
==== Front
Clin Transl Radiat Oncol
Clin Transl Radiat Oncol
Clinical and Translational Radiation Oncology
2405-6308
Elsevier

S2405-6308(24)00126-5
10.1016/j.ctro.2024.100849
100849
Original Research Article
Systemic inflammatory markers and volume of enhancing tissue on post-contrast T1w MRI images in differentiating true tumor progression from pseudoprogression in high-grade glioma
Satragno Camilla satragno.camilla@gmail.com
a2⁎
Schiavetti Irene b2
Cella Eugenia cd
Picichè Federica b
Falcitano Laura f
Resaz Martina f
Truffelli Monica g
Caneva Stefano gh
Mattioli Pietro hi
Esposito Daniela b
Ginulla Alessio b
Scaffidi Claudio b
Fiaschi Pietro gh
D’Andrea Alessandro g
Bianconi Andrea g
Zona Gianluigi gh
Barletta Laura f
Roccatagliata Luca bf
Castellan Lucio f
Morbelli Silvia bj
Bauckneht Matteo bj
Donegani Isabella j
Nozza Paolo k
Arnaldi Dario hi
Vidano Giulia e
Gianelli Flavio e
Barra Salvina e
Bennicelli Elisa c
Belgioia Liliana be
disease management team on Neuroncology of IRCCS Ospedale Policlinico San Martino1
a Dept. of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
b Dept. of Health Science (DISSAL), University of Genoa, Genoa, Italy
c U.O. Oncologia Medica 2, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
d Dept. of Internal Medicine and Medical Speciality (DIMI), University of Genoa, Genoa, Italy
f U.O. Neuroradiologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
g U.O. Clinica Neurochirurgica e Neurotraumatologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
h Department of Neuroscience Ophthalmological Rehabilitation Genetics and Mother and Child Health (DINOGMI), University of Genoa, Genoa, Italy
i U.O. Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
j U.O. Medicina Nucleare, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
k U.O. Anatomia Patologica Ospedaliera, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
e U.O. Radioterapia Oncologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
⁎ Corresponding author at: Department of Experimental Medicine, University of Genoa, Via Leon Battista Alberti, 2, 16132 Genova (GE), Italy. satragno.camilla@gmail.com
1 Collaborators: The Disease Management Team on Neuro-Oncology of IRCCS Ospedale Policlinico San Martino: Gabriella Biffa, Massimo Del Sette, Chiara Dellepiane, Guido Frosina, Martina Garnero, Maria Carmen Lonati, Ilaria Melloni, Paolo Merciadri, Claudia Rolla, Laura Saitta, Angelo Schenone, Carlo Trompetto, Flavio Villani, Concetta Viola, Francesco Lanfranchi.

2 These authors contributed equally to this work, both authors share first authorship.

30 8 2024
11 2024
30 8 2024
49 10084910 3 2024
31 5 2024
3 7 2024
© 2024 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.
2024

https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Highlights

• Shows how increased tissue volume in post-contrast T1w MRI images predicts response to glioma radiotherapy.

• Links leukocyte count changes to glioma radiotherapy outcomes.

• Unveils reproducible, low-cost techniques for glioma detection via MRI and markers.

Background

High-grade glioma (HGG) patients post-radiotherapy often face challenges distinguishing true tumor progression (TTP) from pseudoprogression (PsP). This study evaluates the effectiveness of systemic inflammatory markers and volume of enhancing tissue on post-contrast T1 weighted (T1WCE) MRI images for this differentiation within the first six months after treatment.

Material and Methods

We conducted a retrospective analysis on a cohort of HGG patients from 2015 to 2021, categorized per WHO 2016 and 2021 criteria. We analyzed treatment responses using modified RANO criteria and conducted volumetry on T1WCE and T2W/FLAIR images.

Blood parameters assessed included neutrophil/lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). We employed Chi-square, Fisher’s exact test, and Mann-Whitney U test for statistical analyses, using log-transformed predictors due to multicollinearity. A Cox regression analysis assessed the impact of PsP- and TTP-related factors on overall survival (OS).

