Objective: Chondroblastoma (CB) is a rare and locally growing cartilage-derived tumor. Currently, clinical implications of tumor-associated macrophages (TAMs) in CB remain unclear. In this study, we sought to analyze the relationship between TAM parameters (including densities of CD68+ and CD163+ cells as well as the CD163+/CD68+ ratio) and clinicopathological characteristics and survival of patients. Methods: Immunohistochemistry was used to assess TAM subtypes for CD68 and CD163, as well as the expression levels of p53, CD34, and Ki-67 on tumor cells in 132 tissue specimens retrieved between July 2002 and April 2020. Then, TAM parameters were retrospectively analyzed for their associations with patient outcomes (local recurrence-free survival [LRFS] and overall survival [OS]) and clinicopathological features. Results: TAM densities were significantly higher in axial chondroblastoma tissue than in extra-axial chondroblastoma tissue. Moreover, the number of CD163+ TAMs was positively correlated with tumor invasion of surrounding tissues and high expression of CD34 and Ki-67 on tumor cells, whereas CD163+ cell density and the CD163/CD68 ratio were negatively associated with patient response to adjuvant radiotherapy. Univariate Kaplan-Meier analysis revealed that the number of CD68+ and CD163+ lymphocytes was significantly associated with both LRFS and OS. Multivariate Cox regression analysis showed that CD163+ and CD68 + cell levels were independent prognostic factors of LRFS, while TAM data independently predicted OS. More importantly, in subgroup analysis based on three significant factors in univariate survival analysis (including tumor location, adjuvant radiotherapy, and surrounding tissue invasion by tumors), the TAM parameters still displayed good prognostic performance. Conclusion: These data suggest that TAM may significantly affect the biological behavior of CB. We hypothesize that modulating the TAM level or polarization status in the microenvironment may be an effective approach for CB treatment.

Background: ChatGPT, powered by the GPT model and Transformer architecture, has demonstrated remarkable performance in the domains of medicine and healthcare, providing customized and informative responses. In our study, we investigated the potential of ChatGPT in the field of neurosurgery, focusing on its applications at the patient, neurosurgery student/resident, and neurosurgeon levels. Method: The authors conducted inquiries with ChatGPT from the viewpoints of patients, neurosurgery students/residents, and neurosurgeons, covering a range of topics, such as disease diagnosis, treatment options, prognosis, rehabilitation, and patient care. The authors also explored concepts related to neurosurgery, including fundamental principles and clinical aspects, as well as tools and techniques to enhance the skills of neurosurgery students/residents. Additionally, the authors examined disease-specific medical interventions and the decision-making processes involved in clinical practice. Results: The authors received individual responses from ChatGPT, but they tended to be shallow and repetitive, lacking depth and personalization. Furthermore, ChatGPT may struggle to discern a patient's emotional state, hindering the establishment of rapport and the delivery of appropriate care. The language used in the medical field is influenced by technical and cultural factors, and biases in the training data can result in skewed or inaccurate responses. Additionally, ChatGPT's limitations include the inability to conduct physical examinations or interpret diagnostic images, potentially overlooking complex details and individual nuances in each patient's case. Moreover, its absence in the surgical setting limits its practical utility. Conclusion: Although ChatGPT is a powerful language model, it cannot substitute for the expertise and experience of trained medical professionals. It lacks the capability to perform physical examinations, make diagnoses, administer treatments, establish trust, provide emotional support, and assist in the recovery process. Moreover, the implementation of Artificial Intelligence in healthcare necessitates careful consideration of legal and ethical concerns. While recognizing the potential of ChatGPT, additional training with comprehensive data is necessary to fully maximize its capabilities.

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ObjectivesThe contributing factors for spondylitis after percutaneous vertebroplasty (PVP) or percutaneous kyphoplasty (PKP) remain unclear. Here, we sought to investigate the factors affecting spondylitis occurrence after PVP/PKP. We also compared the clinical characteristics between patients with tuberculous spondylitis (TS) and nontuberculous spondylitis (NTS) following vertebral augmentation.MethodsLiterature searches (from January 1, 1982 to October 16, 2020) using MEDLINE, EMBASE, Google Scholar and Web of science databases were conducted to identify eligible studies according to predefined criteria. The local database was also retrospectively reviewed to include additional TS and NTS patients at our center.ResultsThirty studies from the literature and 11 patients from our local institute were identified, yielding a total of 23 TS patients and 50 NTS patients for analysis. Compared with NTS group, patients in the TS group were more likely to have a history of trauma before PVP/PKP treatment. Univariate analyses of risk factors revealed pulmonary tuberculosis and diabetes were significant factors for TS after PVP/PKP. Analyzing NTS, we found obesity, a history of preoperative trauma, urinary tract infection, diabetes and multiple surgical segments (≥2) were significantly associated with its occurrence following PVP/PKP treatment. Multivariate logistic analyses showed a history of pulmonary tuberculosis and diabetes were independent risk factors for TS after PVP/PKP, while diabetes and the number of surgically treated segments independently influenced NTS development.ConclusionsA history of pulmonary tuberculosis and diabetes were independent risk factors for TS. For NTS, diabetes and the number of surgically treated segments significantly influenced the occurrence of postoperative spinal infection. These data may be helpful for guiding risk stratification and preoperative prevention for patients, thereby reducing the incidence of vertebral osteomyelitis after PVP/PKP.

BACKGROUND:Currently, little is known about the prognostic value of tumor growth rate (TGR) in spinal giant cell tumors of bone (GCTB).OBJECTIVE:To investigate the correlation of TGR with clinicopathological features, immune microenvironment, prognosis, and response to denosumab treatment of spinal GCTB.METHODS:A total of 128 patients with spinal GCTB treated at 5 centers from 2011 to 2021 were included. TGR was assessed by 2 independent neuroradiologists using at least 2 preoperative thin-section magnetic resonance imaging scans at a minimum interval of 2 months. Immunohistochemistry was used to assess tumor-infiltrating lymphocyte subtypes for CD3, CD4, CD8, CD20, PD-1, PD-L1, and Foxp3. Then, these parameters were analyzed for their associations with patient outcomes (progression-free survival and overall survival), clinicopathological features, and denosumab treatment responsiveness.RESULTS:High TGR predicted both poor progression-free survival and overall survival (both P < .001). In addition, TGR was associated with postoperative neurological dysfunction (P < .001), Enneking staging (P = .016), denosumab treatment responsiveness (P = .035), and the number of CD3+ (P < .001), PD-1+ (P = .009), PD-L1+ (P < .001), and FoxP3+ tumor-infiltrating lymphocyte (P = .02). Importantly, TGR outperformed the traditional Enneking, Campanacci, and American Joint Committee on Cancer staging systems in predicting the clinical outcomes of spinal GCTB.CONCLUSION:These data support the use of TGR as a reliable predictive tool for clinically relevant outcomes and response to denosumab therapy of spinal GCTB, which may be helpful in guiding prognostic risk stratification and therapeutic optimization of patients.