Department of Radiation Oncology (MAASTRO), GROW-School for Oncology, Maastricht University Medical Center, Maastricht, The Netherlands.
Department of Radiation Oncology (MAASTRO), GROW-School for Oncology, Maastricht University Medical Center, Maastricht, The Netherlands.
Radiother Oncol. 2022 Oct;175:152-158. doi: 10.1016/j.radonc.2022.08.030. Epub 2022 Sep 5.
Image-guided radiotherapy using cone beam-CT (CBCT) images is used to evaluate patient anatomy and positioning before radiotherapy. In this study we analyzed and optimized a traffic light protocol (TLP) used in lung cancer patients to identify patients requiring treatment adaptation.
First, CBCT review requests of 243 lung cancer patients were retrospectively analyzed and divided into 6 pre-defined categories. Frequencies and follow-up actions were scored. Based on these results, the TLP was optimized and evaluated in the same way on 230 patients treated in 2018.
In the retrospective study, a total of 543 CBCT review requests were created during treatment in 193/243 patients due to changed anatomy of lung (24%), change of tumor volume (24%), review of match (18%), shift of the mediastinum (15%), shift of tumor (15%) and other (4%). The majority of requests (474, 87%) did not require further action. In 6% an adjustment of the match criteria sufficed; in 7% treatment plan adaptation was required. Plan adaptation was frequently seen in the categories changed anatomy of lung, change of tumor volume and shift of tumor outside the PTV. Shift of mediastinum outside PRV and shift of GTV outside CTV (but inside PTV) never required plan adaptation and were omitted to optimize the TLP, which reduced the CBCT review requests by 23%.
The original TLP selected patients that required a treatment adaptation, but with a high false positive rate. The optimized TLP reduced the amount of CBCT review requests, while still correctly identifying patients requiring adaptation.
使用锥形束 CT(CBCT)图像进行图像引导放疗,用于在放疗前评估患者的解剖结构和定位。在这项研究中,我们分析并优化了用于识别需要治疗调整的患者的肺癌患者使用的红绿灯协议(TLP)。
首先,回顾性分析了 243 例肺癌患者的 243 例 CBCT 复查请求,并分为 6 个预先定义的类别。对频率和后续行动进行评分。基于这些结果,以同样的方式在 2018 年治疗的 230 例患者中优化并评估了 TLP。
在回顾性研究中,由于肺部解剖结构变化(24%)、肿瘤体积变化(24%)、匹配复查(18%)、纵隔移位(15%)、肿瘤移位(15%)和其他原因(4%),193/243 例患者在治疗期间共创建了 543 例 CBCT 复查请求。大多数请求(474 例,87%)不需要进一步的行动。6%的请求只需调整匹配标准即可;7%的请求需要调整治疗计划。在解剖结构变化、肿瘤体积变化和肿瘤移出 PTV 等类别中,经常需要调整计划。CTV 内但 PTV 外的 GTV 外移和 PRV 外的纵隔移位从未需要调整计划,因此在优化 TLP 时被省略,这将 CBCT 复查请求减少了 23%。
原始 TLP 选择了需要治疗调整的患者,但假阳性率较高。优化后的 TLP 减少了 CBCT 复查请求的数量,同时仍然正确识别需要调整的患者。