Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care.

Artificial Intelligence (AI) is definitely a transformative tool in orthopedic oncology, enabling advancements in the diagnosis, classification, and treatment response prediction of primary malignant bone tumors (PBT). AI, particularly machine learning and deep learning ,utilizes the power of computational algorithms and large datasets to improve imaging interpretation and clinical decision making. Radiomics, combined with AI, facilitates the extraction of quantitative features from medical images, in order to precisely characterise the tumor and assist in personalized therapeutic strategies. The integration of convolutional neural networks has demonstrated outstanding capabilities in imaging pattern recognition, advancing tumor detection, segmentation, and differentiation.

Background/Objectives: Tenosynovial giant cell tumor (TGCT), formerly known as pigmented villonodular tenosynovitis (PVNS), is a rare, benign, inflammatory mesenchymal neoplasm originating from the synovium of joints, bursae, or tendon sheaths. Although TGCT can affect any joint, the knee is the most commonly involved site, particularly in cases of diffuse-type TGCT. Bifocal or multifocal involvement is exceedingly uncommon. Methods: Herein, we present a case of localized TGCT with bilateral knee involvement in a 48-year-old female. Results: The patient underwent open arthrotomy with marginal excision of the localized lesions in both knees. Histology and immunohistochemistry staining conformed the diagnosis. At the five-year follow-up, the patient remains asymptomatic and free of recurrence. Conclusions: Given the rarity of bilateral TGCT, clinicians should maintain a high index of suspicion when evaluating patients presenting with bilateral knee pain and swelling and include TGCT in the differential diagnosis. To our knowledge, this represents the fifteenth reported case of bilateral knee TGCT in the literature.

Background/Objectives: Distal femur tumor resection with limb salvage is a demanding procedure that offers hope for patients by preserving the limb rather than opting for amputation. While limb salvage can improve both physical function and psychological well-being, there’s limited knowledge on how active patients remain afterward and how their Quality of Life (QoL) is affected, especially regarding physical activities and sports. This study investigates the quality of life of the patients through the development of motor activity, focusing on both physical and sporting activity of the above-mentioned individuals and their physical abilities to participate in activities of daily and sporting life after surgery. Methods: This study involved 16 patients aged 19–47 years who had undergone surgical resection and replacement of the distal femur for the treatment of sarcoma and were selected by random sampling from a total of 72 patients who had undergone a similar procedure. To explore the topic in depth, the researchers followed the triangulation method. From July 2023 to February 2024, we used surveys and interviews to explore their physical activity (PA) levels, sports participation, and QoL. This study included the International Physical Activity (PA) Questionnaire (IPAQ), the University of California and Los Angeles (UCLA) Activity Score, and semi-structured interviews. The data were analyzed using the statistical software packages SPSS 25 and Excel. Results: Most participants reported moderate to high levels of PA, according to IPAQ, and continued engaging in sports, with swimming, walking, and stair climbing being the most common activities. No significant relationship was found between their PA levels and factors such as age, BMI, or the side of the affected limb. Interviews showed that patients’ motivation and their surgeon’s guidance played key roles in their return to regular activities, though many exercised less frequently or intensely than recommended. Conclusions: Patients who undergo limb salvage surgery following distal femur tumor removal generally maintain a good level of physical activity, which supports their QoL. Encouraging these patients to stay active and even engage in sports appears feasible, especially when guided by medical professionals. These findings highlight the potential benefits of tailored rehab programs to improve long-term health and QoL in sarcoma survivors, although larger studies are needed for more comprehensive insights.

Background/Objectives: Distal femur tumor resection with limb salvage is a demanding procedure that offers hope for patients by preserving the limb rather than opting for amputation. While limb salvage can improve both physical function and psychological well-being, there’s limited knowledge on how active patients remain afterward and how their Quality of Life (QoL) is affected, especially regarding physical activities and sports. This study investigates the quality of life of the patients through the development of motor activity focusing on both physical and sporting activity of the above-mentioned individuals and their physical abilities to participate in activities of daily and sporting life after surgery. Methods: This study involved 16 patients aged 19-47 years who had undergone surgical resection and replacement of the distal femur for the treatment of sarcoma and were selected by random sampling from a total of 72 patients who had undergone a similar procedure. To explore the topic in depth, the researchers followed the triangulation method. From July 2023 to February 2024, we used surveys and interviews to explore their physical activity (PA) levels, sports participation, and QoL. The study included the International Physical Activity (PA) Questionnaire (IPAQ), the UCLA Activity Score, and semi-structured interviews. The data were analyzed using the statistical software packages SPSS 25 and Excel. Results: Most participants reported moderate to high levels of PA, according to IPAQ, and continued engaging in sports, with swimming, walking, and stair climbing being the most common activities. No significant relationship was found between their PA levels and factors such as age, BMI, or the side of the affected limb. Interviews showed that patients’ motivation and their surgeon’s guidance played key roles in their return to regular activities, though many exercised less frequently or intensely than recommended. Conclusions: Patients who undergo limb salvage surgery following distal femur tumor removal generally maintain a good level of physical activity, which supports their QoL. Encouraging these patients to stay active and even engage in sports appears feasible, especially when guided by medical professionals. These findings highlight the potential benefits of tailored rehab programs to improve long-term health and QoL in sarcoma survivors, although larger studies are needed for more comprehensive insights.

Background: Total joint arthroplasties (TJAs) are well-established orthopedic procedures that provide effective pain relief and restore functional mobility. Although these procedures are generally highly successful, postoperative complications may still occur. This review examined the literature on how machine learning (ML) can predict postoperative complications based on laboratory and clinical parameters for total knee (TKA) and total hip arthroplasty (THA). Methods: A scoping review was conducted and reported in accordance with the PRISMA criteria for scoping reviews (PRISMA-ScR). We searched PubMed/MEDLINE, Scopus, and EMBASE for studies evaluating the use of ML to predict adverse events following TKA and/or THA, using routinely available laboratory values combined with demographics and clinical parameters. Results: The initial search yielded 144 studies, of which 18 were finally deemed eligible for inclusion. Key predictors of complications in the examined studies included hemoglobin, hematocrit, white blood cell count, platelet count, and renal markers. Need for transfusion was the most frequently studied outcome, followed by severe adverse events and postoperative delirium. The ML models, especially gradient boosting machines (GBMs), random forests (RFs), and neural networks (NNs), demonstrated promising discriminative performance in predicting adverse events. Conclusions: Combining laboratory data with demographics and clinical information may improve the accuracy of complication prediction. This review emphasizes the potential of ML analysis of laboratory parameters to create risk assessment tools, which could detect complication risk and potentially improve outcomes for patients undergoing TKA or THA.