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|NI FEATURE: CENTS (CONCEPTS, ERGONOMICS, NUANCES, THERBLIGS, SHORTCOMINGS) - ORIGINAL ARTICLE
|Year : 2015 | Volume
| Issue : 5 | Page : 727-735
Navigated intraoperative ultrasound for resection of gliomas: Predictive value, influence on resection and survival
Aliasgar V Moiyadi1, Sadhana Kannan2, Prakash Shetty1
1 Department of Neurosurgery, Advanced Centre for Training, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai, Maharashtra, India
2 Department of Biostatistics, ACTREC, Navi Mumbai, Maharashtra, India
|Date of Web Publication||6-Oct-2015|
Dr. Aliasgar V Moiyadi
PS 245, ACTREC, Tata Memorial Centre, Kharghar, Navi Mumbai - 410 210, Maharashtra
Source of Support: None, Conflict of Interest: None
Background: Navigable ultrasound (NUS) is a useful adjunct for controlling resection in intra-axial brain tumors. We investigated its role in predicting residual disease and thereby in influencing the intraoperative decision regarding additional resection as also its influence on survival in glioblastoma patients.
Methods: A prospectively maintained database was accessed to retrieve the data regarding consecutive histologically verified gliomas operated using the NUS. We documented the number of times US images were obtained, the surgeon's impression of each scan and the subsequent decision regarding further resection. Survival (progression-free and overall) was calculated for patients with a glioblastoma, and univariate and multivariate analyses performed.
Results: The NUS was used for resection control in 88 gliomas. In 66 cases, intermediate scans were performed resulting in further resection in 60 of them. Radiological gross total resection was obtained in 46 cases (44%). The US correctly predicted postoperative residue in 83% cases (sensitivity and specificity of 87 and 78% respectively; positive and negative predictive values of 82 and 84%). There were 9 false positives and 6 false negative cases. When the US was false positive, the resolution was more often good (7 of 9 cases); whereas when there were false negatives, it was more likely to be less than optimal (4 of 6). Morbidity was 17% and this was not related to the additional resections. In the subset of glioblastoma patients (n = 28) use of NUS was associated with significantly better progression-free as well as overall survival rates.
Conclusions: NUS is a useful intraoperative adjunct in controlling resections. It positively and decisively influences the intraoperative course of the surgery. Understanding its correct technique and limitations, along with experience in image interpretation can help in maximizing its accuracy without compromising functional outcomes. Optimally utilized, it can improve survival.
Keywords: Navigable intraoperative ultrasound; three-dimensional ultrasound; accuracy; resection control; outcome; gliomas
|How to cite this article:|
Moiyadi AV, Kannan S, Shetty P. Navigated intraoperative ultrasound for resection of gliomas: Predictive value, influence on resection and survival. Neurol India 2015;63:727-35
|How to cite this URL:|
Moiyadi AV, Kannan S, Shetty P. Navigated intraoperative ultrasound for resection of gliomas: Predictive value, influence on resection and survival. Neurol India [serial online] 2015 [cited 2019 Aug 25];63:727-35. Available from: http://www.neurologyindia.com/text.asp?2015/63/5/727/166549
| » Introduction|| |
Navigable ultrasound (NUS) is a useful adjunct for controlling resection of intra-axial brain tumors., Specifically, the role of navigable three-dimensional (3D) US (NUS) has been well established in improving resections in malignant gliomas., The underlying basis of its efficacy is the accurate intraoperative prediction of residual tumor. Besides technical factors, user experience may influence this parameter. Accurate detection of tumor is essential if it is to guide reliably the course of the resection. Despite earlier skepticism, the literature now supports radical resections in malignant gliomas. Radical resection, even when short of a radiological gross total resection, confers a survival advantage provided it can be achieved reliably and safely. In this paper, we evaluated the diagnostic accuracy and role of NUS in predicting residual disease and thereby in influencing the intraoperative decision regarding additional resection. We further evaluated its role in improving outcomes in terms of survival for the subset of glioblastomas (GBM).
