Anatomical Determinants of Epilepsy Surgery Outcomes: A Systematic Review and Individual Patient Data Meta-Analysis

Anatomical Determinants of Epilepsy Surgery Outcomes: A Systematic Review and Individual Patient Data Meta-Analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Anatomical Determinants of Epilepsy Surgery Outcomes: A Systematic Review and Individual Patient Data Meta-Analysis View ORCID Profile Tamir Avigdor , Alyssa Ho , Matthew Moye , William Davalan , Erica Minato , Sana Hannan , Tamzin Holden , Tasha Bouchet , Yingqi Laetitia Wang , Kassem Jaber , Mays Khweileh , Samatha Kaplan , Vojtech Travnicek , David Carlson , Birgit Frauscher doi: https://doi.org/10.1101/2025.05.13.25327534 Tamir Avigdor 1 Montreal Neurological Institute, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tamir Avigdor Alyssa Ho 2 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew Moye 2 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site William Davalan 3 Department of Medicine, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Erica Minato 1 Montreal Neurological Institute, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sana Hannan 4 Department of Biomedical and Life Sciences, Lancaster University , Lancaster, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tamzin Holden 4 Department of Biomedical and Life Sciences, Lancaster University , Lancaster, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tasha Bouchet 3 Department of Medicine, McGill University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yingqi Laetitia Wang 5 Schulich School of Medicine & Dentistry, Western University , London, Ontario, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kassem Jaber 2 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , North Carolina, U.S.A 6 Department of Biomedical Engineering, Duke Pratt School of Engineering , Durham, North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mays Khweileh 2 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samatha Kaplan 7 Duke University Medical Center Library, Duke University School of Medicine , Durham, North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vojtech Travnicek 8 The Czech Academy of Sciences, Institute of Scientific Instruments , Brno, Czech Republic 9 International Clinical Research Centre, St Anne’s University Hospital Brno , Czech Republic Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Carlson 10 Department of Biostatistics and Bioinformatics, Duke University School of Medicine , Durham, North Carolina, U.S.A 11 Department of Computer Science, Department of Civil and Environmental Engineering, Duke University , Durham, North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site Birgit Frauscher 2 Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center , North Carolina, U.S.A 6 Department of Biomedical Engineering, Duke Pratt School of Engineering , Durham, North Carolina, U.S.A Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: birgit.frauscher{at}duke.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Importance To date, epilepsy surgery outcomes remain highly variable, with seizure freedom rates hovering around 50-70%, highlighting the need for a deeper understanding of the factors influencing surgical success. Objective To conduct an individual patient data meta-analysis of epilepsy surgery outcomes in drug-resistant epilepsy, leveraging granular, patient-level data to identify key clinical, demographic, and anatomical factors that influence surgical success. Data Sources MEDLINE (via Ovid), Embase, and Scopus were searched from inception to August 9, 2024. Study Selection Primary studies reporting patient-level surgical outcomes and clinical information in patients with drug-resistant epilepsy. Data Extraction and Synthesis Data were abstracted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Unique patient data from 385 studies were pooled, yielding 5,588 patients with outcomes, localization, demographics, pathology, and other findings. Surgical success rates were reported with 95% Wald confidence intervals. Main Outcome(s) and Measure(s) Measured outcomes were surgical success rates (% Engel 1/ ILAE 1-2) based on key patient and disease-specific factors. Statistical associations were tested with chi-squared tests (p<0.05), effect sizes measured with Cramer’s V, and post-hoc comparisons adjusted using the false discovery rate. Results Surgical success rates (Engel I/ILAE 1-2) have remained stable over the past decades (r=0.25, p=0.13), while seizure freedom rates (Engel Ia/ILAE 1) have significantly improved (r=0.59, p<0.01). This occurred alongside a rise in surgical interventions, including more complex cases, as indicated by increased stereo-EEG use, and