Results

The cohort consisted of 39 patients, where 16 exhibited PsP and 23 showed TTP. Univariate analysis revealed significantly higher NLR and SII in the TTP group [NLR: 4.1 vs 7.3, p = 0.002; SII 546.5 vs 890.5p = 0.009]. T1WCE volume distinctly differentiated PsP from TTP [2.2 vs 11.7, p < 0.001]. In multivariate regression, significant predictors included NLR and T1WCE volume in the “NLR Model,” and T1WCE volume and SII in the “SII Model.” The study also found a significantly lower OS rate in TTP patients compared to those with PsP [HR 3.97, CI 1.59 to 9.93, p = 0.003].

Conclusion

Elevated both, SII and NLR, and increased T1WCE volume were effective in differentiating TTP from PsP in HGG patients post-radiotherapy. These results suggest the potential utility of incorporating these markers into clinical practice, though further research is necessary to confirm these findings in larger patient cohorts.

Keywords

High-grade glioma
Pseudoprogression
Systemic Immune-Inflammation Index
Neutrophil-Lymphocyte Ratio
Post-contrast T1-weighted volume
Volumetric analysis
==== Body
pmc1 Introduction

High-grade gliomas (HGGs) are the most prevalent and aggressive primary malignant brain tumors in adults [1]. Despite advancements in molecular characterization, the prognosis, particularly for glioblastoma (GB) patients, remains bleak, with a low survival rate at one and five years post-treatments [2]. Since 2005, the standard of care has included maximal or supramaximal surgery, followed by radiotherapy (RT) and temozolomide (TMZ) chemotherapy [3], [4].

Accurately assessing treatment response and differentiating true tumor progression (TTP) from pseudoprogression (PsP) – a treatment-related transient image phenomenon – continues to pose significant challenge [5].

PsP, often resembling TTP on magnetic resonance imaging (MRI), results from treatment-related effects like radiation-induced inflammation and transient edema [6], [7].

Correctly distinguishing between PsP from TTP is crucial for informed treatment decisions and enhancing patient outcomes [8]. Misdiagnosing PsP as TTP can lead to unnecessary and potentially harmful interventions, while confusing TTP for PsP might delay crucial treatments [9]. Furthermore, accurate differentiation contributes to better patient management, improving quality of life by avoiding unnecessary procedures and the associated side effects of aggressive treatments [10].

Conventional MRI sequences have limited ability in accurately distinguishing between the two conditions [10]. Typically, PsP occurs within the first few months post-radiotherapy and chemotherapy, temporally overlapping with TTP imaging features [9].

To overcome this diagnostic challenge, several studies have explored advanced imaging modalities, such as positron emission tomography (PET) especially with amino acid tracers, magnetic resonance spectroscopy (MRS), perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and radiomic features, to provide additional differentiation insights [7], [8], [11], [12].

However, particularly within the first three months after treatment completion, these modalities have yet to reliably distinguish between PsP and TTP, often necessitating further examinations [12], [13].

Interestingly, increasing volume of enhancing tissue on post-contrast T1 weighted (T1WCE) has shown significant potential in predicting TTP [5]. Additionally, the analysis of genetic alterations and molecular markers, including O^6methylguanine-DNA methyltransferase (MGMT) promoter methylation status and isocitrate dehydrogenase (IDH) mutation presence, has demonstrated promise in enhancing diagnostic accuracy [8], [12], [14].

In recent years, identifying reliable, noninvasive blood biomarkers has gained attraction as potential markers for predicting treatment response and guiding therapeutic decisions in HGGs [15], [16], [17], [18], [19].

The blood cell-derived indices might complement tumor tissue-derived biomarkers, offering cost-effective and easily reproducible methods to improve prognostic stratification [20].

Inflammation, increasingly recognized as a pivotal factor in cancer progression, can be quantified using various indices [21] like the neutrophil–lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI).

The NLR, a simple measure of systemic inflammation, has been extensively studied and is thought to reflect the balance between pro-tumor inflammation and anti-tumor immunity. Elevated NLR has been associated with poor prognosis in GB patients, correlating with shorter overall survival and progression-free survival [19], [22]. This suggests that patients with higher NLR may exhibit more aggressive tumor behavior and a diminished response to treatments.

