Gros Sebastien A A, Santhanam Anand P, Block Alec M, Emami Bahman, Lee Brian H, Joyce Cara
Loyola University Chicago, Loyola University Medical Center, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, IL, United States.
Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States.
Front Oncol. 2022 Jun 30;12:777793. doi: 10.3389/fonc.2022.777793. eCollection 2022.
This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.
We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients' data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V<95%) and adaptation (V<93%) thresholds were set for PTVs, while OAR adaptation dosimetric endpoints of +10% (DE) were set for all D and D DPs. Any threshold violation at end of treatment (EOT) triggered a review of the DP trends to determine the threshold-crossing fraction when the violations occurred. The prediction model accuracy was determined as the difference between calculated and predicted DP values with 95% confidence intervals (CI).
RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid D at EOT. Twelve PTVs had V<95% (mean coverage decrease of -6.8 ± 2.9%) including six flagged for adaptation at median = 6 (range, 1-16). Seventeen parotids were flagged for exceeding D dose constraints with a median increase of +2.60 Gy (range, 0.99-6.31 Gy) at EOT, including nine with DP>DE. The differences between predicted and calculated PTV V and parotid D was up to 7.6% (mean ± CI, -2.7 ± 4.1%) and 5 Gy (mean ± CI, 0.3 ± 1.6 Gy), respectively. The most accurate predictions were obtained closest to the threshold-crossing fraction. For parotids, the results showed that ranged between fractions 1 and 23, with a lack of specific trend demonstrating that the need for treatment adaptation may be verified for every fraction.
Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.
本研究旨在评估头颈癌（HNC）患者自适应放射治疗（ART）自动决策支持软件平台的临床需求。
我们对RTapp（SegAna）进行了测试，这是一个用于确定何时需要进行治疗重新计划的新型ART软件平台，我们回顾性地研究了一组27例HNC患者的数据。对于每个分次，该软件实时估计ART的关键组成部分，如每日剂量分布以及靶区和危及器官（OAR）从每日三维成像中接受的累积剂量。RTapp还包括一种预测算法，该算法根据用户指定的阈值分析剂量学参数（DP）趋势，以提前多达四个分次主动触发自适应重新计划。用于ART评估的DP基于治疗计划剂量限制。为计划靶体积（PTV）设置了警告（V<95%）和适应（V<93%）阈值，而对所有D和D DP为OAR适应剂量学终点设置了+10%（剂量当量，DE）。治疗结束（EOT）时任何阈值违反都会触发对DP趋势的审查，以确定违规发生时的阈值跨越分次。预测模型的准确性通过计算和预测的DP值之间的差异以及95%置信区间（CI）来确定。
RTapp能够满足治疗适应的需求。具体而言，我们确定在EOT时18/27项研究（67%）违反了PTV覆盖或腮腺D。12个PTV的V<95%（平均覆盖减少-6.8±2.9%），其中6个在中位数=6（范围，1-16）时被标记为适应。17个腮腺在EOT时因超过D剂量限制而被标记，中位数增加+2.60 Gy（范围，0.99-6.31 Gy），其中9个DP>DE。预测和计算的PTV V以及腮腺D之间的差异分别高达7.6%（平均±CI，-2.7±4.1%）和5 Gy（平均±CI，0.3±1.6 Gy）。最准确的预测是在最接近阈值跨越分次时获得的。对于腮腺，结果表明范围在第1分次至第23分次之间，缺乏特定趋势表明每个分次都可能需要验证治疗适应的必要性。
RTapp集成在ART临床工作流程中，有助于提前多达四个分次预测特定治疗是否需要适应。
