Kensen Chavelli M, Simões Rita, Betgen Anja, Wiersema Lisa, Lambregts Doenja M J, Peters Femke P, Marijnen Corrie A M, van der Heide Uulke A, Janssen Tomas M
Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands.
Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands.
Phys Imaging Radiat Oncol. 2024 Sep 16;32:100648. doi: 10.1016/j.phro.2024.100648. eCollection 2024 Oct.
In online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial variation in tumor shape and location between patients. The aim of this work was to investigate different approaches of propagating patient-specific prior information to the online adaptive treatment fractions to improve deep-learning based auto-segmentation of rectal tumors.
243 T2-weighted MRI scans of 49 rectal cancer patients treated on the 1.5T MR-Linear accelerator (MR-Linac) were utilized to train models to segment rectal tumors. As benchmark, an MRI_only auto-segmentation model was trained. Three approaches of including a patient-specific prior were studied: 1. include the segmentations of fraction 1 as extra input channel for the auto-segmentation of subsequent fractions, 2. fine-tuning of the MRI_only model to fraction 1 (PSF_1) and 3. fine-tuning of the MRI_only model on all earlier fractions (PSF_cumulative). Auto-segmentations were compared to the manual segmentation using geometric similarity metrics. Clinical impact was assessed by evaluating post-treatment target coverage.
All patient-specific methods outperformed the MRI_only segmentation approach. Median 95th percentile Hausdorff (95HD) were 22.0 (range: 6.1-76.6) mm for MRI_only segmentation, 9.9 (range: 2.5-38.2) mm for MRI+prior segmentation, 6.4 (range: 2.4-17.8) mm for PSF_1 and 4.8 (range: 1.7-26.9) mm for PSF_cumulative. PSF_cumulative was found to be superior to PSF_1 from fraction 4 onward (p = 0.014).
Patient-specific fine-tuning of automatically segmented rectal tumors, using images and segmentations from all previous fractions, yields superior quality compared to other auto-segmentation approaches.
在在线自适应磁共振成像（MRI）引导的放射治疗（MRIgRT）中，每日图像上手动勾勒直肠肿瘤轮廓既费力又耗时。由于患者之间肿瘤形状和位置存在显著差异，该任务的自动化很复杂。本研究的目的是探讨将患者特异性先验信息传播到在线自适应治疗分次的不同方法，以改善基于深度学习的直肠肿瘤自动分割。
利用在1.5T MR直线加速器（MR-Linac）上治疗的49例直肠癌患者的243次T2加权MRI扫描来训练分割直肠肿瘤的模型。作为基准，训练了一个仅基于MRI的自动分割模型。研究了三种纳入患者特异性先验的方法：1. 将第1分次的分割结果作为后续分次自动分割的额外输入通道；2. 将仅基于MRI的模型微调至第1分次（PSF_1）；3. 将仅基于MRI的模型在所有先前分次上进行微调（PSF_累积）。使用几何相似性指标将自动分割结果与手动分割结果进行比较。通过评估治疗后靶区覆盖情况来评估临床影响。
所有患者特异性方法均优于仅基于MRI的分割方法。仅基于MRI分割的第95百分位数豪斯多夫距离（95HD）中位数为22.0（范围：6.1 - 76.6）mm，MRI + 先验分割为9.9（范围：2.5 - 38.2）mm，PSF_1为6.4（范围：2.4 - 17.8）mm，PSF_累积为4.8（范围：1.7 - 26.9）mm。发现从第4分次起，PSF_累积优于PSF_1（p = 0.014）。
与其他自动分割方法相比，使用所有先前分次的图像和分割结果对自动分割的直肠肿瘤进行患者特异性微调可产生更高质量的结果。
