Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery

Introduction

The advent of functional magnetic resonance imaging (fMRI) has transformed the practice of neurosurgery by offering unparalleled insights into the brain’s functioning structure.^1,2 This imaging approach, which does not require penetration of the body, is crucial for pinpointing important brain areas that are at risk of damage during surgery.³ Recent research has emphasized the crucial importance of fMRI in improving the accuracy of cortical mapping, which helps in performing brain tumor removal surgery while safeguarding vital neurological processes.^4,5 Neurosurgical efforts focus on maximizing tumor removal while minimizing neurological impairments.⁵ Having a proper balance is essential for enhancing the quality of life after surgery and prolonging the survival of patients, especially in cases of glioma, where the degree of resection is closely linked to better treatment results. Yet, attaining this equilibrium is filled with obstacles, as the elimination of brain tissue heightens the likelihood of functional impairment.^6,7 Therefore, the advancement and improvement of brain mapping techniques before and during surgery have become a central focus of study in neurosurgery.^8,9 However, intraoperative mapping demands a substantial amount of effort in the operating room, particularly when working on the brain of a conscious patient.^10,11 Using modern neuroimaging techniques, preoperative functional mapping strategies may assist in identifying individuals who are best suited for mapping in the waking state and provide direct activation of areas of highest interest.¹² In addition to these methods, functional magnetic resonance imaging is one of the most important procedures for preoperative mapping.¹³

Task-based functional magnetic resonance imaging (T-fMRI) has been the primary method used to locate language and motor functions by analyzing changes in the blood oxygen level-dependent (BOLD) signal while performing certain activities.^14,15 However, there are some drawbacks associated with T-fMRI which include long scanning time and requires that the patient must be awake, cooperative, and actively participate during the task, which is sometimes not possible, especially in pediatric or patients’ populations with neurologic frailties as well as those with brain tumours.^14,15 The fact that in the brain tumours practice, maximizing the tumour resection while conserving brain function is the basic goal of the surgery. Resting-state functional magnetic resonance imaging (Rs-fMRI) is a useful method for assessing functional connectivity and network dynamics in patients without requiring them to do any tasks.¹⁶ Its incorporation into clinical processes offers the potential to improve surgical techniques, perhaps reducing operative time and improving postoperative recovery.^17,18

Resting-state functional magnetic resonance imaging (Rs-fMRI) is an alternate technique for localizing the representation of a function in the brain. Rs-fMRI delineates topographies linked with certain tasks, such as somatomotor, executive control, and language tasks. These topographies are sometimes referred to as resting state networks (RSNs).^19–21 Rs-fMRI and T-fMRI are fundamentally distinct in terms of their analytical approach and methodology. RS-fMRI centers on the spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals observed during a resting state, devoid of any specific task execution, as opposed to T-fMRI, which measures brain activity by comparing BOLD signals during specific tasks to baseline periods.²² Rs-fMRI can identify regions of the brain that exhibit synchronous, low-frequency oscillations, generally falling below 0.1 Hz.²³ These oscillations are hypothesized to indicate functional connectivity between distinct brain areas.^24,25 It is believed that brain areas with synchronized spontaneous oscillations of their BOLD signal belong to the same resting-state functional network (RSN).²⁶ Functional maps derived from Rs-fMRI have been used to identify eloquent brain areas and overcome the constraints of T-fMRI for preoperative planning.^27,28 During scanning, patients are not expected to do any specific tasks other than minimizing head movement.²⁹ Therefore, the behavioral protocol is rather straightforward (“keep waiting and try to remain awake”), and no extra task-related devices, such as MR-compatible display systems, are needed.^30,31 Additionally, Rs-fMRI is compatible with light or sleep sedation and may be performed on young children; consequently, the conclusions are more difficult.^32,33 Therefore, this study intends to comprehensively examine the relevance of fMRI in the neurosurgical planning of brain tumor resections, specifically focusing on the shift from task-based to resting-state modalities. The study will evaluate the influence of different imaging methods on surgical results, analyzing their effectiveness in achieving a balance between tumor removal and the preservation of cognitive and motor skills.

