Wang Lei, McQuaid Dualta, Blackledge Matthew, McNair Helen, Harris Emma, Lalondrelle Susan
The Joint Department of Physics at the Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK.
Phys Imaging Radiat Oncol. 2024 Feb 15;29:100554. doi: 10.1016/j.phro.2024.100554. eCollection 2024 Jan.
Interfraction motion during cervical cancer radiotherapy is substantial in some patients, minimal in others. Non-adaptive plans may miss the target and/or unnecessarily irradiate normal tissue. Adaptive radiotherapy leads to superior dose-volume metrics but is resource-intensive. The aim of this study was to predict target motion, enabling patient selection and efficient resource allocation.
Forty cervical cancer patients had CT with full-bladder (CT-FB) and empty-bladder (CT-EB) at planning, and daily cone-beam CTs (CBCTs). The low-risk clinical target volume (CTV) was contoured. Mean coverage of the daily CTV by the CT-FB CTV was calculated for each patient. Eighty-three investigated variables included measures of organ geometry, patient, tumour and treatment characteristics. Models were trained on 29 patients (171 fractions). The Two-CT multivariate model could use all available data. The Single-CT multivariate model excluded data from the CT-EB. A univariate model was trained using the distance moved by the uterine fundus tip between CTs, the only method of patient selection found in published cervix plan-of-the-day studies. Models were tested on 11 patients (68 fractions). Accuracy in predicting mean coverage was reported as mean absolute error (MAE), mean squared error (MSE) and R.
The Two-CT model was based upon rectal volume, dice similarity coefficient between CT-FB and CT-EB CTV, and uterine thickness. The Single-CT model was based upon rectal volume, uterine thickness and tumour size. Both performed better than the univariate model in predicting mean coverage (MAE 7 %, 7 % and 8 %; MSE 82 %, 65 %, 110 %; R 0.2, 0.4, -0.1).
Uterocervix motion is complex and multifactorial. We present two multivariate models which predicted motion with reasonable accuracy using pre-treatment information, and outperformed the only published method.
宫颈癌放疗期间，部分患者的分次间运动幅度较大，而另一些患者则较小。非适应性计划可能会遗漏靶区和/或不必要地照射正常组织。适应性放疗可产生更好的剂量体积指标，但资源消耗大。本研究的目的是预测靶区运动，以便进行患者选择和有效资源分配。
40例宫颈癌患者在计划时进行了膀胱充盈（CT-FB）和膀胱排空（CT-EB）状态下的CT扫描以及每日锥形束CT（CBCT）扫描。勾画了低危临床靶区（CTV）。计算每位患者CT-FB CTV对每日CTV的平均覆盖情况。研究的83个变量包括器官几何形状、患者、肿瘤和治疗特征的测量指标。在29例患者（171个分次）上对模型进行训练。双CT多变量模型可使用所有可用数据。单CT多变量模型排除了CT-EB的数据。使用两次CT扫描之间子宫底尖端移动的距离训练单变量模型，这是已发表的宫颈癌当日计划研究中发现的唯一患者选择方法。在11例患者（68个分次）上对模型进行测试。预测平均覆盖情况的准确性以平均绝对误差（MAE）、均方误差（MSE）和R表示。
双CT模型基于直肠体积、CT-FB与CT-EB CTV之间的骰子相似系数以及子宫厚度。单CT模型基于直肠体积、子宫厚度和肿瘤大小。在预测平均覆盖情况方面，两者均优于单变量模型（MAE分别为7%、7%和8%；MSE分别为82%、65%、110%；R分别为0.2、0.4、-0.1）。
子宫颈运动复杂且受多种因素影响。我们提出了两个多变量模型，它们利用治疗前信息对运动进行了合理准确的预测，且表现优于唯一已发表的方法。
