Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study

Abstract Background While computed tomography (CT) remains an important imaging modality in radiotherapy planning, magnetic resonance imaging (MRI)‐only workflow, often realized via the generation of synthetic computed tomography (sCT), is desired due to superior soft‐tissue contrast and less susceptibility to registration errors. Purpose The aim of this study was to investigate the feasibility of applying sCT, generated from MRI using a deep learning model, to pelvic radiotherapy planning. By comparing differences between conventional CT and sCT in terms of image quality, dosimetric parameters, and three‐dimensional (3D) dose distributions, the clinical potential of an MRI‐only radiotherapy workflow was evaluated. Methods Fifteen patients who underwent both pelvic CT and MRI examinations were retrospectively enrolled. For the same patients, sCT images were generated from the MRI using a pretrained pelvic deep learning model integrated in the syngo.via VB60A software platform. Treatment plans originally created on planning CT (pCT) images were re‐calculated on corresponding sCT datasets. Subsequently, the mean absolute error (MAE) of CT numbers, Dice similarity coefficients (DSCs) for major structures, dose‐volume histogram (DVH) parameters for the planning target volume (PTV) and major organs at risk (OARs), and 3D dose distributions were compared between plans calculated on pCT and sCT. Results In terms of image quality, sCT demonstrated comparable CT number accuracy with pCT for soft tissue structures, with MAEs ranging from 6.8 to 8.5 Hounsfield units (HU) for the PTV, bladder, and rectum. Higher MAEs were observed for bony structures (femoral heads and bone marrow), with average MAEs ranging between 17 and 19 HU. The DSC was 0.94 ± 0.01 for the right femoral head, 0.93 ± 0.01 for the left femoral head, and 0.85 ± 0.03 for bone marrow. Regarding dosimetric comparison, the mean absolute differences in key dosimetric parameters for the PTV (D98%, D95%, and Dmean) were all below 0.6%, remaining within clinically acceptable limits. Among the OARs, except for relatively large differences for the maximum and minimum doses in the bladder, the mean dose (Dmean) differences for the rectum, femoral heads, and bone marrow were minimal (mean absolute value ≤ 0.34%), indicating high reliability. Analysis of 3D dose distributions using a 2% 2 mm γ criterion revealed an average γ passing rate of 95.92% between sCT and pCT, satisfying routine clinical quality assurance requirements. Conclusions The findings of our study demonstrate the feasibility of applying sCT generated from MRI using a deep learning model to pelvic radiotherapy planning. The MRI‐only workflow proposed on this basis eliminates systematic errors introduced by CT‐MRI image registration, optimizes the clinical workflow, and fully exploits the superior soft‐tissue contrast of MRI to improve target delineation accuracy. The proposed approach provides a novel technical paradigm for achieving more precise and efficient individualized radiotherapy.