Methods of the Systematic Article

Search Technique and Study Choice

The present investigation adheres to the requirements set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and has been duly registered in the International Prospective Register of Systematic Reviews (PROSPERO) database under the identifier CRD42022364693. The study encompassed the following components: The process involves many key steps: (1) clearly delineating the objectives, (2) establishing specific criteria for inclusion and exclusion, (3) precisely describing the relevant information to be retrieved from the articles, (4) conducting a thorough analysis of the collected data, (5) interpreting the obtained results, and (6) engaging in a comprehensive discussion of the findings.

We conducted a comprehensive search of PubMed, Scopus, and Web of Science to locate all relevant papers published before October 2022. The search uncovered studies that reported the use of (“resting-state fMRI” OR “functional magnetic resonance imaging” OR “rs-fMRI” OR “resting-state functional MRI” OR “functional neuroimaging”) AND (“preoperative planning” OR “pre-surgical planning” OR “neurosurgery” OR “preoperative assessment” OR “surgical planning” OR “preoperative evaluation” OR “surgical decision-making”) AND (“brain tumor surgery” OR “brain tumor” OR “neuro-oncology” OR “brain neoplasm” OR “glioma” OR “brain cancer”) AND (“reliability” OR “clinical utility” OR “surgical outcomes” OR “surgical success” OR “patient outcomes”)I, and default mode network (DMN) were among the key terms employed. In addition, we monitored referrals to pertinent literature to identify more qualified research. The publication status and publication date stated in Systematic Research Strategies are largely from the last five years (from 2018 to 2022). Furthermore, we conducted a systematic review of relevant literature to ascertain additional scholarly studies. The publication status and publication date reported in Systematic Research Strategies predominantly encompass the period spanning from 2018 to 2022, with a focus on recent scholarship.

Inclusion and Exclusion Criteria

The study employed specific criteria for inclusion, which were as follows:

• English-language manuscript.
• Case studies and cross-sectionals for Rs-fMRI research.
• Investigations of all types of brain cancer.
• Studies on paediatric patients.

While this study had specific exclusion criteria, which were as follows:

• Animal studies.
• Article reviews.
• Conference papers.

Through debate, an agreement on the eligibility of each research was obtained.

Results

Rs-fMRI as a Preoperative Mapping Tool

Rs-fMRI is an emerging technology that may be used to investigate brain physiology.³⁴ Recent research has investigated the possible uses of Rs-fMRI in various neurological disorders (eg, epilepsy, neurodegenerative, and psychiatric disorders).^35,36 Nonetheless, the efficacy of Rs-fMRI in neurosurgical planning for brain tumours has not been exhaustively studied.

Several studies have revealed a significant overlap between Rs-fMRI and T-fMRI when comparing the motor network in different neurological disorders and a high degree of concordance with cortical stimulation mapping.³⁷ According to one study, a change in function on the intraocular sensorimotor network (SMN) after surgery revealed a drop in functional connectivity (FC) 24 h after surgery and considerable restoration of connection at a 3 month of follow-up check, which was related to the restoration of motor skills.³⁸ Other investigations have revealed similar repeatability of motor maps produced from Rs-fMRI and T-fMRI in healthy people.³⁹ Other authors have documented FC alterations following surgery not just in the SMN but also in the DMN, SN, and LN, even at the level of a single subject.⁴⁰

Rs-fMRI may consistently identify common functional connectivity networks in glioma patients and has the potential to predict network modifications after surgical resection. Alterations of RSNs may be studied at both the individual subject and the group level, with functional mapping revealing a good correlation with cortical stimulation mapping. Although T-fMRI can achieve preoperative localization of eloquent regions limit the risk of surgery-induced functional impairments, various functional networks, including linguistic, visual, and sensorimotor networks, may be identified using Rs-fMRI.^41,42

Brain tumour patients exhibited a range of tumour related comorbidities, including ADHD linguistics problems, neurological impairment, and tic disorders, most of the examined trials. Due to the frequency of comorbidities in brain tumours, we decided to include these studies in our review to increase the generalizability of the results. For each of the chosen studies, we examined the previous five years to determine the most recent advancements in fMRI patient diagnosis and prediction accuracy. We had several issues regarding some of the studies that explored the diagnosis of brain tumours by fMRI, and after reviewing the complete texts, we disregarded some of these studies, leaving 18 suitable fMRI investigations, as shown in Figure 1. All test volunteers were adults and teenagers of various ages (ages 2–84 years). Table 1 provides a comprehensive overview of the neoplastic outcomes. Graph theory, independent component analysis (ICA), and seed-based analysis were frequently cited as the most employed analytical techniques.

