Abstract：
OBJECTIVE: Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning.
METHODS: Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman\'s correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model.
RESULTS: CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients\' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05).
CONCLUSIONS: Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.
METHODS: Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman\'s correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model.
RESULTS: CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients\' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05).
CONCLUSIONS: Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.
摘要：
目的：结合肺功能影像的放疗计划有可能降低肺毒性。自由呼吸4DCT导出的通气图像（CTVI）可能有助于量化肺功能。这项研究引入了一种新颖的深度学习模型，直接将计划CT图像转换为CTVI。我们调查了生成图像的准确性以及对功能回避计划的影响。
方法：来自48例NSCLC患者的配对计划CT和4DCT扫描被随机分配到训练（n=41）和测试（n=7）数据集。使用基于Jacobian的算法从4DCT生成通风图，以提供地面实况标签（CTVI4DCT）。训练基于3DU-Net的模型以将CT映射到合成CTVI(CTVIShn)并使用五次交叉验证进行验证。将性能最高的模型应用于测试集。Spearman相关性（rs）和Dice相似性系数（DSC）确定了CTVI4DCT和CTVIShn之间的体素和功能一致性。测试集中为每位患者设计了三个计划：一个没有CTVI的临床计划和两个结合CTVI4DCT或CTVISynn的功能回避计划。旨在保留被定义为百分位数通气范围前50%的高功能肺。记录有关计划目标体积（PTV）和风险器官（OAR）的剂量体积（DVH）参数。使用基于剂量功能（DFH）的正常组织并发症概率（NTCP）模型估计放射性肺炎（RP）风险。
结果：与CTVI4DCT相比，CTVISynn显示平均rs值为0.65±0.04。前50%和60%通气范围内的平均DSC值分别为0.41±0.07和0.52±0.10。在测试集（n=7）中，所有患者的RP风险受益于CTVI4DCT指导计划(Riskmean_4DCT_vs_Clinical：29.24%vs.49.12%,P=0.016),六名患者受益于CTVIShn指导计划(Riskmean_Syn_vs_Clinical：31.13%vs.49.12%,P=0.022)。CTVIShn和CTVI4DCT指导计划的DVH和DFH指标差异无统计学意义(P>0.05)。
结论：使用深度学习技术，从计划CT生成的CTVIShn与CTVI4DCT表现出中等到高度的相关性。CTVIShn指导的计划与CTVI4DCT指导的计划相当，有效降低患者的肺毒性，同时保持可接受的计划质量。需要进一步的前瞻性试验来验证这些发现。
方法：来自48例NSCLC患者的配对计划CT和4DCT扫描被随机分配到训练（n=41）和测试（n=7）数据集。使用基于Jacobian的算法从4DCT生成通风图，以提供地面实况标签（CTVI4DCT）。训练基于3DU-Net的模型以将CT映射到合成CTVI(CTVIShn)并使用五次交叉验证进行验证。将性能最高的模型应用于测试集。Spearman相关性（rs）和Dice相似性系数（DSC）确定了CTVI4DCT和CTVIShn之间的体素和功能一致性。测试集中为每位患者设计了三个计划：一个没有CTVI的临床计划和两个结合CTVI4DCT或CTVISynn的功能回避计划。旨在保留被定义为百分位数通气范围前50%的高功能肺。记录有关计划目标体积（PTV）和风险器官（OAR）的剂量体积（DVH）参数。使用基于剂量功能（DFH）的正常组织并发症概率（NTCP）模型估计放射性肺炎（RP）风险。
结果：与CTVI4DCT相比，CTVISynn显示平均rs值为0.65±0.04。前50%和60%通气范围内的平均DSC值分别为0.41±0.07和0.52±0.10。在测试集（n=7）中，所有患者的RP风险受益于CTVI4DCT指导计划(Riskmean_4DCT_vs_Clinical：29.24%vs.49.12%,P=0.016),六名患者受益于CTVIShn指导计划(Riskmean_Syn_vs_Clinical：31.13%vs.49.12%,P=0.022)。CTVIShn和CTVI4DCT指导计划的DVH和DFH指标差异无统计学意义(P>0.05)。
结论：使用深度学习技术，从计划CT生成的CTVIShn与CTVI4DCT表现出中等到高度的相关性。CTVIShn指导的计划与CTVI4DCT指导的计划相当，有效降低患者的肺毒性，同时保持可接受的计划质量。需要进一步的前瞻性试验来验证这些发现。
