Navigation and Robotics in Interventional Oncology: Current Status and Future Roadmap.
Georgios CharalampopoulosReto BaleDimitrios K FilippiadisBruno C OdisioBradford WoodLuigi SolbiatiPublished in: Diagnostics (Basel, Switzerland) (2023)
Interventional oncology (IO) is the field of Interventional Radiology that provides minimally invasive procedures under imaging guidance for the diagnosis and treatment of malignant tumors. Sophisticated devices can be utilized to increase standardization, accuracy, outcomes, and "repeatability" in performing percutaneous Interventional Oncology techniques. These technologies can reduce variability, reduce human error, and outperform human hand-to-eye coordination and spatial relations, thus potentially normalizing an otherwise broad diversity of IO techniques, impacting simulation, training, navigation, outcomes, and performance, as well as verification of desired minimum ablation margin or other measures of successful procedures. Stereotactic navigation and robotic systems may yield specific advantages, such as the potential to reduce procedure duration and ionizing radiation exposure during the procedure and, at the same time, increase accuracy. Enhanced accuracy, in turn, is linked to improved outcomes in many clinical scenarios. The present review focuses on the current role of percutaneous navigation systems and robotics in diagnostic and therapeutic Interventional Oncology procedures. The currently available alternatives are presented, including their potential impact on clinical practice as reflected in the peer-reviewed medical literature. A review of such data may inform wiser investment of time and resources toward the most impactful IR/IO applications of robotics and navigation to both standardize and address unmet clinical needs.
Keyphrases
- minimally invasive
- palliative care
- endothelial cells
- clinical practice
- systematic review
- robot assisted
- healthcare
- radiofrequency ablation
- ultrasound guided
- climate change
- pluripotent stem cells
- high resolution
- small cell lung cancer
- metabolic syndrome
- machine learning
- adipose tissue
- virtual reality
- fluorescent probe
- atrial fibrillation
- brain metastases
- data analysis