In a comparative study of GA and AC, the largest analysis was performed with the prognostic factors adjusted by propensity score. This study showed that AC for eloquent glioma was comparable with GA in terms of EOR and OS.
Superiority in propensity score-matched analysis for bias adjustment
We compared our study to previous articles comparing AC versus GA-adjusted background factors (Table 4). When considering the effect of AC on OS, it is challenging to consider a control group for AC other than GA. However, in retrospective or prospective designs, there will always be significant differences between the characteristics of patients undergoing AC and GA. The AC group had better KPS score and younger age before propensity score matching (Table 1). The same table also shows that lower-grade cases are biased toward AC, inevitably implying that cases with good prognosis are biased toward the AC group. The most direct way to establish effectiveness of AC is to randomize patients with glioma near eloquent areas. Only one RCT concluded that removal rate and functional preservation were significantly worse in the AC group.[26] As that the superiority of AC in terms of functional preservation has become commonly accepted, it is becoming challenging to implement RCTs ethically. To our knowledge, only one RCT is planned for glioblastoma.[36] The selection criteria include the condition that the neurosurgeon can remove the tumor in both surgical procedures, cleverly avoiding ethical issues. We considered propensity score-matched analysis as the most suitable for examining the effects of AC on OS in a retrospective analysis.
Table 4
Studies comparing patients undergoing awake craniotomy (AC) vs general anesthesia (GA) after bias adjustment
Study | Object | Study design (bias adjustment) | No. of patient AC, GA | EOR AC vs GA | OS | Functional preservation |
Suarez-Meade et al., 202012 | | Review meta-analysis | 437, 1892 | AC > GA | 90.1% vs 81.7% (p = 0.06) | n.a. | AC = GA |
Bu et al., 202013 | | Review meta-analysis | 499, 334 | AC > GA | Pooled risk ratio 0.81 | n.a. | AC > GA |
Brown et al., 201622 | | Review | 411, 540 | AC < GA | Mean GTR 41%vs 44 % | n.a. | AC > GA |
Gerritsen et al., 202016 | GIV | Retrospective (propensity score-matched) | 37, 111 | AC > GA | 94.89% vs 70.30 (p < 0.001) | AC = GA | AC > GA |
Eseonu et al., 201719 | GII-IV | Retrospective (matched pairs) | 27, 31 | AC = GA | 86.3% vs 79.6% (p = 0.136) | n.a. | AC > GA |
Martino et al., 201321 | GII | Retrospective (matched pairs) | 11, 11 | AC > GA | 91.7% vs 48.7% (p = 0.001) | n.a. | AC > GA |
Duffau et al., 200527 | GII | Retrospective (chronological comparison) | 100, 122 | AC > GA | GTR rate 21.6% vs 6.0% | AC > GA | AC > GA |
Pichierri et al., 201915 | GII-IV | Retrospective (matched pairs) | 20, 26 | AC = GA | GTR rate 60.0% vs 53.8% | AC > GA | AC > GA |
Tuominen et al., 201320 | GI-IV | Retrospective (matched pairs) | 20, 20 | AC = GA | GTR rate 50.0% vs 55.0% | n.a. | AC > GA |
This study | GII-IV | Retrospective (propensity score-matched) | 91, 91 | AC = GA | 96.1% vs. 97.4% (p = 0.83) | AC = GA | AC = GA |
AC, awake craniotomy; G, WHO grade; GA, general anesthesia; KPS, Karnofsky performance status; OS, overall survival; RCT, randomized controlled study; IQR, interquartile range; EOR, extent of resection; n.a., not available; GTR, gross total resection |
EOR and OS
There is much debate on whether AC improves the resection rate. Some reports have stated that the removal rate is improved,[12, 13, 16, 21, 23, 24, 27] while others reported a decrease or no significant difference.[14, 15, 18–20, 22, 26] The best way to address this issue is to confirm that the resection rate correlates with prognosis. Most studies are limited to the analysis of the functional outcome. Gerritsen et al. used propensity score-matched analysis limited to grade IV to show that AC improved EOR, but not OS,[16] and attributed the lack of significance to a sample size issue. Pichierri et al. showed the same resection rate for AC and GA, but better OS with AC[15]: Postoperative neural function might have affected prognosis. Although Sacko et al. evaluated the OS of glioma patients, data from tumors other than glioma were included in the analysis of resection rates.[23] OS data of Duffau et al. and Gravesteijn et al. were not quantitative evaluations using Kaplan-Meier curves.[18, 27] Our data are consistent with the absence of significant differences in EOR and OS between the AC and GA groups, proving the validity of the results.