the adoption of minimally invasive techniques. Surgical success varied significantly by lobar anatomy (χ 2 =52, p<0.01), with the highest success rates in temporal (68.6% [67.0–70.1%]) and insular lobes (66.2% [55.4–77.0%]), although only temporal outcomes were statistically significant. Multilobar resections had lower success rates, with outcomes varying significantly by lobar combination (χ 2 =25, p=0.02). Variability in outcomes were also influenced by histopathology and MRI findings (χ 2 =121, p<0.001), and the type of surgical intervention (χ 2 =30.5, p<0.001). Conclusions and Relevance This meta-analysis combined patient-level data from multiple studies to understand how individual patient characteristics influence surgical outcomes. Identifying these prognostic factors can guide more personalized patient selection and surgical planning, and ultimately improve rates of favorable outcomes in epilepsy surgery. Question What are the main factors influencing surgical success in drug-resistant epilepsy patients? Findings A systematic review of 5,588 individual patient data from 385 primary research studies showed that the anatomical region, surgical technique, and histopathological diagnosis impact epilepsy surgery outcomes, with varying success rates based on these factors’ interaction. Meaning Presurgical evaluations and research into potential biomarkers and treatment options should consider these patient-specific factors instead of relying on generalized, population-level outcome statistics. Introduction Epilepsy is one of the most prevalent and disabling neurological conditions, affecting individuals of all ages and impacting over 70 million individuals worldwide. 1 Beyond its debilitating clinical effects, epilepsy carries with it significant comorbidities and substantial economic burden. 2 In 2016, the World Health Organization’s Global Burden of Disease highlighted the significance of epilepsy, ranking it among the top neurological contributors to global disability-adjusted life-years. 2 While antiseizure medication is the first line of treatment for epilepsy, nearly one-third of patients continue to experience seizures despite having been prescribed multiple and varied treatment regimens of antiseizure drugs in sufficient dosages. 3 , 4 This condition, termed drug-resistant epilepsy 5 poses a major challenge in epilepsy treatment, and is responsible for 80% of the total cost associated with the disorder. 6 , 7 For these individuals, surgical intervention is a potentially life-changing option, offering the prospect of seizure freedom and improved quality of life. 8 Epilepsy surgery can be divided into two main categories, resective and minimally invasive surgery, both aimed at removing the tissue indispensable for seizure generation (so-called “epileptogenic zone”), 9 thereby aiming to render the patient seizure free. 8 Resective surgery, the most invasive option, is typically used to remove larger volumes of diseased tissue, whereas ablation, encompassing laser interstitial thermal therapy (LITT), radiofrequency thermocoagulation, and focused ultrasound, offers a less invasive alternative. 10 Despite advancements in epilepsy surgery, 11 rates of post-surgical seizure freedom hover around 50-70% and are highly variable. 12 Several factors contribute to this variability, such as limitations in electroencephalography (EEG)/intracranial-EEG (iEEG) monitoring for accurately capturing the epileptogenic zone, 13 as well as differences in etiology, implicated brain regions, 14 - 16 and demographic factors such as sex and age. 17 Additionally, even in cases where epilepsy surgery may be successful, there is a severe underutilization of surgical interventions, with only 1% of eligible patients being referred for comprehensive epilepsy presurgical evaluations. 18 , 19 This may stem from fear of complications and reservations about the benefits of surgery. 20 These challenges highlight the importance of recognizing the personalized nature of epilepsy and the necessity of selecting tailored surgical interventions. As such, there is a need to understand how different types of surgery, etiology, age, sex and specific brain regions interact and contribute to surgical success. 