Edited by: Cesare Zoia, San Matteo Hospital Foundation (IRCCS), Italy
Reviewed by: Andrea Bianconi, University Hospital of the City of Health and Science of Turin, Italy
Giorgio Carrabba, University of Milano-Bicocca, Italy
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Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving “decision-making processes” for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.
High-grade gliomas (HGG) stand as the most prevalent and deadly primary malignant brain tumors in adults. Among these, glioblastoma (GBM) is the most frequent malignant brain tumor, constituting 14.2% of all tumors and 50.9% of all malignant tumor. In the United States, its incidence is reported at 3.27 per 100,000 population. Typically affecting individuals with a median age of 65 years, GBM exhibits a remarkably poor overall survival, despite the implementation of combined radio-chemotherapy. Survival durations typically range between 15 and 17 months, with a median survival of only 8 months (
The decision-making process for surgical interventions in patients with GBM is inherently challenging, suffering from a lack of clear guidelines, particularly regarding the choice between biopsy and resection. Surgeons are confronted with the intricate task of assessing resectability, carefully balancing the advantages of decompression and cytoreduction against the potential neurological consequences. Navigating the diverse clinical landscape characterized by varied glioma molecular subtypes, tumor locations, sizes, eloquent area involvement, and co-existing medical complexities poses a formidable challenge (
Moreover, while individual surgeons manifest considerable variability in their clinical judgment regarding surgical resectability, aggregated responses from a large number of surgeons prove to be more consistent and predictive. Sonabend et al. (
Recognizing cognitive biases and understanding decision-making processes are pivotal in enhancing patient care, especially in neurosurgery where errors carry significant consequences. The Dual Process Theory (DPT) illuminates two cognitive processes – analytical and rapid, unconscious and biased implicit processes. Despite the prevalent belief in analytical decision-making, much of daily clinical decisions are influenced by the rapid and unconscious implicit system, leading to inherent human biases (
AI encompasses the use of computers and technology to mimic intelligent behavior and critical thinking, similar to humans. Within AI, ML is a subset that employs methods capable of automatically identifying patterns in data for predicting future data or making decisions under uncertainty. The learning process in ML can take the form of supervised or unsupervised learning. Supervised learning establishes a pattern connecting inputs to outputs using a labeled set of input–output pairs, for tasks like classification and regression. In contrast, unsupervised learning extracts patterns or structures from input data without relying on labeled data or predefined outcomes. Such learning algorithms extract patterns from input data without predefined outcomes, revealing insights that may not be immediately apparent, for example, personalized treatment strategies and hidden disease patterns. ML/AI techniques have become increasingly important in healthcare applications, providing innovative solutions to various challenges in clinical settings (
Examples of AI tools for glioma imaging and prediction.
| Focus of the Study | Methodology | Evaluation Technique | Outcome/Performance | Remarks |
|---|---|---|---|---|
| Predicting surgical resectability ( |
Artificial NN | Receiver Operator Characteristic (ROC) curves; Area Under Curve (AUC) and accuracy calculations | AUC of 0.87–0.92; Accuracy of 83–87% | Compared against logistic regression and a standard grading system |
| Differentiation between non-enhancing tumor and vasogenic edema ( |
Support Vector Machine (SVM) Classifier | ROC analysis | Misclassification error reduced to 2.4% with post-processing | Utilized T1 perfusion MRI parameters |
| Prediction of tumor recurrence or necrosis ( |
Convolutional NN combined with Long Short-Term Memory (CNN-LSTM) | AUC, AUPRC, F1-score | AUC of 0.83; AUPRC of 0.87; F1-score of 0.74 | Combined MRI data and clinical features |
| Differentiating vasogenic edema from non-enhancing tumor ( |
DL with multimodal MRI | Accuracy, sensitivity, specificity | Accuracy up to 90.3%; sensitivity and specificity significantly better than neurosurgeons | Histology examination of the resected tissue for validation |
| Differentiation between pseudo-progression and true progression ( |
DL model | ROC analysis; Leave-one-out cross-validation | AUC up to 0.92; Accuracy up to 87% for predicting PsP | Utilized preoperative and intraoperative MRI data |
| Predicting regions of local recurrence in GBM ( |
ML with voxel-based radiomic features | AUC, Accuracy, Precision, Recall, F1 Score, Cohen’s Kappa | AUC of 0.81 ± 0.09, Accuracy of 0.84 ± 0.06, Precision of 0.48 ± 0.24, Recall of 0.76 ± 0.22, F1 Score of 0.53 ± 0.17, Cohen’s Kappa of 0.45 ± 0.18 | Utilized postoperative MRI data |
NN, Neural network; DL, Deep learning; ML, Machine Learning; MRI, Magnetic resonance imaging.
