Limb salvage surgeries utilizing endoprostheses and allografts are performed for a variety of oncologic conditions. These reconstructions can fail and require revision for many reasons, which are outlined and classified into mechanical failures (soft tissue failures, aseptic loosening, structural failure), non-mechanical failures (infection, tumor progression), and pediatric failures (physeal arrest, growth dysplasia). Distinct radiologic and clinical findings define specific failure subtypes but are sparsely illustrated in the radiology literature. Specifically, an understanding of the organizational structure of the failure modes can direct radiologists’ search for post-reconstruction complications, enhance an appreciation of their prognostic significance, and facilitate research by standardizing the language and conceptual framework around outcomes. The purpose of this review is to highlight the key radiologic findings and imaging studies of each failure mode in orthopedic oncologic reconstructive surgery in the context of risk factors, failure rates, prognosis and survival statistics, and clinical decision-making regarding chemotherapy, radiation, and revision surgery.

Radiomics entails a data-driven approach to imaging with a wide array of potential uses in characterizing soft tissue sarcomas, enabling extraction of quantitative features from routine clinical CT and MRI examinations. These features—encompassing descriptors of size, shape, and internal heterogeneity—can improve diagnostic accuracy, tumor grading, and treatment response assessment. Radiomics has shown promise in distinguishing benign from malignant lesions, subtyping sarcomas, and predicting metastatic potential. In particular, models integrating radiomic data with clinical variables have demonstrated performance comparable to expert radiologists in challenging diagnostic scenarios. Machine learning enhances radiomics by automating feature selection and improving predictive modeling. Despite its potential, challenges remain in standardizing imaging protocols, ensuring reproducibility, and integrating radiomics into clinical workflows. Multi-institutional collaboration is essential for broader model validation and clinical integration. By leveraging specific radiomics features as novel quantitative imaging biomarkers, radiomics can drive precision oncology in sarcoma, supporting tailored therapies and improving prognostic accuracy.

Giant cell tumor (GCT) of bone is an uncommon indolent tumor, typically occurring in the meta-epiphysis of long bones in young adults. GCT arising in the clivus is exceedingly rare, and even more uncommon in the pediatric population. We present a case of a 13-year-old patient diagnosed with a large GCT in the clivus. Initial clinical and radiographic findings were suspicious for a GCT, although other more common skull base tumors were also considered in the differential diagnosis. Certain key radiographic features suggested the diagnosis of a GCT, including the low T2 signal within the mass on MRI and the T2 hypointense rim corresponding with a cortical shell present on CT. An endoscopic biopsy confirmed the diagnosis. This report highlights the unique diagnostic challenges and broad differential in this case while underscoring the role of imaging in detection and precise anatomic delineation that helps guide therapeutic decisions and improve patient prognosis.