effectively utilizing the comprehensive information including genotyping, tumor mask, and prognosis, multitask technique, on the one hand, realized multi-purpose modeling and simultaneously, on the other hand, promoted the accuracy of the molecular subgrouping. One hundred and thirteen medulloblastoma cases were collected from 4 hospitals during the 8-year period in the retrospective study, which were divided into 3-fold cross-validation cohorts (N = 74) from 2 hospitals and independent testing cohort (N = 39) from the other 2 hospitals. Comparative experiments of different auxiliary tasks were designed to illustrate the effect of multitasking in molecular subgrouping. Results. Compared to the single-task framework, the multitask framework that combined 3 tasks increased the average accuracy of molecular subgrouping from 0.84 to 0.93 in cross-validation and from 0.79 to 0.85 in independent testing. The average area under the receiver operating characteristic curves (AUCs) of molecular subgrouping were 0.97 in cross-validation and 0.92 in independent testing. The average AUCs of prognostication also reached to 0.88 in cross-validation and 0.79 in independent testing. The tumor segmentation results achieved the Dice coefficient of 0.90 in both cohorts. Conclusions. The multitask Mask-RCNN is an effective method for the molecular subgrouping and prognostication of medulloblastomas with high accuracy in few-shot learning.