In recent years, AI applications in medicine, spanning various medical specialties, have experienced significant growth. A notable advancement in radiology involves the transformation of biomedical images, such as magnetic resonance imaging (MRI), into mineable data, coupled with their analysis using AI techniques—commonly referred to as “radiomics” (
The main goal of this study is to conduct a thorough literature review, concentrating on current AI tools. The objective is to analyze a wide range of predictive factors, both tumoral and non-tumoral, along with their potential interactions. This review explores the use of AI tools in the context of surgical decision-making for GBM patients, with a specific focus on predicting resectability and surgery related early postoperative neurological outcomes.
The literature search for the study involved the use of three bibliographic databases (Pubmed, Web of Science, Google Scholar, and Scopus) from their respective inception date to January 2024. The search term constructs used in all three databases were “connectomics” or “radiomics” and “AI” or “deep learning” or “machine learning” and “glioblastoma” and “surgical” or “decision making.” This search string generated a total of 117 articles. Two investigators (AMM and FF) independently screened the titles, abstracts, and full texts retrieved from all three databases to determine the eligibility of the studies. Publications outside the scope of neurosurgery, preclinical studies, non-peer reviewed, duplicates, and GBM/HGG studies focused on AI or ML at the molecular level were excluded from the study. The study’s inclusion criteria involved the application of an AI model developed by the researchers to patients with GBM. The focus was on using the AI model for surgical decision-making, specifically assessing resectability and estimating surgery-related neurological outcomes and complications. Out of the 117 studies generated, only 10 studies were included in this study.
Studies utilizing AI tools in surgical decision-making are grouped under 2 headings: predicting resectability and predicting postoperative complications and neurological outcome. The studies are listed in
Studies utilizing AI tools in surgical decision-making.
| Author, Year | Data | AI model description |
|---|---|---|
| Fathi-Kazerooni, 2015 | MRI | DL for segmentation, using advanced NN to delineate tumor boundaries |
| Marcus, 2020 | MRI | Artificial NN to recognize complex patterns in imaging data |
| Kommers, 2021 | MRI | Deep NN for enhanced segmentation, along with sophisticated algorithms for tumor and tissue differentiation |
| Zanier, 2023 |
|
ML for tumor segmentation |
| Yeung, 2021 | Connectome | ML with a connectome-based approach, analyzing brain network connectivity to assess tumor impact |
| Osipowicz, 2023 | Connectome | Connectomics software [for analyzing T1 MRI, DWI and resting-state fMRI data and Hollow-tree Super (HoTS) method for mapping and analyzing abnormal brain connectivity] |
| Morell, 2022 | Connectome | Connectomics platform, a ML tool for comprehensive brain network mapping and analysis to make surgical decisions |
| Luckett, 2023 | Resting State fMRI | 3D convolutional NN for fMRI data, extracting features from brain activity patterns to predict surgical outcomes |
| Caverzasi, 2016 | Residual bootstrap q-ball fiber tracking | ML for DTI analysis for surgical planning |
| Ille, 2022 | nTMS, Connectome | Integrated ML for non-invasive mapping with nTMS and connectome data to enhance surgical accuracy |
ML, Machine learning; DL, Deep Learning; NN, Neural network; MRI, Magnetic resonance imaging; DWI, diffusion weighted imaging; DTI, Diffusion tensor imaging; fMRI, Functional Magnetic resonance imaging; nTMS, Navigated transcranial magnetic stimulation.
