Zeng Hongwei, E Xiangyu, Lv Minghe, Zeng Su, Feng Yue, Shen Wenhao, Guan Wenhui, Zhang Yang, Zhao Ruping, Yu Jingping
Department of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Zhangheng Road, Pudong New Area, Shanghai, 201203, China.
Department of Radiotherapy, Changzhou Cancer Hospital, Honghe Road, Xinbei Area, Changzhou, 213032, China.
Radiat Oncol. 2025 Jan 22;20(1):12. doi: 10.1186/s13014-024-02568-6.
Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics.
This study employed cone-beam computed tomography from 115 lung cancer patients to develop a U-Net + + deep learning model for generating synthetic CT (sCT). The clinical feasibility of sCT was thoroughly evaluated in terms of image clarity, Hounsfield Unit (HU) consistency, and computational accuracy. For large lung tumors, accumulated doses to the gross tumor volume (GTV) and organs at risk (OARs) during 20 fractions of CRT were precisely monitored using matrices derived from the deformable registration of sCT and planning CT (pCT). Additionally, for patients with minimal tumor shrinkage during CRT, an sCT-based adaptive LRT boost plan was introduced, with its dosimetric properties, treatment safety in high dose regions, and delivery accuracy quantitatively assessed.
The image quality and HU consistency of sCT improved significantly, with dose deviations ranging from 0.15% to 1.25%. These results indicated that sCT is feasible for inter-fraction dose monitoring and adaptive planning. After rigid and hybrid deformable registration of sCT and pCT, the mean distance-to-agreement was 0.80 ± 0.18 mm, and the mean Dice similarity coefficient was 0.97 ± 0.01. Monitoring dose accumulation over 20 CRT fractions showed an increase in high-dose regions of the GTV (P < 0.05) and a reduction in low-dose regions (P < 0.05). Dosimetric parameters of all OARs were significantly higher than those in the original treatment plan (P < 0.01). The sCT based adaptive LRT boost plan, when combined with CRT, significantly reduced the dose to OARs compared to CRT alone (P < 0.05). In LRT plan, high-dose regions for the GTV and D exhibited displacements greater than 5 mm from the tumor boundary in 19 randomly scanned sCT sequences under free breathing conditions. Validation of dose delivery using TLD phantom measurements showed that more than half of the dose points in the sCT based LRT plan had deviations below 2%, with a maximum deviation of 5.89%.
The sCT generated by the U-Net + + model enhanced the accuracy of monitoring the actual accumulated dose, thereby facilitating the evaluation of therapeutic efficacy and toxicity. Additionally, the sCT-based LRT boost plan, combined with CRT, further minimized the dose delivered to OARs while ensuring safe and precise treatment delivery.
传统放疗（CRT）对大体积肺肿瘤的局部控制有限，且存在严重毒性的高风险。本研究旨在制定一种将CRT与点阵式增强放疗（LRT）相结合的综合治疗方案，并监测其剂量学特征。
本研究采用115例肺癌患者的锥形束计算机断层扫描，开发了一种用于生成合成CT（sCT）的U-Net++深度学习模型。从图像清晰度、亨氏单位（HU）一致性和计算准确性方面对sCT的临床可行性进行了全面评估。对于大体积肺肿瘤，使用从sCT与计划CT（pCT）的可变形配准得出的矩阵，精确监测CRT的20次分割期间大体肿瘤体积（GTV）和危及器官（OARs）的累积剂量。此外，对于CRT期间肿瘤缩小不明显的患者，引入了基于sCT的自适应LRT增强计划，并对其剂量学特性、高剂量区域的治疗安全性和照射准确性进行了定量评估。
sCT的图像质量和HU一致性显著提高，剂量偏差范围为0.15%至1.25%。这些结果表明sCT可用于分割间剂量监测和自适应计划。sCT与pCT进行刚性和混合可变形配准后，平均配准距离为0.80±0.18毫米，平均骰子相似系数为0.97±0.01。监测20次CRT分割期间的剂量累积显示，GTV的高剂量区域增加（P<0.05），低剂量区域减少（P<0.05）。所有OARs的剂量学参数均显著高于原始治疗计划（P<0.01）。与单独使用CRT相比，基于sCT的自适应LRT增强计划与CRT联合使用时，显著降低了OARs的剂量（P<0.05）。在LRT计划中，在自由呼吸条件下，19个随机扫描的sCT序列中，GTV和D的高剂量区域与肿瘤边界的位移大于5毫米。使用热释光剂量仪体模测量进行剂量照射验证表明，基于sCT的LRT计划中超过一半的剂量点偏差低于2%，最大偏差为5.89%。
U-Net++模型生成的sCT提高了监测实际累积剂量的准确性，从而有助于评估治疗效果和毒性。此外，基于sCT的LRT增强计划与CRT联合使用，在确保安全精确的治疗照射的同时，进一步将输送至OARs的剂量降至最低。
