Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma.

BACKGROUND AND
PURPOSE: Disruption of the tumor-brain barrier in meningioma plays a critical role in the development of peritumoral brain edema (PTBE). We hypothesized that osteoporotic conditions may be associated with PTBE occurrence after radiation in patients with intracranial meningioma.
METHODS: We measured Hounsfield units (HU) of the frontal skull on simulation brain CT in patients who underwent linear accelerator (LINAC)-based radiation treatment for intracranial meningioma. Receiver operating characteristic curve analysis was performed to determine the optimal cut-off values for several predictive factors. The cumulative hazard for PTBE was estimated and classified according to these factors. Hazard ratios were then estimated to identify independent predictive factors associated with the development of PTBE after radiation in intracranial meningioma patients.
RESULTS: A total of 83 intracranial meningiomas in 76 patients who received LINAC-based radiation treatment in our hospital over an approximate 5-year period were included for the study. We found mean frontal skull HU ≤630.625 and gross tumor volume >7.194 cc to be independent predictors of PTBE after radiation treatment in patients with meningioma (hazard ratio, 8.41; P = 0.019; hazard ratio, 5.92; P = 0.032, respectively). In addition, patients who were ≥65 years showed a marginally significant association with PTBE.
CONCLUSIONS: Our study suggests that possible osteoporotic conditions, large tumor volume, and older age may be associated with PTBE occurrence after LINAC-based radiation treatment for intracranial meningioma. In the future we anticipate that these findings may enhance the understanding of the underlying mechanisms of PTBE after radiation in meningioma patients.

Introduction

Meningiomas are the most common extra-axial primary intracranial benign tumors and account for 13–26% of all primary intracranial tumors [1]. Although microsurgical tumor resection is the treatment of choice for symptomatic meningiomas, gross total resection of meningiomas is not always possible due to various conditions such as tumor size, location, adjacent neurovascular structures, or the patient’s medical status. Radiation therapy is used as a treatment for meningiomas when the remnant tumor is present after surgery or when surgical resection is not an option [2]. Radiotherapy for meningioma is accepted as a safe treatment modality. Approximately 5% to 40% of patients experience treatment-related complications [3]. It was reported that symptomatic brain edema occurs in 37.5% of patients with parasagittal meningiomas after gamma knife radiosurgery [4]. Previously, several risk factors associated with peritumoral brain edema (PTBE) after radiosurgery in meningioma were reported. These include greater radiation dose, greater tumor size or volume, tumor location, brain-tumor interface, no prior resection for meningioma, and presence of pretreatment edema [5].

Disruption of the tumor-brain barrier in meningioma plays a critical role in the development of PTBE [6]. A previous study regarding microscopic anatomy of the brain–meningioma interface reported the presence of arachnoid trabeculae at the brain–meningioma contact interface [7]. We previously demonstrated a close correlation between bone mineral density (BMD) and Hounsfield unit (HU) values [8]. In addition, we suggested that systemic osteoporosis is negatively associated with the integrity of arachnoid trabeculae as both the bone and the arachnoid trabeculae are composed of type 1 collagen [8,9]. We hypothesized that osteoporotic conditions may be associated with PTBE after radiation in intracranial meningioma patients. To the best of our knowledge, there are no previous studies describing the possible relationship between osteoporotic conditions and PTBE after radiotherapy in meningioma which have been published [5].

To test this hypothesis, we measured HU values in the frontal bone from simulation brain computed tomography (CT) of patients who underwent linear accelerator (LINAC)-based radiation treatment for intracranial meningioma in our hospital. We evaluated other predictive risk factors for PTBE in meningioma after radiation treatment.

Methods

Study patients

This study was approved by the Institutional Review Board of Hanyang University Medical Center, Korea, and conformed to the tenets of the Declaration of Helsinki. Owing to the retrospective nature of the study, the need for informed consent was waived. All patient records were anonymized prior to analysis.

We retrospectively extracted data for all consecutive patients who were diagnosed with intracranial meningioma and received LINAC-based radiation treatment for the first time from the database of our hospital’s NOVALIS registry, from July 7, 2014 to July 31, 2019. The registry has been designed for prospective research since July 7, 2014. Demographic patient information, prescribed radiation dose, and fractionation data were extracted from the NOVALIS registry.

All intracranial meningiomas were diagnosed by radiologic findings or histological confirmation following resection. All radiologic findings were confirmed by experienced neuro-radiologists. We only included patients with meningioma who underwent at least one follow-up imaging (CT/magnetic resonance imaging [MRI]) after LINAC-based radiation treatment in order to assess the occurrence of PTBE. The last imaging follow-up period after treatment was investigated in all study patients. PTBE was defined as the radiological confirmation of newly developed PTBE or the progression of preexisting PTBE after radiation treatment with newly developed neurological deficits. All patients had no preexisting PTBE among the patients who did not underwent surgery for meningioma before radiation treatment. Two patients were excluded due to no measurable cancellous bone of the frontal skull on brain CT.

Radiation technique

All patients were treated using the NOVALIS Tx system (Varian Medical Systems, CA, USA; Brainlab, Feldkirchen, Germany) in our hospital. A noninvasive thermoplastic mask was used to perform simulation-computed tomography (CT) for radiation treatment. The Novalis ExacTrac image system and robotic couch of the NOVALIS Tx system allowed us to adjust the patients’ positions according to the information from the real-time image acquisition. Patients were treated with a 6 MV LINAC-based radiation treatment within 1 week from the day when the CT simulation was performed.

Gross tumor volume (GTV) was defined as the contrast-enhanced area on T1-weighted MRI images. In surgery patients, the GTV was defined as the postoperative resection cavity and the area of residual tumor in cases of subtotal resection. The clinical target volume (CTV) was identical to the GTV. The planning target volume (PTV) was defined as a symmetrical 0 to 2-mm expansion from the CTV. In case the tumor was located near an organ at risk, we adjusted the PTV with no expansion in the area of the tumor that was close to the organ at risk. The iPlan (Brainlab, Feldkirchen, Germany) and Eclipse (Varian, CA, USA) that are 3D treatment/planning systems of the NOVALIS Tx, were used for radiation planning using MRI/CT-fusion images in all intracranial meningioma patients. The 3D treatment/planning system automatically calculated the GTV, CTV, and PTV in all treated patients. We attempted to achieve tight conformality of the treatment isodose to the 3D reconstructed meningioma geometry.

