Wang Yunxiang, Zhang Yihang, Chen Si-Ye, Lv Tie, Liu Yuxiang, Fang Hui, Jing Hao, Lu Ning-Ning, Zhai Yi-Rui, Song Yong-Wen, Liu Yue-Ping, Zhang Wen-Wen, Qi Shu-Nan, Tang Yuan, Chen Bo, Li Ye-Xiong, Men Kuo, Chen Xinyuan, Zhao Wei, Wang Shu-Lian
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
School of Physics, Beihang University, Beijing 102206, China.
Phys Med. 2025 May;133:104964. doi: 10.1016/j.ejmp.2025.104964. Epub 2025 Apr 26.
Currently there is no widely used upfront selection method to determine whether patients are suitable for deep inspiration breath-hold (DIBH) in left-sided breast radiotherapy.
To establish an upfront patient selection strategy to improve the decision-making efficiency of DIBH and avoid extra computed tomography (CT) exposure to patients.
A total of 174 patients who underwent both free-breathing (FB) and DIBH scans were enrolled. A general principal component analysis model for DIBH-CT synthesis was trained and consists of principal component feature vectors extracted from paired FB-CT and DIBH-CT in training set. The coefficients of the vectors were optimized to minimize the difference between synthetic CT and breath-hold scout image of each patient in test set, leading to personalized DIBH-CT synthesis. An upfront patient selection strategy was established based on cardiac dose in synthetic DIBH-CT plan. The performance of DIBH-CT synthesis was analyzed in terms of geometric and dosimetric consistency between synthetic and scanned DIBH-CTs. The accuracy of the patient selection strategy was evaluated. Time assumption of the patient selection workflow was analyzed.
Synthetic DIBH-CTs had average Dice similarity coefficients of 0.84 for the heart and 0.91 for the lungs compared with scanned DIBH-CTs. Synthetic DIBH-CT plans revealed an average mean heart dose reduction of 1.46 Gy, which was not significantly different from 1.51 Gy in scanned DIBH-CT plans (p = 0.878). The patient selection strategy yielded the correct benefit results with accuracy of 86.7 %. The average time assumption for patient selection was 11.9 ± 3.6 min.
The proposed patient selection strategy can accurately identify patients benefiting from DIBH and provides a more efficient workflow for DIBH.
目前尚无广泛应用的前期筛选方法来确定左侧乳腺癌放疗患者是否适合深吸气屏气（DIBH）技术。
建立一种前期患者筛选策略，以提高DIBH技术的决策效率，并避免患者接受额外的计算机断层扫描（CT）。
共纳入174例同时进行了自由呼吸（FB）和DIBH扫描的患者。训练了一个用于DIBH-CT合成的通用主成分分析模型，该模型由从训练集中的配对FB-CT和DIBH-CT中提取的主成分特征向量组成。对向量的系数进行优化，以最小化测试集中每个患者的合成CT与屏气定位像之间的差异，从而实现个性化的DIBH-CT合成。基于合成的DIBH-CT计划中的心脏剂量建立了前期患者筛选策略。从合成的和扫描的DIBH-CT之间的几何和剂量学一致性方面分析了DIBH-CT合成的性能。评估了患者筛选策略的准确性。分析了患者筛选工作流程的时间假设。
与扫描的DIBH-CT相比，合成的DIBH-CT心脏的平均骰子相似系数为0.84，肺的平均骰子相似系数为0.91。合成的DIBH-CT计划显示心脏平均剂量降低了1.46 Gy，与扫描的DIBH-CT计划中的1.51 Gy无显著差异（p = 0.878）。患者筛选策略得出正确受益结果的准确率为86.7%。患者筛选的平均时间假设为11.9±3.6分钟。
所提出的患者筛选策略可以准确识别从DIBH技术中受益的患者，并为DIBH提供更高效的工作流程。
