Navigation Guidance for Percutaneous Splanchnic Nerve Radiofrequency Neurolysis: Preliminary Results.
Stavros GrigoriadisDimitrios FilippiadisVasiliki StamatopoulouEfthimia AlexopoulouNikolaos KelekisAlexis KelekisPublished in: Medicina (Kaunas, Lithuania) (2022)
Background and Objectives : To describe preliminary results upon the application of the "Cube Navigation System" (CNS) for computed tomography (CT)-guided splanchnic nerve radiofrequency neurolysis. Materials and Methods : CT-guided splanchnic nerve neurolysis was performed in five patients; in all cases, neurolysis was performed under CT guidance using the CNS. The mean patient age was 71.6 years (range 54-81 years; male/female: 5/0). Technical success, parameters of the neurolysis session and complications were evaluated. Technical success was defined as a needle position on the defined target. Session parameters included procedure time and number of scans. The CIRSE reporting system was used for complications' classification and grading. Results : Technical success was obtained in all cases; in 1/5 patients, a slight correction in needle orientation was necessary. Mean procedure time was 12.4 min (range 8-19 min); an average of four CT scans was recorded in the five neurolysis sessions. There were no complications or material failures reported in the present study. Conclusions : Preliminary results of the present study show that computed tomography (CT)-guided splanchnic nerve radiofrequency neurolysis using the CNS is an accurate and time-efficient percutaneous procedure. More prospective and comparative studies with larger patient samples are necessary for verification of this system as well as for drawing broader conclusions.
Keyphrases
- computed tomography
- dual energy
- contrast enhanced
- image quality
- ultrasound guided
- positron emission tomography
- magnetic resonance imaging
- end stage renal disease
- minimally invasive
- ejection fraction
- newly diagnosed
- blood brain barrier
- risk factors
- prognostic factors
- chronic kidney disease
- magnetic resonance
- machine learning
- emergency department
- peripheral nerve
- mass spectrometry
- high resolution