BACKGROUND: Reconstructing bone defects in the upper extremities and restoring their functions poses a significant challenge. In this study, we describe a novel workflow for designing and manufacturing customized bone cement molds using 3D printing technology to reconstruct upper extremity defects after bone tumor resection.
METHODS: Computer tomography data was acquired from the unaffected upper extremities to create a detachable mold, which can be customized to fit the joint precisely by shaping the bone cement accordingly. Fourteen patients who underwent reconstructive surgery following bone tumor resection in the proximal humerus (13 cases) or distal radius (1 case) between January 2014 and December 2022 were retrospectively evaluated. The medical records of this case series were reviewed for the demographic, radiological, and operative data. Metastasis, local recurrence, and complication were also reviewed. Additionally, Musculoskeletal Tumor Society Score (MSTS) and Visual Analogue Scale (VAS) were used to assess clinical outcomes.
RESULTS: The mean follow-up period was 49.36 ± 15.18 months (range, 27-82 months). At the end of follow-up, there were no cases of metastasis or recurrence, and patients did not experience complications such as infection, dislocation, or implant loosening. Two cases complicated with subluxation (14.3%), and 1 case underwent revision surgery for prosthetic fracture (7.1%). The average MSTS score was 23.2 ± 1.76 (77.4%, range, 66.7%-86.7%), and the postoperative VAS score was 1.86 ± 1.03 (range, 1-4), which was significantly lower than that before surgery (average preoperative VAS score was 5.21 ± 2.00 (range, 2-8)) (P < .001).
CONCLUSIONS: Customized 3D molds can be utilized to shape bone cement prostheses, which may serve as a potential alternative for reconstructing the proximal humerus and distal radius following en bloc resection of bone tumors. This reconstruction strategy offers apparent advantages, including precise matching of articular surfaces and comparatively reduced costs.

BACKGROUND: A solitary plasmacytoma is classified into a solitary plasmacytoma of the bone (SBP) and a solitary extramedullary (soft tissue mass) plasmacytoma, based on the site of the lesion. Despite the high local control rate with radiotherapy, approximately half of patients\' conditions progress to multiple myeloma (MM) within 3-5 years after diagnosis, with SBP having a worse prognosis.
METHODS: We retrospectively assessed the treatment and outcomes of patients with SBP in a hospital in China from 2008 to 2021. Twenty-four patients treated over 13 years with SBP were enrolled in this retrospective study.
RESULTS: The most common sites for SBP were the axial skeleton and femur. The M protein was detected in 11 patients (46 %), of which 8 (33 %) had light chains, 2 (8 %) had immunoglobulin G kappa and 1 (4 %) had immunoglobulin D kappa. Flow cytometry revealed that 5 patients (21 %) had minimal bone marrow involvement. The treatment included chemotherapy, surgery, and radiotherapy in 18 (75 %), 12 (50 %), and 9 (38 %) patients, respectively, of whom 13 (54 %) received combined treatment. Over a median follow-up period of 67.2 months, 9 patients (38 %) developed MM in a median time of 101.5 months. The 5- and 10-year progression-free survival rates were 67.3 % and 37.4 %, respectively. One patient died due to pneumonia without progression and the other died due to relapse.
CONCLUSIONS: This study confirmed the high rate of progression of SBP to MM, indicating a need for adjunct chemotherapy for the management of SBP.

