Advancing Gamma Knife Radiosurgery with Artificial Intelligence: A New Era of Precision and Efficacy

Abstract

The recent surge in artificial intelligence (AI) applications is revolutionizing the medical field by offering astute data analysis and decision-making advancements. AI, via its algorithmic techniques, improves its performance over time by analyzing the data continuously and learning, identifying, and implementing subtle changes in the data presented to it. Gamma Knife radiosurgery is a noninvasive technique that represents an advanced and refined approach within the realm of stereotactic radiosurgery, predominantly utilized for the management of several brain pathologies by facilitating the exact targeting of aberrant brain tissue through the deployment of highly focused beams of gamma radiation, ensuring unparalleled precision and efficacy in treatment. This article delves into the transformative impact of AI on Gamma Knife radiosurgery, examining its influence across imaging, treatment planning, and posttreatment evaluation.

Keywords

artificial intelligence - Gamma Knife radiosurgery - neurosurgery

Introduction

The birth of stereotaxic surgery dates back to the early 20th century when Sir Victor Horsley and Robert Clarke used an apparatus and cartesian coordinates to probe a subcortical structure of an animal subject.[1] Later, with the serendipitous discovery of some destructive lobar procedures' role, stereotaxy was extrapolated to human patients in 1947.[2] Since then, the stereotactic radiosurgery field has flourished with Dr. Lars Leksell's game-changing contributions. It was rightly considered an avenue for functional preservation and less invasive surgery. In its initial days, it was used for functional disorders; however, later, its efficacy was established in other brain pathologies like vestibular schwannomas, pituitary tumors, craniopharyngiomas, cavernous sinus hemangiomas, metastasis, and other vascular malformations. Their application was also extended to conditions like trigeminal neuralgia, movement disorders, mesial temporal lobe epilepsy, and certain psychiatric illnesses like obsessive compulsive disorders. Over time, significant technological advancements have been witnessed in Gamma Knife treatment in the form of computerized planning and image guidance, making it one of the most sought-after treatments.

The delivery of gamma radiation using the Gamma Knife involves a meticulous sequence of events to ensure precision and safety. It starts with patient selection using suitable imaging methods and identifying the right candidate for this form of management. A stereotactic frame is placed on the day of the procedure, following which the patient is subjected to appropriate imaging. The imaging data are uploaded into the Gamma Knife console that fuses the diagnostic images with the frame-based images, allowing the precise localization of the target in three-dimensional space. Several multiparametric thin slice MR images (i.e., T1W, T1W + C, and T2W) characterize the target lesion's anatomical details. Manual contouring of the target lesion is performed, and the structures at risk are defined. The dose matrices are placed, and treatment fields are defined and revised. The treatment parameters are verified, and the plan is exported to the Gamma Knife table. The patient is docked to the treatment table, and the highly focused gamma radiation from ^6°Co sources is delivered to the target. Most of the described steps in the radiation delivery have subjective involvement and thus have a margin of error. Artificial intelligence (AI) can reshape Gamma Knife radiosurgery (GKRS) by improving imaging, treatment planning, and posttreatment assessment ([Table 1]).

Table 1 Studies on the application of Artificial Intelligence in GKRS

Study	Study focus	Findings and conclusion
Buzea et al[6]	To investigate three deep learning models, namely, the CNN model, the TL model, and the FT model	The CNN model, trained from scratch, provided the most accurate predictions of SRS responses for unlearned BM images
Ranjbarzadeh et al[7]	To obtain a flexible and effective brain tumor segmentation system	Several machines and deep learning models, namely, C-CNN model, can accurately obtain a segmentation result
Tangsrivimol et al[8]	Review effectiveness of AI in GKRS by reviewing the published literature	ML techniques have proven effective in tumor identification, surgical outcome prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery
Shapey et al[10]	To develop a novel artificial intelligence (AI) framework to be embedded in the clinical routine for automatic delineation and volumetry of VS	A robust AI framework for automatically delineating and calculating VS tumor volume and have achieved excellent results, equivalent to those achieved by an independent human annotator
Klinge et al[11]	To investigate the feasibility of employing inverse planning methods to generate treatment plans exhibiting desirable BED characteristics using the per isocenter beam-on times and delivery sequence	They demonstrated the feasibility of using an inverse planning approach within a reasonable time frame to ensure BED-based objectives are achieved across varying treatment times and highlight the prospect of further improvements in treatment plan quality
Khouy et al[13]	They propose a new approach called GA-U Net that employs genetic algorithms to automatically design a U-shaped convolution neural network	GA-U Net, a more viable option for deployment in resource-limited environments or real-world implementations that demand more efficient and faster inference times
Feng et al[14]	To develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation	A high prediction accuracy in both low-grade glioma and glioblastoma patients was seen using a deep learning model
Ahn et al[16]	A deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction was seen
Goyal et al[18]	Artificial neural network (ANN) model could predict the outcomes in trigeminal neuralgia with 90% accuracy
Ertiaei et al[20]	ANN can predict postoperative outcomes in patients who underwent GKRS with a high level of accuracy

Abbreviations: BED, biologically effective dose; BM, brain metastases; CNN, convolutional neural network; C-CNN, Cascade Convolutional Neural Network; FT, fine-tuning; GA, genetic algorithm; GKRS, Gamma Knife radiosurgery; ML, machine learning; SRS, stereotactic radiosurgery; TL, transfer learning; VS, vestibular schwannoma.

