Department of Diagnostic Radiology, King Abdulaziz University, Jeddah, Saudi Arabia.
School of Medicine, University of Leeds, Leeds, UK.
J Appl Clin Med Phys. 2024 May;25(5):e14345. doi: 10.1002/acm2.14345. Epub 2024 Apr 25.
To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-based models. The correlation between geometric and dosimetric measures was also investigated to establish whether dosimetric assessment is required for clinical validation.
CT and MRI-based deep learning autosegmentation models were trained using edited and unedited clinical contours. Autosegmentations were dosimetrically compared to gold standard contours for a test cohort. D1%, D5%, D50%, and maximum dose were used as clinically relevant dosimetric measures. The statistical significance of dosimetric differences between the gold standard and autocontours was established using paired Student's t-tests. Clinically significant cases were identified via dosimetric headroom to the OAR tolerance. Pearson's Correlations were used to investigate the relationship between geometric measures and absolute percentage dose changes for each autosegmentation model.
Except for the right orbit, when delineated using MRI models, the dosimetric statistical analysis revealed no superior model in terms of the dosimetric accuracy between the CT DL-AC models or between the MRI DL-AC for any investigated brain OARs. The number of patients where the clinical significance threshold was exceeded was higher for the optic chiasm D1% than other OARs, for all autosegmentation models. A weak correlation was consistently observed between the outcomes of dosimetric and geometric evaluations.
Editing contours before training the DL-AC model had no significant impact on dosimetry. The geometric test metrics were inadequate to estimate the impact of contour inaccuracies on dose. Accordingly, dosimetric analysis is needed to evaluate the clinical applicability of DL-AC models in the brain.
为了确定深度学习危及器官自动勾画模型（DL-AC）在脑部放射治疗中的临床适用性。评估了在 CT 和 MRI 模型中，在模型训练之前对勾画轮廓进行编辑对性能的影响。还研究了几何和剂量学测量之间的相关性，以确定是否需要进行剂量评估来进行临床验证。
使用编辑和未编辑的临床轮廓来训练基于 CT 和 MRI 的深度学习自动勾画模型。将自动勾画与测试队列的金标准轮廓进行剂量学比较。D1%、D5%、D50%和最大剂量用作相关的剂量学测量。使用配对学生 t 检验确定金标准轮廓和自动勾画轮廓之间的剂量学差异的统计学意义。通过 OAR 耐受剂量的剂量学余量来识别具有临床意义的病例。使用 Pearson 相关系数研究每个自动勾画模型的几何测量与绝对剂量变化百分比之间的关系。
除了右侧眼眶，当使用 MRI 模型进行勾画时，在剂量学准确性方面，与 CT DL-AC 模型或任何研究的脑部 OAR 的 MRI DL-AC 模型相比，没有一个模型具有优越性。对于所有的自动勾画模型，在视交叉 D1%方面，超过临床意义阈值的患者数量高于其他 OAR。在剂量学和几何评估的结果之间始终观察到弱相关性。
在训练 DL-AC 模型之前编辑轮廓对剂量学没有显著影响。几何测试指标不足以估计轮廓不准确对剂量的影响。因此，需要进行剂量分析来评估 DL-AC 模型在脑部的临床适用性。
