Automated Diffusion Analysis for Non-Invasive Prediction of IDH Genotype in WHO Grade 2-3 Gliomas.

Jiaming Wu, Stefanie C Thust, Stephen J Wastling, Gehad Abdalla, Massimo Benenati, John A Maynard, Sebastian Brandner, Ferran Prados Carrasco, Frederik Barkhof
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引用次数: 0

Abstract

Background and purpose: Glioma molecular characterization is essential for risk stratification and treatment planning. Noninvasive imaging biomarkers such as apparent diffusion coefficient (ADC) values have shown potential for predicting glioma genotypes. However, manual segmentation of gliomas is time-consuming and operator-dependent. To address this limitation, we aimed to establish a single-sequence-derived automatic ADC extraction pipeline using T2-weighted imaging to support glioma isocitrate dehydrogenase (IDH) genotyping.

Materials and methods: Glioma volumes from a hospital data set (University College London Hospitals; n=247) were manually segmented on T2-weighted MRI scans using ITK-Snap Toolbox and co-registered to ADC maps sequences using the FMRIB Linear Image Registration Tool in FSL, followed by ADC histogram extraction (Python). Separately, a nnUNet deep learning algorithm was trained to segment glioma volumes using T2w only from BraTS 2021 data (n=500, 80% training, 5% validation and 15% test split). nnUnet was then applied to the University College London Hospitals (UCLH) data for segmentation and ADC read-outs. Univariable logistic regression was used to test the performance manual and nnUNet derived ADC metrics for IDH status prediction. Statistical equivalence was tested (paired two-sided t-test).

Results: nnUnet segmentation achieved a median Dice of 0.85 on BraTS data, and 0.83 on UCLH data. For the best performing metric (rADCmean) the area under the receiver operating characteristic curve (AUC) for differentiating IDH-mutant from IDHwildtype gliomas was 0.82 (95% CI: 0.78-0.88), compared to the manual segmentation AUC 0.84 (95% CI: 0.77-0.89). For all ADC metrics, manually and nnUNet extracted ADC were statistically equivalent (p<0.01). nnUNet identified one area of glioma infiltration missed by human observers. In 0.8% gliomas, nnUnet missed glioma components. In 6% of cases, over-segmentation of brain remote from the tumor occurred (e.g. temporal poles).

Conclusions: The T2w trained nnUnet algorithm achieved ADC readouts for IDH genotyping with a performance statistically equivalent to human observers. This approach could support rapid ADC based identification of glioblastoma at an early disease stage, even with limited input data.

Abbreviations: AUC = Area under the receiver operating characteristic curve, BraTS = The brain tumor segmentation challenge held by MICCAI, Dice = Dice Similarity Coefficient, IDH = Isocitrate dehydrogenase, mGBM = Molecular glioblastoma, ADCmin = Fifth ADC histogram percentile, ADCmean = Mean ADC value, ADCNAWM = ADC in the contralateral centrum semiovale normal white matter, rADCmin = Normalized ADCmin, VOI rADCmean = Normalized ADCmean.

自动扩散分析在WHO 2-3级胶质瘤中无创预测IDH基因型。
背景与目的:胶质瘤分子特征对风险分层和治疗计划至关重要。无创成像生物标志物,如表观扩散系数(ADC)值已经显示出预测胶质瘤基因型的潜力。然而,人工分割胶质瘤是费时且依赖于操作者。为了解决这一限制,我们旨在建立一个单序列衍生的自动ADC提取管道,使用t2加权成像来支持胶质瘤异柠檬酸脱氢酶(IDH)基因分型。材料和方法:来自医院数据集的胶质瘤体积(伦敦大学学院医院;n=247)在t2加权MRI扫描上使用ITK-Snap工具箱手动分割,并使用FMRIB线性图像配准工具在FSL中共同配准到ADC映射序列,然后进行ADC直方图提取(Python)。另外,训练nnUNet深度学习算法仅使用来自BraTS 2021数据的T2w分割胶质瘤体积(n=500, 80%训练,5%验证和15%测试分割)。然后将nnUnet应用于伦敦大学学院医院(UCLH)数据进行分割和ADC读出。单变量逻辑回归用于测试性能手册和nnUNet派生的ADC指标,用于IDH状态预测。采用配对双侧t检验进行统计等价性检验。结果:nnUnet分割在BraTS数据上的中位数Dice为0.85,在UCLH数据上的中位数Dice为0.83。对于表现最好的指标(rADCmean),区分idh -突变型和idh -野生型胶质瘤的受试者工作特征曲线下面积(AUC)为0.82 (95% CI: 0.78-0.88),而人工分割的AUC为0.84 (95% CI: 0.77-0.89)。对于所有ADC指标,手工和nnUNet提取的ADC在统计上是相等的(p结论:T2w训练的nnUNet算法实现了IDH基因分型的ADC读数,其性能在统计上与人类观察者相当。这种方法可以支持在疾病早期基于ADC的胶质母细胞瘤快速识别,即使输入数据有限。缩写:AUC =受者工作特征曲线下面积,BraTS = MICCAI控制的脑肿瘤分割挑战,Dice =骰子相似系数,IDH =异酸脱氢酶,mGBM =分子胶质母细胞瘤,ADCmin = ADC直方图第五百分位,ADCmean = ADC均值,ADCNAWM =对侧半瓣中央正常白质ADC, rADCmin =归一化ADCmin, VOI rADCmean =归一化ADCmean。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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