ArticleFrontiers in oncology2020
Artificial Intelligence (AI) Assisted CT/MRI Image Fusion Technique in Preoperative Evaluation of a Pelvic Bone Osteosarcoma.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 19 citations in OpenAlex.
- Real-time virtual sonography for prone-position breast MRI: technical feasibility and accuracy in locating 125 breast lesions.European radiology experimental · 2026Article
- The pedicled sartorius flap and mesh (PSM) technique vs no reconstruction in repairing the defect after type III pelvic bone tumor resection: a retrospective study.World journal of surgical oncology · 2023Article
- Propofol Regulates ER Stress to Inhibit Tumour Growth and Sensitize Osteosarcoma to Doxorubicin.International journal of clinical practice · 2023Article
- Unveiling the prognostic implications of RPLP1 upregulation in osteosarcoma.American journal of cancer research · 2023Article
- Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection.Journal of healthcare engineering · 2022Article
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4 authors at 3 institutions in 1 country.
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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.
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