UNASSIGNED: This study was designed to delve into the complexities involved in diagnosing of benign and malignant bone tumors and to assess the potential of AI technologies like ChatGPT in improving diagnostic accuracy and efficiency. The study also explores the few-shot learning as a method to optimize ChatGPT\'s performance in specialized medical domains such as benign and malignant bone tumors diagnosis.
UNASSIGNED: A total of 1366 benign and malignant bone tumors-related imaging reports were collected and diagnosed by 25 experienced physicians. The gold standard of diagnosis was established by combining clinical, imaging and pathological principles.These reports were then input into the ChatGPT model which underwent a few-shot learning method to generate diagnostic results. The diagnostic results of the physicians and the AI model were compared to evaluate the performance of ChatGPT. An experiment was conducted to assess the influence of different radiologist\'s reporting styles on the model\'s diagnostic performance. Furthermore, in-depth analysis of misdiagnosed cases was carried out, categorizing diagnostic errors and exploring possible causes.
UNASSIGNED: The diagnostic results generated by ChatGPT showed an accuracy of 0.73, sensitivity of 0.95, and specificity of 0.58. After few-shot learning, ChatGPT demonstrated significant improvement, achieving an accuracy of 0.87, sensitivity of 0.99, and specificity of 0.73, bringing it much closer to the level of physician diagnostics. In an experiment analyzing the influence of the radiologist\'s reporting style, the model demonstrated higher sensitivity when interpreting reports written by high-level radiologists. In 56 benign cases, ChatGPT misdiagnosed them as malignant. Among these, 35 benign lesions- fibrous dysplasia and osteofibrous dysplasia- were incorrectly identified as metastatic tumors or osteosarcomas; 8 cases of myositis ossificans were wrongly diagnosed as extraosseous osteosarcoma. 7 cases of giant cell tumor of bone at the end of long bone were misdiagnosed as osteosarcoma by intermediate doctors. Chondroblastoma was misdiagnosed as malignant tumor in 6 cases -2 osteosarcoma and 4 chondrosarcoma-In this study, 23 osteosarcoma cases were misdiagnosed by ChatGPT as osteomyelitis; Chondrosarcoma was misdiagnosed as fibrous dysplasia or aneurysmal bone cyst in 8 cases. Four cases of spinal chordoma were misdiagnosed as spinal tuberculosis.
UNASSIGNED: Our findings highlight the potential of ChatGPT in the diagnosis of benign and malignant bone tumors, offering advantages like enhanced efficiency and a reduction in missed diagnoses. However, the necessity of collaborative interactions between physicians and ChatGPT in practical settings was underscored. With an examination into AI\'s capacity in benign and malignant bone tumors diagnosis, this study lays the groundwork for future AI advancements in medicine. Additionally, the benefits of few-shot learning in fine-tuning ChatGPT applications in specialized fields were also demonstrated.

UNASSIGNED: For the best possible outcomes from therapy, proximal femur bone cancers must be accurately classified. This work creates an artificial intelligence (AI) model based on plain radiographs to categorize bone tumor in the proximal femur.
UNASSIGNED: A tertiary referral center\'s standard anteroposterior hip radiographs were employed. A dataset 538 images of the femur, including malignant, benign, and tumor-free cases, was employed for training the AI model. There is a total of 214 images showing bone tumor. Pre-processing techniques were applied, and DenseNet model utilized for classification. The performance of the DenseNet model was compared to that of human doctors using cross-validation, further enhanced by incorporating Grad-CAM to visually indicate tumor locations.
UNASSIGNED: For the three-label classification job, the suggested method boasts an excellent area under the receiver operating characteristic (AUROC) of 0.953. It scored much higher (0.853) than the diagnosis accuracy of the human experts in manual classification (0.794). The AI model outperformed the mean values of the clinicians in terms of sensitivity, specificity, accuracy, and F1 scores.
UNASSIGNED: The developed DenseNet model demonstrated remarkable accuracy in classifying bone tumors in the proximal femur using plain radiographs. This technology has the potential to reduce misdiagnosis, particularly among non-specialists in musculoskeletal oncology. The utilization of advanced deep learning models provides a promising approach for improved classification and enhanced clinical decision-making in bone tumor detection.

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.

Bone tumor has become a common disease that endangers human health. Surgical resection of bone tumors not only causes biomechanical defects of bone but also destroys the continuity and integrity of bone and cannot completely remove the local tumor cells. The remaining tumor cells in the lesion bring a hidden danger of local recurrence. To improve the chemotherapeutic effect and effectively clear tumor cells, traditional systemic chemotherapy often requires higher doses, and high doses of chemotherapeutic drugs inevitably cause a series of systemic toxic side effects, often intolerable to patients. PLGA-based drug delivery systems, such as nano delivery systems and scaffold-based local delivery systems, can help eliminate tumors and promote bone regeneration and therefore have more significant potential for application in bone tumor treatment. In this review, we summarize the research progress of PLGA nano drug delivery systems and PLGA scaffold-based local delivery systems in bone tumor treatment applications, expecting to provide a theoretical basis for developing novel bone tumor treatment strategies.

