Improving the Efficiency of Single-Isocenter Multiple Metastases Stereotactic Radiosurgery Treatment

IF 2.2 Q3 ONCOLOGY

Advances in Radiation Oncology Pub Date : 2024-05-08 DOI:10.1016/j.adro.2024.101538

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引用次数: 0

Abstract

Purpose

Multiple brain metastases can be treated efficiently with stereotactic radiosurgery (SRS) using a single-isocenter dynamic conformal arc (SIDCA) technique. Currently, plans are manually optimized, which may lead to unnecessary table angles and arcs being used. This study aimed to evaluate an automatic 4π optimization SIDCA algorithm for treatment efficiency and plan quality.

Methods and Materials

Automatic 4π-optimized SIDCA plans were created and compared with the manually optimized clinical plans for 54 patients who underwent single-fraction SRS for 2 to 10 metastases. The number of table angles and number of arcs were compared with a paired t test using a Bonferroni-corrected significance level of P < .05/4 = .0125. The reduction in treatment time was estimated from the difference in the number of table angles and arcs. Plan quality was assessed through the volume-averaged inverse Paddick Conformity Index (CI) and Gradient Index (GI) and the volume of normal brain surrounding each metastasis receiving 12 Gy (local V12 Gy). For a 5-patient subset, the automatic plans were manually adjusted further. CI and GI were assessed for noninferiority using a 1-sided t test with the noninferiority limit equal to the 95% interobserver reproducibility limit from a separate planning study (corrected significance level P < .05/[4 − 1] = .017).

Results

The automatic plans significantly improved treatment efficiency with a mean reduction in the number of table angles and arcs of −0.5 ± 0.1 and −1.3 ± 0.2, respectively (±SE; both P < .001). Estimated treatment time saving was −2.7 ± 0.5 minutes, 14% of the total treatment time. The volume-averaged CI and GI were noninferior to the clinical plans (both P < .001), although there was a small systematic shift in CI of 0.07 ± 0.01. The resulting difference in local V12 Gy, 0.25 ± 0.04 cm³, was not clinically significant. Minor manual adjustment of the automatic plans removed these slight differences while preserving the improved treatment efficiency.

Conclusions

Automatic 4π optimization can generate SIDCA SRS plans with improved treatment efficiency and noninferior plan quality.

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提高单中心多发性转移立体定向放射外科治疗的效率

目的使用单等中心动态适形弧线（SIDCA）技术进行立体定向放射手术（SRS）可有效治疗多发性脑转移瘤。目前，计划需要手动优化，这可能会导致不必要的工作台角度和弧度的使用。本研究旨在评估自动4π优化SIDCA算法的治疗效率和计划质量。方法和材料为54名接受单分次SRS治疗2至10个转移灶的患者创建了自动4π优化SIDCA计划，并将其与人工优化的临床计划进行了比较。台角数和弧线数的比较采用配对 t 检验，Bonferroni 校正显著性水平为 P < .05/4 = .0125。根据工作台角度和弧度数量的差异估算出治疗时间的缩短。计划质量通过体积均值的反Paddick一致性指数（CI）和梯度指数（GI）以及每个转移灶周围接受12 Gy治疗的正常脑体积（局部V12 Gy）进行评估。对于 5 名患者的子集，还需进一步手动调整自动计划。采用单侧 t 检验评估 CI 和 GI 的非劣效性，非劣效性限值等于另一项计划研究中 95% 的观察者间可重复性限值（校正显著性水平 P < .05/[4 - 1] = .017）。结果自动计划显著提高了治疗效率，工作台角度和弧度的平均减少量分别为 -0.5 ± 0.1 和 -1.3 ± 0.2（±SE；均为 P < .001）。估计节省的治疗时间为 -2.7 ± 0.5 分钟，占总治疗时间的 14%。容积平均 CI 和 GI 均不劣于临床计划（均为 P <.001），但 CI 有 0.07 ± 0.01 的微小系统性偏移。由此产生的局部 V12 Gy 差异（0.25 ± 0.04 cm3）并无临床意义。结论自动 4π 优化可以生成 SIDCA SRS 计划，并提高治疗效率和计划质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Radiation Oncology Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

4.60

自引率

4.30%

发文量

208

审稿时长

98 days

期刊介绍： The purpose of Advances is to provide information for clinicians who use radiation therapy by publishing: Clinical trial reports and reanalyses. Basic science original reports. Manuscripts examining health services research, comparative and cost effectiveness research, and systematic reviews. Case reports documenting unusual problems and solutions. High quality multi and single institutional series, as well as other novel retrospective hypothesis generating series. Timely critical reviews on important topics in radiation oncology, such as side effects. Articles reporting the natural history of disease and patterns of failure, particularly as they relate to treatment volume delineation. Articles on safety and quality in radiation therapy. Essays on clinical experience. Articles on practice transformation in radiation oncology, in particular: Aspects of health policy that may impact the future practice of radiation oncology. How information technology, such as data analytics and systems innovations, will change radiation oncology practice. Articles on imaging as they relate to radiation therapy treatment.