CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study
Abstract Background Pseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective. Methods 34 cases (< 30 cm³) were retrospectively incl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-024-11844-3 |
| _version_ | 1797355696838148096 |
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| author | Xin Yang Bin Feng Han Yang Xiaoqi Wang Huanli Luo Liyuan Chen Fu Jin Ying Wang |
| author_facet | Xin Yang Bin Feng Han Yang Xiaoqi Wang Huanli Luo Liyuan Chen Fu Jin Ying Wang |
| author_sort | Xin Yang |
| collection | DOAJ |
| description | Abstract Background Pseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective. Methods 34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation. Results There was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5–99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation. Conclusions This is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis. |
| first_indexed | 2024-03-08T14:14:49Z |
| format | Article |
| id | doaj.art-1bd9486dfe6e40dda54d1e44011bfa7f |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-03-08T14:14:49Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-1bd9486dfe6e40dda54d1e44011bfa7f2024-01-14T12:26:28ZengBMCBMC Cancer1471-24072024-01-0124111110.1186/s12885-024-11844-3CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility studyXin Yang0Bin Feng1Han Yang2Xiaoqi Wang3Huanli Luo4Liyuan Chen5Fu Jin6Ying Wang7Departments of Radiation Oncology, Chongqing University Cancer HospitalDepartments of Radiation Oncology, Chongqing University Cancer HospitalDepartments of Radiation Oncology, Chongqing University Cancer HospitalApodibot MedicalDepartments of Radiation Oncology, Chongqing University Cancer HospitalDepartments of Radiation Oncology, Chongqing University Cancer HospitalDepartments of Radiation Oncology, Chongqing University Cancer HospitalDepartments of Radiation Oncology, Chongqing University Cancer HospitalAbstract Background Pseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective. Methods 34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation. Results There was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5–99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation. Conclusions This is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis.https://doi.org/10.1186/s12885-024-11844-3Multi-modal radiomics analysisPseudo-CTMRI-only radiotherapy |
| spellingShingle | Xin Yang Bin Feng Han Yang Xiaoqi Wang Huanli Luo Liyuan Chen Fu Jin Ying Wang CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study BMC Cancer Multi-modal radiomics analysis Pseudo-CT MRI-only radiotherapy |
| title | CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study |
| title_full | CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study |
| title_fullStr | CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study |
| title_full_unstemmed | CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study |
| title_short | CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study |
| title_sort | cnn based multi modal radiomics analysis of pseudo ct utilization in mri only brain stereotactic radiotherapy a feasibility study |
| topic | Multi-modal radiomics analysis Pseudo-CT MRI-only radiotherapy |
| url | https://doi.org/10.1186/s12885-024-11844-3 |
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