Enhanced Imaging and Diagnosis

Imaging is fundamental to GKRS, serving as the basis for diagnosis, treatment planning, and execution. The success of the procedure depends largely on the precision of imaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). Machine learning is a significant subset of AI, which refers to a system's ability to gain statistical insights by deciphering patterns from training data and applying learned rules to predict a specific task. Apart from the derivation of rules for optimal behavior, it also adapts to evolving circumstances. Deep machine learning, commonly known as deep learning (DL), is a subset of machine learning that advances this process by allowing the computer to analyze the extracted patterns and create complex visual representations.[3] This is achieved through a series of simple mathematical operations organized into layers of progressively more abstract feature extraction mechanisms using various neural networks. The amount of information that can be interpreted using DL methods can be quite enormous and accurate, thus improving diagnostic accuracy and even predicting outcomes based on preprocedure imaging.[4]

Convolutional neural networks (CNNs) are the most common type of DL model used, and they have been shown to remove noise from MR images while preserving resolution, leading to better visualization of tumors and abnormalities.[5] This improved clarity is crucial for accurate targeting during GKRS. To make the adjuvant therapy more meaningful, lesion segmentation is used to derive crucial information about the tumor. A region-based CNN is a family of methods that can be utilized for object detection and localization in the images. It can divide an image into multiple regions and process them individually to search for the desired pathology. This method has shown its potential application for the prediction of brain metastasis.[6] Traditional methods are labor intensive and time-consuming. Several machines and DL models, namely, multi-atlas registration algorithms and DL methods, can be trained to accurately distinguish between tumor and healthy brain tissue, along with providing clinicians with precise data on the treatment area's location, size, and shape, thus defining the target accurately.[7] This supplements treatment planning and can lead to better patient outcomes.[8] AI also supports multimodal image fusion, which combines data from various imaging techniques (such as MRI and PET) to offer a more comprehensive view of the target area. AI algorithms can automate this fusion process, ensuring accurate image alignment and increasing the radiosurgery's precision.[9] By delivering a detailed understanding of the biological characteristics of the target tissue, AI-powered image fusion helps optimize radiation dose selection and treatment strategies. Shapey et al have successfully demonstrated a robust AI framework that has accurately annotated vestibular schwannomas in their cohort and has promising potential for its application in other tumors.[10]

Optimized Treatment Planning

In GKRS, we intend to deliver focused radiation on the target lesion and prevent scattering to surrounding normal tissue, thus minimizing radiation-related side effects on normal structures. The delivery of radiation dose is influenced by the geometry of the radiation unit, the patient positioning system, and the collimator selection.[11]

Traditionally, this process is complex and iterative, relying on the expertise of medical physicists and neurosurgeons. AI can significantly change treatment planning by enhancing predictive modeling, automating contouring, and improving dose calculation. AI algorithms, particularly reinforcement learning and DL, can create predictive models that simulate treatment scenarios and forecast patient outcomes based on various parameters. By analyzing vast amounts of historical data, these models can identify patterns and correlations that may be obscure to human planners. Using deep neural network algorithms, the dose distribution can be derived depending on the patient's anatomy and the beam angles, and doses are adjusted accordingly.[12] Currently, in Gamma Knife, a semiautomated in-plane segmentation method is used, which requires manually segmenting each axial slice, thus making the entire process time-consuming. An automated segmentation tool using DL models can delineate the target tumor tissue accurately and more quickly.[10] This approach is relatively time-consuming and subjective and could be made more efficient by introducing an automated segmentation tool.

AI-based segmentation algorithms, such as U-Net, which is an artificial neural network (ANN) architecture primarily used for segmentation in computer vision, have demonstrated high accuracy in automating the contouring process.[13] [14] AI reduces interobserver variability and promotes uniformity in treatment planning by ensuring consistent and precise delineation of the target area and adjacent structures. AI can also optimize dose calculation, a key component of GKRS planning. Traditional methods, such as Monte Carlo simulations, are accurate but require significant computational resources and time.[15] DL models can accurately compute dosage requirements based on the datasets of previous cases, thus making the treatment delivery process more efficient and significantly shorter in duration.[16]

Real-Time Monitoring and Adaptive Treatment

The efficacy of Gamma Knife is, apart from imaging and treatment planning, significantly affected by controlled and precise delivery of radiation to the target, which is affected by factors like patient movement, anatomical changes, and physiological variations. AI-integrated systems can monitor patients' anatomy and position during the procedure and mitigate these challenges.

