Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy

IF 3.4 Q2 ONCOLOGY
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Abstract

Background and purpose

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.

Materials and methods

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.

Results

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).

Conclusion

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.

为在线自适应磁共振图像引导放射治疗开发直肠肿瘤自动分割模型时纳入患者特异性信息
背景和目的在在线自适应磁共振成像(MRI)引导放疗(MRIgRT)中,在日常图像上手动绘制直肠肿瘤轮廓是一项劳动密集型且耗时的工作。由于不同患者的肿瘤形状和位置存在很大差异,这项任务的自动化非常复杂。这项工作的目的是研究向在线自适应治疗分数传播患者特定先验信息的不同方法,以改进基于深度学习的直肠肿瘤自动分割。作为基准,训练了一个仅使用 MRI 的自动分割模型。研究了包含患者特异性先验的三种方法:1.将第 1 部分的分割作为后续部分自动分割的额外输入通道;2.对第 1 部分的纯 MRI 模型进行微调(PSF_1);3.对所有早期部分的纯 MRI 模型进行微调(PSF_cumulative)。使用几何相似度指标将自动分割与手动分割进行比较。通过评估治疗后的目标覆盖范围来评估临床效果。仅核磁共振成像分割的第 95 百分位数 Hausdorff (95HD) 中值为 22.0(范围:6.1-76.6)毫米,核磁共振成像+先前分割为 9.9(范围:2.5-38.2)毫米,PSF_1 为 6.4(范围:2.4-17.8)毫米,PSF_cumulative 为 4.8(范围:1.7-26.9)毫米。PSF_cumulative从第4分段开始就优于PSF_1(p = 0.014)。结论与其他自动分段方法相比,使用之前所有分段的图像和分段对患者特定的直肠肿瘤自动分段进行微调可获得更高的质量。
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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
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