| » Methods|| |
This was a retrospective analysis of a prospectively maintained database. The database was accessed to retrieve the data regarding consecutive, histologically verified gliomas operated on using the NUS between June 2011 and May 2013 at our center. The study was conducted as per institutional review board (IRB) approval (as per local IRB stipulations, retrospective analyses of routine treatment can be performed with waiver of patient consent). Some of these cases were part of an earlier report. Tumors were classified based on resectability and enhancement as described in our earlier report. Eloquence was defined as per the criteria described by Sawaya et al. We used the Sonowand (SONOWAND AS, Trondheim, Norway) system for performing the NUS. The system functions as a navigation unit with an integrated US scanner capable of obtaining 3D scans which are automatically registered and serve to provide real-time intraoperative updates. Routine microsurgical techniques were followed. Awake craniotomy with clinical and electrophysiological monitoring was used as necessary by the operating surgeon (typically in cases close to eloquent cortex). Surgery, as is routinely performed for all cases, included a baseline US scan at the time of dural opening as well as a final scan at the end of the resection. In addition, the surgeons employed multiple intermediate scans to update the images as required. The surgeons filled out a form after each surgery [Supplement 1] and this was retrieved to extract the relevant information. Besides detailing various technical and procedural parameters (including US resolution), the form also specifically documented the number of times US images were obtained in the course of the surgery. Further, the surgeon's impression of each scan (tumor seen or not) and the subsequent decision (to proceed with resection or not) were noted. The final US scan impression (at the end of resection) with respect to the residual tumor was also noted. As is our routine practice, a uniformly thin (<5 mm) hyperechoic rim along the cavity (excluding acoustic enhancement artifacts) is generally regarded as normal. As this was a retrospective analysis, the number of times the US scans were obtained were not controlled and varied as per the operating surgeon's discretion. Postoperative neurological events were noted, and postoperative magnetic resonance (MR) scan findings recorded. Usually, the MR imaging (MRI) scan was obtained within 72 h as a routine protocol, except in a few cases where due to logistical reasons it may not have been possible. Residual disease was confirmed on the postoperative imaging (in cases where only an immediate postoperative computed tomography was available, this was interpreted in conjunction with a subsequent delayed MR scan, whenever available). Both post-contrast T1 volume as well as unequivocal T2/fluid-attenuated inversion recovery component, were factored in while deducing the residual volume. Volumetry was, however, not performed routinely. The extent of resection (EOR) was semi-quantified as gross-total (GTR = 100% resection), near-total (NTR >90%), sub-total (STR = 50–90%), and partial (PR <50%).
Accuracy analysis: One hundred and six gliomas were used for this analysis. The postresection US-defined residual disease status was correlated with the postoperative imaging-based residual disease status and 2-by-2 tables were used for assessing the diagnostic efficacy. Sensitivity (Sn), specificity (Sp), positive and negative predictive values (PPV and NPV), as well as overall accuracy (Ac) were calculated and represented as percentages with 95% confidence intervals (CI).
Survival analysis of GBM: Only histologically confirmed GBMs were included in this subset analysis (n = 28 of the 106 gliomas). GBMs operated at our center in the same time period where the NUS was not used (n = 80), were taken as controls. Postoperative adjuvant therapy details were retrieved. Follow-up data was obtained by reviewing case records and by telephonic contact and recorded through March 2014. Progression was defined as documented clinico-radiological progression where available. Death was taken as end point if no progression was documented. Kaplan–Meier analysis was used for univariate analysis of risk factors. Factors with P < 0.05 were regarded as statistically significant. The significant factors (along with the use of NUS which was the variable of interest) were entered into a multivariate model using Cox Regression analysis. The results were displayed as forest plots. Data were analyzed using PASW-21 statistical software (PASW 21, IBM Corp., Armonk, NY, USA).
| » Results|| |
In this time period, 140 cases of various brain tumors were operated using the NUS. 106 consecutive intra-axial gliomas (M: F = 7:3) operated using NUS were included in the diagnostic efficacy analysis. Of these, 28 GBMs were included in the survival analysis [Figure 1], study schema].
