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Abstract：
OBJECTIVE: To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.
METHODS: Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman\'s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics.
RESULTS: FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics.
CONCLUSIONS: Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.
METHODS: Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman\'s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics.
RESULTS: FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics.
CONCLUSIONS: Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.
摘要：
目的：研究基于深度学习的危险器官自动分割（OAR）对鼻咽癌和直肠癌的剂量学影响。
方法：20名患者，包括10名鼻咽癌（NPC）患者和10名直肠癌患者，在我们部门接受放疗的患者被纳入本研究.两个基于深度学习的自动分割系统，包括内部开发的系统(FD)和商业产品(UIH)，用于生成两个自动分段的OAR集(OAR_FD和OAR_UIH)。在每个OAR集合(Plan_FD和Plan_UIH)上为每个患者生成基于自动分段OAR并遵循我们的临床要求的治疗计划。几何度量(Hausdorff距离(HD)，平均协议距离(MDA)，计算Dice相似系数（DICE）和Jaccard指数）进行几何评估。通过将Plan_FD和Plan_UIH与具有剂量-体积度量和3D伽马分析的原始临床批准的计划(Plan_Manual)进行比较来评估剂量测定影响。进行Spearman相关分析以探讨剂量学差异与几何指标之间的相关性。
结果：FD和UIH可以在腮腺中提供相似的几何性能，颞叶,镜头,和眼睛（DICE，p>0.05)。OAR_FD在视神经中具有较好的几何性能,口腔,喉部,和股骨头(DICE，p<0.05)。OAR_UIH在膀胱中具有更好的几何性能(DICE，p<0.05)。在剂量学分析中，对于大多数PTV和OARs剂量-体积指标,Plan_FD和Plan_UIH与Plan_Manual相比剂量差异不显著.唯一显著的剂量学差异是Plan_FD与左颞叶的最大剂量。Plan_Manual(p=0.05)。股骨头的平均剂量与其HD指数之间仅发现一个显着相关性（R=0.4，p=0.01），没有OAR显示其剂量学差异与所有四个几何指标之间的强相关性。
结论：基于深度学习的NPC和直肠癌OARs自动分割对大多数PTV和OARs剂量-体积指标没有显著影响。对于大多数OAR，未观察到自动分割几何度量与剂量学差异之间的相关性。
方法：20名患者，包括10名鼻咽癌（NPC）患者和10名直肠癌患者，在我们部门接受放疗的患者被纳入本研究.两个基于深度学习的自动分割系统，包括内部开发的系统(FD)和商业产品(UIH)，用于生成两个自动分段的OAR集(OAR_FD和OAR_UIH)。在每个OAR集合(Plan_FD和Plan_UIH)上为每个患者生成基于自动分段OAR并遵循我们的临床要求的治疗计划。几何度量(Hausdorff距离(HD)，平均协议距离(MDA)，计算Dice相似系数（DICE）和Jaccard指数）进行几何评估。通过将Plan_FD和Plan_UIH与具有剂量-体积度量和3D伽马分析的原始临床批准的计划(Plan_Manual)进行比较来评估剂量测定影响。进行Spearman相关分析以探讨剂量学差异与几何指标之间的相关性。
结果：FD和UIH可以在腮腺中提供相似的几何性能，颞叶,镜头,和眼睛（DICE，p>0.05)。OAR_FD在视神经中具有较好的几何性能,口腔,喉部,和股骨头(DICE，p<0.05)。OAR_UIH在膀胱中具有更好的几何性能(DICE，p<0.05)。在剂量学分析中，对于大多数PTV和OARs剂量-体积指标,Plan_FD和Plan_UIH与Plan_Manual相比剂量差异不显著.唯一显著的剂量学差异是Plan_FD与左颞叶的最大剂量。Plan_Manual(p=0.05)。股骨头的平均剂量与其HD指数之间仅发现一个显着相关性（R=0.4，p=0.01），没有OAR显示其剂量学差异与所有四个几何指标之间的强相关性。
结论：基于深度学习的NPC和直肠癌OARs自动分割对大多数PTV和OARs剂量-体积指标没有显著影响。对于大多数OAR，未观察到自动分割几何度量与剂量学差异之间的相关性。