Stereotactic radiosurgery (SRS) was defined as a single session treatment, hypofractionated SRS (hf-SRS) as 2 to 5 fractions, hypofractionated stereotactic radiotherapy (hFSRT) as 6 to 10 fractions, and fractionated stereotactic radiotherapy (FSRT) as doses delivered in >10 sessions (1.8–2.0 Gy/fraction) [10,11]. The biologically equivalent dose (BED) for the tumor was calculated according to the following equation: BED = nd × (1 + d/3), where n is the number of fractions, d is the dose per fraction, and α/β = 3 [12].

Measurement of frontal skull HU

Simulation-CT images (Philips Brilliance Big Bore CT Simulators) for radiation planning were used to measure the frontal skull HU values in all study patients. A previous study reported that variations in HU values across five CT scanners were in the range of 0–20 HU [13]. We previously demonstrated detailed methods for measuring HU values at each of four lines on the frontal cancellous bone. This was between the right and left coronal sutures on axial CT slices at the point where the lateral ventricles disappear [8,14]. The HU value of the frontal cancellous bone was measured using the “Linear histogram graph” function in the picture archiving and communication system (PACS) (PiViewSTAR version 5.0, INFINITT Healthcare, Seoul, Korea). The PACS automatically calculated the maximum, minimum, and mean HU values according to the values on the drawn line. We recorded the mean HU value on each line of cancellous bone at the frontal bone region (Fig 1).

Fig 1

Measurement of HU values at each of four lines on the frontal bone.

The PACS automatically calculated the maximum, minimum, and mean HU values according to the values on the drawn line. The mean HU value on each of the four lines was recorded. (A) Right lateral; (B) right medial; (C) left medial; (D) left lateral. HU = Hounsfield unit; PACS = picture archiving and communication system.

To avoid including cortical bone, all brain CT images were magnified for HU measurement. All frontal skull HU measurements were conducted by a trained neurosurgeon blinded to the clinical data of all patients.

Other study variables

Clinical data including height, weight, hypertension, and diabetes were extracted from electronic medical records. Body mass index (BMI) was calculated as weight/ (height × height) and expressed in kg/m2. Tumor location was confirmed by neuro-radiologists using the PACS.

Statistical methods

Continuous variables were expressed as mean ± SD or median with an interquartile range (IQR) and categorical variables were expressed as counts and percentage. The chi-square test and Student’s t-test were used to assess statistical differences between non-PTBE and PTBE groups. The mean frontal skull HU value ([mean right lateral HU + mean right medial HU + mean left medial HU + mean left lateral HU]/4) was used in all analyses.

Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off values of several factors for predicting PTBE after radiation treatment in meningioma patients. The optimal cut-off value was defined as the shortest distance from the upper left corner. The distance between each point on the ROC curve and the upper left corner was calculated as [15].

The cumulative hazard for PTBE was estimated using Kaplan-Meier analysis classified according to several predictive factors, with censoring of patients who had no PTBE on the last brain CT/MRI. Hazard ratios (HRs) with 95% confidence intervals (CIs) were then calculated using univariate and multivariate Cox regression analysis. This was used to identify independent predictive factors associated with the development of PTBE after LINAC-based radiation treatment in intracranial meningioma patients. The P-values less than 0.05 were considered statistically significant.

All statistical analyses were performed using R version 3.5.2 (https://www.r-project.org/).

Results

Characteristics of study patients

Seventy-Six patients with 83 intracranial meningiomas who received LINAC-based radiation treatments in our hospital over an approximate 5-year period were enrolled in the study. The mean patient age was 62.8 years and 80.7% of patients were female. The median imaging follow-up period was 456 days and 45.8% of patients had surgical resection before radiation treatment. The mean GTV and BED were 8.4 cc and 90.8 Gy, respectively. Non-PTBE and PTBE patients demonstrated significant differences in age. Details of patient characteristics are presented in Table 1.

Table 1

Characteristics of patients with intracranial meningioma who underwent LINAC-based radiation treatment in our hospital.

Characteristics	PTBE (-)	PTBE (+)	Total	P
Number (%)	70 (84.3)	13 (15.7)	83 (100)
Sex, female, n (%)	56 (80.0)	11 (84.6)	67 (80.7)	1.000
Age, mean ± SD, y	61.4 ± 11.6	70.2 ± 9.0	62.8 ± 11.7	0.012
Imaging follow-up period, median (IQR), days	477.0 (194.8–788.0)	435.0 (198.5–1062.5)	456.0 (198.0–862.0)	0.251
BMI, mean ± SD, kg/m2	24.7 ± 3.7	24.2 ± 3.2	24.6 ± 3.6	0.675
Height, mean ± SD, cm	159.1 ± 9.4	155.9 ± 7.8	158.6 ± 9.2	0.247
Weight, mean ± SD, kg	62.5 ± 11.5	58.5 ± 6.7	61.9 ± 10.9	0.228
Prior surgical resection, n (%)	35 (50.0)	3 (23.1)	38 (45.8)	0.128
Pathology, n (%)				0.317
WHO grade I	24 (34.3)	2 (15.4)	26 (31.3)
WHO grade II	8 (11.4)	0	8 (9.6)
WHO grade III	3 (4.3)	1 (7.7)	4 (4.8)
GTV, mean ± SD, cc	7.6 ± 9.9	12.4 ± 9.8	8.4 ± 10.0	0.116
PTV, mean ± SD, cc	11.7 ± 13.7	17.6 ± 11.1	12.7 ± 13.4	0.153
Location, n (%)				0.733
Convexity	22 (31.4)	5 (38.5)	27 (32.5)
Parasagittal or parafalcine	14 (20.0)	4 (30.8)	18 (21.7)
Sphenoid ridge	7 (10.0)	1 (7.7)	8 (9.6)
Cerebellopontine angle	7 (10.0)	2 (15.4)	9 (10.8)
Posterior fossa	7 (10.0)	1 (7.7)	8 (9.6)
Parasellar or petroclival	10 (14.3)	0	10 (12.0)
Other	3 (4.3)	0	3 (3.6)
Marginal radiation dose, mean ± SD, Gy	31.5 ± 12.0	26.7 ± 5.6	30.8 ± 11.4	0.161
Fractionation, n (%)				0.372
SRS	13 (18.6)	3 (23.1)	16 (19.3)
hf-SRS (2–5 fractions)	39 (55.7)	9 (69.2)	48 (57.8)
hFSRT (6–10 fractions)	4 (5.7)	1 (7.7)	5 (6.0)
FSRT	14 (20.0)	0	14 (16.9)
Dose per fraction median (IQR), Gy	5.8 (4.8–7.0)	6.0 (5.4–11.3)	5.8 (5.3–7.0)	0.418
BED (α/β = 3), mean ± SD, Gy	90.5 ± 16.2	92.5 ± 20.5	90.8 ± 16.8	0.695
BED (α/β = 3), median (IQR), Gy	86.4 (80.6–95.1)	90.0 (75.6–102.1)	86.4 (80.6–95.1)	0.695
Past medical history, n (%)
Hypertension	29 (41.4)	6 (46.2)	35 (42.2)	0.768
Diabetes	13 (18.6)	3 (23.1)	16 (19.3)	0.708

