Iterative Metal Artifact Reduction (iMAR) of the Non-adhesive Liquid Embolic Agent Onyx in Computed Tomography : An Experimental Study

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

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.

Visibility of liquid embolic agents in fluoroscopy: a systematic in vitro study

BACKGROUND

Endovascular embolization using liquid embolic agents (LEAs) is frequently applied for the treatment of intracranial vascular malformations. Appropriate visibility of LEAs during embolization is essential for visual control and to prevent complications. Since LEAs contain different radiopaque components of varying concentrations, our aim was the systematic assessment of the visibility of the most used LEAs in fluoroscopy.

METHODS

A specifically designed in vitro model, resembling cerebral vessels, was embolized with Onyx 18, Squid 18, Squid 12, PHIL (precipitating hydrophobic injectable liquid) 25%, PHIL LV (low viscosity) and NBCA (n-butyl cyanoacrylate) mixed with iodized oil (n=3 for each LEA), as well as with contrast medium and saline, both serving as a reference. Fluoroscopic image acquisition was performed in accordance with clinical routine settings. Visibility was graded quantitatively (contrast to noise ratio, CNR) and qualitatively (five-point scale).

RESULTS

Overall, all LEAs provided at least acceptable visibility in this in vitro model. Onyx and Squid as well as NBCA mixed with iodized oil were best visible at a comparable level and superior to the formulations of PHIL, which did not differ in quantitative and qualitative analyses (eg, Onyx 18 vs PHIL 25% along the 2.0 mm sector: mean CNR±SD: 3.02±0.42 vs 1.92±0.35; mean score±SD: 5.00±0.00 vs 3.75±0.45; p≤0.001, respectively).

CONCLUSION

In this systematic in vitro study, relevant differences in the fluoroscopic visibility of LEAs in neurointerventional embolization procedures were demonstrated, while all investigated LEAs provided acceptable visibility in our in vitro model.