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.
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。