Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L Houk, Jeffrey D Rudie, Haotian Zou, Pranav Warman, Maciej A Mazurowski, Evan Calabrese
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Abstract

Background and purpose: To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center.

Materials and methods: We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment algorithm was developed using automated, volumetric tumor segmentation. AI-based response assessments were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BTRADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated.

Results: For all BT-RADS categories, AI-based response assessments showed moderate agreement with radiologists' response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P=0.007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P=0.012).

Conclusions: AI-based volumetric glioblastoma MRI response assessment following BT-RADS criteria yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.

Abbreviations: GBM= Glioblastoma; RANO= Response Assessment in Neuro-Oncology; BTRADS= Brain Tumor Reporting and Data System; NLP = Natural Language Processing.

基于人工智能的胶质母细胞瘤患者脑肿瘤反应自动评估的开发与评估
背景和目的:在一个高容量脑肿瘤中心接受治疗的大样本患者中,开发并评估一种基于人工智能的自动化脑肿瘤核磁共振成像容积反应评估算法:我们回顾性分析了在一家脑肿瘤中心接受治疗的 634 名胶质母细胞瘤患者的数据,时间跨度为 5 年(2017-2021 年)。平均年龄为 56 +/-13 岁。372/634(59%)名患者为男性,262/634(41%)名患者为女性。研究数据包括 3403 次脑磁共振成像检查和相应的标准化、基于放射医师的脑肿瘤反应评估(BT-RADS)。利用自动肿瘤体积分割技术开发了基于人工智能(AI)的脑肿瘤反应评估算法。对基于人工智能的反应评估与基于放射科医生的反应评估的一致性以及按总生存期对患者进行分层的能力进行了评估。以 BT-RADS 为基础真相,计算了评估一致性的指标,进行了固定时间点生存分析以评估生存分层,并计算了相关的 P 值:对于所有 BT-RADS 类别,基于人工智能的反应评估与放射科医生的反应评估显示出中等程度的一致性(F1 = 0.587-0.755)。卡普兰-梅耶尔生存分析显示,与单独使用人工智能相比,单独使用人工智能评估图像恶化等同于 RANO 进展的患者的总体固定时间点生存率在统计学上更低(log-rank P=0.007)。Cox比例危险模型分析显示,基于人工智能的评估在预测总生存率方面处于劣势(P=0.012):结论:基于AI的胶质母细胞瘤MRI容积反应评估遵循BT-RADS标准,在复制人类反应评估方面取得了中等程度的一致性,但在总生存期分层方面稍差:缩写:GBM=胶质母细胞瘤;RANO=神经肿瘤学反应评估;BTRADS=脑肿瘤报告和数据系统;NLP=自然语言处理。
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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