Demographic and clinicoradiological features: The demographic and clinical details of the 106 gliomas are shown in [Table 1]. The mean duration of surgery was 3.9 h. Awake surgery was performed in 19 cases (four with electrophysiological monitoring, which was available only during the end of the present study period). Histologically, the majority were high-grade gliomas (79%). The US mode was utilized in 98 cases (in 8, only the navigation function using preoperative MRI was used) of which resection control was intended in 91. However, in three cases due to technical problems, the NUS could not be used for resection control. Hence, overall resection control was actually performed in 88 cases. US resolution was recorded as good in 43%, moderate in 46% and poor in the remaining patients.
|Table 1: Clinical and demographic characteristics of the cohort (n = 106)|
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Influence of intraoperative US (IOUS) on the course of resection: In 66 cases, intermediate scans were performed (between the first and the final postresection scan). In 60 of these cases (68% of all cases where US was used) this intermediate scan prompted a further resection. Final postresection US scans were interpreted as residual disease in 50 cases (where further resection was deemed unsafe or impossible). In the remaining 38, US-confirmed complete resections were obtained. Based on postoperative imaging, GTR was obtained in 46 cases (44%). Of the 59 gliomas which were considered to be resectable, GTR was achieved in 43 (73%) cases and <90% resection (STR and PR) in only 6 cases (10%). In the 47 gliomas considered unresectable, a radical resection (at least NTR) was still possible in 9 cases (19%).
Accuracy Assessment: We correlated postresection US scan findings with the postoperative imaging [Table 2]. The US correctly predicted postoperative residue in 83% of cases. NUS had a sensitivity and specificity of 87 and 78% respectively; the PPV and NPV were 82 and 84%, providing an overall accuracy of 83%. The 17% discordance rate was contributed to by 9 false positive and 6 false negative cases. In view of the 17% discordance rates, we were interested in finding out whether the resolution of the US scan was related to the predictive accuracy [Table 3]. The US resolution was found to be good in 44% and moderate in 49% cases. When the US was false positive, the resolution was more often good (7 of 9 cases); whereas when there were false negatives, it was more likely to be less than optimal (4 of 6). Furthermore, in the enhancing tumors, there were equal number of false positives and negatives (four each); whereas in the nonenhancing tumor group, there were far more false positives (five) and few false negatives (two).
|Table 2: Diagnostic accuracy of the intraoperative US in predicting residual tumor (n = 88)|
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|Table 3: Correlation of predictive value of US during the resection control of gliomas with respect to its resolution and enhancement pattern of the tumor (n = 88)|
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Additional resections and morbidity: Neurological worsening was encountered postoperatively in 18 cases. In 8 of them it was transient. Further resection after an intermediate US scan was not correlated with postoperative worsening. However, worsening occurred significantly more often (P = 0.01) in tumors within eloquent areas and this was irrespective of whether a further resection was carried out after an intermediate scan [Table 4].
|Table 4: Morbidity and its correlation with location and additional resection intraoperatively|
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Survival in GBMs: There were 28 cases of glioblastoma (GBM) in the cohort of gliomas. In addition, 80 patients with a GBM were operated upon in the same time period without the use of NUS. The two subsets were more or less matched except for a higher proportion of patients with better Karnofsky Performance Scale (KPS) in the NUS group [Table 5]. Interestingly, the GTR rate as well as perioperative morbidity was also higher in the NUS subset; however, this was not statistically significant. Follow-up was available in 100 cases. Sixty-five patients had died at the time of analysis. Median follow-up was 18.4 months (95% CI: 13.5–23.4). Median overall survival (OS) and progression-free survival (PFS) were 10.5 (95% CI: 7.7–13.4) and 9.2 (95% CI: 6.8–11.7) months respectively. Univariate analysis revealed age, KPS, EOR, postoperative morbidity, completion of radiotherapy as well as adjuvant chemotherapy to be highly significant for the OS and PFS [Table 6]. Use of the NUS was borderline significant for OS, but not for PFS. Since the use of NUS was of interest to us, we included it in the multivariate model, in addition to the other significant variables. On multivariate analysis, except KPS and radiotherapy completion, the others (including the use of NUS) were highly significant. [Figure 2] and [Figure 3] show the forest plots for the multivariate analysis.