Difference between AC and GA resection rates
According to Chang et al., a false eloquent area is presumed to be eloquent based on preoperative fluid-attenuated inversion recovery (FLAIR)/T2 imaging but is not eloquent based on the AC mapping.[37] A true eloquent area is presumed to be eloquent based on anatomical preoperative FLAIR/T2 imaging and is confirmed as such by AC mapping. They concluded that AC mapping improved the removal rate and prognosis when the T2/FLAIR tumor area was a false eloquent. By contrast, if it was a true eloquent, the prognosis did not change, and the EOR was the same as in the GA group. Considering the lack of significant difference in KPS scores between the AC and GA groups before and 3 months after surgery in diffuse gliomas including grades II-IV, the lack of difference in AC and GA removal rates suggested that a false eloquent contained more of the iMRI removal target than the true eloquent. In the sub-analysis of grade II gliomas, no difference was found in the removal rate, OS, or postoperative KPS between the two groups (Table 3, Fig. 1.B). Our data suggest that there was little true eloquence in the grade II T2 regions. Grade IV sub-analysis showed no difference in EOR, but KPS scores tended to be superior in the GA group than in the AC group, contrary to what was observed in other grades. The EOR result can confirm the long-standing belief that there are no functional fields in the gadolinium-enhanced lesions of glioblastoma. Although there was no significance for OS, a divergence could be observed at the beginning of the Kaplan-Meier curves. Surprisingly, in glioblastoma, AC may have reduced early mortality by preventing an early postoperative decline in KPS (Fig. 1D, Table 3). The recent meta-analysis by Gerritsen et al. of AC for high-grade glioma also suggests the efficacy of AC and is in the process of being validated by RCTs in grade IV tumors.[16, 36, 38]
Effect of genetics and postoperative treatment
This study was not based on the WHO 2016 classification, as we could not analyze the 1p19q codeletion status in the early 2000s. In principle, we could have eliminated these cases, but since OS analysis was among the main purposes of the study, these cases and those with grades II-III require long follow-up. Recently, however, IDH status and 1p19q coding have been considered strict prognostic factors.[39, 40] The IDH status of 16 patients was unknown, but no significant difference was found between the AC and GA groups (p = 0.131). Most of the cases whose IDH status could not be identified were glioblastoma. At our facility, gene mutation search was not actively performed in the past for cases pathologically classified as glioblastoma. Approximately 90% of glioblastoma cases are IDH-wild type, in agreement with previous estimates.[40] In 26 cases, the 1p19q status could not be identified. However, since the percentage of 1p19q codeletion was not significantly different between the groups (p = 0.397), the effects of prognostic genetic factors are considered small, although we could not analyze O6-methylguanine (O6-MeG)-DNA methyltransferase promoter methylation status.
The inclusion of all gliomas in our analysis has an effect on post-treatment and may affect the analysis of prognosis. Our facility does not actively perform AC when we suspect glioblastoma. Indeed, glioblastoma is typically already symptomatic at the time of discovery and is often not indicated for AC, as shown in Table 1. Besides, it is not possible to accurately assess the WHO grade in the preoperative image. The reason for simultaneously analyzing tumors of different WHO grades was to reduce the selection bias. Furthermore, in the post-hoc analysis, no difference was noted after treatment between the AC group and the GA group, and the effect on prognosis was considered small.
Limitations
Our study is inherently limited by its retrospective design, leading to selection bias, lack of randomization, limited control of confounding factors, and difficulty in establishing causes and effects. Moreover, it did not adjust for eloquent area lesions when comparing the AC and GA groups. The selection of glioma patients should be limited to near eloquent lesions to investigate the effects of AC on EOR and OS, and previous studies have attempted to do so. Paradoxically, adjusting for eloquent lesions makes it difficult to adjust for KPS and age, which affects the OS; moreover, the small number of eloquent cases in the GA group leads to small sample size studies. There were 12/91 (13%) patients with near eloquent lesions in the GA group, although the analysis performed when excluding them did not change the results (data not shown). However, excluding the eloquent group from the GA group creates a bias for OS analysis. Besides, the cohort adjusted for eloquence is fundamentally enriched in perirolandic glioma cases, because eloquent glioma, which is an indication for GA, is abundant in the motor area and is considered less prevalent in the language-related area. Given that the most prominent feature of AC is preserving language function, this method is also limited. We believe that propensity score-matched analysis is the best adjustment for assessing EOR and OS in non-RCT studies.