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

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Japanese Journal of Radiology Aims and scope Submit manuscript

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 (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).

Conclusion

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.

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Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (No. 82202300) and a grant from Jiangsu Provincial Double-Innovation Doctor Program.

Author information

Author notes

  1. Zhen Hou, Youyong Kong and Junxian Wu have been contributed equally to this work.

Authors and Affiliations

  1. The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China

    Zhen Hou, Juan Liu, Shanbao Gao, Yicai Yin, Ling Zhang, Yongchao Han & Shuangshuang Li

  2. School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China

    Youyong Kong, Junxian Wu & Jiabing Gu

  3. Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China

    Jian Zhu

  4. Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China

    Jian Zhu

  5. Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China

    Youyong Kong & Jian Zhu

  6. Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France

    Youyong Kong & Jian Zhu

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Contributions

Original idea and study design (SSL, ZH, JZ, YYK, JBG); Data collection and analysis (SBG, YCY, LZ, YCH, JL, ZH, JXW); Manuscript writing (ZH, SSL, JZ); Important contributions to radiotherapy plan design (SSL, ZH). All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jian Zhu or Shuangshuang Li.

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Conflict of interest

The authors declare that they have no competing interests.

Ethical approval and consent to participate

The ethics committee approved this retrospective research at Nanjing Drum Tower Hospital, and informed consent was waived. The CT data were anonymized for the scientific purpose of this work. This retrospective study was performed in line with the principles of the Declaration of Helsinki.

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Not applicable.

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Hou, Z., Kong, Y., Wu, J. et al. A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning. Jpn J Radiol (2024). https://doi.org/10.1007/s11604-024-01550-2

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