|Table 5: Risk factor distribution within glioblastoma subgroup (n = 108)|
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|Table 6: Univariate analysis for overall and progression free survival in the glioblastoma subgroup (n = 108)|
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|Figure 2: Forest plot showing the multivariate analysis of factors involved in overall survival|
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|Figure 3: Forest plot showing the multivariate analysis of factors involved in progression-free survival|
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| » Discussion|| |
Impact of NUS on the course of surgery and its use in guiding the resections
NUS prompted further resections in 68% of our cases. Since this was not a controlled study, the surgeons performed intermediate scans as and when desired. Though it is possible that these scans would have been intentionally performed at a point much before presumed complete resection, it is also likely that because the surgeon did not always perform intermediate scans (it was not performed in 22 cases) the need for the intermediate scan was genuine and not superfluous in the cases where it was performed. Nonetheless, the availability of an intraoperative imaging update improves the surgeon's comfort levels and positively impacts the course of the resection.
Accuracy of NUS
Central to the utility of the NUS is its accuracy. Overall accuracy in predicting residual disease was 83% in our study. Rygh et al., have demonstrated that the diagnostic accuracy of US is maximum at the beginning of the resection and drops during the course of surgery. Many factors, both technical and procedural, affect the overall accuracy. The former are a function of the technical makeup of the particular tool being used and are usually unchangeable. Careful selection of the tool and fine tuning of certain settings can optimize these. The latter (procedural) variables are the major determinants of the accuracy and have to be diligently focused upon to ensure the maximum benefit and accuracy. Both false positives and false negatives need to be minimized. In our experience, false positives were more often seen when the image resolution was good. It is known that artifacts increase during the course of the resection and these are mainly related to blood and tissue handling effects. These artifacts may be exaggerated when the resolution is good. Understanding this and paying attention to tissue handling is crucial and can be minimized by a meticulous technique, which improves with experience. This can increase the accuracy significantly. Another possible source of false positives is the likelihood that in nonenhancing tumors, postoperative MR may have underestimated the residual volume. It is known that lack of objective criteria for defining noncontrast enhancing tumor component can lead to errors. In our series too, the false positive rate was higher in the nonenhancing tumors (5 of 7) compared to the enhancing ones (4 of 8). Histological correlation of the US images would be a better way to ascertain its true accuracy. Unsgaard et al., have actually demonstrated that the NUS may be better for intraoperatively detecting gliomas. False positives can, however, lead to unnecessary resections, which may jeopardize neurological function. Hence, it is important to use monitoring techniques to complement the NUS. More worrying than false positives are the false negatives. These lead to incomplete resections. Many factors could lead to these “missed” remnants. These include inadequate volume scanned, insufficient exposure and hence suboptimal scanning area, blind corners and overhanging regions, using an incorrect technique of insonation or even using the wrong probe. As in our experience, when we had false negatives, more often the resolution was less than optimal. Solheim et al., have shown that when the image resolution was suboptimal, the EOR was inferior. Careful attention to the technique of insonation can help to reduce this error. To overcome both these sources of error, it is imperative to learn the correct technique and meticulously perform an optimal scan. Most importantly, one needs to develop the experience to intraoperatively interpret the images correctly. Mathematical modeling using image interpretation software can be explored to reduce subjective variation but this is still not standardized.
Extended resections and morbidity
An important criticism of any intraoperative imaging adjunct is the risk of developing neurological deficits with increased radicality of resections. Though it may be argued that over-resection may jeopardize neurological function, our experience does not show that. In our experience, the morbidity was not more in those cases where the US prompted additional resection. It is not the act of performing the additional resection which causes additional morbidity; it is more often related to the location of the tumor in close proximity to eloquent areas. It is important to remember that anatomical image guidance is to be complemented with functional monitoring especially around eloquent areas where perioperative deficits are anyway higher irrespective of the imaging modality utilized. Judicious interpretation of intraoperative anatomical and functional information is crucial to minimizing the morbidity while at the same time ensuring a substantial resection.