Figure 1 Diagram illustrating the search strategy used for the studies included in this systematic review.

Table 1 provides a comprehensive summary of the key characteristics of the investigation. The study was conducted in multiple countries. Five studies were conducted entirely with right-handed persons.^43–47 Eight investigations included both right-handed and left-handed individuals.^18,48–53 The remaining studies did not document the hand preference of the participants.^54–56 Most of the research used participants who were matched in terms of group, age, and gender. No analyses relevant to age or gender were conducted in the studies.

All studies included in the analysis reported age ranges that spanned from 2 to 84 years. Table 1 enumerates the many classifications of brain tumors, as outlined in numerous scholarly investigations, encompassing low-grade gliomas. Brain tumors are categorized based on their postulated cellular source and cellular behavior, ranging from the least aggressive (benign) to the most aggressive (malignant).⁵⁷ Gliomas are the most primary intracranial tumors, including 81% of malignant brain tumors and 16% of all primary tumors.⁵⁸ Glioblastoma (GBM) is the most common and most fatal primary malignant brain tumor encountered in the adult population.⁵⁹

Table 1 A Collection of Research Analyzing Brain Function Using Rs-fMRI

Resting-State fMRI Networks

Recent implementations of the Rs-fMRI analysis for the localization of functional networks involved regions that rely heavily on independent component analysis (ICA) and neural-network (NN), which were also used in the reviewed investigations. The majority of studies concentrated on the localization of the motor network (MN) and language network (LN) since these networks are used by the T-fMRI method^{37–45,48–51,54,55} to measure the quality of the reconstruction. In addition, cognitive and motor skills are among the most crucial talents that must be preserved to guarantee the patient’s quality of life following surgery. However, Rs- fMRI has the potential to isolate other functional networks, as demonstrated by Lemée et al,⁴⁹ who demonstrated that Rs-fMRI could detect brain language regions during cortical mapping (CM) with a sensitivity level 100% compared to 65.6% with T-fMRI.

Gianvincenzo et al⁴⁸ reliably reported common functional connectivity networks in patients with glioma and were able to anticipate network changes after surgical removal through an Rs-fMRI. Moreover, Met- wally et al⁵⁵ showed that the conversion rate between networks is significantly higher in patients with brain tumours than in healthy subjects. Although brain tumours affect FC and RSNs, their study showed by analysing data that Rs-fMRI indicates higher baseline connectivity between networks in patients with brain tumours, which may suggest an intrinsic neural compensatory mechanism.

Seed-Based Analysis

Six studies employed a seed-based approach to examine brain activity.^{18,45,48,50,51,53} Various brain regions were employed as seed regions to assess the locations of different cancers and networks. A study was conducted that combined seed-based analysis with independent component analysis (ICA).⁵⁶ The study included individuals diagnosed with brain tumors in the sensorimotor area who exhibited normal motor strength, individuals diagnosed with brain tumours in the sensorimotor region who exhibited low motor strength, and a group of healthy individuals serving as controls. Before the surgical excision procedure, a certain researcher successfully ascertained the precise topology of the eight resting-state networks of each participant.⁴⁸ Furthermore, it was determined by neurosurgeons that resting-state functional magnetic resonance imaging (Rs-fMRI) proved to be unequivocally beneficial in 26 cases (60%) and moderately beneficial in 13 cases (30%) for the purpose of identifying potentially significant eloquent language areas.

However, in six cases, the utilization of Rs-fMRI was unsuccessful due to factors such as head movement, non-specific functional connectivity beyond the posterior language network, or system instability of unknown origin.⁵³ According to previous studies,¹⁸ it has been demonstrated that the localization of the supplementary movement region by the utilization of manual motor seed regions yields higher rates of success compared to seeding with the orofacial motor areas for individual patients with brain tumors. In their study, Cho et al⁴⁵ discovered that brain tumors had a disruptive effect on the language network within the cerebral and cerebellar regions. This observation suggests the presence of neuronal impairments and cortical reconfiguration as a consequence of the lesions.

