The complex heterogeneity of brain tumours is increasingly recognized to demand data of magnitudes and richness only fully-inclusive, large-scale collections drawn from routine clinical care could plausibly offer. This is a task contemporary machine learning could facilitate, especially in neuroimaging, but its ability to deal with incomplete data common in real world clinical practice remains unknown. Here we apply state-of-the-art methods to large scale, multi-site MRI data to quantify the comparative fidelity of automated tumour segmentation models replicating the various levels of sequence availability observed in the clinical reality. We compare deep learning (nnU-Net-derived) segmentation models with all possible combinations of T1, contrast-enhanced T1, T2, and FLAIR sequences, trained and validated with five-fold cross-validation on the 2021 BraTS-RSNA glioma population of 1251 patients, with further testing on a real-world 50 patient sample diverse in not only MRI scanner and field strength, but a random selection of pre- and post-operative imaging also. Models trained on incomplete imaging data segmented lesions well, often equivalently to those trained on complete data, exhibiting Dice coefficients of 0.907 (single sequence) to 0.945 (full datasets) for whole tumours, and 0.701 (single sequence) to 0.891 (full datasets) for component tissue types. Incomplete data segmentation models could accurately detect enhancing tumour in the absence of contrast imaging, quantifying its volume with an R2 between 0.95-0.97, and were invariant to lesion morphometry. Deep learning segmentation models characterize tumours well when missing data and can even detect enhancing tissue without the use of contrast. This suggests translation to clinical practice, where incomplete data is common, may be easier than hitherto believed, and may be of value in reducing dependence on contrast use.
翻译:大脑肿瘤的复杂异质性日益得到人们的承认,要求获得数量和丰富程度的数据,只有充分包容,从常规临床护理中获得的大规模采集才能令人信服地提供。这是一项当代机器学习的任务,可以促进特别是神经成形学,但是它处理在现实世界临床实践中常见的不完全数据的能力仍然未知。我们在这里对大规模使用最先进的方法,多点磁RI数据来量化自动肿瘤分解模型的相对真实性,这些模型复制了临床现实中观察到的不同程度的准确性。我们比较了深度学习(NNU-NET-派生)分解模型与T1、对比增强T1、T2和FLAIR等所有可能的组合。我们用5倍交叉校正的2021 BRATS-RSNA星座人口使用高亮度数据的能力,在实际50个病人样本中可以进一步测试,不仅进行MRI扫描,而且在实地进行不同,而且随机选择了完全的成像。我们比较了深度的(991的DNA数据分解数据分解模式,经常用来显示不完全的血型数据序列数据,这些分解的模型可以显示整个数据。