Evolving and novel applications of artificial intelligence in interventional oncology

Table 1 Artificial intelligence applications by procedural phase in interventional oncology

IO phase	AI application
Pre-procedural	Lesion segmentation (CNNs, radiomics)
	Needle path planning (CT-guided)
	Outcome prediction (e.g., TACE response, recurrence risk)
	Imaging enhancement (denoising, SNR/CNR improvement)
Intra-procedural	Real-time motion correction. Image fusion (CT/US)
	Needle tracking and trajectory optimization
	Multimodal image registration
	Robotic assistance
Post-procedural	Structured reporting (NLP, LLMs)
	Margin assessment (3D modeling, deformable registration)
	Recurrence prediction (radiomics, XGBoost)
	Longitudinal lesion tracking
System-level/patient-centered	Predictive maintenance (equipment)
	Workflow optimization (scheduling, triage)
	Patient education (AR tours, chatbots)
	Research support (trial matching, literature mining)

IO: Interventional oncology; AI: Artificial intelligence; CNN: Convolutional neural network; CT: Computed tomography; TACE: Transarterial catheter embolization; SNR: Signal-to-noise ratio; CNR: Contrast-to-noise ratio; US: Ultrasound; NLP: Natural language processing; LLMs: Large language models; 3D: Three-dimensional; XGBoost: Extreme gradient boosting; AR: Augmented reality.

Table 2 Current and potential patient-focused artificial intelligence applications and validation gaps in interventional oncology

Application	Description	Highest level of clinical evidence currently available	Most significant validation gap	Ref.
Patient selection & response prediction	Analyzes clinical data to predict therapeutic response, enabling patient-specific treatment recommendations while accounting for potential risks	Retrospective studies, simulation platforms, some prospective modeling	Lack of prospective trials and integration into real-time clinical decision-making	[41,54,55,60-63,67,111,115]
Patient triage	Prioritizes patients for urgent interventional oncology procedures by evaluating imaging and clinical indicators of disease severity	Retrospective validation in diagnostic settings	Limited IO-specific validation and real-time deployment in procedural prioritization	[128]
Personalized post-procedural follow-up and long-term management	Integrates clinical, procedural, and imaging data to customize follow-up schedules and management plans, improving patient outcomes over time	Retrospective studies, mobile health feasibility trials	Lack of prospective trials with integration with EMRs and imaging systems	[23,111-113,115,116,129,130]
Improved patient experience	Delivers personalized education, procedural tours, and recovery support to foster a more comfortable and informed perioperative experience	Feasibility studies and pilot implementations, RCT	Limited usability testing and lack of standardized patient outcome metrics	[129,133,134]
Comprehension enhancement	Provides clear, individualized instructions, interactive virtual tours, and tailored recovery feedback to improve patient understanding and adherence	Systematic reviews and NLP-based readability studies	Lack of prospective validation and integration into clinical education workflows	[129,131-134]

IO: Interventional oncology; EMR: Electronic medical record; RCT: Randomized controlled trial; NLP: Natural language processing.

Table 3 Human-centered artificial intelligence features for enhancing clinician trust and ensuring safe deployment

Feature	Purpose	Example	Deployment considerations
Feature attribution	Identifies the imaging or clinical features that most influenced the AI’s recommendation	In ablation planning, feature attribution can highlight lesion boundaries, proximity to critical structures, or perfusion metrics that guided probe placement	Should be integrated into procedural consoles with toggleable overlays for real-time validation
Uncertainty quantification	Provides confidence scores or probability distributions to help clinicians assess risk and determine whether to rely on or override the output	During catheter navigation, an AI system might suggest a path with 92% confidence, giving the proceduralist a quantifiable basis for trust	Must be displayed in plain language (e.g., “low confidence”) and updated dynamically during the procedure
Saliency maps or visual overlays	Highlight relevant anatomical regions on live imaging by overlying AI-derived insights (e.g., tumor margins, vessel segmentation) to support real-time targeting	Enhances targeting precision in ultrasound- or CT-guided procedures by showing which regions the AI model considers most relevant	Requires seamless integration with imaging feeds and adjustable settings
Counterfactual examples	Illustrate how small changes in input (e.g., lesion size or location) would alter the AI’s recommendation, helping assess model robustness	Could be used pre-procedurally to simulate alternative probe placements or embolization strategies	Should be available pre-procedurally for simulation and intra-procedurally for real-time adjustment

AI: Artificial intelligence; CT: Computed tomography.

Table 4 Current business case for hospital investment in artificial intelligence-interventional oncology platforms

Strengths of AI	Supporting claim
Clinical efficacy & cost savings	Reduced procedure time
	Fewer complications with better precision
	Optimized resource use
	Cost savings
Improved outcomes & quality metrics	Higher technical success rates
	Personalized treatment
	Better documentation
Strategic positioning & innovation	Competitive differentiation
	Academic and research value
	Patient experience
Scalability & long-term ROI	Data-driven learning systems
	Alignment with value-based care models

AI: Artificial intelligence; ROI: Return of investment.

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.