LINAC, linear accelerator; PTBE, peritumoral brain edema; SD, standard deviation; IQR, interquartile range; BMI, body mass index; WHO, world health organization; GTV, gross tumor volume; PTV, planning target volume; SRS, stereotactic radiosurgery; hf-SRS, hypofractionated stereotactic radiosurgery; hFSRT, hypofractionated stereotactic radiotherapy; FSRT, fractionated stereotactic radiotherapy; BED, biologically equivalent dose

Mean frontal skull HU values, according to PTBE in study patients

Table 2 shows descriptive statistics of frontal skull HU values according to PTBE after radiation treatment.

Table 2

Descriptive statistics of the mean frontal skull HU values according to peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma.

Characteristics	PTBE (-)	PTBE (+)	Total	P
Overall mean frontal skull HU value, median (IQR)	733.6 (559.3–870.1)	547.8 (415.6–677.5)	725.8 (527.0–853.3)	0.018
Overall mean frontal skull HU value, mean ± SD	735.4 ± 246.2	564.4 ± 161.7	708.6 ± 242.4	0.018
Mean HU value at each of four sites in the frontal skull, mean ± SD
Right lateral	707.3 ± 245.1	579.2 ± 124.9	687.2 ± 234.6	0.070
Right medial	773.6 ± 268.7	588.9 ± 191.3	744.7 ± 265.9	0.021
Left medial	738.8 ± 271.4	566.2 ± 201.9	711.8 ± 268.2	0.032
Left lateral	722.1 ± 259.0	523.2 ± 166.3	690.9 ± 256.5	0.009
Average, medial	756.2 ± 266.0	577.5 ± 190.6	728.2 ± 262.9	0.024
Average, lateral	714.7 ± 247.9	551.2 ± 143.3	689.1 ± 241.4	0.024
Classification of skull HU, n (%)				0.005
Mean frontal skull HU ≤630.6	23 (32.9)	10 (76.9)	33 (39.8)
Mean frontal skull HU >630.6	47 (67.1)	3 (23.1)	50 (60.2)

HU, Hounsfield unit; LINAC, linear accelerator; PTBE, peritumoral brain edema; IQR, interquartile range; SD, standard deviation

We observed significant differences in values of the mean frontal skull HU and classification of the skull HU between non-PTBE and PTBE groups. The overall average mean frontal skull HU value was 725.8 in all study patients, 733.6 in the non-PTBE group and 547.8 in the PTBE group.

Determination of the optimal cut-off values of predictive factors for PTBE after radiation

The optimal cut-off values of age, mean frontal skull HU, and GTV for the prediction of PTBE in patients with intracranial meningioma after radiation were 65 years (area under the curve [AUC] = 0.730; sensitivity = 84.6%; specificity = 65.7%; P = 0.009), 630.625 (AUC = 0.716; sensitivity = 76.9%; specificity = 67.1%; P = 0.014), and 7.194 cc (AUC = 0.706; sensitivity = 69.2%; specificity = 71.4%; P = 0.019), respectively (Fig 2A–2C).

Fig 2

Comparisons of age mean frontal skull HU value, GTV, and BED between PTBE and non-PTBE groups This includes the determination of the optimal cut-off values of the predictive factors for PTBE occurrence after radiation in intracranial meningioma.

(A) Boxplots with dot plots of age according to the PTBE and ROC curve to identify the optimal cutoff value of age for the prediction of PTBE; (B) Boxplots with dot plots of mean frontal skull HU according to the PTBE and ROC curve to identify the optimal cutoff value of mean frontal skull HU for the prediction of PTBE; (C) Boxplots with dot plots of GTV according to the PTBE and ROC curve to identify the optimal cutoff value of GTV for the prediction of PTBE; (D) Boxplots with dot plots of BED according to the PTBE and ROC curve to identify the optimal cutoff value of BED for the prediction of PTBE. PTBE = peritumoral brain edema; AUC = area under the curve; HU = Hounsfield unit; GTV = gross tumor volume; BED = biologically equivalent dose; ROC = receiver operating characteristic.

However, BED did not show statistical significance in the ROC analysis (P = 0.920), (Fig 2D).

According to the cut-off values, the study patients were classified into (1) ≥65 years (2) mean frontal skull HU ≤630.625, and (3) GTV >7.194 cc groups.

Cumulative hazard of PTBE after radiation according to several predictive factors

The incidence of PTBE was significantly higher among patients who were ≥65 years, with a mean frontal skull HU ≤630.625, and a GTV >7.194 cc in the clinical course of intracranial meningioma after LINAC-based radiation treatment (Fig 3A–3C).

Fig 3

Cumulative hazard of PTBE after LINAC-based radiation treatment for intracranial meningioma according to the several predictive factors.

(A) age group (cut-off value of 65); (B) mean frontal skull HU (cut-off value of 630.625); (C) GTV (cut-off value of 7.194); (D) two fractionation categories (SRS or hf-SRS versus hFSRT or FSRT). PTBE = peritumoral brain edema; HU = Hounsfield unit; GTV = gross tumor volume; SRS = stereotactic radiosurgery; hf-SRS = hypofractionated stereotactic radiosurgery; hFSRT = hypofractionated stereotactic radiotherapy; FSRT = fractionated stereotactic radiotherapy.

