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基于CT的妇科恶性肿瘤近距离放疗自动轮廓勾画的前瞻性评估

Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies.

作者信息

Kraus Abigayle C, Iqbal Zohaib, Cardan Rex A, Popple Richard A, Stanley Dennis N, Shen Sui, Pogue Joel A, Wu Xingen, Lee Kevin, Marcrom Samuel, Cardenas Carlos E

机构信息

Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.

Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.

出版信息

Adv Radiat Oncol. 2023 Dec 10;9(4):101417. doi: 10.1016/j.adro.2023.101417. eCollection 2024 Apr.

Abstract

PURPOSE

The use of deep learning to auto-contour organs at risk (OARs) in gynecologic radiation treatment is well established. Yet, there is limited data investigating the prospective use of auto-contouring in clinical practice. In this study, we assess the accuracy and efficiency of auto-contouring OARs for computed tomography-based brachytherapy treatment planning of gynecologic malignancies.

METHODS AND MATERIALS

An inhouse contouring tool automatically delineated 5 OARs in gynecologic radiation treatment planning: the bladder, small bowel, sigmoid, rectum, and urethra. Accuracy of each auto-contour was evaluated using a 5-point Likert scale: a score of 5 indicated the contour could be used without edits, while a score of 1 indicated the contour was unusable. During scoring, automated contours were edited and subsequently used for treatment planning. Dice similarity coefficient, mean surface distance, 95% Hausdorff distance, Hausdorff distance, and dosimetric changes between original and edited contours were calculated. Contour approval time and total planning time of a prospective auto-contoured (AC) cohort were compared with times from a retrospective manually contoured (MC) cohort.

RESULTS

Thirty AC cases from January 2022 to July 2022 and 31 MC cases from July 2021 to January 2022 were included. The mean (±SD) Likert score for each OAR was the following: bladder 4.77 (±0.58), small bowel 3.96 (±0.91), sigmoid colon 3.92 (±0.81), rectum 4.6 (±0.71), and urethra 4.27 (±0.78). No ACs required major edits. All OARs had a mean Dice similarity coefficient > 0.86, mean surface distance < 0.48 mm, 95% Hausdorff distance < 3.2 mm, and Hausdorff distance < 10.32 mm between original and edited contours. There was no significant difference in dose-volume histogram metrics (D2.0 cc/D0.1 cc) between original and edited contours ( values > .05). The average time to plan approval in the AC cohort was 19% less than the MC cohort. (AC vs MC, 117.0 + 18.0 minutes vs 144.9 ± 64.5 minutes, = .045).

CONCLUSIONS

Automated contouring is useful and accurate in clinical practice. Auto-contouring OARs streamlines radiation treatment workflows and decreases time required to design and approve gynecologic brachytherapy plans.

摘要

目的

深度学习用于妇科放射治疗中危及器官(OARs)的自动轮廓勾画已得到广泛应用。然而,关于自动轮廓勾画在临床实践中的前瞻性应用的数据有限。在本研究中,我们评估了基于计算机断层扫描的近距离放射治疗计划中,妇科恶性肿瘤OARs自动轮廓勾画的准确性和效率。

方法和材料

一个内部轮廓勾画工具在妇科放射治疗计划中自动勾勒出5个OARs:膀胱、小肠、乙状结肠、直肠和尿道。使用5点李克特量表评估每个自动轮廓的准确性:5分表示轮廓无需编辑即可使用,1分表示轮廓不可用。在评分过程中,对自动轮廓进行编辑,随后用于治疗计划。计算原始轮廓和编辑后轮廓之间的骰子相似系数、平均表面距离、95%豪斯多夫距离、豪斯多夫距离和剂量学变化。将前瞻性自动轮廓勾画(AC)队列的轮廓批准时间和总计划时间与回顾性手动轮廓勾画(MC)队列的时间进行比较。

结果

纳入了2022年1月至2022年7月的30例AC病例和2021年7月至2022年1月的31例MC病例。每个OAR的平均(±标准差)李克特评分为:膀胱4.77(±0.58),小肠3.96(±0.91),乙状结肠3.92(±0.81),直肠4.6(±0.71),尿道4.27(±0.78)。没有AC需要进行重大编辑。所有OARs在原始轮廓和编辑后轮廓之间的平均骰子相似系数>0.86,平均表面距离<0.48毫米,95%豪斯多夫距离<3.2毫米,豪斯多夫距离<10.32毫米。原始轮廓和编辑后轮廓之间的剂量体积直方图指标(D2.0 cc/D0.1 cc)没有显著差异(P值>.05)。AC队列中计划批准的平均时间比MC队列少19%。(AC与MC,117.0 + 18.0分钟对144.9 ± 64.5分钟,P =.045)。

结论

自动轮廓勾画在临床实践中是有用且准确的。OARs的自动轮廓勾画简化了放射治疗工作流程,并减少了设计和批准妇科近距离放射治疗计划所需的时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ded/10906166/7f49596e8a5b/gr1.jpg

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