Navigable US and survival in GBMs
In the subset of GBMs, our results showed that the use of NUS on multivariate analysis positively influenced progression-free as well as OS. Though the association of NUS with improved resection rates has been shown earlier, its effect on survival has not been adequately described. Wang et al., in a case-control study, have shown that the use of IOUS improved survival for gliomas. They compared 79 malignant gliomas (54 grade 3, and 25 grade 4) where they used two-dimensional (2D) US with 30 randomly selected high grade glioma controls during the same period in whom 2D US was not used. They reported gross total resection rates of 69%. Specifically in malignant gliomas, the 1- and 2-year survival rates were significantly different between the control group and the group where IOUS was used (43% vs. 59% and 13% vs. 33%, respectively). This study, however, had several limitations. The selection of cases for use of the 2D US was not mentioned. Further, details of adjuvant therapy and other known prognostic factors were not discussed. In another larger series, Sæther et al., analyzed 192 glioblastoma patients operated at their center over a 15 year period. Whereas Wang et al., used conventional 2D US, Sæther et al. used the navigable 3D US modality. In the first half of their study (up to 1997), they performed surgery in 85 cases without any intraoperative guidance. In the subsequent period, they used the navigable 3D US system (Sonowand) in 102 cases. They showed a statistically significant improvement in median survival (11.9 vs. 9.6 months) in GBMs when navigable 3D US was used. This difference was significant even after adjusting for other known prognostic factors such as age, performance status and EOR. Though they did not find a difference in gross total resection rates in the two groups, in the multivariate analysis, the EOR was a significant predictor of survival. Our study is a consecutive series of GBMs operated at a single center. In consecutive patients operated on during the same time period, and with a similar profile, NUS use was associated with a statistically significant improvement in survival in GBMs (14.6 vs. 9.4 months) as compared to a matched control group in the same time period. Both EOR (GTR) and use of NUS were independently significant predictors of PFS and OS. These findings concur with the earlier reported study by Sæther et al. Though the EOR was higher in the NUS group, this difference was not statistically significant. NUS use itself was still an important predictor of survival. This may be because EOR (when dichotomized into GTR vs. non-GTR groups) disregards the benefits of radical resections which fall just short of a GTR, and which may actually have a survival benefit as demonstrated in other studies., These uncertainties would possibly be captured in the NUS group where we would expect a higher percentage resection (even though it may not be accurately recorded).
Strengths and limitations of the study
The accuracy of US was measured with respect to MR imaging. It would have been ideal to use histological correlation as described in another study. This was not a prospective study and hence is likely to be influenced by selection bias. It could be argued that there may have been a bias in selecting more “resectable” tumors for the NUS group and hence a better survival. In an earlier study (which included a subset of the patients in the present study), we have shown that “resectable” gliomas made up only approximately half of all the cases in which NUS was used. Though we have not analyzed the resectability in the present series, we can presume based on the earlier study, that so called “unresectable” gliomas were not intentionally excluded while choosing when to use the NUS in routine practice. We have earlier also highlighted the fact that IOUS is a multipurpose tool and may not always be used just for improving resection rates. It is more likely that the case mix was probably only influenced by the scheduling of cases and availability of the system. It is also noted that the NUS group had a better KPS. One reason could be that poor KPS patients would have been operated as emergencies when the NUS may not have been available. However, it is to be noted that the NUS group also had a higher morbidity. This, as we have shown earlier, could be more likely because of the eloquent location of these tumors. Whereas a better KPS was a good prognostic indicator, morbidity was an adverse factor and may have offset the potential benefit of better KPS in the NUS group. Nonetheless, on multivariate analysis, the use of NUS was an independent predictor whereas KPS was not, underlying its stronger influence on the survival. Another source of selection bias could be the intent to be radical in the US group. In the period after the introduction of NUS at our center, there has been a gradual increase in the utilization of US for radical surgeries. Solheim et al., have shown, that when the surgical intent is radical, better resections are achieved; this could be a confounding factor leading to bias toward better resections in the NUS group. We have excluded intended biopsies from the analysis and hence we can assume that the intent was a radical debulking in the remaining patients, which comprise this study group. Nonetheless, whether the increased GTR rates reflects a strong association with the use of NUS or just indicates a much more radical mindset is difficult to establish. Even if we attribute the increased radical mindset to the use of the NUS, it only reaffirms its utility and benefit. Regardless of the association, NUS turned out to be significantly associated with better survival, independent of the EOR. Furthermore, survival is a more reliable and clinically valid endpoint and this is the major strength of our study.
| » Conclusions|| |
Navigable US is a reliable and useful intraoperative adjunct in controlling resections and positively and decisively influences the intraoperative course of surgery in gliomas. Understanding its correct technique and limitations is essential to minimize false positive and false negative scans. Experience in image interpretation can help in improving its accuracy. This can be achieved without any increase in morbidity. Survival has improved in GBMs after the introduction of NUS for intraoperative guidance, and its routine use is advisable when resective surgery is planned.