Patients with ≤5 fractionation (SRS or hf-SRS) also tended to have higher rates of PTBE after radiation (Fig 3D, P = 0.159).

Independent predictive factors for PTBE after radiation in meningioma patients

The multivariate Cox regression analysis identified a mean frontal skull HU ≤630.625 and GTV >7.194 cc as independent predictors of PTBE after LINAC-based radiation treatment in intracranial meningioma patients (HR, 8.41; 95% CI, 1.42–49.83; P = 0.019; HR, 5.92; 95% CI, 1.16–30.19; P = 0.032, respectively); (Table 3).

Table 3

Univariate and multivariate Cox regression analyses for the development of peritumoral brain edema in patients with intracranial meningioma after LINAC-based radiation treatment based on predictive variables.

	Univariate analysis		Multivariate analysis
Variable	HR (95% CI)	P	HR (95% CI)	P
Sex
Male	Reference		Reference
Female	1.27 (0.28–5.80)	0.759	0.84 (0.17–4.26)	0.836
Age group
<65 years	Reference		Reference
≥65 years	11.24 (2.47–51.27)	0.002	5.12 (0.99–26.65)	0.052
BMI (per 1 BMI increase)	0.95 (0.80–1.14)	0.610	0.97 (0.76–1.24)	0.826
Mean frontal skull HU
≤630.6	9.83 (2.13–45.23)	0.003	8.41 (1.42–49.83)	0.019
>630.6	Reference		Reference
GTV
≤7.2 cc	Reference		Reference
>7.2 cc	4.17 (1.27–13.74)	0.019	5.92 (1.16–30.19)	0.032
Location
Convexity	2.41 (0.74–7.88)	0.145	1.90 (0.52–7.02)	0.333
Other regions	Reference		Reference
BED (α/β = 3) (per 1 Gy increase)	1.01 (0.97–1.04)	0.725	1.00 (0.95–1.05)	0.989
Fractionation (per 1 fraction increase)	0.92 (0.82–1.04)	0.184	0.78 (0.45–1.36)	0.381

HR, hazard ratio; CI, confidence interval; BMI, body mass index; HU, Hounsfield unit; GTV, gross tumor volume; BED, biologically equivalent dose

Patients who were ≥65 years showed a marginal statistically significant association with PTBE occurrence after full adjustment (HR, 5.12; 95% CI, 0.99–26.65; P = 0.052).

Although we adjusted the age group in the multivariate analysis, a negative relationship between age and BMD may affect our results. We also identified a close association between age and mean frontal skull HU values in the study patients in S1 Fig. We further performed additional multivariate Cox regression with the adjustment for age as a continuous variable in the S1 Table. The results showed that the mean frontal skull HU ≤630.625 was maintained as an independent predictor of PTBE (HR, 7.04; 95% CI, 1.16–42.85; P = 0.034). When we adjusted for the past medical history, mean frontal skull HU ≤630.625 showed a strong association with PTBE in the study patients (S2 Table).

When the patients were divided into the risk factor group (age ≥65 years and skull HU ≤630.625 and GTV >7.194 cc) and others, the rate of PTBE was significantly higher in the risk factor group than in the others (Fig 4).

Fig 4

Cumulative hazard of PTBE after LINAC-based radiation treatment for intracranial meningioma according to the risk factor group (age ≥65 years, skull HU ≤630.625 and GTV >7.194 cc).

PTBE = peritumoral brain edema; HU = Hounsfield unit; GTV = gross tumor volume; HR = hazard ratio; CI = confidence interval.

The univariate Cox analysis showed a strong significant association between PTBE and the risk factor group (HR, 21.92; 95% CI, 6.10 to 78.74; P<0.001).

Discussion

We found that PTBE was independently associated with possible low BMD and large tumor volume in the clinical course of intracranial meningioma after LINAC-based radiation treatment. Older age showed a marginal independent association with PTBE occurrence after radiation. The possible low BMD group (mean skull HU ≤630.6) had an approximate 7.0 to 9.0-fold increased risk of PTBE after adjusting for other predictive factors including age. To our knowledge, this study is the first to suggest that BMD is associated with PTBE after radiation treatment in patients with intracranial meningioma.

It is well accepted that the tumor-brain barrier disruption may be an essential component of PTBE formation [6]. Glioblastoma and metastatic tumors usually induce PTBE. However, in contrast to glioblastomas and metastases, meningiomas are encapsulated and are separated from the underlying normal cerebral cortex by the arachnoid membrane and pia mater. The arachnoid membrane is impermeable to fluids due to its’ tight intercellular junctions [16]. It is thought that the arachnoid membrane may act as a mechanical and biochemical buffer against mediators released from a meningioma [17]. It is probable that the arachnoid membrane blocks the spread of edema-associated proteins such as endothelial growth factor/vascular permeability factor and vasogenic edema fluids from meningiomas from the peritumoral brain tissue [3]. A previous study that examined the microscopic anatomy of the brain-meningioma interface, also reported that the degree of arachnoid disruption correlated with the presence of perifocal edema [7].

Interestingly, a microscopic examination of the brain-meningioma interface revealed proliferation of hyperplastic arachnoid trabeculae, (below the arachnoid membrane at the brain–meningioma interface) in the meningioma with a thin connective capsule (shown in Fig 1A of the study) [7]. After the study, it was reported that the arachnoid trabeculae and granulations are composed of type 1 collagen [18]. The arachnoid is composed of two layers. An outer part of the arachnoid is the arachnoid barrier layer and is an actual membrane cover. An inner part is the arachnoid trabeculae maintaining the stability of the subarachnoid space and cerebrospinal fluid flow to support the arachnoid barrier layer [19]. Arachnoid cap cells are believed to be of meningioma cell origin [20]. Therefore, it is possible to postulate that meningioma from arachnoid cap cells may naturally push the arachnoid trabeculae into the pia mater [21]. As the tumor grows, it could also be assumed that arachnoid trabeculae may be sandwiched between the pia mater and meningioma. This may form part of the tumor-brain contact interface. Compression due to the growth of a tumor on adjacent venous structures, leptomeninges, and the cerebral cortex may lead to an increase in hydrostatic pressure [22].

