Preliminary application of improved 3D printed pathological section box to assisting stitching pathological images of bone tumor

Abstract

Objective·To explore the feasibility of applying improved pathological section boxes based on image post-processing and 3D printing technology to assisting artificial intelligence (AI) in stitching pathological images of bone tumors.Methods·Bone tumor patients who underwent tumor resection surgery between February 2022 and August 2022 were enrolled. Patients and their postoperative tumor specimens were examined by CT and MRI. The skeletal anatomical landmarks of bone tumors were used to register the preoperative CT, and MRI and the postoperative CT images. The personalized pathological slice box was improved and 3D printed to guide the cutting direction of bone tumor specimens. The large pathological slices were grid-like segmented and performed with hematoxylin-eosin staining. After AI stitching the pathological images, the pathological tumor boundaries were compared with the preoperative radiological boundaries.Results·Four patients with bone tumors (2 bone metastatic neoplasm, 1 osteosarcoma, and 1 chondrosarcoma; 3 males and 1 female) were collected to design the pathological section box with an average age of (40.25±25.70) years. The lesions included 3 cases of femur and 1 case of ilium. The mean maximum tumor diameter was (12.10±4.02) cm. The modified 3D printed pathological section box could fit the surface of bone tumor individually,and overcame the problems of difficult cutting or inaccurate cutting location caused by excessive movement of bone tumor specimens in the primary pathological section box. The pathological boundaries could be completely obtained and compared with the preoperative MRI boundaries for colocalization. The outline features of the section boxes could help AI to restore the permutation of pathological fragments, and the image stitching time decreased from the previous seventy hours to one hour while the boundary coincidence rates increased to 90%.Conclusion·The improved pathological section box of 3D printing can accurately assist AI in stitching pathological images, greatly improve the efficiency of pathological image stitching and achieve the colocalization between preoperative MRI and pathological images of bone tumor specimens.

Keywords

• 3d printing
• magnetic resonance imaging
• pathology image
• bone tumor
• registration fusion
• co-localization