14 - 16 Despite the known influences of these factors in affecting the likelihood of seizure freedom following surgery, their relative importance in determining surgical success remains to be determined. Many meta-analyses have examined seizure outcomes after epilepsy surgery, but they often focus on specific cohorts and rely on aggregate data, which may overlook key sources of heterogeneity. To address this, we conducted a comprehensive literature review and meta-analysis of individual patient data (IPD) to systematically quantify the rates of surgical success within a large international dataset of patients with drug-resistant epilepsy. As the gold standard for evaluating treatment effectiveness across diverse patient subgroups, IPD meta-analysis provides a more precise and nuanced assessment of surgical outcomes across various epilepsy and surgery types. This approach allowed us to identify key sources of heterogeneity between these rates, such as sex, age, and pathology, offering deeper insights into factors influencing surgical success. It also improves data handling, facilitates novel insights, and ensures greater transparency and reliability in findings. By integrating these findings, we aim to equip clinicians and patients with a more data-driven understanding of epilepsy surgery, thus optimizing patient outcomes and improving the accessibility and acceptance of surgical interventions in the management of drug-resistant epilepsy. Methods Search strategy and selection criteria We conducted a systematic review to identify studies reporting patient-level data on epilepsy surgery outcomes in drug-resistant epilepsy for the purpose of an IPD meta-analysis. Our data collection and analyses followed a prespecified protocol (registered on Prospero on 17 April 2024: CRD42024530397). The search strategy was developed in collaboration with a medical librarian (S.K.) who identified potential cohorts by searching OVID MEDLINE, Embase, and Scopus using a comprehensive search strategy (see Appendix 1). The search was conducted on August 9, 2024. The Covidence software was used for the review and management of studies. Title and abstract screening, as well as full-text reviews, were performed by two reviewers, with any conflicts resolved by a third reviewer. For the full list of inclusion and exclusion criteria, please see Supplementary Figure 1. Data extraction was performed by one reviewer and cross-checked by the first authors (T.A. or A.H.). Data extraction For each patient, we extracted the following patient-level information: sex, age, pathology, type of epilepsy, affected hemisphere including brain region (if available), type of surgical intervention, region(s) resected, MRI information, and surgical outcome. Missing data was denoted as “unknown” and cross-checked for validation. To ensure consistency across studies with varied terminology, we standardized the format of each variable using established definitions and classifications defined by the ILAE whenever possible. 21 - 24 Additionally, to mitigate duplicate entries from multiple papers sharing the same patient cohorts, we implemented a two-step screening process. First, an automated screening identified potential duplicates by matching key demographic and clinical variables, including sex, age, etiology, and center/country of origin. Suspected duplicates were then manually reviewed for confirmation before removal to ensure data integrity. Definition of surgical success The definition of surgical success varies largely across studies. To standardize outcomes, we used two widely recognized measures. The Engel Outcome Scale 25 classifies outcomes into four classes (Engel 1-4) based on the presence and improvement of disabling seizures, with Engel 1 indicating a favorable outcome and Engel 1a representing a completely seizure free outcome. The 2001 ILAE classification 26 categorizes outcomes into six classes (ILAE 1-6) based on seizure frequency and semiology, with ILAE 1-2 indicating a favorable outcome, and ILAE 1 representing a seizure free outcome. For the main results of this meta-analysis, surgical success is defined as Engel 1 and ILAE 1-2, while supplementary analyses also report results associated with seizure-freedom using Engel 1a and ILAE 1. Parcellation Cortical areas were grouped into five main lobes: temporal, parietal, insular, frontal, and occipital. Sublobar parcellation was performed using the Brainnetome Atlas, 27 which provides a standardized framework for categorizing the epileptic focus and resected regions. All subsequent regional analyses were conducted based on these predefined groupings. Statistical Analysis Surgical success rates were calculated as the percentage of patients in the positive group relative to those in the negative group, with 95% confidence intervals using Wald’s coefficient. Chi-squared tests were used to assess statistical associations between variables and surgical success in each sub-analysis, with significance set at p<0.05. Cramer’s V was used to measure the effect size. Post-hoc comparisons were adjusted for multiple corrections using the false discovery rate. We further explored data heterogeneity using a logistic regression model to estimate the predicted probability of surgical success based on all available parameters. Role of the funding source There was no funding source for this study. Results Demographics and clinical characteristics A total of 385 studies, providing patient-level data on 5,588 patients, met the selection criteria and were included in this meta-analysis (see inclusion flowchart, Supplementary Figure 1). 