ArticleFrontiers in oncology2021
MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Real-Time AI-Based Radiotherapy Planning for Nasopharyngeal Carcinoma: Development and Validation.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Supervised versus unsupervised GAN for pseudo-CT synthesis in brain MR-guided radiotherapy.Physical and engineering sciences in medicine · 2025Article
- Clinical implementation of deep learning-based synthetic CT for MRI-only volumetric modulated arc therapy in head and neck and pelvic cancer patients.Radiation oncology (London, England) · 2025Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.Biomedical engineering letters · 2024Article
- Narrow band imaging-based radiogenomics for predicting radiosensitivity in nasopharyngeal carcinoma.European journal of radiology open · 2024Article
- Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2024Review
- Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
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6 authors at 2 institutions in 1 country.
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Abstract
backgroundRadical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide electron density (ED) information for radiotherapy planning. Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy.
methodsTwenty patients with early-stage NPC who received radiotherapy in our hospital were investigated. First, 1433 sets of paired T1 weighted magnetic resonance (MR) simulation images and CT simulation images were rigidly registered and preprocessed. A 16-layer U-Net was used to train the pCT generative model and a "pix2pix" generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality. Second, the contours of all target volumes and organs at risk in the original CT were transferred to the pCT for planning, and the beams were copied back to the original CT for reference dose calculation. Finally, the dose distribution calculated on the pCT was compared with the reference dose distribution through gamma analysis and dose-volume indices.
resultsThe average time for pCT generation for each patient was 7.90 ± 0.47 seconds. The average mean (absolute) error was -9.3 ± 16.9 HU (102.6 ± 11.4 HU), and the mean-root-square error was 209.8 ± 22.6 HU. There was no significant difference between the pCT quality of pix2pix GAN and that of pure U-Net (p > 0.05). The dose distribution on the pCT was highly consistent with that on the original CT. The mean gamma pass rate (2 mm/3%, 10% low dose threshold) was 99.1% ± 0.3%, and the mean absolute difference of nasopharyngeal PGTV D
conclusionThe proposed deep learning model can accurately predict CT from MRI, and the generated pCT can be employed in precise dose calculations. It is of great significance to realize MRI-only planning in NPC radiotherapy, which can improve structure delineation and considerably reduce additional imaging dose, especially when an MR-guided linear accelerator is adopted for treatment.
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