Suter Yannick, Notter Michelle, Meier Raphael, Loosli Tina, Schucht Philippe, Wiest Roland, Reyes Mauricio, Knecht Urspeter
ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Front Radiol. 2023 Sep 7;3:1211859. doi: 10.3389/fradi.2023.1211859. eCollection 2023.
Automated tumor segmentation tools for glioblastoma show promising performance. To apply these tools for automated response assessment, longitudinal segmentation, and tumor measurement, consistency is critical. This study aimed to determine whether BraTumIA and HD-GLIO are suited for this task. We evaluated two segmentation tools with respect to automated response assessment on the single-center retrospective LUMIERE dataset with 80 patients and a total of 502 post-operative time points. Volumetry and automated bi-dimensional measurements were compared with expert measurements following the Response Assessment in Neuro-Oncology (RANO) guidelines. The longitudinal trend agreement between the expert and methods was evaluated, and the RANO progression thresholds were tested against the expert-derived time-to-progression (TTP). The TTP and overall survival (OS) correlation was used to check the progression thresholds. We evaluated the automated detection and influence of non-measurable lesions. The tumor volume trend agreement calculated between segmentation volumes and the expert bi-dimensional measurements was high (HD-GLIO: 81.1%, BraTumIA: 79.7%). BraTumIA achieved the closest match to the expert TTP using the recommended RANO progression threshold. HD-GLIO-derived tumor volumes reached the highest correlation between TTP and OS (0.55). Both tools failed at an accurate lesion count across time. Manual false-positive removal and restricting to a maximum number of measurable lesions had no beneficial effect. Expert supervision and manual corrections are still necessary when applying the tested automated segmentation tools for automated response assessment. The longitudinal consistency of current segmentation tools needs further improvement. Validation of volumetric and bi-dimensional progression thresholds with multi-center studies is required to move toward volumetry-based response assessment.
用于胶质母细胞瘤的自动肿瘤分割工具显示出了良好的性能。要将这些工具应用于自动反应评估、纵向分割和肿瘤测量，一致性至关重要。本研究旨在确定BraTumIA和HD-GLIO是否适合这项任务。我们在包含80例患者和总共502个术后时间点的单中心回顾性LUMIERE数据集上，针对自动反应评估对两种分割工具进行了评估。根据神经肿瘤学反应评估（RANO）指南，将体积测量和自动二维测量结果与专家测量结果进行了比较。评估了专家与各方法之间的纵向趋势一致性，并根据专家得出的进展时间（TTP）对RANO进展阈值进行了测试。使用TTP与总生存期（OS）的相关性来检验进展阈值。我们评估了不可测量病变的自动检测及影响。分割体积与专家二维测量结果之间计算得出的肿瘤体积趋势一致性较高（HD-GLIO：81.1%，BraTumIA：79.7%）。使用推荐的RANO进展阈值时，BraTumIA与专家TTP的匹配度最高。HD-GLIO得出的肿瘤体积在TTP与OS之间的相关性最高（0.55）。两种工具在跨时间准确计数病变方面均未成功。手动去除假阳性并限制可测量病变的最大数量并无益处。在将经过测试的自动分割工具应用于自动反应评估时，仍需要专家监督和手动校正。当前分割工具的纵向一致性需要进一步改进。需要通过多中心研究对体积和二维进展阈值进行验证，以迈向基于体积测量的反应评估。