Numerous prior studies conducting volumetric assessments and assessing resection extent have concentrated on the percentage of the removed tumor volume. A classification system that integrates both relative tumor reduction and absolute residual tumor volume has been suggested. However, there is a presumption that the absolute residual volume might carry more significance as a prognostic factor than the relative reduction of tumor volume. In 2022, the Response Assessment in Neuro-Oncology (RANO) Resect Group introduced a revised classification system, which, in contrast to the earlier systems, incorporates only absolute residual tumor volumes. Upon application of the resulting extent of resection (EOR) classification system, distinct survival outcomes were observed among the respective categories. Patients stratified into “supramaximal contrast enhancing (CE) resection” demonstrated superior outcomes compared to those with “maximal CE resection,” with the latter group being superior to patients with “submaximal CE resection.” Patients designated as “biopsy” exhibited the least favorable progression-free survival (
Segmentation proves valuable not only for evaluating tumor borders but also for AI tools to efficiently segment and quantify the volume of both CE and non-CE areas of GBMs. An integral aspect of image processing in GBM, characterized by heterogeneity, is the precise segmentation of distinct tumor components, including viable tumor, edema, and necrosis. Fathi Kazerooni et al. (
Marcus et al. (
Kommers et al. (
Zanier et al. (
The conventional strategy in surgical neuro-oncology aims to preserve function in eloquent areas, primarily within the left dominant hemisphere to prevent aphasia. In non-eloquent regions, particularly outside the left perisylvian areas, surgery is often performed in asleep patients, potentially utilizing motor-evoked potentials to prevent hemiplegia in cases involving or near the central area. Surgical selection and planning traditionally focus on the local topography of the glioma, with limited considerations for the entire brain circuitry. However, the emerging field of mapping macro-scale neural connectivity has led to a reevaluation of classical cognitive models. This paradigm shift advocates moving from a localized understanding of brain processing to adopting a meta-networking theory of cerebral functions (
Many researchers performed AI tools to predict the resectability based on connectomics to better assess the postoperative neurological outcome. Connectomics, the investigation of the brain’s entire neural connections, known as the ‘connectome,’ is centered around the complex white matter pathways responsible for transmitting information between cortical and subcortical structures. The initiation of the Human Connectome Project (HCP) in 2010 has been a driving force behind the surge in interest in connectomics within both cognitive neuroscience and neurosurgery, serving as a watershed moment, instigating extensive exploration into the realm of functional brain connectivity. The persistent effort to unravel the intricate functional networks and connections within the nervous system remains an area of compelling potential for glioma surgery (
The definition of eloquence in neurosurgery has evolved, with the primary objective of brain tumor surgery being the optimal balance between oncologic treatment and preserving neurological function (
The potential of Quicktome™ appears promising for evaluating the influence of brain tumors on large-scale networks, including both traditional and non-traditional eloquent areas, during preoperative planning. Morrell et al. (
Luckett et al. (
Cepeda et al. (
Although complete resection is linked to improved survival, it poses risks of neurological deficits, occurring in approximately 1 in 10 patients (
Predicting the outcome of a surgical procedure is a multifaceted and intricate decision-making process that takes into consideration various parameters. This encompasses factors specific to the tumor, individual patient characteristics, elements related to the health system, and considerations tied to the surgeon’s expertise. These considerations encompass the accessibility of surgical tools, the patient’s frailty, neurological condition, existing comorbidities, and even psychological aspects. Additional factors such as geographical location, ethical and social considerations, healthcare and malpractice systems, and the availability of post-operative management and care by the neuro-oncology team add layers of complexity to this prediction. Surgeon-related factors also play a crucial role in this comprehensive assessment. The surgeon’s experience and their approach to the functional neurooncology concept are pivotal elements in this convoluted assessment. Gerritsen et al. (
Efforts to formulate the decision-making process have been made in the literature. Ferroli et al. (
Our search revealed only two studies to predict postoperative complication or surgical outcome by AI tools.
Caverzasi et al. (
Ille et al. (
The optimal surgical approaches for GBM remain a subject of ongoing debate among surgeons due to the intricate heterogeneity of gliomas, including factors such as location, grade, and patient-specific considerations. The AI tools have emerged as transformative elements in addressing these challenges, enhancing resectability and outcome prediction by capturing intricate relationships among variables. Real-time decision support, integrating automated segmentation systems with immediate feedback during preoperative and intraoperative evaluations, is a groundbreaking concept empowering neurosurgeons to make informed decisions based on imaging data.
The advancement and deployment of advanced AI algorithms are pivotal in enhancing imaging capabilities for GBM surgery. These algorithms excel in precisely delineating tumors and continuously refining their performance through learning from diverse datasets. The utilization of DL techniques becomes essential for managing the intricate patterns and variability inherent in GBM imaging. Integration of multimodal imaging, incorporating data from functional MRI, diffusion tensor imaging (DTI), and positron emission tomography (PET), offers a comprehensive perspective of tumors and surrounding structures, thereby enhancing diagnostic precision and treatment strategies. Bianconi et al. (
While preoperative connectome analysis holds promise for predicting the risk of neurological deficits before surgery, the challenge lies in developing an AI system to evaluate postoperative complications and neurological outcomes. Creating such a system would provide neurosurgeons with access to an evidence-based therapeutic blueprint tailored to the diverse needs of individual patients. Future AI models in intracranial tumor surgery may draw insights from existing literature based on surgeons’ predictions for surgery related neurological outcomes and postoperative complications. The dependence on surgeons’ predictions, whether through AI tools or other methods, presents a fundamental flaw. Currently, there is a lack of studies, including those involving AI tools, specifically focused on developing a tool for predicting postoperative deterioration or surgical outcomes. Surgeons typically make treatment decisions based on factors such as tumor location, size, and interaction with surrounding structures. In a prospective study involving 299 patients undergoing intracranial tumor surgery, neurosurgeons displayed a consistent tendency to overestimate postoperative functional levels, especially regarding the ability to perform normal activities at 30 days. The assessment, using the Karnofsky Performance Scale, revealed that neurosurgeons underestimated in 15% of cases, accurately estimated in 23%, and overestimated in 62% (
Utilizing AI tools in the education and training of residents raises additional concerns. A proactive approach is crucial to mitigate biases and enhance decision-making quality, beginning with acknowledging inherent biases in thought processes. Many clinicians, particularly during their early training years, lack formal education on the cognitive aspects of medical decision-making and bias recognition. Therefore, the introduction of training programs, especially in graduate medical education, becomes essential. These programs should empower physicians to identify cognitive biases, understand decision-making processes, and reflect on past errors. Integrating AI into such programs could further enhance bias recognition and decision-making by providing data-driven insights, potentially transforming the way physicians navigate cognitive challenges throughout their careers (
The application of AI in neurosurgery introduces both promise and challenges. To effectively leverage AI in this field, several key considerations and hurdles must be addressed.
