Osteosarcoma is the most common bone cancer. Despite advances, molecular mechanisms associated with osteosarcoma have not been fully understood. Hence, an effective treatment for osteosarcoma has yet to be developed. Even though signal transducer and activator of transcription3 (STAT3) has been implicated, its role in pathogenesis of osteosarcoma is not fully determined. In this study, we investigated the antitumor effect of napabucasin (NP) (BBI608), an inhibitor of STAT3 on osteosarcoma in vitro and in vivo and studied the underlying molecular mechanism. Cell viability, colony formation, apoptosis, tumor growth and metastasis assays were performed to examine the effect of NP on osteosarcoma in vitro and in vivo. Real-time RT-PCR, western analysis, immunofluorescence and reporter assays were used to monitor the expression and activity of proteins and underlying molecular pathways. Protein synthesis, co-immunoprecipitation and CAP binding assays were carried out to understand NP-mediated mechanism of actions in osteosarcoma cells. Our results show that NP treatment decreases cell viability and induces apoptosis in several osteosarcoma cell lines. NP treatment suppresses both expression and phosphorylation of STAT3 in addition to blocking STAT3-mediated transcription and downstream target proteins in osteosarcoma cells. Furthermore, NP inhibits protein synthesis through regulation of the eukaryotic initiation factor 4E (eIF4E) and eIF4E-binding protein 1 (4E-BP1). NP also inhibits the progression of osteosarcoma tumors and metastasis in vivo in an orthotopic tibial model of osteosarcoma. Taken together, our investigation reveals that NP acts through a novel mechanism and inhibits osteosarcoma growth and metastasis, and could be investigated clinically for treating osteosarcoma patients alone or in combination with other drugs.

The reconstruction of an intercalary bone defect after a tumor resection of a long bone remains a challenge to orthopedic surgeons. Though several methods have been adopted to enhance the union of long segmental allografts or retrieved segmental autografts to the host bones, still more progresses are required to achieve a better union rate. Several methods have been adopted to devitalize tumor bone for recycling usage, and the results varied. We describe our experiences of using devitalized tumor-bearing bones for the repairing of segmental defects after tumor resection. Twenty-seven eligible patients treated from February 2004 to May 2012 were included. The segmental tumor bone (mean length, 14 cm) was resected, and then devitalized in 20 % sterile saline at 65 °C for 30 min after the tumor tissue was removed. The devitalized bone was implanted back into the defect by using nails or plates. Complete healing of 50 osteotomy ends was achieved at a median time of 11 months (interquartile range (IQR) 9–13 months). Major complications included bone nonunion in four bone junctions (7.4 %), devitalized bone fracture in one patient (3.7 %), deep infection in three patients (11.1 %), and fixation failure in two patients (7.4 %). The bone union rates at 1 and 2 years were 74.1 and 92.6 %, respectively. The average functional score according to the Musculoskeletal Tumor Society (MSTS) 93 scoring system was 93 % (IQR 80–96.7 %). Incubation in 20 % sterile saline at 65 °C for 30 min is an effective method of devitalization of tumor-bearing bone. The retrieved bone graft may provide as a less expensive alternative for limb salvage. The structural bone and the preserved osteoinductivity of protein may improve bone union.

Extended reality (XR) technology refers to any situation where real-world objects are enhanced with computer technology, including virtual reality, augmented reality, and mixed reality. Augmented reality and mixed reality technologies have been widely applied in orthopedic clinical practice, including in teaching, preoperative planning, intraoperative navigation, and surgical outcome evaluation. The primary goal of this narrative review is to summarize the effectiveness and superiority of XR-technology-assisted intraoperative navigation in the fields of trauma, joint, spine, and bone tumor surgery, as well as to discuss the current shortcomings in intraoperative navigation applications. We reviewed titles of more than 200 studies obtained from PubMed with the following search terms: extended reality, mixed reality, augmented reality, virtual reality, intraoperative navigation, and orthopedic surgery; of those 200 studies, 69 related papers were selected for abstract review. Finally, the full text of 55 studies was analyzed and reviewed. They were classified into four groups—trauma, joint, spine, and bone tumor surgery—according to their content. Most of studies that we reviewed showed that XR-technology-assisted intraoperative navigation can effectively improve the accuracy of implant placement, such as that of screws and prostheses, reduce postoperative complications caused by inaccurate implantation, facilitate the achievement of tumor-free surgical margins, shorten the surgical duration, reduce radiation exposure for patients and surgeons, minimize further damage caused by the need for visual exposure during surgery, and provide richer and more efficient intraoperative communication, thereby facilitating academic exchange, medical assistance, and the implementation of remote healthcare.

