Ibragimov Bulat, Toesca Diego A S, Chang Daniel T, Yuan Yixuan, Koong Albert C, Xing Lei, Vogelius Ivan R
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Med Phys. 2020 Aug;47(8):3721-3731. doi: 10.1002/mp.14235. Epub 2020 Jun 3.
Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced toxicities of healthy organs accompany many RTs and represent one of the main limiting factors during dose delivery. The existing RT planning solutions generally discard spatial dose distribution information and lose the ability to recognize radiosensitive regions of healthy organs potentially linked to toxicity manifestation. This study proposes a universal deep learning-based algorithm for recognitions of consistent dose patterns and generation of toxicity risk maps for the abdominal area.
We investigated whether convolutional neural networks (CNNs) can automatically associate abdominal computed tomography (CT) images and RT dose plans with post-RT toxicities without being provided segmentation of abdominal organs. The CNNs were also applied to study RT plans, where doses at specific anatomical regions were reduced/increased, with the aim to pinpoint critical regions sparing of which significantly reduces toxicity risks. The obtained risk maps were computed for individual anatomical regions inside the liver and statistically compared to the existing clinical studies.
A database of 122 liver stereotactic body RT (SBRT) executed at Stanford Hospital from July 2004 and November 2015 was assembled. All patients treated for primary liver cancer, mainly hepatocellular carcinoma and cholangiocarcinoma, with complete follow-ups were extracted from the database. The SBRT treatment doses ranged from 26 to 50 Gy delivered in 1-5 fractions for primary liver cancer. The patients were followed up for 1-68 months depending on the survival time. The CNNs were trained to recognize acute and late grade 3+ biliary stricture/obstruction, hepatic failure or decompensation, hepatobiliary infection, liver function test (LFT) elevation or/and portal vein thrombosis, named for convenience hepatobiliary (HB) toxicities. The toxicity prediction accuracy was of 0.73 measured in terms of the area under the receiving operator characteristic curve. Significantly higher risk scores (P < 0.05) of HB toxicity manifestation were associated with irradiation for the hepatobiliary tract in comparison to the risk scores for liver segments I-VIII and portal vein. This observation is in strong agreement with anatomical and clinical expectations.
In this work, we proposed and validated a universal deep learning-based solution for the identification of radiosensitive anatomical regions. Without any prior anatomical knowledge, CNNs automatically recognized the importance of hepatobiliary tract sparing during liver SBRT.
约50%的实体瘤患者接受放射治疗（RT）用于根治性和姑息性治疗。许多放疗过程中会伴随健康器官的放射性毒性，这是剂量输送过程中的主要限制因素之一。现有的放疗计划解决方案通常会丢弃空间剂量分布信息，失去识别可能与毒性表现相关的健康器官放射敏感区域的能力。本研究提出一种基于深度学习的通用算法，用于识别腹部区域的一致剂量模式并生成毒性风险图。
我们研究了卷积神经网络（CNN）能否在不提供腹部器官分割的情况下，自动将腹部计算机断层扫描（CT）图像和放疗剂量计划与放疗后毒性相关联。CNN还被应用于研究放疗计划，其中特定解剖区域的剂量减少/增加，目的是找出显著降低毒性风险的关键区域。针对肝脏内各个解剖区域计算得到风险图，并与现有的临床研究进行统计学比较。
收集了2004年7月至2015年11月在斯坦福医院执行的122例肝脏立体定向体部放疗（SBRT）数据库。从该数据库中提取了所有接受原发性肝癌（主要是肝细胞癌和胆管癌）治疗且有完整随访的患者。原发性肝癌的SBRT治疗剂量为26至50 Gy，分1 - 5次给予。根据生存时间对患者进行了1至68个月的随访。训练CNN识别急性和晚期3级及以上胆管狭窄/梗阻、肝衰竭或失代偿、肝胆感染、肝功能检查（LFT）升高或/和门静脉血栓形成，为方便起见称为肝胆（HB）毒性。以接受者操作特征曲线下面积衡量，毒性预测准确率为0.73。与肝段I - VIII和门静脉的风险评分相比，与胆道照射相关的HB毒性表现的风险评分显著更高（P < 0.05）。这一观察结果与解剖学和临床预期高度一致。
在这项工作中，我们提出并验证了一种基于深度学习的通用解决方案，用于识别放射敏感解剖区域。在没有任何先验解剖学知识的情况下，CNN自动识别了肝脏SBRT期间保护胆道的重要性。
