Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA.
J Appl Clin Med Phys. 2024 Feb;25(2):e14168. doi: 10.1002/acm2.14168. Epub 2023 Oct 5.
Knowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions.
The six models differed in training size and plan composition: The KBP (n = 203 plans), KBP (n = 101), KBP (n = 50), and KBP (n = 25) were trained with plans from two head-and-neck physicians. KBP and KBP each contained n = 101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000-7000 cGy by a third physician was re-planned with all KBP models for validation. Standard head-and-neck dosimetric parameters were used to compare resulting plans. KBP plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBP plans were presented to another physician for blind review. Dosimetric comparison of KBP against KBP , KBP , and KBP investigated the effect of model size. Finally, KBP versus KBP tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t-test (p < 0.05).
Compared to manual plans, KBP significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBP plan over the manual plan in 20/39 cases. Dosimetric differences between KBP , KBP , KBP , and KBP plans did not exceed 187 cGy on aggregate, except for the cochlea. Further, average differences between KBP and KBP were below 110 cGy.
Overall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models.
基于知识的计划(KBP)旨在实现治疗计划的自动化和标准化。新的 KBP 用户面临许多问题:模型大小有多重要,是否需要多个模型来适应特定医生的偏好?在这项研究中,训练了六个头颈部 KBP 模型来解决这些问题。
这六个模型在训练规模和计划组成上有所不同:KBP(n=203 个计划)、KBP(n=101 个计划)、KBP(n=50 个计划)和 KBP(n=25 个计划)是由两位头颈部医生的计划进行训练的。KBP 和 KBP 分别仅包含来自一位医生的 n=101 个计划。由第三位医生以 6000-7000cGy 治疗的 39 名患者的独立集用所有 KBP 模型重新进行了计划,以进行验证。使用标准的头颈部剂量学参数比较生成的计划。将 KBP 计划与临床计划进行比较,以评估整体模型质量。此外,还将临床和 KBP 计划呈现给另一位医生进行盲审。KBP 与 KBP 、KBP 、KBP 的剂量学比较研究了模型大小的影响。最后,KBP 与 KBP 的比较测试了仅在一位医生的计划上训练 KBP 模型是否会影响输出结果。使用配对 t 检验(p<0.05)测试剂量学差异的显著性。
与手动计划相比,KBP 显著增加了 PTV Low D95%和左腮腺平均剂量,但降低了剂量耳蜗、缩肌和喉。在 39 例患者中,有 20 例医生更喜欢 KBP 计划而不是手动计划。KBP 、KBP 、KBP 、KBP 计划之间的剂量学差异除了耳蜗外,总体上不超过 187cGy。此外,KBP 和 KBP 之间的平均差异低于 110cGy。
总体而言,所有模型都被证明可以生成高质量的计划。与处方相比,模型输出之间的差异很小。这表明,当增加模型大小时,只有很小的改进,而当选择头颈部 KBP 模型的治疗计划时,医生的影响最小。