In a study conducted by Daniel et al,⁵¹ it was shown that functionally linked pixels, which are not evident in conventional structural images, were frequently observed within the tumor mass. Notably, these linked pixels did not exhibit a significant correlation with tumor size. After accounting for clinical and demographic variables, it was observed that a higher level of functional connectivity (FC) within the network of Glioblastoma (GBM) tumors was associated with a better overall survival outcome in persons with documented survival periods (n = 31).

Independent Component Analysis (ICA)

Six investigations scanned the brain using an ICA method.^43,44,47,49 Lemée et al⁴⁹ identified cerebral language areas during CM with a sensitivity level of 100% when using Rs-fMRI compared to a sensitivity level of 65.6% when using the fMRI task; this improves the use of Rs-fMRI for preoperative language mapping. Liouta et al⁴⁴ found that Rs-fMRI BOLD signalling of motor and linguistic networks was considerably impacted by tumours, demonstrating the method’s utility for measuring baseline functioning in patients with brain tumours. Alternatively, Sharaev et al used a three-step method for the efficient and automated mapping of functional brain areas. For both the T-fMRI and Rs-fMRI approaches, blind and semi-cryptic ICA analysis was used. The findings demonstrated that the semi-blind ICA had more specificity than the blind ICA.

Rs-fMRI is an excellent approximation for T-fMRI maps, particularly in cases with nearly full ICA decomposition. Zacà, D. et al⁴⁷ demonstrated that Rs-fMRI networks, including the pleura cortex, are automatically spatially adjacent to the excised lesion in all patients. For 78% of motor instances, 100% of visual cases, 87.5% of language cases, and 100% of speech-expression mapping cases the estimated distance between direct electrical stimulation (DES) sites and matched Rs-fMRI networks is smaller than 1 cm. Lastly, Yahyavi et al¹⁵ reveal the identification of ventral somatomotor network in 81% of the 26 patients by Rs-fMRI presented for presurgical brain mapping. Enhanced Rs-fMRI reliability in ventral motor network mapping with higher ICA orders was similarly observed. At the single-subject level, mutable concordance of the ventral somatomotor network between Rs-fMRI and T-fMRI was demonstrated.

Convolutional Neural Networks Analysis

Two investigations scanned the brain using convolutional neural networks method.^60,61 Recently, well recorded transformation in image classification and subnetworks or segmentation related problems by deep CNN method was achieved. Language network was specifically and precisely localized using three dimensional CNN in subjects with brain tumors by Luckett and his colleagues. The investigators demonstrate that with limited quantities of data and patient cooperation the method was able to deliver strong results which is an added advantage in clinical practice.

The utilization of noninvasive resting-state functional magnetic resonance imaging (Rs-fMRI) at the preoperative planning stage holds significance for localizing the eloquent cortex. This application serves to expedite and improve the involved processes, despite the inherent challenges it presents. In their study, Nandakumar et al⁶¹ employed a convolutional neural network (CNN) that leverages the generalization capabilities of multi-task learning, a unique deep learning technique. The aim was to achieve simultaneous localization of various areas of eloquent cortex using resting-state functional magnetic resonance imaging (Rs-fMRI) connections. This technique effectively captured shared representation among various subnetworks of interest by employing a graph-based design. Following a thorough process of quantitative validation and evaluation, the researchers discovered that the utilization of resting-state functional magnetic resonance imaging (Rs-fMRI) in this innovative approach holds significant promise in enhancing the preoperative assessment by facilitating faster and more reliable tumor removal.

Comparison of Rs-fMRI and T-fMRI

The present review centers on the utilization of resting-state functional magnetic resonance imaging (Rs fMRI) as a pre-operative mapping technique for the identification of functional areas. Out of the total of eighteen studies examined, seven of them employed the combination of task-based functional magnetic resonance imaging (T-fMRI) and resting-state functional magnetic resonance imaging (Rs-fMRI) to accurately identify and map language and sensorimotor regions in the brain. The aforementioned investigations are delineated in Table 2.

