Abstract
Surgeries of pelvic bone tumors are very challenging due to the complexity of anatomical structures and the irregular bone shape. CT and MRI are used in clinic for tumor evaluation, each with its own advantages and shortcomings. Combining the data of both CT and MRI images would take advantage of the merits of both images and provide better model for preoperative evaluation. We utilized an artificial intelligence (AI)-assisted CT/MRI image fusion technique and built a personalized 3-D model for preoperative tumor margin assessment. A young female patient with pelvic osteosarcoma was evaluated with our novel image fusion 3-D model in comparison with the 3-D model based solely on CT images. The fusion image model showed more detailed anatomical information and discovered multiple emboli within veins which were previously neglected. The discovery of emboli implied abysmal prognosis and discouraged any attempts for complex reconstruction after tumor resection. Based on the experience with this pelvic osteosarcoma, we believe that our image fusion model can be very informative with bone tumors. Though further validation with a large number of clinical cases is required, we propose that our model has the potential to benefit the clinic in the preoperative evaluation of bone tumors.
Highlights
Based on the shapes, bones can be generally divided into long bones, short bones, flat bones, sesamoid bones, and irregular bones
artificial intelligence (AI)-assisted CT/MRI image fusion technique was utilized, and a personalized 3-D pelvic model (Figures 2A–C) showing tumor was constructed together with adjacent structures such as blood vessels, nerves, and L5 vertebra. This fusion technique used was based on symmetric diffeomorphic image registration with cross-correlation [9,10,11]
Osteosarcoma is a highly deadly disease mostly affecting skeletal immature adolescents [12]. It preferentially occurs in long bones around the knee joint but can present in other irregular bones such as pelvic bone in our case here
Summary
Bones can be generally divided into long bones, short bones, flat bones, sesamoid bones, and irregular bones. MRI, on the other hand, can present sharp margins of soft tissue masses but could not distinguish changes within bones or calcification lesions properly Despite their limitations, they do provide valuable information in tumor planning. AI-assisted CT/MRI image fusion technique was utilized, and a personalized 3-D pelvic model (Figures 2A–C) showing tumor was constructed together with adjacent structures such as blood vessels, nerves, and L5 vertebra. This fusion technique used was based on symmetric diffeomorphic image registration with cross-correlation [9,10,11]. The patient died after 1 year of surgery due to lung metastasis but with no sign of local recurrence
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