28 - 140 Publication bias was assessed by a funnel plot for studies reporting patient-level data. Visual examination revealed mild asymmetry in the rates of surgical success (Supplementary Figure 2). A description of included papers is described in Supplementary Table 1. Of the 5,588 patients, 2,220 were male, 2,129 were female, and sex was not specified for 1,239 patients. The most common epilepsy types in our cohort were temporal lobe epilepsy (n=3,252) and frontal lobe epilepsy (n=754). The median age at intervention was 24 years (0.5 to 77 years), with a median disease duration of 16.5 years (10 months to 60 years) between epilepsy diagnosis and surgical intervention. The median post-surgical follow-up interval was 24 months (12 to 480 months). The dataset represents patients from 36 countries, highlighting the global scope of this IPD meta-analysis (Supplementary Figure 3). Overall, 3,894 patients (success rate 64.14% [95% CI 62.88 – 65.39%]) achieved a favorable surgical outcome (Engel 1 or ILAE 1-2) at the last follow-up. There were no significant differences between favorable and unfavorable surgical outcomes for sex, duration of disease, and post-surgical follow up time (p>0.05), while age at intervention showed a negligible effect (p=0.006, d=0.05). A summary of patient characteristics and corresponding rates of seizure freedom is presented in Figure 1 . Download figure Open in new tab Figure 1. Summary of patient demographics. (a) Percentage of males and females with good outcomes and seizure-freedom. (b) Distribution of post-surgical follow-up duration in the cohort. (c) Distribution of patients and their age at time of surgical intervention. (d) Distribution of disease duration prior to surgical intervention. Type of surgical intervention Surgical success rates (Engel I/ILAE 1-2) have remained stable over time ( Figure 2a ; r=0.25, p=0.13), while the rate of seizure freedom (Engel Ia/ILAE 1) has significantly improved (Supplementary Figure 4a). This trend persists despite the increasing adoption of minimally invasive surgeries and a growing number of patients undergoing surgical interventions ( Figure 2b , Supplementary Figure 4b). There was a significant association between the type of surgical intervention and the rate of surgical success (χ 2 =30.5, p<0.001, V=0.07) ( Figure 2c ). Patients who underwent hemispherectomies (success rate 67.3% [95% CI 60.7 – 74.0%]) and resections (success rate 64.8% [95% CI 63.5 – 66.2%]) had a higher, albeit not statistically significant, success rate than those who received disconnective surgeries (success rate 60.9% [95% CI 40.9 – 80.8%]) or minimally invasive procedures such as LITT or radiofrequency thermocoagulation (success rate 60.5% [54.5 – 66.4%]). In contrast, corpus callosotomy (success rate 45.0% [95% CI 37.0 – 53.0%]), typically offered as a palliative surgery, had the lowest success rate, with comparisons with hemispherectomies, resections, and minimally invasive procedures reaching statistical significance (all p<0.001). Download figure Open in new tab Figure 2. Type of surgical interventions. (a) Reported success rates of resections (blue), defined as Engel I or ILAE 1–2 outcomes, over time, alongside success rates for minimally invasive surgeries (yellow). Each dot represents the overall success rate calculated from all the patients pooled across studies conducted in a given year. Linear regression revealed no significant trend (r=0.25, p=0.13). (b) Number of patients undergoing resections and those undergoing minimally invasive surgery over time. Only years with ≥20 patients are shown. (c) Surgical success rates by intervention type. The highest success rate was observed in hemispherectomies (n=190, 67.4% [60.7–74.0%]), followed by resections (n=4,967, 64.8% [63.5–66.2%]), disconnections (n=23, 60.9% [40.9–80.8%]), and minimally invasive surgeries (n=263, 60.5% [54.5–66.4%]), with no significant differences among these groups (p>0.05). In contrast, success rates were significantly lower for corpus callosotomies (n=145, 43.4% [35.4–51.5%]) compared to hemispherectomies, resections, and minimally invasive surgeries (all p<0.01). Surgical success by brain region We analyzed surgical success rates and rates of seizure freedom (Supplementary Figure 5) separately for patients who had procedures confined to a single lobe and those who underwent surgeries