BACKGROUND: The present study aimed to evaluate the indications, feasibility, clinical effectiveness and complications of the treatment with microwave in situ inactivation followed by curettage and bone grafting assisted with internal fixation, for the proximal humerus tumors.
METHODS: The clinical data of 49 patients with primary or metastatic tumor of the proximal humerus who received intraoperative microwave inactivation in situ with curettage and bone grafting in our hospital from May 2008 to April 2021 were retrospectively analyzed.
RESULTS: There were 25 males and 24 females, with an average age of 57.6 ± 19.9 years (range, 20-81). All patients were followed up for 7 to 146 months, with an average period of 69.2 ± 39.8 months. Up to the last follow-up, 14 patients died. The 5-year overall survival was 67.3%, and 5-year tumor-specific survival was 71.4%. The 5-year tumor-specific survival rates were 100% for aggressive benign tumors or low potential malignancy tumors, 70.1% for primary malignancies, and 36.9% for metastatic tumors. The average preoperative MSTS, constant-Murley and VAS scores were 16.81 ± 3.85, 62.71 ± 12.56 and 6.75 ± 2.47, which were all significantly improved at 6 weeks after operation and at the final follow-up (P < 0.05).
CONCLUSIONS: Microwave inactivation in situ and curettage and bone grafting are a feasible treatment for tumors of proximal humeral, especially for malignant tumors and metastases, without the necessity of the replacement of the shoulder, with little trauma and good upper limb function, and with low local recurrence and distant metastasis.

Osteoblastoma is a rare, benign, bone-forming tumor that is frequently observed in the spine and long tubular bones. There are very few reports available on osteoblastoma of the patella. The present study reported an extremely rare case of a 22-year-old male adult who presented with an osteoblastoma of the patella. He was treated via intralesional curettage of the patella with subsequent bone grafting. After the intervention, he made an uneventful recovery with no recurrence after a follow-up of 2 years. Making an accurate diagnosis of osteoblastoma of the patella is challenging and important for determining the correct treatment modality and prognosis, therefore, the present case may be helpful in the diagnosis and treatment of osteoblastoma of the patella.

Bone tumors, including primary bone tumors, invasive bone tumors, metastatic bone tumors, and others, are one of the most clinical difficulties in orthopedics. Once these tumors have grown and developed in the bone system, they will interact with osteocytes and other environmental cells in the bone system\'s microenvironment, leading to the eventual damage of the bone\'s physical structure. Surgical procedures for bone tumors may result in permanent defects. The dual-efficacy of tissue regeneration and tumor treatment has made biomaterial scaffolds frequently used in treating bone tumors. 3D printing technology, also known as additive manufacturing or rapid printing prototype, is the transformation of 3D computer models into physical models through deposition, curing, and material fusion of successive layers. Adjustable shape, porosity/pore size, and other mechanical properties are an advantage of 3D-printed objects, unlike natural and synthetic material with fixed qualities. Researchers have demonstrated the significant role of diverse 3D-printed biological scaffolds in the treatment for bone tumors and the regeneration of bone tissue, and that they enhanced various performance of the products. Based on the characteristics of bone tumors, this review synthesized the findings of current researchers on the application of various 3D-printed biological scaffolds including bioceramic scaffold, metal alloy scaffold and nano-scaffold, in bone tumors and discussed the advantages, disadvantages, and future application prospects of various types of 3D-printed biological scaffolds. Finally, the future development trend of 3D-printed biological scaffolds in bone tumor is summarized, providing a theoretical foundation and a larger outlook for the use of biological scaffolds in the treatment of patients with bone tumors.

Interleukin (IL)-10 family cytokines include IL-10, IL-19, IL-20, IL-22, IL-24, IL-26, IL-28A, IL-28B, and IL-29. These cytokines play crucial regulatory roles in various biological reactions and diseases. In recent years, several studies have shown that the IL-10 family plays a vital role in bone and joint diseases, including bone metabolic diseases, fractures, osteoarthritis, rheumatoid arthritis, and bone tumors. Herein, the recent progress on the regulatory role of IL-10 family of cytokines in the occurrence and development of bone and joint diseases has been summarized. This review will provide novel directions for immunotherapy of bone and joint diseases.