Similarly, the SII, which incorporates platelet counts along with neutrophil and lymphocyte counts, has been demonstrated to have prognostic value in GB. Studies have indicated that patients with a higher SII tend to have a worse prognosis, potentially due to the role of platelets in promoting tumor growth and protecting circulating tumor cells [18], [21].

Lastly, the SIRI, which combines neutrophil, monocyte, and lymphocyte counts, is a newer index that has been shown to predict clinical outcomes in GB. A higher SIRI has been linked with a more immunosuppressive microenvironment, leading to poorer survival outcomes for patients [15], [20]. However, the utility of SIRI as a distinct prognostic tool compared to other indices is still under investigation.

While these indices show promise in enhancing our understanding of GB prognosis and treatment response, it’s important to note that their predictive power and clinical utility are not yet fully established, particularly in distinguishing between PsP and TTP, necessitating further research and validation in larger, prospective studies [16], [22], [23].

This study aims to integrate the systemic inflammatory indices and volume of enhancing tissue on post-contrast T1 weighted images to improve differentiation between PsP and TTP in HGG patients.

2 Materials and methods

This study was conducted following the approval from the ethical committee, under the protocol number 368/2021 – DB id 11595, ensuring adherence to ethical standards and guidelines for research.

2.1 Patient data

We conducted a retrospective cohort study from 2015 to 2021, enrolling patients with HGGs as classified by WHO 2016 criteria, adapted to the WHO 2021 standards, and by the WHO 2021 classification [24].

Clinical, MRI, and therapeutic data were collected at three distinct time points: at diagnosis, post-surgery or biopsy, and within six months post-RT (in most cases, PsP occurs within the first 3 months after completion of treatment but can occur up to 6 months after treatment [6]).

Inclusion criteria required:• Integrated histopathological and molecular diagnosis of HGG,

• MRI follow-up suspected of PsP or TTP, within 6 months post-RT,

• Presence of a subsequent MRI to confirm PsP or TTP,

• A complete blood count at the same timing as MRI follow up. All parameters in the complete blood count, including Neutrophil, Lymphocyte, and Platelet counts, are measured in ×10^9 cells per liter (×10^9/L).

From the complete blood count, inflammation indices were analyzed: NLR (Neutrophils/Lymphocytes), SII (Platelet count x Neutrophil count/Lymphocyte count), and SIRI (Neutrophil count x Monocyte count)/Lymphocyte count).

Patient profiles were characterized by age and gender at the time of diagnosis.

The tumor sites were categorized based on diagnostic MRI, findings into two distinct categories, frontal lobe and other cerebral lobe, each bearing prognostic implications as referenced in studies [25], [26], [27], [28], [29], [30], [31]. Moreover, we accounted for the presence of multicentric disease at diagnosis.

Additionally, the administered corticosteroid dosages, standardized to dexamethasone equivalents exceeding 4 mg, were recorded during both the treatment phase and the subsequent follow-up period.

Surgical interventions for each patient were evaluated based on the residual disease post-operation: subtotal resection (STR) indicated the presence of residual disease, whereas gross total resection (GTR) denoted its absence. Cases where only a biopsy was performed were also noted.

We considered also the IDH mutation and the MGMT methylation status.

Furthermore, we took into account the presence of infections during and after radiotherapy.

Chemotherapy regimens were distinctly categorized based on their temporal relationship with radiotherapy, identifying whether they were administered concomitantly or sequentially.

2.2 Imaging data and response to treatment identification

Each MRI included standard sequences, (T1W, T1WCE, T2W and T2W/FLAIR).

The imaging was reviewed by a specialized neuroradiologists team.

MRIs were reviewed at diagnosis, within 72h post-surgery and during follow-ups.

Treatment response was evaluated using modified RANO criteria [32], then to define TTP and PsP we applied: mandatory confirmation of progression with a repeat MRI and measurement of the maximum tumour cross-sectional area with which we associated volumetric measurements.

Radiological patterns of relapse were defined as local or distal, considering growth of residual and/or new lesion.

2.3 Radiotherapy data

Patients were treated according to their performance status, age and extent of surgery, following protocols including the STUPP protocol [4] or alternatives for specific age and performance categories [33], [34].

