• Metastasis accounts for nearly half of pathologic acetabular reconstructions. • Increasing use of combined percutaneous ablation, cementoplasty, and osteosynthesis. • Harrington or MAC classification may lead to overtreatment of acetabular lesions. • Know indications for joint-sparing reconstruction; consider use in select patients.

Background: Total femur replacement (TFR) has become increasingly significant as a salvage procedure for both oncologic reconstruction and complex nononcologic conditions such as revision arthroplasty. Despite its effectiveness in limb salvage, TFR is associated with high complication and failure rates, which vary depending on the underlying indication. Methods: This systematic review and meta-analysis adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive search of MEDLINE, EMBASE, Web of Science, and Cumulative Index to Nursing and Allied Health Literature databases was conducted, focusing on studies that reported outcomes of TFR in oncologic and nononcologic cases. Primary outcomes included failure mode and rates according to the Henderson classification, functional outcomes scores, and mobility status. Data were analyzed using random-effects models and generalized linear mixed models. Results: A total of 35 studies involving 1,002 patients were included. The majority of TFRs were performed for oncologic reasons (63.7%). The mean Musculoskeletal Tumor Society (MSTS) score was 66%, with a limb salvage rate of 89%. The meta-analysis revealed a combined failure rate of 34%. For type 4 failures (infection), nononcologic patients exhibited a significantly higher rate at 18% (95% confidence interval [CI], 12%-26%, I² = 46%, p < 0.01) compared with 8% in oncologic patients (95% CI, 6%-12%, I² = 0%). Regarding combined types 1 to 4 failures, oncologic patients had a rate of 20% (95% CI, 25%-52%, I² = 60%), whereas nononcologic patients faced a higher rate of 37% (95% CI, 12%-26%, I² = 63%) (p < 0.05), indicating a significant difference. There were no significant differences in the MSTS score. In addition, there were no notable differences when comparing failure modes 1, 2, and 3 independently. Mobility analysis showed that approximately 70% of patients required walking aids after surgery. Conclusion: TFR offers a valuable limb salvage option in both oncologic and nononcologic scenarios, despite its high failure rates. Although functional outcomes were similar between groups, the higher failure rate in nononcologic cases and the poor overall quality of evidence warrant further comprehensive assessments into predictors of outcomes to optimize results. Level of Evidence: Level III. See Instructions for Authors for a complete description of levels of evidence.

Background: Accurate prediction of postoperative metastasis and mortality risks in patients undergoing lower-limb oncological resection and endoprosthetic reconstruction is essential for guiding adjuvant therapies and managing patient expectations. Current prediction methods are limited by variability in patient-specific factors. This study aims to develop and internally validate explainable machine learning (ML) models to predict the 1-year risk of new distant metastases and mortality in these patients. Methods: We performed a secondary analysis of data from the Prophylactic Antibiotic Regimens in Tumor Surgery trial, which included 604 patients. Candidate features were selected based on availability and clinical relevance and then narrowed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Boruta algorithms. Six ML classification algorithms were tuned and calibrated: logistic regression, support vector machines, random forest, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and neural networks. Models were developed with and without including percent tumor necrosis due to its high missing data rate (>30%). Hyperparameters were tuned using Bayesian optimization. Internal validation was conducted using a 30% hold-out set. Model explainability was assessed using permutation-based feature importance and SHapley Additive exPlanations. Results: LightGBM was identified as the best-performing algorithm for both outcomes. For 1-year mortality prediction without percent necrosis, LightGBM achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% confidence interval [CI] 0.70-0.86) during cross-validation and 0.72 on internal validation. For distant metastasis prediction, the LightGBM model without percent necrosis achieved an AUC-ROC of 0.77 (95% CI 0.71-0.84) during cross-validation and 0.77 on internal validation. Including percent necrosis did not significantly improve model performance. The top predictors identified were patient age, largest tumor dimension, and tumor stage. Conclusions: Explainable ML models can effectively predict the 1-year risk of mortality and new distant metastases in patients undergoing lower-limb oncological resection and endoprosthetic reconstruction. Further external validation and consideration of other data modalities are required before integrating these ML-driven risk assessments into routine clinical practice. Level of Evidence: Level II, Prognostic Study. See Instructions for Authors for a complete description of levels of evidence.

Targeted therapy has greatly improved the outlook for patients with spinal metastatic cancers. Scoring systems like the Tokuhashi or Tomita scores are commonly used to predict prognosis and inform surgical decisions, but they are outdated and fail to consider recent advancements. We aimed to investigate the current state of the literature and treatment options pertaining to advancements in targeted therapy compared to other forms of medical management for metastatic spinal tumors. This study represents the first comprehensive systematic review that encompasses the most common primary cancers that metastasize to the spine and evaluates the median overall survival (mOS) across five different medical treatment modalities as well as surgical intervention. Additionally, our study analyzes the tumor receptor status in conjunction with these treatments. A PubMed search was conducted, and according to the PRISMA guidelines, 28 articles out of 1834 met the inclusion criteria. The pooled data analysis highlighted the superior efficacy of targeted therapy, evidenced by a significant improvement in the mOS and lower hazard ratios in patients with lung and breast cancers who received targeted therapy compared to those who did not. Our study provides valuable insights into the recent advancements in the medical management of metastatic spinal tumors. Future indications include incorporating this literature into personalized treatment approaches for metastatic spinal tumors.

Histology has not been accepted as a valid predictor of the biological behavior of extra-meningeal solitary fibrous tumors (SFTs). Based on the lack of a histologic grading system, a risk stratification model is accepted by the WHO to predict the risk of metastasis; however, the model shows some limitations to predict the aggressive behavior of a low-risk/benign-appearing tumor. We conducted a retrospective study based on medical records of 51 primary extra-meningeal SFT patients treated surgically with a median follow-up of 60 months. Tumor size (p = 0.001), mitotic activity (p = 0.003), and cellular variants (p = 0.001) were statistically associated with the development of distant metastases. In cox regression analysis for metastasis outcome, a one-centimeter increment in tumor size enhanced the expected metastasis hazard by 21% during the follow-up time (HR = 1.21, CI 95% (1.08–1.35)), and each increase in the number of mitotic figures escalated the expected hazard of metastasis by 20% (HR = 1.2, CI 95% (1.06–1.34)). Recurrent SFTs presented with higher mitotic activity and increased the likelihood of distant metastasis (p = 0.003, HR = 12.68, CI 95% (2.31–69.5)). All SFTs with focal dedifferentiation developed metastases during follow-up. Our findings also revealed that assembling risk models based on a diagnostic biopsy underestimated the probability of developing metastasis in extra-meningeal SFTs.