论文标题
WHO 2016年使用多任务深度学习的神经胶质瘤的亚型和自动分割
WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
论文作者
论文摘要
神经胶质瘤的准确表征对于临床决策至关重要。在最初的决策阶段，肿瘤的描述也是可取的，但是一项耗时的任务。利用最新的GPU功能，我们开发了一个单一的多任务卷积神经网络，该神经网络使用完整的3D，结构，术前MRI扫描可以预测IDH突变状态，1P/19Q共同删除状态和肿瘤等级，同时分割肿瘤。迄今为止，我们使用最大，最多样化的患者队列培训了我们的方法，其中包含来自16个机构的158例神经胶质瘤患者。我们在来自13个不同机构的240名患者的独立数据集上测试了我们的方法，并达到了0.90、1p/19q-AUC的IDH-AUC，为0.85，AUC级别为0.81，平均肿瘤骰子得分为0.84。因此，我们的方法非侵入性地预测了多个临床相关参数，并将其推广到更广泛的临床人群。
Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean whole tumor DICE score of 0.84. Thus, our method non-invasively predicts multiple, clinically relevant parameters and generalizes well to the broader clinical population.