Volumetry of suspected progression was performed on T1WCE and T2W/FLAIR sequences.

Three labels were identified, respectively, the suspected disease with contrast enhancement, the FLAIR hyperintensity indicative of perilesional oedema, and the whole tumour covering the whole FLAIR hyperintensity [35], [36], excluding the surgical cavity, where it was present.

Segmentation was conducted by our department's team of neuro-radiation oncologists using our Treatment Planning System (TPS).

2.4 Statistical analysis

Associations between clinicopathological factors and progression were assessed using Chi-square and Fisher's exact tests.

Continuous variables were analyzed using independent sample t-tests or Mann-Whitney U tests as appropriate.

Variables with a p-value ≤ 0.05 in univariate analysis were included in the multivariate logistic regression.

Logistic regression models were developed for prognostic factors of progression, with log-transformed predictors to address skewed distributions.

Two separate binary regression models addressed multicollinearity between the “NLR” and “SII index.”

In the “Multivariate Model NLR,” the SII index was not considered, while in the “Multivariate Model SII index,” the NLR was excluded.

Cox regression analysis evaluated the impact of PsP- and TTP-related factors on OS.

The discriminative capacity of significant variables was assessed using Receiver Operating Characteristic (ROC) curve analysis and Area Under the Curve (AUC) curve values. The curve comparison was carried out. The optimal threshold for maximizing sensitivity and specificity was identified, contributing to our diagnostic accuracy.

3 Results

The study included 39 patients out of initial cohort of 121, of 41.0% being females.

Among these, 16 patients exhibited PsP, while 23 showed TTP.

Of the 39 patients according to the WHO 2016 classification, 5 (12.8%) were grade 3 astrocytomas and 34 (87.2%) were glioblastomas. Based on molecular biology and thus transitioning to the 2021 classification, of the 5 considered grade 3, only 2 (40%) were confirmed as such, while the other 3 (60%) were reclassified as molecular glioblastomas due to the absence of the IDH gene mutation. Of the 34 considered glioblastomas, 1 (2.9%) presenting the IDH mutation was reclassified to IDH-mutant grade 4 astrocytoma [2].

The demographic and clinical characteristics, along with key imaging and blood test findings, are detailed in Table 1a, Table 1b, without showing notable differences between the two groups. In our cohort no patient was infected during and/or after the end of radiotherapy.Table 1a Part 1. Differences at baseline between TTP and PsP.

	TTP (N=23)	PsP (N=16)	p	
sex	female	9 (39.1 %)	7 (43.8 %)	0.77	
male	14 (60.9 %)	9 (56.3 %)	
age at the time of diagnosis	61.1 ± 14.17	57.0 ± 14.62	0.39	
cerebral lobe	other lobes	14 (60.9 %)	11 (68.8 %)	0.61	
frontal lobe	9 (39.1 %)	5 (31.3 %)	
multicentric disease	no	19 (82.6 %)	15 (93.8 %)	0.31	
yes	4 (17.4 %)	1 (6.3 %)	
grade 4 WHO 2016	G3	3 (13.0 %)	2 (12.5 %)	0.99	
G4	20 (87.0 %)	14 (87.5 %)	
IDH mutation	no	22 (95.7 %)	14 (87.5 %)	0.56	
yes	1 (4.3 %)	2 (12.5 %)	
extent of surgery	GTR	5 (21.7 %)	7 (43.8 %)	0.30	
STR	15 (65.2 %)	7 (43.8 %)	
Biopsy	3 (13.0 %)	2 (12.5 %)	
total Gy	60	9 (39.1 %)	7 (43.8 %)	0.75	
50	12 (52.2 %)	9 (56.3 %)	
40.05	2 (8.7 %)	0 (0.0 %)	
steroid dose 1st follow up	</=4mg	9 (69.2 %)	9 (81.8 %)	0.48	
>4mg	4 (30.8 %)	2 (18.2 %)	
progression in field	no	2 (8.7 %)	1 (6.3 %)	0.99	
yes	21 (91.3 %)	15 (93.8 %)	
progression out field	no	17 (73.9 %)	15 (93.8 %)	0.11	
yes	6 (26.1 %)	1 (6.3 %)	
T2/FLAIR edema volume	cm3	60.8 ± 60.61	38.6 ± 34.75	0.37	
T2/FLAIR whole volume	cm3	80.6 ± 82.29	42.2 ± 37.48	0.15	
TTP true tumour progression, PsP pseudoprogression, WHO world health organization, G grade, IDH isocitrate dehydrogenase, GTR gross total resection, STR subtotal resection, Gy gray, 1st first, mg milligrams, T2/FLAIR T2-weighted Fluid-Attenuated Inversion Recovery relapse edema and whole volume.

