Papagelopoulos, Panayiotis J.

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.

This is a technical note of pelvic reconstruction performed by an advanced multidisciplinary team. The authors report a new 3-stage reconstruction of the hemipelvis after Type 3 sacrectomy involving instrumented spinoiliac arthrodesis and pedicled fibula grafting in 2 patients. The anterior stage of the procedure begins with a transabdominal approach to mobilize the viscera and to free up the tumor from the vessels. The posterior divisions of internal iliac vessels, the middle sacral vessels, and the lateral sacral vessels are then ligated. An anterior vertebrectomy is done at the appropriate level, followed by an anterior osteotomy through the lateral planed surgical margin of the sacrum close to the salvaged sacroiliac joint. The second stage includes a major sacral resection with lower-extremity amputation from the pubic symphysis through the intact side of the sacrum, ipsilateral pedicled fibula harvesting, and closure with an ipsilateral pedicled quadriceps flap. The final stage involves reconstruction with lumboiliac instrumentation. The pedicled fibular graft left from the second stage is then placed distally within the previously created iliopectineal docking site and proximally within the L-5 docking site. The authors believe that this is a feasible and reproducible technique with theoretical advantages that have to be proved in the long-term follow-up.