For example, DL algorithms can analyze real-time imaging data to detect any shifts in patient positioning or changes in the target location. If deviations are identified, the system can alert the medical team or automatically adjust the treatment parameters to maintain precise radiation delivery. This reduces the risk of radiation exposure to healthy tissue, enhancing the procedure's safety and effectiveness.

Additionally, AI enables adaptive treatment planning, allowing the treatment plan to be dynamically adjusted based on real-time data. This is particularly useful when the target tissue or surrounding anatomy changes during the procedure. AI algorithms can rapidly process new data, recalculate optimal treatment parameters, and update the plan accordingly. Adaptive planning ensures more personalized and precise radiosurgery, ultimately improving patient outcomes.[17] This can be of great use during administration of Gamma Knife for functional disorders, namely, trigeminal neuralgia or performing ventral intermediate (VIM) thalamotomy for control of tremors where the target tissue is of small volume and a large dose of radiation is to be administered. Thus, precision and adaptive treatment would play a vital role.

Posttreatment Assessment and Outcome Prediction

Predicting long-term patient outcomes is one of the critical aspects of management. AI can potentially predict the treatment responses and outcomes based on the preoperative scans and the pathology, which otherwise would involve long-term clinical and radiological follow-ups. For instance, DL models can be trained to identify residual tumor tissue or early signs of recurrence with high sensitivity and specificity, allowing for timely interventions and adjustments to the treatment plan if necessary. Goyal et al assessed an ANN model trained on 16 variables to forecast postoperative outcomes after GKRS in patients suffering from trigeminal neuralgia. An accuracy of 90.9% in predicting treatment responses was achieved using this model.[18] The use of ANNs in medicine began in the late 1980s, particularly for diagnosis, assessing disease severity, and outcome prediction. While ANNs were conceptualized before modern computers, advancements in computational models led to their rapid adoption.[19] Ertiaei et al used ANNs to predict outcomes in patients with trigeminal neuralgia who underwent stereotactic radiosurgery and to categorize the relative importance of individual risk factors.[20] [21]

Moreover, AI can support the creation of personalized treatment strategies by identifying patient-specific factors that influence treatment outcomes. By analyzing data from a vast number of cases, AI can reveal patterns and correlations that may not be apparent in smaller datasets. This insight can inform the development of tailored treatment protocols, optimizing the likelihood of a successful outcome for each patient. A Chinese group used machine learning models to their advantage to satisfactorily predict post-Gamma Knife edema in patients with meningioma.[22] They could thus counsel their patients better, tailor their treatment decisions, and generate a customized follow-up plan.

Challenges

AI has been quite instrumental when it was used for pathologies like vestibular schwannoma, trigeminal neuralgia, or metastasis, but it does have its share of limitations. The algorithms used for the above conditions have been used in a small cohort of patients, and their generalizability and reproducibility are yet to be established using prospective studies. This warrants collaborative efforts from clinicians across the globe and validates its efficacy and reliability in diverse populations.

Adopting and seamlessly integrating the practice of AI demands substantial adjustments from clinicians. Clinicians must be trained to incorporate these tools into daily use and comprehend outputs from AI software. Infrastructural amendments in the form of provision of secure networks, processing power, and data storage have to be carried out. Also, concerns about maintaining privacy crop up. Above all, the analysis, interpretation, and precision offered by AI are based on the data fed to build the algorithms. Their interpretation and analysis can considerably differ from that of an astute clinician. Implementation of new pathologies might also take a long time before one can confidently implement them for patient care.[22] There are several ethical issues that have to be addressed before AI technology is widely incorporated into routine practice, which include patients' consent to utilize the medical data, safety and transparency, and algorithmic biases. The inaccuracy resulting from the algorithmic biases in treatment delivery can prove catastrophic for the patient, and the extent of the harm it can result is difficult to gauge. In addition, we cannot hold anyone accountable for faulty decision-making. This also warrants prior intimation to the patients for whom AI technology is going to be used, and one should stress on obtaining proper consent.[23]

Despite these challenges, the future of AI in GKRS is auspicious. Ongoing advancements in AI algorithms, computational power, and data accessibility are poised to fuel continued innovation. Furthermore, the convergence of AI with other emerging technologies—such as robotics and augmented reality—can potentially elevate precision and personalization in radiosurgical procedures to new heights. We also believe that introducing AI can jeopardize the age-old clinician–patient connection, which can affect patient trust, and we will have to devise ideas to circumvent these issues.

Conclusion

AI is set to revolutionize GKRS by advancing imaging capabilities, refining treatment planning, enabling real-time monitoring, and enhancing posttreatment evaluations. Through the integration of AI, clinicians can attain unprecedented levels of precision, efficiency, and personalization in radiosurgical procedures, significantly improving patient outcomes, as highlighted in [Table 1]. Although challenges persist, the ongoing development and adoption of AI-driven innovations offer tremendous potential for the future of GKRS and the broader discipline of neurosurgery.

Conflict of Interest

None declared.

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Publication History

Article published online:
02 April 2025