• Dataset Challenges:
o Extensive datasets are essential for AI training, but often lack verification in clinical settings.
o Variability in model performances and controversial findings add complexity.
• Radiomic Workflow Optimization:
o Optimizing parameters in radiomic workflows, covering tumor segmentation, feature extraction, and model training, is crucial.
o Comparing multiple ML algorithms within the same population is vital for understanding performance impacts.
• Focus on Resectability Prediction:
o Current studies predominantly focus on developing AI tools for predicting resectability.
o Surgeons base treatment decisions on factors such as tumor location, size, and interaction with surrounding structures.
• Challenges in Predicting Neurological Outcomes:
o AI faces hurdles in predicting postoperative neurological outcomes.
o Data quality and quantity are critical, emphasizing the need for interpretability in medical applications.
• Surgeon Bias and Shared Decision-Making:
o Surgeons tend to exhibit an optimistic bias in predicting postoperative functional levels.
o Shared decision-making, involving patients in complex treatment choices, is considered viable.
• Clinical Validation for Generalizability:
o Clinical validation is a rigorous requirement to ensure the reliability and generalizability of AI models.
o Testing models on independent datasets and diverse patient populations is necessary.
• Collaboration and Continuous Feedback:
o Collaboration with research institutions and participation in clinical trials are imperative.
o Establishing a continuous feedback loop in AI systems is pivotal for ongoing improvements and knowledge incorporation, in accuracy, and reliability.
• Ethical and Regulatory Considerations:
o Patient privacy, transparent decision-making, and adherence to regulatory standards are critical ethical and regulatory considerations.
o Responsible integration of AI in neurosurgery is essential for patient safety and trust.
• AI in Education and Training:
o Using AI tools in the education and training of residents raises concerns without establishing formal education based on established curriculum.
o A proactive approach is crucial, starting with acknowledging inherent biases in thought processes.
o Introducing training programs, particularly in graduate medical education, becomes essential, with the potential integration of AI to enhance bias recognition and decision-making.
Addressing these challenges and considerations is essential for the successful integration of AI in neurosurgery. This involves not only technical advancements but also ethical, regulatory, and educational initiatives to ensure the responsible and effective use of AI in improving patient outcomes.
AI relies significantly on high-quality and annotated data for accurate and trustworthy predictions. Particularly, the fields of radiomics and connectomics are advancing, incorporating enhanced imaging technologies. Collaboration with research institutions and participation in clinical trial initiatives remains imperative for integrating automated segmentation systems into ongoing studies. This collaborative effort contributes valuable data to research endeavors focused on understanding GBM heterogeneity, treatment responses, and patient outcomes. Establishing a continuous feedback loop in AI systems, wherein the system learns from new patient data and outcomes, is pivotal. This iterative process leads to ongoing improvements in accuracy, reliability, and the incorporation of emerging knowledge in GBM research. Looking ahead, the goal is to enhance automated GBM segmentation and reporting systems, ultimately improving patient care and contributing to a deeper understanding of GBM radiomics and connectomics. While navigating through these challenges and considerations, the integration of AI tools in GBM management has immense potential for advancing patient care, refining treatment strategies, and contributing to the broader comprehension of surgical decision making.
The primary aim is to contribute to comprehensive effectiveness research and offer valuable insights for well-informed decision-making in surgeries for GBM. An innovative AI system should seamlessly integrate imaging, radiomics, RANO criteria, resectability studies, and connectomics, along with surgery related neurological outcomes, to enhance assessment and contribute to education and training. Despite challenges, these approaches are transforming medicine, and healthcare providers should prepare for the era of AI. However, it is crucial to acknowledge that despite these advancements, the technology remains distant from replicating the nuanced and educated decision-making of an experienced neurosurgeon.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
MM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing. MZ: Data curation, Investigation, Supervision, Writing – review & editing. IG: Investigation, Writing – review & editing. PF: Supervision, Writing – review & editing. FF: Investigation, Writing – review & editing. DN: Investigation, Writing – review & editing.
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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