Table 2 A Comparison of Rs-fMRI and T-fMRI Studies

Four studies^15,43,44,49 utilized independent components analysis (ICA) to evaluate the Rs-fMRI data, and the T-fMRI task paradigm consisted of performing the various activities listed in Table 2. Lemée et al⁴⁹ discovered that using Rs-fMRI for preoperative language mapping is simple and permits the identification of functional brain language networks with better sensitivity than T-fMRI. Liouta et al⁴⁴ discovered that tumours significantly impacted Rs-fMRI BOLD signals of motor and linguistic networks, showing the method’s utility for assessing baseline function. Yahyavi et al¹⁵ demonstrated consistency in the somatic network between Rs-fMRI and T-fMRI at the level of the individual participant.

Luckett et al⁶⁰ found that resting-state fMRI can be utilized for all patients and since it has a lower failure rate than T-fMRI, it is beneficial for patients who cannot engage in task-based testing. According to a study by Nandakumar et al,⁶¹ the ability of Rs-fMRI as a preoperative mapping tool is supported, and the benefits of a study-specific graph convolution design coupled with multitask learning are highlighted. Kumar et al⁵³ demonstrated that in the absence of T-fMRI, Rs-fMRI may serve as a suitable substitute for clinical language mapping. The utility of Rs-fMRI as a preoperative mapping technique is therefore supported by these investigations. Thus, we conclude that Rs-fMRI is a potential method for evaluating and accurately mapping brain areas before surgery.

Comparison of Rs-fMRI Data to Cortical Stimulations (DCS and ECS)

Many studies have used RS-fMRI for preoperative planning, although the concordance, specificity, and sensitivity of Rs-fMRI with intraoperative cortical mapping (DCS, the gold standard or ECS) are not well studied. Researchers have found good connection between Rs-fMRI findings and DCS in locating the eloquent motor cortex, sensorimotor network, and Wernicke’s region.^18,62,63 Three researchers compared preoperative Rs-fMRI data to intraoperative DES and ECS outcomes. According to Zaca et al,⁴⁷ the difference between Rs-fMRI networks and intraoperative DES results in 27 positive cortical sites from six patients with presurgical Rs-fMRI was less than 1 cm in cases involving visual and speech articulation, 78% for motor cases, and 87.5% for language mapping. This indicated good spatial agreement between Rs-fMRI and DES results during awake surgery. According to Lemée et al,⁴⁹ 32 of 50 persons with preoperative language mapping (Rs-fMRI and T-fMRI) had cerebral language areas identified by intraoperative ECS mapping. Rs-FMRI localized eloquent cerebral language networks with 100% sensitivity, while T-fMRI had 65.6%. ECS instrument confirmed the four functional brain language areas identified by Rs-fMRI, but not by T-fMRI, during surgery. The study found that the Rs-fMRI method is easy to use and more sensitive than T-fMRI in identifying functional brain language networks, although with certain restrictions. Yordanova et al⁵⁰ mapped the face-based mentalizing network using Rs-fMRI and perioperative DES. Positive stimulation locations were verified and attached to presurgical normalized MRI data, serving as seeds for subsequent seed-to-voxel functional connectivity analyses. Their findings confirmed many prior studies and showed that the combined approaches can strongly detect functional networks in brain-damaged people. See Table 3.

Table 3 Correlation and Validation of Preoperative Rs-fMRI by Intraoperative Cortical Mapping (DES and ECS)

Discussion

A new method for localizing functional brain areas, Rs-fMRI, is gaining favor.²⁷ The therapeutic application of this method for diagnostic and therapeutic purposes is being worked on.²⁸ Niu et al⁴³ found that Rs-fMRI data may predict motor activation based on preoperative mapping in adult motor brain tumor patients, however they recommended include pediatric data in future studies to test the neural network (NN) technique.

Research by Li-outa et al⁴⁴ presented contrasting results. Given the limitations of T-fMRI, RS-fMRI may be a helpful supplemental method for preoperative brain mapping in a pediatric investigation. Future research is required to compare the BOLD signals of Rs-fMRI networks with the functional results after surgery to confirm the functional importance of this approach. Leuthardt et al⁵⁴ provided evidence of this. In his study, including children, the failure rate of Rs-fMRI was 13%, which was much lower than the failure rate of T-MRI (38.5%) (P 0.001). In conclusion, Daniel et al⁵¹ found that most glioblastoma patients had functionally linked brain tissue, as shown by Rs-fMRI. The degree of functional connection inside the tumour also has prognostic significance. Based on research by Kumar et al,⁵³ there is evidence that Rs-fMRI may be useful for preoperative clinical language mapping when a patient cannot complete T-fMRI or if findings are insufficient. In neurosurgical procedure, accurate brain function localization is vital, while DCS is the gold standard for brain function assessment, reduction in operative time and enhanced operative planning are obtained when preoperative Rs-fMRI a noninvasive tool is performed. Many predictions of Rs-fMRI tool have been validated by DCS and demonstrated good agreement between the two findings. Zaca et al⁴⁷ demonstrated that, excellent spatial agreement between Rs-fMRI predictions and DCS were achieved in the mapping of motor, language, visual and speech articulation areas during the awake operation.