Precise delineation of surgical resection margins is a pivotal determinant of oncologic surgery outcomes, with R0 resection serving as the gold standard for maximizing local control and long-term survival. However, intraoperative identification of tumor boundaries remains challenging. Conventional tools such as frozen section pathology offer limited field coverage, are labor-intensive, time-consuming, and may yield sampling errors, particularly in anatomically complex or highly infiltrative lesions. Near-infrared II (NIR-II, 1000–1700 nm) fluorescence imaging has emerged as a transformative modality for real-time intraoperative navigation. Leveraging reduced photon scattering, minimal tissue autofluorescence, and deeper penetration compared with NIR-I and other optical approaches, NIR-II imaging enables high-contrast visualization of tumor margins while preserving adjacent normal structures. Over the past decade, the molecular probe landscape for NIR-II imaging has rapidly evolved—from early non-specific small molecules and inorganic nanomaterials to the latest generation of targeted agents designed to bind tumor-specific biomarkers. These advances have facilitated more accurate tumor–normal tissue contrast in a range of surgical contexts, including glioma, hepatobiliary malignancies, colorectal cancer, and head–neck tumors. Several first-in-human and ongoing clinical trials have demonstrated the feasibility, safety, and intraoperative utility of NIR-II molecular probes. Nevertheless, issues related to regulatory approval, probe pharmacokinetics, real-time quantitative imaging, and integration with multimodal surgical systems remain to be addressed. This review summarizes current concepts of precise surgical resection margins, outlines the origin and principles of NIR-II fluorescence imaging, and provides an in-depth discussion of targeted molecular probe development, preclinical and clinical progress, and their role in tumor margin delineation. Finally, we highlight existing limitations and future opportunities for translating NIR-II probes into routine surgical oncology practice.

Multimodal images contain complementary information that is valuable for deep learning (DL)-based image segmentation. To enable effective multimodal feature learning and fusion for accurate segmentation, multimodal images usually need to be registered to achieve anatomical alignment.However, in clinical settings, multimodal image registration is often challenging. For instance, to reduce radiation exposure, CT scans usually have a smaller field of view (FoV) than MR, i.e., inconsistent anatomical content in CT and MR images, hindering accurate registration. Using such misaligned multimodal images, segmentation performance could be significantly degraded. This study aims to develop a DL-based multimodal image segmentation method that is capable of learning high-quality and strongly related image features from misaligned multimodal images without registration and produce accurate segmentation results comparable to that obtained with well-aligned multimodal images. In our method, a unified body space (UBS) module is presented, where image patches cropped from misaligned modalities are encoded to positions and projected into a unified body space, thereby largely mitigating the misalignment among multimodal images. Built upon the UBS module, a new spatial-attention is proposed and integrated into a multilevel feature fusion (MFF) module, where features learned from misaligned multimodal images are effectively fused at internal-, spatial-, and modal-levels, leading the segmentation of misaligned multimodal images to a high accuracy level. We validate our method on both public and inhouse multimodal image datasets containing 1472 patients. Experimental results demonstrate that our method outperforms state-of-the-art (SOTA) methods. The ablation study further confirms that the UBS modules can accurately project image patches from different modalities into the unified body space. Moreover, the internal-, spatial-, and modal-level feature fusion in the MFF module substantially enhances segmentation accuracy for misaligned multimodal images. Codes are available at https://github.com/BH-MICom/Patch2Space.