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BackgroundOne of the major challenges in orthopedics is to develop implants that overcome current postoperative problems such as osteointegration, proper load bearing, and stress shielding. Current implant techniques such as allografts or endoprostheses never reach full bone integration, and the risk of fracture due to stress shielding is a major concern. To overcome this, a novel technique of reverse engineering to create artificial scaffolds was designed and tested. The purpose of the study is to create a new generation of implants that are both biocompatible and biomimetic.Methods3D-printed scaffolds based on physiological trabecular bone patterning were printed. MC3T3 cells were cultured on these scaffolds in osteogenic media, with and without the addition of Calcitonin Receptor Fragment Peptide (CRFP) in order to assess bone formation on the surfaces of the scaffolds. Integrity of these cell-seeded bone-coated scaffolds was tested for their mechanical strength.ResultsThe results show that cellular proliferation and bone matrix formation are both supported by our 3D-printed scaffolds. The mechanical strength of the scaffolds was enhanced by trabecular patterning in the order of 20% for compression strength and 60% for compressive modulus. Furthermore, cell-seeded trabecular scaffolds modulus increased fourfold when treated with CRFP.ConclusionUpon mineralization, the cell-seeded trabecular implants treated with osteo-inductive agents and pretreated with CRFP showed a significant increase in the compressive modulus. This work will lead to creating 3D structures that can be used in the replacement of not only bone segments, but entire bones.

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3D-printed guides, which have recently been introduced in orthopedic oncology, improve resection accuracy compared with traditional bone resection methods, but there are inaccuracies associated with them. These inaccuracies could lead to disastrous outcomes such as positive tumor resection margins. In this Sawbone study, we sought to quantitatively investigate the margin of error for various jig types and to determine a “safety margin” that could serve as a guide for surgeons and jig engineers in creating 3D-printed jigs that would reduce the risk of potential disastrous results such as positive margins. Various 3D-printed jigs were used to simulate wide resection of a distal femoral bone sarcoma on Sawbone specimens by 10 individuals with no specific prior expertise in cutting guides. We developed a mathematical model using kinematic theory. We defined a safety margin as the amount of change in the osteotomy lines that must be incorporated into the jig design to ensure that the surgeon is at least 98% likely not to have a positive tumor margin. Experiments were conducted to determine the mean deviation experienced in placing cutting guides on the bones. The mean deviation for the four types of cutting guides ranged from 2.86 mm to 6.54 mm. We determined that a jig design should have a safety margin of 4.8 mm for standard guides and 8.65 mm for gusset guides to minimize the possibility of cutting into the tumor as a result of human error in guide placement. Further studies involving cadavers and patients are warranted. [ Orthopedics . 2022;45(3):169–173.]

Intraoperative confirmation of negative resection margins is an essential component of soft tissue sarcoma surgery. Frozen section examination of samples from the resection bed after excision of sarcomas is the gold standard for intraoperative assessment of margin status. However, it takes time to complete histologic examination of these samples, and the technique does not provide real-time diagnosis in the operating room (OR), which delays completion of the operation. This paper presents a study and development of sensing technology using Raman spectroscopy that could be used for detection and classification of the tumor after resection with negative sarcoma margins in real time. We acquired Raman spectra from samples of sarcoma and surrounding benign muscle, fat, and dermis during surgery and developed (i) a quantitative method (QM) and (ii) a machine learning method (MLM) to assess the spectral patterns and determine if they could accurately identify these tissue types when compared to findings in adjacent H&E-stained frozen sections. High classification accuracy (>85%) was achieved with both methods, indicating that these four types of tissue can be identified using the analytical methodology. A hand-held Raman probe could be employed to further develop the methodology to obtain spectra in the OR to provide real-time in vivo capability for the assessment of sarcoma resection margin status.