Based on the current research, twelve out of fifteen studies (80%) concurred that Rs-fMRI is acceptable and beneficial for preoperative mapping, as illustrated in Figure 2. The study by Cho et al⁴⁵ showed that Rs- fMRI is clinically quick and effective for language network mapping. Lemée et al⁴⁹ demonstrate that Rs-fMRI is more sensitive than T-fMRI in identifying functional brain language networks. This was confirmed by Kumar et al⁵³ suggested that if T-fMRI was not available, Rs-fMRI might be a suitable alternative for clinical language mapping. Although these studies highlight the effectiveness of Rs-fMRI, it is important to note that direct comparative outcome studies between Rs-fMRI and T-fMRI are limited. The current data suggests that Rs-fMRI can detect the location of the cortex consistently and with a high success rate, showing outcomes that are equivalent to those of T-fMRI. This indicates that Rs-fMRI could be a promising technique for preoperative functional localization, but further comparative studies are needed to substantiate this suggestion more robustly.

Figure 2 The findings of the content analysis of the studies included in this study.

Previous studies indicate that Rs-fMRI is a sensitive and reliable preoperative mapping tool for detecting neural networks like RSNs efficiently and effectively. As shown in Figure 2, Rs-fMRI is a promising technology that can be used in many clinical situations where T-fMRI cannot be used and could improve surgical and therapeutic techniques and include more patients in preoperative detection and evaluation. Thus, there is evidence that Rs-fMRI has interesting clinical applications. During cDCS testing is currently the gold standard for mapping palpebral areas for neurosurgical operations. This method has several disadvantages, such as requiring the patient to stay willing and interactive during the surgical process.⁶⁴ Furthermore, there is a risk of intraoperative seizures (3 to 18%) or respiratory difficulties (15 to 24%) in infants who are awake, which might reduce overall efficacy.⁶⁵ If any of these occur during surgery, the awake part must be ended promptly; this would restrict the scope of the resection.⁶⁶ Rs-fMRI correlation mapping, on the other hand, offers information throughout the gray matter and may provide information on previously inaccessible regions, such as language regions inside the brain.⁶⁷

Study Limitations

The implementation of numerous procedures and protocols in scientific investigations may pose challenges in regulating head movement and physiological fluctuations. MRI technique and methodological limitations in terms of technology may also compromise the dependability of results. Furthermore, a significant number of studies lack independent data examination by neuroradiologists, which underscores a deficiency in the validation procedure. In addition, further dependable validation studies are required to enhance the preoperative mapping precision of Rs-fMRI.

Conclusion

Results suggest Rs-fMRI could be employed for preoperative mapping. This method may aid patients, especially those who struggle with T-fMRI tasks. When planning brain tumor surgery, Rs-fMRI may be the only option to determine functional connectivity. It serves as an essential alternative in scenarios where T-fMRI is not possible, such as language mapping. Moreover, resting fMRI may be the only tool for measuring functional connectivity in brain tumour surgical planning. Despite its significant utility, Rs-fMRI can produce incorrect results due to head movement during data collection. To tackle this issue, it is imperative to incorporate real-time motion correction algorithms, improve scanner hardware to mitigate motion sensitivity and utilize sophisticated image processing methods such as machine learning to correct artifacts. In addition, the integration of hybrid imaging modalities including diffusion tensor imaging (DTI) with fMRI could yield mappings that are more exhaustive and precise. Additionally, the incorporation of artificial intelligence can enhance interpretation and analysis, resulting in more accurate tumor mappings. These technological developments are crucial in providing neurosurgeons with the necessary tools to evaluate the advantages and disadvantages of surgical procedures, thus maximizing patient results.