基于人工智能的决策支持可提高神经肿瘤学中肿瘤反应评估的可重复性：一项国际多读者研究。
Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study.
机构信息
Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
出版信息
Neuro Oncol. 2023 Mar 14;25(3):533-543. doi: 10.1093/neuonc/noac189.
BACKGROUND
To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria.
METHODS
A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling.
RESULTS
The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02).
CONCLUSIONS
AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).
背景
为了评估人工智能（AI）辅助决策支持是否可以在神经肿瘤学中比使用反应评估神经肿瘤学（RANO）标准的手动二维肿瘤负担测量更具可重复性和标准化地评估治疗反应。
方法
选择了一系列 30 名患者（15 名低级别胶质瘤，15 名胶质母细胞瘤），并具有连续 MRI 扫描的可用性。15 名研究人员在两轮中分别单独评估每位患者的 MRI 进展时间（TTP）。在第一轮中，根据 RANO 标准评估 TTP，而在第二轮中，通过整合 AI 增强 MRI 序列中描述肿瘤体积纵向变化的附加信息来评估 TTP。使用一致性相关系数（CCC）及其置信区间（CI）评估研究人员之间 TTP 测量的一致性，并使用自举重采样获得 P 值。
结果
仅使用 RANO 时，研究人员之间 TTP 测量的 CCC 为 0.77（95%CI=0.69，0.88），而使用基于 AI 的决策支持时，增加至 0.91（95%CI=0.82，0.95）（P=0.005）。对于低级别胶质瘤患者，这种效果更为显著（P=0.008）（CCC=0.70[95%CI=0.56，0.85]，无基于 AI 的决策支持与 0.90[95%CI=0.76，0.95]，基于 AI 的决策支持）与胶质母细胞瘤相比（CCC=0.83[95%CI=0.75，0.92]，无基于 AI 的决策支持与 0.86[95%CI=0.78，0.93]，基于 AI 的决策支持）。经验较少的研究人员认为 AI 决策支持更有帮助（P=0.02）。
结论
与手动二维肿瘤负担测量相比，基于 AI 的决策支持具有在神经肿瘤学中更具可重复性和标准化地评估治疗反应的潜力，特别是在低级别胶质瘤患者中。提供了这个基于 AI 的处理管道的全功能版本作为开源（https://github.com/NeuroAI-HD/HD-GLIO-XNAT）。
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