Department of Radiation Oncology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Department of Management Science and Statistics, University of Texas at San Antonio, San Antonio, Texas, USA.
Med Phys. 2021 Sep;48(9):5152-5164. doi: 10.1002/mp.14925. Epub 2021 Aug 11.
We propose a treatment planning framework that accounts for weekly lung tumor shrinkage using cone beam computed tomography (CBCT) images with a deep learning-based model.
Sixteen patients with non-small-cell lung cancer (NSCLC) were selected with one planning CT and six weekly CBCTs each. A deep learning-based model was applied to predict the weekly deformation of the primary tumor based on the spatial and temporal features extracted from previous weekly CBCTs. Starting from Week 3, the tumor contour at Week N was predicted by the model based on the input from all the previous weeks (1, 2 … N - 1), and was evaluated against the manually contoured tumor using Dice coefficient (DSC), precision, average surface distance (ASD), and Hausdorff distance (HD). Information about the predicted tumor was then entered into the treatment planning system and the plan was re-optimized every week. The objectives were to maximize the dose coverage in the target region while minimizing the toxicity to the surrounding healthy tissue. Dosimetric evaluation of the target and organs at risk (heart, lung, esophagus, and spinal cord) was performed on four cases, comparing between a conventional plan (ignoring tumor shrinkage) and the shrinkage-based plan.
he primary tumor volumes decreased on average by 38% ± 26% during six weeks of treatment. DSCs and ASD between the predicted tumor and the actual tumor for Weeks 3, 4, 5, 6 were 0.81, 0.82, 0.79, 0.78 and 1.49, 1.59, 1.92, 2.12 mm, respectively, which were significantly superior to the score of 0.70, 0.68, 0.66, 0.63 and 2.81, 3.22, 3.69, 3.63 mm between the rigidly transferred tumors ignoring shrinkage and the actual tumor. While target coverage metrics were maintained for the re-optimized plans, lung mean dose dropped by 2.85, 0.46, 2.39, and 1.48 Gy for four sample cases when compared to the original plan. Doses in other organs such as esophagus were also reduced for some cases.
We developed a deep learning-based model for tumor shrinkage prediction. This model used CBCTs and contours from previous weeks as input and produced reasonable tumor contours with a high prediction accuracy (DSC, precision, HD, and ASD). The proposed framework maintained target coverage while reducing dose in the lungs and esophagus.
我们提出了一种治疗计划框架，该框架使用基于深度学习的模型来考虑每周肺部肿瘤缩小的情况。
选择了 16 名患有非小细胞肺癌（NSCLC）的患者，每位患者均有一次计划 CT 和六次每周的 CBCT。基于从以前的每周 CBCT 中提取的空间和时间特征，应用基于深度学习的模型来预测原发性肿瘤的每周变形。从第 3 周开始，基于输入所有前几周（1、2……N-1），模型预测第 N 周的肿瘤轮廓，并使用 Dice 系数（DSC）、精度、平均表面距离（ASD）和 Hausdorff 距离（HD）评估与手动勾画的肿瘤的吻合程度。然后，有关预测肿瘤的信息将输入到治疗计划系统中，每周重新优化计划。目标是在最大限度地覆盖目标区域的剂量的同时，最小化对周围健康组织的毒性。对四个病例的靶区和危及器官（心脏、肺、食管和脊髓）进行了剂量评估，比较了传统计划（忽略肿瘤缩小）和基于缩小的计划。
在六周的治疗过程中，原发性肿瘤体积平均缩小了 38%±26%。第 3、4、5、6 周的预测肿瘤与实际肿瘤的 DSCs 和 ASD 分别为 0.81、0.82、0.79、0.78 和 1.49、1.59、1.92、2.12mm，显著优于刚性转移肿瘤的评分（忽略缩小）和实际肿瘤的 0.70、0.68、0.66、0.63 和 2.81、3.22、3.69、3.63mm。在重新优化的计划中保持了靶区覆盖度的度量标准的同时，与原始计划相比，四个样本病例的肺平均剂量分别降低了 2.85、0.46、2.39 和 1.48Gy。对于某些病例，食管等其他器官的剂量也有所降低。
我们开发了一种基于深度学习的肿瘤缩小预测模型。该模型使用 CBCT 和前几周的轮廓作为输入，并产生了具有高预测准确性（DSC、精度、HD 和 ASD）的合理肿瘤轮廓。所提出的框架在降低肺和食管剂量的同时保持了靶区的覆盖度。
