Validating clinical feasibility of MRCAT and deep learning-based synthetic CT images for cervical cancer patient

Various methods have been developed to generate synthetic computed tomography (CT) images from magnetic resonance (MR) images, including segmentation-based approach with MR calculating attenuation (MRCAT) and deep learning (DL)-based approach.

In this study, we aimed to validate the conventional radiotherapy (RT) planning process with MRCAT and DL-based synthetic CT images for five patients with cervical cancer.

DL-based synthetic CT images of the five patients were inferred using a network trained with 40 pairs of CT and deformed, normalized T2-weighted MR scans; MRCAT images were obtained from mDixon sequences for the tested cases only. On the synthetic CT images, the contouring process for organs-at-risk (OARs) was automatically performed with minor adjustments, while two experienced radiation oncologists defined target volumes. Simultaneous integrated boost plans (2.2/2.0/1.8 Gy with 25 fractions) were produced from a commercial treatment planning system (TPS) TomoTherapy.

The plans with two synthetic CT images were compared with those based on genuine CT images for the five test cases. High geometric similarity was confirmed for the planning target volume (PTV), with average dice similarity coefficient (DSC) of 0.844 for the DL-based and 0.829 for the MRCAT images. The mean percentage difference in gross tumor volume (GTV) was 20.71 $\pm$ 34.28% for DL-based synthetic CT and 30.31 $\pm$ 46.20% for MRCAT images. By contrast, PTV, encompassing GTV, exhibited minimal changes with an average increase of 0.37 $\pm$ 3.10% and 1.66 $\pm$ 7.62%, respectively. MRCAT images and DL-based synthetic CT revealed significant differences, relative to true CT images, in the entire volume (p = 0.03) of the bladder and in V_20Gy and V_30Gy of the resultant plans for the bladder (p = 0.029 and 0.063), all plans generated on the synthetic CTs were clinically acceptable and met institutional for target coverage.

MRCAT and DL-based synthetic CT images demonstrated clinical applicability, achieving plan quality similar to that of plans based on genuine planning CT images.