It is well documented that type 1 collagen is a major component of bone. Osteoporosis is a systemic disease that affects systemic bone mass and microarchitecture throughout the body. We previously reported the close association between mean frontal skull HU and BMD [8,14]. We also demonstrated that systemic osteoporosis may negatively affect the integrity of arachnoid trabeculae and granulations because bone, arachnoid trabeculae, and granulations are all composed of type 1 collagen [8,9]. Supporting our hypothesis, osteogenesis imperfecta, that is caused by mutations in type 1 procollagen genes (COL1A1/COL1A2), is associated with communicating hydrocephalus [23]. We believe that trabeculae, which are sandwiched between the pia mater and meningioma, may be more impaired and weakened in osteoporotic patients when compared to healthy patients.

Previous studies described that irradiation affects collagen structure and can lead to collagen changes and damage [24,25]. When the meningioma is not treated with surgery or radiation therapy, tumor growth is the primary cause of damage to the tumor-brain contact interface including the arachnoid trabeculae. After radiation, this contact interface may be damaged by radiation activities [3].

Based on the above findings and assumptions, we propose the following hypothetical mechanism as an explanation for the association between possible low BMD, large tumor volume, and PTBE after radiation for intracranial meningioma. As tumor grows, the tumor may push more of the arachnoid trabeculae into the pia mater and cause damage to the tumor-brain contact interface. The larger the tumor, the greater the likelihood of damage to the tumor-brain contact interface including the arachnoid trabeculae. The damage to the arachnoid trabeculae due to compression by the tumor will be more severe in osteoporotic patients. Radiation may aggravate the damaged tumor-brain contact interface including the arachnoid trabeculae and may lead to tumor-brain barrier disruption. We hypothesized that the more damaged the arachnoid trabeculae are at the tumor-brain interface due to low BMD and large tumor volume, the higher the possibility will be of tumor-brain barrier disruption after radiation therapy. Tumor-brain barrier disruption may result in PTBE formation in meningioma patients after radiation.

Loosening of the microstructure network and the volume reduction of aging white matter may increase the possibility of PTBE. This allows direct transmission of edematous fluids into the white matter [26]. We believe that thorough precautions are required with older patients with osteoporosis and large tumor volume, after radiation therapy for intracranial meningioma. We also found that BED was not associated with PTBE occurrence in our study. We propose that this was because we did not use extremely high radiation doses and the narrow BED range may not have resulted in significant differences in PTBE occurrence [3]. We believe that the status of the brain-meningioma contact interface, including the arachnoid trabeculae, is a more important factor than the radiation dosage in predicting PTBE occurrence after radiation for meningioma. Although it falls short of significance, multi-fraction seems to be important for prevention of PTBE after radiation for meningioma.

Our study has several limitations. First, due to the retrospective nature of the study, the length of follow-ups and the number of follow-up images varied widely. Second, HU measurement errors may have occurred. However, all brain CT images were magnified for HU measurement to reduce errors. We excluded patients with no measurable cancellous bone of the frontal skull in the simulation brain CT. To reduce measurement errors, we estimated mean HU values in four areas of the frontal skull and averaged them. Third, although HU values are correlated with BMD, HU values may not reflect the exact BMD values. Fourth, heterogeneity in tumor location and absence of histological confirmation in many cases may affect the results of the study. Fifth, several previously reported risk factors for PTBE after radiosurgery for meningioma were not included for the study. These include adjacency to vein or sinus, tight vs smooth brain-tumor interface, plasma levels of vascular endothelial growth factor, or a few others [5]. Lastly, the small number of cases may have reduced the statistical power and validation. In addition, our single-center study may be limited in its broad applicability. A further multi-center study with a larger sample size would be required to validate our results.

In conclusion, our study suggests that possible osteoporotic conditions, large tumor volume, and older age may be associated with PTBE occurrence after LINAC-based radiation treatment for intracranial meningioma. We believe that these findings may be helpful for predicting PTBE occurrence during the clinical course of meningioma after radiation. In the future, we anticipate that the findings of this study may enhance the understanding of the underlying mechanisms of PTBE after radiation in meningioma patients.

Scatterplot with linear regression line showing the association between age and mean frontal skull HU values.

HU = Hounsfield unit.

(TIF)

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Uni- and multivariate Cox regression analyses for the development of peritumoral brain edema in patients with intracranial meningioma after LINAC-based radiation treatment based on predictive variables (adjusted for age as continuous variable).

(DOCX)

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Uni- and multivariate Cox regression analyses for the development of peritumoral brain edema in patients with intracranial meningioma after LINAC-based radiation treatment based on predictive variables (adjusted for age as continuous variable and past medical history).

(DOCX)

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22 Jan 2020

PONE-D-19-32359

Predictive factors for the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. Please comment on the authors' question and originality of their findings.

Intriguing question, with relatively original findings.

2. Please comment on the appropriateness of the study approach and experimental design. (Examples: retrospective or prospective cohort, case-control, cross-sectional, ecological, case series; clinical trial or secondary analysis of clinical trial; registry-based; critical review; metaanalysis or systematic review; experimental, based on cell cultures, animal models, physical models, or method/technique development.)

Appears to be a single-center study, limiting its broad applicability.

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The authors claim this study was IRB-approved. The methods and analysis described is appropriate and reproducible. I suspect other groups will rapidly run similar analyses on databases in an attempt to validate the findings reported here.

4. Please comment on the analysis and interpretations of the data. Do you agree with the proposed conclusions?

Not yet -- please see my suggestions, particularly the first comment.

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Given the relatively small sample size, these preliminary results are intriguing but require validation in a larger cohort. The authors acknowledge this.

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Well-written.

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They support the manuscript.

8. Please comment on any need for formal statistical review.

Always useful.

Reviewer #2: The authors present data from 76 patients and 83 meningioma lesions treated with LINAC based radiation over a 5 year period. They present several factors correlated to the development of peri-tumoral brain edema (PTBE) after radiation. Their study finds a significant and independent association of low frontal skull hounsfeld units (HU) to the development of PTBE in addition to well known factors such as tumor volume. The association of frontal skull HU is a novel finding and to my knowledge, not previously reported or studied in the literature.