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Conflicts of interest
There are no conflicts of interest.
| » References|| |
Unsgaard G, Selbekk T, Brostrup Müller T, Ommedal S, Torp SH, Myhr G, et al.
Ability of navigated 3D ultrasound to delineate gliomas and metastases – Comparison of image interpretations with histopathology. Acta Neurochir (Wien) 2005;147:1259-69.
Moiyadi AV, Shetty PM, Mahajan A, Udare A, Sridhar E. Usefulness of three-dimensional navigable intraoperative ultrasound in resection of brain tumors with a special emphasis on malignant gliomas. Acta Neurochir (Wien) 2013;155:2217-25.
Solheim O, Selbekk T, Jakola AS, Unsgård G. Ultrasound-guided operations in unselected high-grade gliomas – Overall results, impact of image quality and patient selection. Acta Neurochir (Wien) 2010;152:1873-86.
Sanai N, Polley MY, McDermott MW, Parsa AT, Berger MS. An extent of resection threshold for newly diagnosed glioblastomas. J Neurosurg 2011;115:3-8.
Sawaya R, Hammoud M, Schoppa D, Hess KR, Wu SZ, Shi WM, et al.
Neurosurgical outcomes in a modern series of 400 craniotomies for treatment of parenchymal tumors. Neurosurgery 1998;42:1044-55.
Rygh OM, Selbekk T, Torp SH, Lydersen S, Hernes TA, Unsgaard G. Comparison of navigated 3D ultrasound findings with histopathology in subsequent phases of glioblastoma resection. Acta Neurochir (Wien) 2008;150:1033-41.
Moiyadi A. Intraoperative ultrasound in neurosurgical oncology – Scope and utility. In: Hayat M, editor. Tumors of the Central Nervous System. Vol. 13. Dordrecht, Springer Netherlands; 2014. p. 149-58.
Selbekk T, Jakola AS, Solheim O, Johansen TF, Lindseth F, Reinertsen I, et al.
Ultrasound imaging in neurosurgery: Approaches to minimize surgically induced image artefacts for improved resection control. Acta Neurochir (Wien) 2013;155:973-80.
Vogelbaum MA, Jost S, Aghi MK, Heimberger AB, Sampson JH, Wen PY, et al.
Application of novel response/progression measures for surgically delivered therapies for gliomas: Response Assessment in Neuro-Oncology (RANO) Working Group. Neurosurgery 2012;70:234-43.
Alberola-López C, Martín-Fernández M, Ruiz-Alzola J. Comments on: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 2004;23:658-60.
Wang J, Liu X, Ba YM, Yang YL, Gao GD, Wang L, et al.
Effect of sonographically guided cerebral glioma surgery on survival time. J Ultrasound Med 2012;31:757-62.
Sæther CA, Torsteinsen M, Torp SH, Sundstrøm S, Unsgård G, Solheim O. Did survival improve after the implementation of intraoperative neuronavigation and 3D ultrasound in glioblastoma surgery? A retrospective analysis of 192 primary operations. J Neurol Surg A Cent Eur Neurosurg 2012;73:73-8.
Chaichana KL, Jusue-Torres I, Navarro-Ramirez R, Raza SM, Pascual-Gallego M, Ibrahim A, et al.
Establishing percent resection and residual volume thresholds affecting survival and recurrence for patients with newly diagnosed intracranial glioblastoma. Neuro Oncol 2014;16:113-22.
Moiyadi A, Shetty P. Objective assessment of utility of intraoperative ultrasound in resection of central nervous system tumors: A cost-effective tool for intraoperative navigation in neurosurgery. J Neurosci Rural Pract 2011;2:4-11.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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