Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors
Abstract Purpose Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics. Meth...
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BMC
2025-01-01
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| Series: | Radiation Oncology |
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| Online Access: | https://doi.org/10.1186/s13014-024-02568-6 |
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| author | Hongwei Zeng Xiangyu E Minghe Lv Su Zeng Yue Feng Wenhao Shen Wenhui Guan Yang Zhang Ruping Zhao Jingping Yu |
| author_facet | Hongwei Zeng Xiangyu E Minghe Lv Su Zeng Yue Feng Wenhao Shen Wenhui Guan Yang Zhang Ruping Zhao Jingping Yu |
| author_sort | Hongwei Zeng |
| collection | DOAJ |
| description | Abstract Purpose Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics. Methods This study employed cone-beam computed tomography from 115 lung cancer patients to develop a U-Net + + deep learning model for generating synthetic CT (sCT). The clinical feasibility of sCT was thoroughly evaluated in terms of image clarity, Hounsfield Unit (HU) consistency, and computational accuracy. For large lung tumors, accumulated doses to the gross tumor volume (GTV) and organs at risk (OARs) during 20 fractions of CRT were precisely monitored using matrices derived from the deformable registration of sCT and planning CT (pCT). Additionally, for patients with minimal tumor shrinkage during CRT, an sCT-based adaptive LRT boost plan was introduced, with its dosimetric properties, treatment safety in high dose regions, and delivery accuracy quantitatively assessed. Results The image quality and HU consistency of sCT improved significantly, with dose deviations ranging from 0.15% to 1.25%. These results indicated that sCT is feasible for inter-fraction dose monitoring and adaptive planning. After rigid and hybrid deformable registration of sCT and pCT, the mean distance-to-agreement was 0.80 ± 0.18 mm, and the mean Dice similarity coefficient was 0.97 ± 0.01. Monitoring dose accumulation over 20 CRT fractions showed an increase in high-dose regions of the GTV (P < 0.05) and a reduction in low-dose regions (P < 0.05). Dosimetric parameters of all OARs were significantly higher than those in the original treatment plan (P < 0.01). The sCT based adaptive LRT boost plan, when combined with CRT, significantly reduced the dose to OARs compared to CRT alone (P < 0.05). In LRT plan, high-dose regions for the GTV and D95% exhibited displacements greater than 5 mm from the tumor boundary in 19 randomly scanned sCT sequences under free breathing conditions. Validation of dose delivery using TLD phantom measurements showed that more than half of the dose points in the sCT based LRT plan had deviations below 2%, with a maximum deviation of 5.89%. Conclusions The sCT generated by the U-Net + + model enhanced the accuracy of monitoring the actual accumulated dose, thereby facilitating the evaluation of therapeutic efficacy and toxicity. Additionally, the sCT-based LRT boost plan, combined with CRT, further minimized the dose delivered to OARs while ensuring safe and precise treatment delivery. |
| format | Article |
| id | doaj-art-4a646875c4f34ed8a829b58d71fd1640 |
| institution | Kabale University |
| issn | 1748-717X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | Radiation Oncology |
| spelling | doaj-art-4a646875c4f34ed8a829b58d71fd16402025-01-26T12:45:58ZengBMCRadiation Oncology1748-717X2025-01-0120111310.1186/s13014-024-02568-6Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumorsHongwei Zeng0Xiangyu E1Minghe Lv2Su Zeng3Yue Feng4Wenhao Shen5Wenhui Guan6Yang Zhang7Ruping Zhao8Jingping Yu9Department of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese MedicineDepartment of Radiotherapy, Changzhou Cancer HospitalAbstract Purpose Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and monitors its dosimetric characteristics. Methods This study employed cone-beam computed tomography from 115 lung cancer patients to develop a U-Net + + deep learning model for generating synthetic CT (sCT). The clinical feasibility of sCT was thoroughly evaluated in terms of image clarity, Hounsfield Unit (HU) consistency, and computational accuracy. For large lung tumors, accumulated doses to the gross tumor volume (GTV) and organs at risk (OARs) during 20 fractions of CRT were precisely monitored using matrices derived from the deformable registration of sCT and planning CT (pCT). Additionally, for patients with minimal tumor shrinkage during CRT, an sCT-based adaptive LRT boost plan was introduced, with its dosimetric properties, treatment safety in high dose regions, and delivery accuracy quantitatively assessed. Results The image quality and HU consistency of sCT improved significantly, with dose deviations ranging from 0.15% to 1.25%. These results indicated that sCT is feasible for inter-fraction dose monitoring and adaptive planning. After rigid and hybrid deformable registration of sCT and pCT, the mean distance-to-agreement was 0.80 ± 0.18 mm, and the mean Dice similarity coefficient was 0.97 ± 0.01. Monitoring dose accumulation over 20 CRT fractions showed an increase in high-dose regions of the GTV (P < 0.05) and a reduction in low-dose regions (P < 0.05). Dosimetric parameters of all OARs were significantly higher than those in the original treatment plan (P < 0.01). The sCT based adaptive LRT boost plan, when combined with CRT, significantly reduced the dose to OARs compared to CRT alone (P < 0.05). In LRT plan, high-dose regions for the GTV and D95% exhibited displacements greater than 5 mm from the tumor boundary in 19 randomly scanned sCT sequences under free breathing conditions. Validation of dose delivery using TLD phantom measurements showed that more than half of the dose points in the sCT based LRT plan had deviations below 2%, with a maximum deviation of 5.89%. Conclusions The sCT generated by the U-Net + + model enhanced the accuracy of monitoring the actual accumulated dose, thereby facilitating the evaluation of therapeutic efficacy and toxicity. Additionally, the sCT-based LRT boost plan, combined with CRT, further minimized the dose delivered to OARs while ensuring safe and precise treatment delivery.https://doi.org/10.1186/s13014-024-02568-6Deep learningSynthetic CTDose accumulationLattice radiotherapyLarge lung tumor |
| spellingShingle | Hongwei Zeng Xiangyu E Minghe Lv Su Zeng Yue Feng Wenhao Shen Wenhui Guan Yang Zhang Ruping Zhao Jingping Yu Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors Radiation Oncology Deep learning Synthetic CT Dose accumulation Lattice radiotherapy Large lung tumor |
| title | Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors |
| title_full | Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors |
| title_fullStr | Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors |
| title_full_unstemmed | Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors |
| title_short | Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors |
| title_sort | deep learning based synthetic ct for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors |
| topic | Deep learning Synthetic CT Dose accumulation Lattice radiotherapy Large lung tumor |
| url | https://doi.org/10.1186/s13014-024-02568-6 |
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