involving a combination of different lobes. For unilobar surgeries, there was a significant association between the involved lobe and the rate of surgical success (χ 2 =52, p<0.001, V=0.12). Success rates were significantly higher in the temporal lobe (68.6% [67.0 – 70.1%]) compared to the occipital (55.8% [46.6 – 65.1%]), parietal (56.0% [49.0 – 63.0%]), and frontal lobe (57.3% [53.8-60.8%]) (all p<0.05) ( Figure 3a ). The insula demonstrated the second highest success rate at 66.2% [55.4–77.0%], although this was not statistically significant. Within the temporal lobe, we further conducted a sublobar analysis ( Figure 3b ), revealing no significant difference between surgeries of the mesial temporal lobe (63.1% [59.0 – 67.1%]) and those of the lateral and basal temporal lobe (71.0% [62.1 – 80.0%]). Download figure Open in new tab Figure 3. Surgical success rates across unilobar (n=4384), sublobar (n=655) and multilobar (n=491) surgeries. (a) For unilobar surgeries, surgical success significantly varied by lobe (χ 2 =52, p<0.001, V=0.12). Success rates were as follows: temporal lobe (n=3,252, 68.6% [67.0–70.2%]), insula (n=74, 66.2% [55.4–77.0%]), frontal lobe (n=754, 57.3% [53.8– 60.8%]), parietal lobe (n=193, 56.0% [49.0–63.0%]), and occipital lobe (n=111, 55.9% [46.6–65.1%]). Only the differences between the temporal and frontal lobe, temporal and parietal lobe and temporal and occipital lobe were significant (p<0.01). (b) Within the temporal lobe, sublobar analysis showed that mesial temporal surgeries (n=555) had a success rate of 63.1% (59.0–67.1%), whereas procedures targeting the lateral and basal temporal regions (n=100) had a success rate of 71.0% (62.1–79.9%). This difference was not significantly different. (c) Multilobar surgeries involved combinations of the frontal, temporal, insular, and parietal lobes. There was a significant association between surgical region and success rate (χ 2 =25, p=0.02, V=0.22). Success rates for different multilobar combinations were: frontal-insula (77.4% [66.1–88.6%]), temporal-occipital (66.7% [55.8– 77.6%]), frontal-parietal (65.1% [54.8–75.3%]), temporal-parietal (60.0% [45.7–74.3%]), parietal-insula (60.0% [38.5–81.5%]), parietal-occipital (53.3% [38.8–67.9%]), temporal-insula (48.6% [32.5–64.8%]), temporal-frontal (48.3% [39.2–57.4%]), and temporal-frontal-parietal (35.0% [14.1–55.9%]). Significant differences were observed between the following pairs: temporal-frontal vs. temporal-occipital (p=0.02, V=0.17), temporal-frontal vs. frontal-parietal (p=0.03, V=0.16), temporal-occipital vs. temporal-frontal-parietal (p=0.02, V=0.24), and frontal-parietal vs. temporal-frontal-parietal (p=0.03, V=0.22). For multilobar surgeries ( Figure 3c ), the procedures performed included combinations involving the frontal, temporal, insular, and parietal lobes. Overall, there was a significant association between the surgical region and success rate (χ 2 =25, p=0.02, V=0.22). Surgeries involving the frontal and insular lobes had the highest success rate (77.4% [66.1 – 88.6%]). In contrast, procedures affecting the temporal, frontal and parietal lobes had the lowest success rate (35.0% [14.1 – 55.9%]). This difference was statistically significant (p=0.03, V=0.22). Direct surgery vs. iEEG-informed surgery The use of iEEG, particularly stereo-EEG, has expanded globally and gained popularity over the past decade, 141 becoming an integral tool for improving the localization of the epileptic focus, particularly for more complex cases ( Figure 4a and 4b ). Surgical success rates for patients who had SEEG did not change significantly over time (n=1330, p=0.27). Among unilobar cases ( Figure 4c ), surgical success varied significantly by lobe (χ 2 =6.2, p=0.004, V=0.11). These trends were consistent with those observed in the overall cohort. Download figure Open in new tab Figure 4. Utilization of stereo-electroencephalography (SEEG) in presurgical evaluation and its impact on surgical outcomes. (a) Surgical success rates among patients who underwent SEEG remained relatively stable over time, with no significant trend observed (p = 0.27). (b) The number of patients receiving SEEG as part of their presurgical evaluation increased over the years. (c) In unilobar cases, surgical success rates varied significantly by lobe (p = 0.18, V = 0.08), with the highest success in the insula (69.2% [44.1-94.3%]) and temporal lobe (64.2% [60.4-68.1%]), followed by the frontal (54.9% [48.9-60.9%]), parietal (49.1% [35.6-62.5%]), and occipital lobes (47.6% [26.3-69.0%]). The differences in surgical success rates between the temporal and frontal lobes (p = 0.004, V = 0.11) and between the temporal and parietal lobes (p = 0.02, V = 0.11) were statistically significant. Surgical