治疗前和治疗后弥漫性胶质瘤组织亚区(包括切除腔)的分割。

IF 3.7 Q1 CLINICAL NEUROLOGY Neuro-oncology advances Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI:10.1093/noajnl/vdae140
Saif Baig, Igor Vidic, George M Mastorakos, Robert X Smith, Nathan White, Suzie Bash, Anders M Dale, Carrie R McDonald, Thomas Beaumont, Tyler M Seibert, Jona Hattangadi-Gluth, Santosh Kesari, Nikdokht Farid, Jeffrey D Rudie
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引用次数: 0

摘要

背景:评估胶质瘤的纵向变化是一个时间密集型的过程,评定者之间的差异很大。自动分割可减少评定者之间的差异,提高评估治疗反应的工作流程效率。我们试图评估神经网络对弥漫性胶质瘤组织亚区域(包括切除腔)治疗前后的评估是否与专家评估相当:我们将癌症成像档案库中 7 个公开资料库中的 647 例弥漫性胶质瘤患者(平均 55.1 岁;29%/36%/34% 女性/男性/未知;396 例治疗前和 251 例治疗后,中位数为手术后 237 天)的 MRI 图像分为训练样本(536 例)和测试/归纳样本(111 例)。T1、T1-后对比、T2 和 FLAIR 图像作为三维 nnU 网络的输入,用于预测 3 个肿瘤亚区和切除腔。我们评估了在治疗前训练病例(Pre-Rx 网络)、治疗后训练病例(Post-Rx 网络)以及治疗前和治疗后病例(组合网络)上训练的网络的性能:结果:结果显示,神经网络对主要肿瘤亚区的骰子评分中位数在 0.82 至 0.94 之间,人工分割的病灶体积与预测的病灶总体积之间存在很强的相关性(0.94 R 2 值):能准确分割治疗前和治疗后弥漫性胶质瘤的神经网络有可能改善临床试验中的反应评估,并减少提供者的负担和测量误差。
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Segmentation of pre- and posttreatment diffuse glioma tissue subregions including resection cavities.

Background: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities.

Methods: A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-contrast, T2, and FLAIR images were used as inputs into a 3D nnU-Net to predict 3 tumor subregions and resection cavities. We evaluated the performance of networks trained on pretreatment training cases (Pre-Rx network), posttreatment training cases (Post-Rx network), and both pre- and posttreatment cases (Combined networks).

Results: Segmentation performance was as good as or better than interrater reliability with median dice scores for main tumor subregions ranging from 0.82 to 0.94 and strong correlations between manually segmented and predicted total lesion volumes (0.94 < R 2 values < 0.98). The Combined network performed similarly to the Pre-Rx network on pretreatment cases and the Post-Rx network on posttreatment cases with fewer false positive resection cavities (7% vs 59%).

Conclusions: Neural networks that accurately segment pre- and posttreatment diffuse gliomas have the potential to improve response assessment in clinical trials and reduce provider burden and errors in measurement.

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