A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning

Abstract

Purpose: 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.

Materials and 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 (CTVI_4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVI_Syn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (r_s) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI_4DCT and CTVI_Syn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI_4DCT or CTVI_Syn, 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: CTVI_Syn showed a mean rs value of 0.65 ± 0.04 compared to CTVI_4DCT. 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 CTVI_4DCT-guided plans (Risk_{mean_4DCT_vs_Clinical}: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVI_Syn-guided plans (Risk_{mean_Syn_vs_Clinical}: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVI_Syn and CTVI_4DCT-guided plan (P > 0.05).

Conclusion: Using deep-learning techniques, CTVI_Syn generated from planning CT exhibited a moderate-to-high correlation with CTVI_4DCT. The CTVI_Syn-guided plans were comparable to the CTVI_4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.

Keywords: 4DCT; Deep learning; Lung cancer; Radiotherapy; Synthetic imaging; Ventilation imaging.