Categorical variables are reported as number (percentage). Continuous variables are reported as mean ± standard deviation.

Table 1b Part 2. Differences between patients with psp and ttp.

		TTP (N=23)	PsP (N=16)	p	
mgmt methylation	No	14 (60.9 %)	9 (56.3 %)	0.77	
Yes	9 (39.1 %)	7 (43.8 %)	
T1WCE volume cm3	19.8 ± 25.2111.7
(0.2–94.4)	3.6 ± 4.492.2
(0.1–13.9)	<0.001	
platelets	212.7 ± 88.12209.0
(52.0–481.0)	199.7 ± 70.99196.5
(82.0–323.0)	0.62	
neutrophils	5.9 ± 2.395.5
(1.9–10.1)	4.5 ± 2.554.1
(1.8–10.3)	0.061	
lymphocytes	1.1 ± 0.560.8
(0.3–2.0)	1.4 ± 0.621.3
(0.5–2.5)	0.18	
monocytes	0.7 ± 0.440.7
(0.0–2.0)	0.5 ± 0.220.5
(0.3–1.1)	0.51	
NLR	7.3 ± 7.264.7
(2.6–36.1)	4.1 ± 3.722.8
(1.1–16.0)	0.002	
SII	1511.0 ± 1615.33
890.5
(397.5–7538.9)	915.5 ± 1215.24
546.5
(152.0–5157.0)	0.009	
SIRI	5.8 ± 8.173.3
(0.0–36.1)	2.1 ± 2.201.5
(0.7–9.9)	0.06	
MGMT O6-Methylguanine-DNA methyltransferase), T1WCE T1-weighted contrast-enhanced MRI relapse volume, NLR Neutrophil-to-Lymphocyte Ratio, SII Systemic Immune-Inflammation Index, calculated as (Platelets x Neutrophils) / Lymphocytes, SIRI Systemic Inflammation Response Index, calculated as (Neutrophils x Monocytes) / Lymphocytes.

Categorical variables are reported as number (percentage). Continuous variables are reported as mean ± standard deviation.

3.1 Univariate analysis

In comparing PsP and TTP groups, our univariate analysis revealed that the neutrophil/lymphocyte ratio (NLR), systemic immune-inflammatory index (SII), and T1WCE volume were notably higher in patients with TTP.

Specifically, NLR values were 7.3 in TTP compared to 4.1 in PsP (p = 0.002), SII values were 1511.0 vs. 546.5 (p = 0.009), and T1WCE values were 19.8 cm^3 vs. 2.2 cm^3 (p < 0.001). Although absolute neutrophil counts and systemic inflammation response index (SIRI) were also higher in the TTP group, these differences did not achieve statistical significance.

These results are detailed in Table 1a, Table 1b.

3.2 Multivariate analysis

Our multivariate analysis differentiated two models: the “NLR Model” and the “SII Model.”.

In the NLR Model, both NLR (OR 7.9, 95% CI: 1.4 to 45.3, p = 0.020) and T1WCE volume (OR 3.0, 95% CI: 1.4 to 6.7, p = 0.007) were identified as significant predictors of TTP.

Similarly, the SII Model confirmed T1WCE volume (OR 2.7, 95% CI: 1.3 to 5.5, p = 0.006) and SII (OR 4.2, 95% CI: 1.1 to 15.3, p = 0.030) as significant predictors.

These findings underscore the predictive value of these markers in differentiating TTP from PsP, as elaborated in Table 2.Table 2 Multivariate model of prognostic factors for TTP vs PsP.

