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最小化人类在膀胱癌在线全自动化日常自适应放射治疗工作流程中的干预。

Minimizing human interference in an online fully automated daily adaptive radiotherapy workflow for bladder cancer.

机构信息

Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.

Cancer Center Amsterdam, Cancer Therapy, Treatment and quality of life, Amsterdam, The Netherlands.

出版信息

Radiat Oncol. 2024 Oct 7;19(1):138. doi: 10.1186/s13014-024-02526-2.

Abstract

PURPOSE

The aim was to study the potential for an online fully automated daily adaptive radiotherapy (RT) workflow for bladder cancer, employing a focal boost and fiducial markers. The study focused on comparing the geometric and dosimetric aspects between the simulated automated online adaptive RT (oART) workflow and the clinically performed workflow.

METHODS

Seventeen patients with muscle-invasive bladder cancer were treated with daily Cone Beam CT (CBCT)-guided oART. The bladder and pelvic lymph nodes (CTV) received a total dose of 40 Gy in 20 fractions and the tumor bed received an additional simultaneously integrated boost (SIB) of 15 Gy (CTV). During the online sessions a CBCT was acquired and used as input for the AI-network to automatically delineate the bladder and rectum, i.e. influencers. These influencers were employed to guide the algorithm utilized in the delineation process of the target. Manual adjustments to the generated contours are common during this clinical workflow prior to plan reoptimization and RT delivery. To study the potential for an online fully automated workflow, the oART workflow was repeated in a simulation environment without manual adjustments. A comparison was made between the clinical and automatic contours and between the treatment plans optimized on these clinical (D) and automatic contours (D).

RESULTS

The bladder and rectum delineated by the AI-network differed from the clinical contours with a median Dice Similarity Coefficient of 0.99 and 0.92, a Mean Distance to Agreement of 1.9 mm and 1.3 mm and a relative volume of 100% and 95%, respectively. For the CTV these differences were larger, namely 0.71, 7 mm and 78%. For the CTV the median target coverage was 0.42% lower for D compared to D. For CTV this difference was 0.03%. The target coverage of D met the clinical requirement of the CTV-coverage in 65% of the sessions for CTV and 95% of the sessions for the CTV.

CONCLUSIONS

While an online fully automated daily adaptive RT workflow shows promise for bladder treatment, its complexity becomes apparent when incorporating a focal boost, necessitating manual checks to prevent potential underdosage of the target.

摘要

目的

研究使用焦点增敏和基准标记物的在线全自动自适应放疗(RT)膀胱肿瘤治疗流程的潜力。该研究侧重于比较模拟自动在线自适应 RT(oART)工作流程与临床执行工作流程的几何和剂量学方面。

方法

17 例肌层浸润性膀胱癌患者接受每日锥形束 CT(CBCT)引导的 oART 治疗。膀胱和盆腔淋巴结(CTV)接受 40Gy/20 次分割总剂量,肿瘤床接受 15Gy 同步整合增敏(SIB)(CTV)。在在线治疗期间,采集 CBCT 并将其作为输入提供给 AI 网络,以自动描绘膀胱和直肠,即影响因素。这些影响因素用于指导目标描绘过程中使用的算法。在计划重新优化和 RT 实施之前,在临床工作流程中,手动调整生成的轮廓很常见。为了研究在线全自动工作流程的潜力,在没有手动调整的模拟环境中重复 oART 工作流程。比较临床和自动轮廓之间以及在这些临床(D)和自动轮廓(D)上优化的治疗计划之间的差异。

结果

AI 网络描绘的膀胱和直肠与临床轮廓存在差异,其 Dice 相似系数中位数分别为 0.99 和 0.92,平均吻合距离分别为 1.9mm 和 1.3mm,相对体积分别为 100%和 95%。CTV 方面的差异更大,分别为 0.71、7mm 和 78%。与 D 相比,CTV 的中位靶区覆盖率低 0.42%。CTV 方面,差异为 0.03%。65%的 CTV 治疗计划和 95%的 CTV 治疗计划满足靶区覆盖率的临床要求。

结论

虽然在线全自动自适应 RT 工作流程为膀胱癌治疗带来了希望,但当纳入焦点增敏时,其复杂性变得明显,需要进行手动检查以防止靶区潜在的剂量不足。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6855/11457325/cf5a4e16195f/13014_2024_2526_Fig1_HTML.jpg

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