success by pathology Subgroup analyses were performed based on pathology, with malformations of cortical development (n=1,340), hippocampal sclerosis (n=950), tumors (n=588), glial scar (n=397), and vascular malformation (n=188) identified as the most common diagnoses in our cohort ( Figure 5a ). There was a significant association between the type of pathology and the rate of surgical success (χ 2 =121, p<0.001, V=0.16). Surgical success was highest in patients with tumors (81.6% [74.9 – 81.6%]) and lowest in those with glial scars (56.3% [46.5 – 56.3%]). This difference was statistically significant (p<0.01, V=0.28). Download figure Open in new tab Figure 5. Surgical success rates by pathology. Pathology was determined by MRI, histology, or both. (a) Success rates for the top five pathologies were: tumors (78.2% [74.9–81.6%]), hippocampal sclerosis (72.5% [69.7–75.4%]), vascular malformation (70.7% [64.2–77.2%]), malformation of cortical development (59.7% [57.1–62.3%]), and glial scars (51.4% [46.5– 56.3%]). All paired comparisons were statistically significant (p<0.01), except for hippocampal sclerosis vs. vascular malformation (p=0.68, V=0.01). (b) For MRI negative epilepsy, success rate by lobe was: insula (n=23, 65.2% [45.8–84.7%]), temporal lobe (n=341, 58.1% [52.8–63.3%]), frontal lobe (n=139, 57.6% [49.3–65.8%]), and parietal lobe (38.7% [21.6–55.9%]). Only the difference between the temporal and parietal lobes was statistically significant (p=0.02, V=0.13). (c) In malformation of cortical development (MCD), success rates by lobe were: occipital (n=36, 66.7% [51.3–82.1%]), insula (n=40, 65.0% [50.2–79.8%]), temporal (n=497, 65.4% [61.2–69.6%]), frontal (n=385, 60.3% [55.4– 65.1%]), and parietal (n=86, 52.3% [41.8–62.9%]). The only statistically significant difference was between the temporal and parietal lobes (p=0.03, V=0.09). (d) For focal cortical dysplasia (FCD) type II, there was no significant association between lobe and success rate (p=0.18, V=0.08). Success rates by lobe was: temporal (n=162, 69.1% [62.0– 76.2%]), frontal (n=171, 69.0% [62.1–75.9%]), insula (n=22, 59.1% [38.5–79.6%]), and parietal (n=45, 55.6% [41.0–70.1%]). Black regions indicate insufficient sample size, defined as n<20. For “MRI-negative” epilepsy ( Figure 5b ), surgical success was significantly associated with the brain region (χ 2 =26, p=0.02, V=0.23), with the temporal lobe (58.1% [52.8-63.3%]) having a significantly higher success rate compared to the parietal lobe (38.7% [21.6 – 55.9%]) (p=0.02, V=0.13). Similarly, in patients with malformations of cortical development, surgical success was significantly associated with the brain region (χ 2 =26, p=0.03, V=0.22) ( Figure 5c ). Resections targeting malformations in the temporal lobe (65.4% [61.2-69.6%]) had a significantly higher success rate compared to those in the parietal lobe (52.3% [41.8 – 62.9%]). There was no significant association between brain regions and surgical success in cases of focal cortical dysplasia (FCD) type II (χ 2 =6.2, p=0.18, V=0.08) ( Figure 5d ). Results for seizure freedom by pathology are presented in Supplementary Figure 6. For the mesial temporal lobe, surgical success rates varied by pathology type (p<0.006, V=0.21). Rates were highest for hippocampal sclerosis (n=172; 70%) and lowest for glial scars (n=22; 55%) (Supplementary Figure 7). These differences were not significant. Discussion This study presents a comprehensive IPD meta-analysis of surgical outcomes in drug-resistant epilepsy, incorporating data from 385 studies and 5,588 patients. Our findings highlight that surgical success varies based on the affected brain region, type of surgical intervention, and underlying pathology, whereas there was no significant association between surgical outcomes and patient sex, disease duration, and post-surgical follow up duration. While there have been numerous meta-analyses exploring seizure outcomes following epilepsy surgery, they often focus on specific epilepsy types or patient cohorts, 142 - 144 and primarily rely on aggregate data. 145 , 146 However, IPD meta-analyses have been shown to produce different results in 20% of cases and are 15% more likely to identify statistically significant differences that aggregated analyses may overlook. 147 This is particularly relevant for epilepsy surgery outcomes, given its diverse etiology and the persistent variability in surgical outcomes. Indeed, despite technological and diagnostic advancements over the years, seizure freedom rates remain between 50–70%, with long-term prognosis often being even less favorable. 12 , 145 Our analysis highlights the notably high success rates of insular lobe surgeries, particularly when guided by SEEG. This challenges conventional expectations and suggests that the insula may be a more favorable surgical target than previously recognized. 