I request the following edits and clarifications:

Title:

1) The title is generic and indistinguishable from many other articles on this topic, and if frontal skull HU is a significant finding, then I suggest making the title more specific. One possible title is "Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma" - however, I leave this at the author's discretion.

Abstract:

Ok. No additional comments.

Introduction:

A major review of the topic of radiation induced edmea was published by Milano et al in 2018 in IJROBP (Pubmed ID 29726362). It may be helpful to cite this review and discuss the features identified in this review article that were significant for PTBE and how frontal bone HU was not previously reported as a relevant factor in this and other reviews.

Methods:

Study Patients: ok

Radiation Technique:

I am not sure that the alpha/beta ratio of 10 for calculating BED for meningiomas and for estimating toxicity is accurate in this paper. For instance, it is known that meningioma, being a benign tumor, has an alpha/beta ratio of closer to 3.7 (Pubmed ID 20470198) Furthermore, this study relates to the effects of radiation on normal brain tissue since the endpoint is edema. Normal CNS tissue is thought to have an alpha/beta ratio of close to 2 (pubmed ID 3891621 among others) although using a value of 3 is also acceptable. I believe the BED estimates in this paper need to be re-calculated with these more appropriate values (select 2 or 3 for normal brain, and select 3 or 3.7 for meningioma). As an example of a paper that used alpha/beta=3, see Pubmed ID 18812954.

Measurement of frontal skull HU:

1) As the mean HU value is acquired from PACS, the PACS software name and version should be reported in the methods of the paper.

Statistics: OK

Results:

Characteristics of study patients: OK.

Mean frontal skull HU values: OK.

Determination of optimal cut off values: OK.

Cumulative hazard: OK, but see figure 3 comment below.

Independent predictive factors: Ok.

Discussion:

Previously reported risk factors for PTBE have included tumor location, marginal dose, adjacency to vein or sinus, tight vs smooth brain-tumor interface, and a few others per review in Pubmed ID 29726362. Although this was not the focus of the paper, can you hypothesize why your paper identified your factors and not others as contributing to PTBE?

Tables:

Table 1: OK

Table 2: OK

Figures:

Figure 1: Ok.

Figure 2: Ok.

Figure 3: All of the cumulative hazard graphs have different ranges (roughly between 0.5 and 0.8). Please have the cumulative hazard graphs all have a scale of 0 to 1.0 for more reasonable visual comparison.

Figure 4: The cumulative hazard appears to go above 1.0 in the risk factor group. Please explain as there may be an error in the analysis that results in >100% of lesions in this group having an event.

Supplement figures and tables: Ok.

**********

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Reviewer #1: No

Reviewer #2: Yes: Yuan James Rao

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28 Jan 2020

Response to reviewers

Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma

Ryang-Hun Lee, Jae Min Kim, Jin Hwan Cheong, Je Il Ryu, Young Soo Kim, Myung-Hoon Han

Reviewer #1

2. Please comment on the appropriateness of the study approach and experimental design. (Examples: retrospective or prospective cohort, case-control, cross-sectional, ecological, case series; clinical trial or secondary analysis of clinical trial; registry-based; critical review; metaanalysis or systematic review; experimental, based on cell cultures, animal models, physical models, or method/technique development.)

Appears to be a single-center study, limiting its broad applicability.

4. Please comment on the analysis and interpretations of the data. Do you agree with the proposed conclusions?

Not yet -- please see my suggestions, particularly the first comment.

5. Please comment on weaknesses or limitations of the study. (Examples are: selection biases, sample size limitations, missing data.)

Given the relatively small sample size, these preliminary results are intriguing but require validation in a larger cohort. The authors acknowledge this.

Thank you. We agree with your comment. We also agree that the hypothesis of our study requires validation in a larger cohort as indicated by you. We have added relevant sentences in the limitations section of the Discussion as follows:

Discussion

Lastly, the small number of cases may have reduced the statistical power and validation. In addition, our single-center study may be limited in its broad applicability. A further multi-center study with a larger sample size would be required to validate our results.

Reviewer #2

1. Title:

1) The title is generic and indistinguishable from many other articles on this topic, and if frontal skull HU is a significant finding, then I suggest making the title more specific. One possible title is "Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma" - however, I leave this at the author's discretion.

Thank you. We agree with your comment. We have revised the title based on your suggestion as follows:

Title: Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma Predictive factors for the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma

2. Introduction:

A major review of the topic of radiation induced edmea was published by Milano et al in 2018 in IJROBP (Pubmed ID 29726362). It may be helpful to cite this review and discuss the features identified in this review article that were significant for PTBE and how frontal bone HU was not previously reported as a relevant factor in this and other reviews.

Thank you for your valuable comment. We have cited and discussed the review (Pubmed ID 29726362) in the Introduction as suggested by you. Please find the revised text below:

Introduction

It was reported that symptomatic brain edema occurs in 37.5% of patients with parasagittal meningiomas after gamma knife radiosurgery [4]. Previously, several risk factors associated with peritumoral brain edema (PTBE) after radiosurgery in meningioma were reported. These include greater radiation dose, greater tumor size or volume, tumor location, brain-tumor interface, no prior resection for meningioma, and presence of pretreatment edema [5].

We hypothesized that osteoporotic conditions may be associated with PTBE after radiation in intracranial meningioma patients. To the best of our knowledge, there are no previous studies describing the possible relationship between osteoporotic conditions and PTBE after radiotherapy in meningioma which have been published [5].

3. Methods:

Study Patients: ok

Radiation Technique:

I am not sure that the alpha/beta ratio of 10 for calculating BED for meningiomas and for estimating toxicity is accurate in this paper. For instance, it is known that meningioma, being a benign tumor, has an alpha/beta ratio of closer to 3.7 (Pubmed ID 20470198) Furthermore, this study relates to the effects of radiation on normal brain tissue since the endpoint is edema. Normal CNS tissue is thought to have an alpha/beta ratio of close to 2 (pubmed ID 3891621 among others) although using a value of 3 is also acceptable. I believe the BED estimates in this paper need to be re-calculated with these more appropriate values (select 2 or 3 for normal brain, and select 3 or 3.7 for meningioma). As an example of a paper that used alpha/beta=3, see Pubmed ID 18812954.

