Search

Simple Summary: In recent years, advances in deep learning have transformed the analysis of medical imaging, especially in spine oncology. Computed Tomography (CT) imaging is crucial for diagnosing, planning treatment, and monitoring spinal tumors. This review aims to comprehensively explore the current uses of deep learning tools in CT-based spinal oncology. Additionally, potential clinical applications of AI designed to address common challenges in this field will also be addressed. In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Apart from skin cancer, prostate cancer is the most prevalent cancer in men. The treatment options typically entail active surveillance, surgery, radiation, or a combination of the above. The treatment options for recurrent disease also include surgery, radiation, and focal therapy. In this review, we look at the use of cryoablation for recurrent prostate cancer following radiation treatment. The treatment options for prostate cancer typically entail active surveillance, surgery, radiation, or a combination of the above. Disease recurrence remains a concern, with a wide range of recurrence rates having been reported in the literature. In the setting of recurrence, the salvage treatment options include salvage prostatectomy, salvage high-intensity focused ultrasound (HIFU), stereotactic body radiotherapy (SBRT), salvage brachytherapy, and salvage cryoablation. In this review, we analyze the currently available data related to salvage cryoablation for recurrent prostate cancer following radiation. [ABSTRACT FROM AUTHOR]