148 , 149 Insular lobe epilepsy has long been considered difficult to diagnose due to its reputation as a “great mimicker” of other focal epilepsies and its poor visibility in non-invasive investigations. 149 , 150 Indeed, it was the least frequently performed unilobar surgery in our cohort. However, with increased SEEG sampling of the insula, more patients are being accurately diagnosed and successfully treated with surgery, leading to improved outcomes. 151 , 152 Similarly, temporal lobe surgeries continued to demonstrate high rates of favorable outcomes, reinforcing their well-established role as an effective surgical target. 142 , 151 In contrast, surgeries involving the posterior cortex, including the occipital and parietal lobes, were associated with the lowest success rates. This is likely due to the functional significance of both the occipital and parietal lobes. 40 , 135 , 153 The occipital lobe is crucial for visual processing, 138 , 139 and the parietal lobe is involved in spatial and sensory integration, 40 , 135 , 140 making surgical resection more complex and less likely to be fully successful. Both lobes present challenges in achieving complete resection while preserving function. Given these findings, increased awareness and refined surgical approaches for insular and posterior cortex epilepsies are warranted. Expanding the use of SEEG in evaluating complex cases may further improve patient selection and surgical outcomes, particularly for insular epilepsy, where growing evidence supports its viability as a surgical target. Our results further show that patients with malformations of cortical development, particularly those with FCD type II, achieve more favorable surgical outcomes. This aligns with recent findings from the MELD consortium, 16 reinforcing FCD type II as a strong prognostic marker for surgical success. Given these promising outcomes, surgery should be more readily considered for this patient group, as it offers a high likelihood of seizure freedom and represents a highly effective treatment option. These findings are also consistent with recommendations from the Surgical Therapies Commission of the ILAE, which, through a Delphi consensus process involving 61 experts, advocated for broader surgical referral criteria. 154 Among their recommendations, they emphasized considering surgery even for non–drug-resistant patients with brain lesions in non-eloquent cortex, 154 highlighting the strong prognostic value of pathological indicators identified through MRI. This expert consensus supports the growing body of evidence promoting earlier and more widespread consideration of epilepsy surgery to optimize patient outcomes. Strengths and limitations Our meta-analysis has several key strengths, including its rigorous methodology and the use of individual-level patient data from a large international cohort of patients with drug-resistant epilepsy. This approach allowed for a more precise and nuanced evaluation of surgical success across diverse epilepsy types and procedures. Additionally, we meticulously screened the data and conducted thorough tests for publication bias, ensuring the robustness and reliability of our findings. Despite these strengths, several challenges arose in pooling data due to variability in the level of detail reported across studies and the lack of standardization in published data. To mitigate this, we applied standardized terminology defined by the ILAE whenever possible. 21 - 24 However, the inconsistencies between studies highlight the need for greater uniformity in reporting. Future large-scale, prospective, multicenter studies with standardized methodologies are essential to refine our understanding of surgical outcomes and enhance the ability to model disease trajectories more accurately. Conclusions In summary, the findings of this IPD meta-analysis suggest key factors associated with surgical success, including age at intervention, the type of surgical approach. Moreover, the observed variability in outcomes by anatomical region may inspire the broader consideration of surgical interventions for epilepsy types traditionally associated with poor prognoses, such as insular lobe epilepsy. These insights can aid clinicians and patients in presurgical decision-making and risk counseling. Data Availability All data produced are available online at surveyed papers Acknowledgments We would like to thank Arpana Wadhwani and Marah Abdelkader for their valuable assistance with the title and abstract screening of manuscripts. References ↵ Ngugi , A. 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