Evolving and novel applications of artificial intelligence in interventional oncology

Table 5 Perioperative artificial intelligence applications in interventional oncology and barriers to clinical adoption

Application	Description	Highest level of clinical evidence currently available	Most significant validation gap	Ref.
Lesion segmentation1	Improves accuracy in delineating tumors for precise targeting	Retrospective studies, phantom trials	Limited prospective validation and generalizability across modalities and institutions	[7,8,20,21,34,44]
Procedural path planning1	Generates patient-specific needle or probe trajectories that, reducing preparation time and improving procedural accuracy	Retrospective studies, phantom trial	Fails to integrate real-time procedural variables and thermal interactions, especially in multi-needle procedures	[21,22,41-43,45,46]
Radiomics integration1	Incorporates radiomic features into planning to predict tumor characteristics and genetic profiles, enabling personalized treatment strategies	Retrospective studies	Limited prospective validation, lack of standardized radiomic pipelines, and poor reproducibility across institutions and imaging platforms	[32,54,59,60,63]
Catheter planning1	Analyzes vascular anatomy and perfusion patterns to provide individualized catheter placement recommendations, improving efficiency and accuracy of transarterial therapies	Retrospective studies, simulation models	Insufficient real-time validation and integration with hemodynamic data	[31,49-55,89,90]
Personalized treatment1 planning	Uses imaging and clinical data to tailor treatments and avoid unnecessary procedures. Digital twin simulations model patient-specific procedural outcomes, aiding in decision-making	Retrospective studies, simulation models	Lack of prospective trials and real-time clinical deployment	[41,61-63,67,69,213]
Imaging analysis2	Enhances image fusion to overlay of intra- and pre-procedural imaging in real time, improving precise lesion localization	Retrospective studies, phantom trials	Latency and lack of seamless fusion across modalities	[70,71,74-77,81]
Needle tracking2	Provides real-time needle localization and trajectory prediction, reducing procedure time and improving first-attempt success rates	Retrospective studies, phantom trials	Limited clinical validation and integration with robotic systems	[21,44,78-80]
Motion correction2	Maintains spatial alignment and alerts to tool deviation, enhancing procedural safety and efficiency	Retrospective studies, phantom trials, simulation models	Lack of real-time deployment and anatomical variability handling	[21,70,71,81]
Safety monitoring2	Detects intra-procedural risks, such as hemorrhage, vascular injury, or thermal injury, alerting clinicians in real time	Retrospective studies, preclinical models	Limited IO-specific validation and standardization of margin assessment	[82-84]
Treatment delivery & dosing optimization2	Optimizes dosing, dose mapping, and targeted therapy delivery using real-time imaging features to improve safety and precision	Feasibility trials in systemic therapy	Lack of IO-specific prospective trials and adaptive dosing platforms	[69,86,88,90,91]
Quality assurance3	Evaluates documentation and ablation margins to ensure procedural consistency	Retrospective studies	Limited prospective validation and standardization of margin assessment	[95,105-108,151]
Retrospective trajectory analysis3	Simulates alternative procedural approaches using image navigation and fusion, accounting for anatomical constraints	Retrospective studies, phantom trials	Lack of integration into intraoperative workflows	[109,110]
Treatment outcome prediction3	Predicts survival, recurrence risk, treatment outcomes, and complications, enabling proactive risk mitigation and individualized adjustments for future procedures	Retrospective studies, systematic reviews	Need for prospective validation and integration into decision-making	[62,111,113,117]
Response monitoring3	Evaluates lesion response and/or recurrence following treatment through imaging features and radiomics	Retrospective studies	Limited real-time deployment and standardization of response metrics	[23,112,114,115]
Longitudinal lesion tracking3	Tracks lesions AI across serial imaging for accurate identification and consistent follow-up guidance	Retrospective studies, algorithm benchmarking	Limited clinical integration and validation across imaging platforms	[116]

¹Pre-procedural.

²Intra-procedural.

³Post-procedural.

IO: Interventional oncology; AI: Artificial intelligence.