| 张丽媛,胡金炎,辜石勇,等.基于鼻咽癌危及器官自动分割可变性及剂量学影响[J].中华放射医学与防护杂志,2024,44(11):944-952.Zhang Liyuan,Hu Jinyan,Gu Shiyong,et al.Auto-segmentation variability of organs at risk in patients with nasopharyngeal carcinoma and its dosimetric impacts[J].Chin J Radiol Med Prot,2024,44(11):944-952 |
| 基于鼻咽癌危及器官自动分割可变性及剂量学影响 |
| Auto-segmentation variability of organs at risk in patients with nasopharyngeal carcinoma and its dosimetric impacts |
| 投稿时间：2023-12-20 |
| DOI：10.3760/cma.j.cn112271-20231220-00216 |
| 中文关键词: 危及器官 自动分割 剂量差异 重叠体积直方图 |
| 英文关键词:Organ at risk Auto-segmentation Dose difference Overlap volume histogram |
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| 中文摘要: |
| 目的 探讨鼻咽癌危及器官(OAR)自动分割轮廓的调整范围,并评估来自不同源头的轮廓对放疗计划剂量效应的影响。方法 对25例临床早期鼻咽癌患者的脊髓、脑干、视神经、视交叉、腮腺、口腔、下咽和下颌骨进行放疗专家勾画、深度学习和图谱库自动勾画,以及专家对这些自动勾画的校正,形成共5个结构集。靶区及其他OAR的专家轮廓被复制到上述图像中用于后续研究。计算结构集的戴斯相似性系数(DSC)。以专家轮廓优化的放疗计划为模板,为每个结构集生成相应的放疗计划和剂量分布。定义专家轮廓及经专家校正的自动轮廓为临床轮廓,设定3个研究目标观察者内临床轮廓变化的剂量学效应、轮廓变化对计划优化的影响和轮廓变化对计划评估的影响。结果 视觉通路平均DSC为0.62±0.10,低于其他OAR的0.86±0.04,经专家校正后,深度学习和图谱库勾画轮廓的DSC分别提高了7.61%和10.69%。在观察者内临床轮廓变化的剂量效应中,视交叉Dmax 最大为(3.96±6.02) Gy,而下咽Dmean 最小为(0.81±0.55) Gy。在专家轮廓上评估轮廓变化对计划优化影响中,脊髓、脑干、视神经和视交叉的剂量差异(ΔD)分别有22%、14%、46%和42%超过±3 Gy,而其他OAR的ΔD超过±3 Gy的比例仅2%。经过专家校正后,自动轮廓与专家轮廓的ΔD有所减小,其中视神经和视交叉超过±3 Gy的Δ D分别降低了16%和14%。当重叠体积直方图(OVH)平均距离超过3.5 cm时,ΔDmax均在±3 Gy 内;当OVH平均距离>1.5 cm时,ΔDmean均在±2 Gy内。深度学习和图谱库轮廓分别有50.0%±17.3%和52.6%±19.3%患者剂量落在临床勾画剂量范围内;脊髓、视神经和视交叉Dmax及下颌骨D1 cm3两种自动轮廓剂量落在临床轮廓范围的患者数差异具有统计学意义(t=-4.24、-3.99、-3.16、3.51,P<0.05)。结论 专家参考不同来源的自动勾画结果校正后仍显示出一定的几何差异,校正降低了自动勾画轮廓对计划优化的影响,OVH平均距离是判定剂量差异的重要特征,对于小体积和距离靶区较近串行器官,自动分割应用于临床前需进行细致的校正。 |
| 英文摘要: |
| Objective To explore the adjustment ranges of auto-segmentation contours for organs at risk (OAR) in patients with nasopharyngeal carcinoma and assess the dosimetric impacts of the contours from varying sources on radiotherapy plans. Methods Twenty-five patients with early-stage nasopharyngeal carcinoma were investigated. Through expert delineation, deep learning-based automatic delineation, and atlas-based automatic delineation of their spinal cord, brainstem, optic nerves, optic chiasm, parotid glands, oral cavity, hypopharynx, and mandible, as well as expert correction of these automatic delineations, five structure sets were formed. Moreover, the contours delineated by experts (also referred to as the expert contours) of the target volumes and other OARs were copied into the images for subsequent research. The Dice similarity coefficients (DSCs) of the structure sets were calculated. Using the radiotherapy plans optimized based on expert contours as templates, the radiotherapy plans and dose distributions of all the structure sets were established. The expert contours and contours determined using automatic delineation and corrected by experts (also referred to as the corrected contours) were defined as clinical contours. Then, three research objectives were set: the dosimetric effects of inter-observer clinical contour variations, the impacts of contour variations on plan optimization, and the impacts of contour variations on plan evaluation. Results The average DSC of the visual pathway was 0.62±0.10, lower than that of other OARs (0.86±0.04). After expert correction, the DSCs of contours obtained using deep learning- and atlas-based automatic delineation increased by 7.61% and 10.69%, respectively. For the dosimetric effects of inner-observer contour variations, the Dmax of the optic chiasm was the maximum (3.96±6.02) Gy, while the Dmean of the hypopharynx was the minimum (0.81±0.55 Gy). When the impacts of contour variations on plan optimization were assessed based on expert contours, the dose differences (ΔD) exceeding ±3 Gy accounted for 22%,14%, 46%, and 42%, respectively for the spinal cord, brainstem, optic nerve, and optic chiasm and accounted for only 2% for other OARs. After expert correction, the ΔD between automatic and expert contours decreased, with ΔD exceeding ±3 Gy decreased by 16% and 14%, respectively for the optic nerves and optic chiasm. When the average distance of the overlap volume histogram (OVH) exceeded 3.5 cm, all ΔDmax fell within ±3 Gy. When the average distance of OVH was greater than 1.5 cm, all ΔDmean fell within ±2 Gy. For contours obtained using deep learning and atlas-based automatic delineation, the doses of 50.0%±17.3% and 52.6%±19.3% of patients fell within the dose ranges of clinical contours, respectively. The numbers of patients for whom the Dmax of the spinal cord, optic nerve, optic chiasm and the D1 cm3 of the mandible in the two types of automatic contours fell within the dose ranges of clinical contours were statistically different (t = -4.24, -3.99, -3.16, 3.51, P < 0.05). Conclusions After expert correction, the automatic delineation results from different sources exhibited certain geometric differences. The expert correction reduced the impacts of automatic contours on plan optimization. The average distance of OVH is identified as an important feature used to determine dose differences. For small-volume serial organs close to the target volumes, meticulous corrections are required before applying auto-segmentation to clinical practice. |
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