We agree with your comment. We re-calculated the BED with α/β=3 and have revised the relevant Tables, Figures, and content of the main text as shown below. We also cited the paper (Pubmed ID 18812954) in the Methods section.

Table 1.

BED (α/β =310), mean ± SD, Gy 90.5 ± 16.2 49.2 ± 8.7 92.5 ± 20.5 46.4 ± 4.7 90.8 ± 16.8 48.8 ± 8.2 0.695

0.264

BED (α/β =310), median (IQR), Gy 86.4

(80.6–95.1)

46.8

(44.5–52.7) 90.0

(75.6–102.1)

45.8

(41.6–49.2) 86.4

(80.6–95.1)

45.9

(43.7–51.2) 0.695 0.264

Table 3.

Univariate analysis Multivariate analysis

Variable HR (95% CI) P HR (95% CI) P

Sex

Male Reference Reference

Female 1.27 (0.28–5.80) 0.759 0.84 (0.17–4.26) 0.83 (0.16–4.16) 0.836 0.818

Age group

<65 years Reference Reference

≥65 years 11.24 (2.47–51.27) 0.002 5.12 (0.99–26.65) 5.20 (1.00–27.13) 0.052 0.050

BMI (per 1 BMI increase) 0.95 (0.80–1.14) 0.610 0.97 (0.76–1.24) 0.98 (0.77–1.26) 0.826 0.893

Mean frontal skull HU

≤630.6 9.83 (2.13–45.23) 0.003 8.41 (1.42–49.83) 8.38 (1.38–50.73) 0.019 0.021

>630.6 Reference Reference

GTV

≤7.2 cc Reference Reference

>7.2 cc 4.17 (1.27–13.74) 0.019 5.92 (1.16–30.19) 5.78 (1.14–29.39) 0.032 0.034

Location

Convexity 2.41 (0.74–7.88) 0.145 1.90 (0.52–7.02) 1.96 (0.53–7.23) 0.333 0.310

Other regions Reference Reference

BED (α/β=310)

(per 1 Gy increase) 1.01 (0.97–1.04) 0.96 (0.89–1.04) 0.725 0.352 1.00 (0.95–1.05) 0.97 (0.85–1.11) 0.989 0.688

Fractionation

(per 1 fraction increase) 0.92 (0.82–1.04) 0.184 0.78 (0.45–1.36) 0.77 (0.49–1.20) 0.381 0.240

S1 Table.

Univariate analysis Multivariate analysis

Variable HR (95% CI) P value HR (95% CI) P value

Sex

Male Reference Reference

Female 1.27 (0.28–5.80) 0.759 0.96 (0.18–5.30) 0.90 (0.16–5.03) 0.967 0.908

Age (per 1-year increase) 1.09 (1.03–1.16) 0.002 1.05 (0.97–1.13) 1.05 (0.97–1.14) 0.229 0.212

BMI (per 1 BMI increase) 0.95 (0.80–1.14) 0.610 0.98 (0.77–1.25) 0.99 (0.78–1.27) 0.889 0.952

Mean frontal skull HU

≤630.6 9.83 (2.13–45.23) 0.003 7.04 (1.16–42.85) 6.99 (1.12–43.60) 0.034 0.037

>630.6 Reference Reference

GTV

≤7.2 cc Reference Reference

>7.2 cc 4.17 (1.27–13.74) 0.019 7.20 (1.37–37.81) 7.19 (1.39–37.35) 0.020 0.019

Location

Convexity 2.41 (0.74–7.88) 0.145 1.89 (0.50–7.11) 1.92 (0.51–7.18) 0.348 0.333

Other regions Reference Reference

BED (α/β=310)

(per 1-Gy increase) 1.01 (0.97–1.04) 0.96 (0.89–1.04) 0.725 0.352 1.00 (0.95–1.05) 0.97 (0.85–1.10) 0.888 0.613

Fractionation

(per 1-fraction increase) 0.92 (0.82–1.04) 0.184 0.72 (0.43–1.22) 0.73 (0.48–1.11) 0.221 0.140

S2 Table.

Univariate analysis Multivariate analysis

Variable HR (95% CI) P value HR (95% CI) P value

Sex

Male Reference Reference

Female 1.27 (0.28–5.80) 0.759 1.06 (0.19–6.02) 1.03 (0.18–5.94) 0.950 0.974

Age (per 1-year increase) 1.09 (1.03–1.16) 0.002 1.06 (0.98–1.15) 1.06 (0.98–1.15) 0.130 0.139

BMI (per 1 BMI increase) 0.95 (0.80–1.14) 0.610 1.03 (0.80–1.33) 1.03 (0.80–1.33) 0.814 0.820

Mean frontal skull HU

≤630.6 9.83 (2.13–45.23) 0.003 9.41 (1.45–61.16) 9.43 (1.41–62.86) 0.019 0.020

>630.6 Reference Reference

GTV

≤7.2 cc Reference Reference

>7.2 cc 4.17 (1.27–13.74) 0.019 6.02 (1.06–34.06) 5.80 (1.04–32.23) 0.042 0.045

Location

Convexity 2.41 (0.74–7.88) 0.145 1.65 (0.43–6.35) 1.75 (0.46–6.60) 0.465 0.412

Other regions Reference Reference

BED (α/β=310)

(per 1-Gy increase) 1.01 (0.97–1.04) 0.96 (0.89–1.04) 0.725 0.352 1.01 (0.96–1.06) 1.00 (0.88–1.15) 0.646 0.948

Fractionation

(per 1-fraction increase) 0.92 (0.82–1.04) 0.184 0.77 (0.45–1.31) 0.71 (0.45–1.12) 0.326 0.137

Past medical history

Hypertension 1.12 (0.37–3.39) 0.837 0.44 (0.10–1.88) 0.48 (0.11–2.10) 0.266 0.326

Diabetes 1.12 (0.30–4.12) 0.870 0.57 (0.12–2.73) 0.61 (0.13–2.86) 0.483 0.531

Figure 2.

Abstract

Results:

We found mean frontal skull HU ≤630.625 and gross tumor volume >7.194 cc to be independent predictors of PTBE after radiation treatment in patients with meningioma (hazard ratio, 8.418.38; P=0.0190.021; hazard ratio, 5.925.78; P=0.0320.034, respectively). In addition, patients who were ≥65 years showed a marginally significant association with PTBE.

Methods

Radiation technique

The biologically equivalent dose (BED) for the tumor was calculated according to the following equation: BED = nd × (1 + d/10), where n is the number of fractions, d is the dose per fraction, and α/β=310 [12].

Results

Characteristics of study patients

The mean GTV and BED were 8.4 cc and 90.848.8 Gy, respectively.

Determination of the optimal cut-off values of predictive factors for PTBE after radiation

However, BED did not show statistical significance in the ROC analysis (P=0.9200.335), (Fig. 2D).

Independent predictive factors for PTBE after radiation in meningioma patients

The multivariate Cox regression analysis identified a mean frontal skull HU ≤630.625 and GTV >7.194 cc as independent predictors of PTBE after LINAC-based radiation treatment in intracranial meningioma patients (HR, 8.418.38; 95% CI, 1.42–49.831.38–50.73; P=0.0190.021; HR, 5.925.78; 95% CI, 1.16–30.191.14–29.39; P=0.0320.034, respectively); (Table 3). Patients who were ≥65 years showed a marginal statistically significant association with PTBE occurrence after full adjustment (HR, 5.125.20; 95% CI, 0.99–26.651.00–27.13; P=0.0520.050).

We further performed additional multivariate Cox regression with the adjustment for age as a continuous variable in the S1 Table. The results showed that the mean frontal skull HU ≤630.625 was maintained as an independent predictor of PTBE (HR, 7.046.99; 95% CI, 1.16–42.851.12–43.60; P=0.0340.037). When we adjusted for the past medical history, mean frontal skull HU ≤630.625 showed a strong association with PTBE in the study patients (S2 Table).

4. Measurement of frontal skull HU:

1) As the mean HU value is acquired from PACS, the PACS software name and version should be reported in the methods of the paper.

We added a relevant sentence in the Methods section as per your suggestion. The revised text is as follows:

Methods

Measurement of frontal skull HU

The HU value of the frontal cancellous bone was measured using the “Linear histogram graph” function in the picture archiving and communication system (PACS) (PiViewSTAR version 5.0, INFINITT Healthcare, Seoul, Korea).

5. Discussion:

Previously reported risk factors for PTBE have included tumor location, marginal dose, adjacency to vein or sinus, tight vs smooth brain-tumor interface, and a few others per review in Pubmed ID 29726362. Although this was not the focus of the paper, can you hypothesize why your paper identified your factors and not others as contributing to PTBE?

We appreciate your comment. We previously described the limitation regarding heterogeneity in tumor location due to small sample size in the Discussion section as follows:

Discussion

Fourth, heterogeneity in tumor location and absence of histological confirmation in many cases may affect the results of the study.

Although short of achieving statistical significance, Table 3 shows a tendency towards higher PTBE occurrence in the convexity meningioma as compared to meningiomas in other locations.

Table 3.

Location

Convexity 2.41 (0.74–7.88) 0.145 1.90 (0.52–7.02) 1.96 (0.53–7.23) 0.333 0.310

Other regions Reference Reference

However, we agree that the hypothesis of our study requires validation in a larger cohort with consideration to tumor location. In addition, marginal radiation dose was not a risk factor for PTBE after radiation in meningioma in the study. We also previously discussed a possible reason in the Discussion section as follows:

Discussion

We also found that BED was not associated with PTBE occurrence in our study. We propose that this was because we did not use extremely high radiation doses and the narrow BED range may not have resulted in significant differences in PTBE occurrence [3]. We believe that the status of the brain-meningioma contact interface, including the arachnoid trabeculae, is a more important factor than the radiation dosage in predicting PTBE occurrence after radiation for meningioma.

Other reported risk factors were not included for the study. Therefore, we added a relevant sentence and cited a review article (Pubmed ID 29726362) in the limitations section of the Discussion section as follows:

Discussion

Fourth, heterogeneity in tumor location and absence of histological confirmation in many cases may affect the results of the study. Fifth, several previously reported risk factors for PTBE after radiosurgery for meningioma were not included for the study. These include adjacency to vein or sinus, tight vs smooth brain-tumor interface, plasma levels of vascular endothelial growth factor, or a few others [5].

6. Figures:

Figure 3: All of the cumulative hazard graphs have different ranges (roughly between 0.5 and 0.8). Please have the cumulative hazard graphs all have a scale of 0 to 1.0 for more reasonable visual comparison.

We agree with your comment. We have adjusted the scale of the y-axes to be (0.0 to 1.0) of the Figure 3 as follows:

Figure 3.

7. Figure 4: The cumulative hazard appears to go above 1.0 in the risk factor group. Please explain as there may be an error in the analysis that results in >100% of lesions in this group having an event.

We appreciate your comment. However, it is known that the cumulative hazard can go above 1.0 according to previous studies [1-3].

1. Bybee KA, Lee JH, O’Keefe JH. Cumulative clinical trial data on atorvastatin for reducing cardiovascular events: the clinical impact of atorvastatin. Curr Med Res Opin. 2008;24: 1217–1229. doi:10.1185/030079908x292001

2. Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HAW, Livingstone SJ, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet Lond Engl. 2004;364: 685–696. doi:10.1016/S0140-6736(04)16895-5

3. Jahn-Eimermacher A, Ingel K, Ozga A-K, Preussler S, Binder H. Simulating recurrent event data with hazard functions defined on a total time scale. BMC Med Res Methodol. 2015;15: 16. doi:10.1186/s12874-015-0005-2

We estimated the cumulative hazard using the ggplot2 and ggsurvplot functions of R version 3.5.2 as described in the Methods as follows:

Submitted filename: Point-by-Point_Response_to_Reviewers.docx

Click here for additional data file.

3 Feb 2020

Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma

PONE-D-19-32359R1

Dear Dr. Han,

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed and thanks for your quick turn around on the edits. I have also learned something new about cumulative hazard curves, so thank you.

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Reviewer #1: No

Reviewer #2: Yes: Yuan James Rao

5 Feb 2020

PONE-D-19-32359R1

Significance of skull osteoporosis to the development of peritumoral brain edema after LINAC-based radiation treatment in patients with intracranial meningioma

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