	Multivariate Model NLROR
(95 %CI), p value	Multivariate Model SII indexOR
(95 %CI), p value	
T1WCE volume (cm3)	3.02 (1.36–6.72); 0.007	2.72 (1.34–5.51); 0.006	
NLR	7.92 (1.38–45.30); 0.020	−	
SII index	−	4.18 (1.14–15.27); 0.030	

3.3 Impact on overall survival

Our survival analysis indicated a significantly poorer overall survival (OS) in patients with TTP compared to those with PsP (Fig. 1).Fig. 1 The Kaplan-Meier curve.

The hazard ratio (HR) for TTP was 3.97 (95% CI: 1.59 to 9.93, p = 0.003).

However, when considering other factors in the multivariate model, only the distinction between progression and pseudoprogression remained a significant predictor of OS, as shown in Table 3.Table 3 Effect of different factors on the time to death according to univariable and multivariable Cox regression models.

	Univariable	Multivariable	
	HR (95 % CI)	p-value	HR (95 % CI)	p-value	
TTP vs PsP	4.41 (1.90–10.24)	0.001	3.97 (1.59–9.93)	0.003	
T1WCE volume (cm3)	1.27 (1.02–1.59)	0.033	1.07 (0.85–1.34)	0.56	
NLR	1.41 (0.88–2.26)	0.15			
SII index	1.27 (0.87–1.87)	0.22			
TTP true tumour progression, PsP pseudoprogression, OR odds ratio, NLR neutrophils/lymphocytes ratio, SII Systemic immune-inflammation index, T1WCE volume of enhancing tissue on post-contrast T1w MRI images.

TTP true tumour progression, PsP pseudoprogression, HR hazard ratio, NLR neutrophils/lymphocytes ratio, SII Systemic immune-inflammation index, T1WCE Volume of enhancing tissue on post-contrast T1w MRI images.

3.4 Diagnostic accuracy and optimal thresholds

The ROC curve and AUC analysis provided the thresholds for the three variables.

For NLR, the threshold was 3.18 with a specificity of 75.0% and a sensitivity of 87.0%. For SII, it was 620.55 with a specificity of 68.8% and a sensitivity of 82.6%. Lastly, for hyperintensity volume on T1- weighted contrast-enhanced the threshold was 5.00 cm3 with a specificity of 81.3% and a sensitivity of 78.3%.

These thresholds, depicted in Fig. 2, are instrumental in enhancing the diagnostic accuracy of our predictive model. The comparison between the 3 curves was not significant.Fig. 2 The ROC curve and AUC. Cut-off. NLR: 3.18 (Specificity: 75.0 %; Sensitivity: 87.0 %). SII: 620.55 (Specificity: 68.8 %; Sensitivity: 82.6 %). Volume of enhancing tissue on post-contrast T1w: 5.00 (Specificity: 81.3 %; Sensitivity:78.3 %).

4 Discussion

This study aimed to delineate clear markers for differentiating true tumor TTP from PsP in patients with HGGs [7], [8] within the critical early period of the first three months post-radiotherapy, extending to a maximum of six months [5].

This distinction is pivotal for effective patient management, particularly during this early and often ambiguous phase where treatment responses are most variable [37], [38], [39].

All patients were re-evaluated by a team of experienced neuroradiologists, ensuring a consistent and uniform distinction between PsP and TTP, thereby minimizing the risk of misclassification, particularly false negatives among PsP cases [12], [40].

Our findings highlight the significant role of systemic inflammation markers and T1WCE volume in this differentiation within this timeframe, which aligns with and expands upon the existing body of research [7].

A significant strength of our study is the comprehensive consideration of various clinical data often associated with worse prognosis and early true tumor progression (TTP) post-treatment. Specifically, we took into account critical factors such as the absence of the IDH mutation, the absence of MGMT methylation, and the increased use of corticosteroids in the early post-treatment follow-up. These factors are well-documented in the literature for their impact on patient outcomes and provide a robust framework for distinguishing TTP from PsP. By incorporating these variables into our analysis, we were able to enhance the accuracy and relevance of our findings, contributing valuable insights to the existing body of knowledge on high-grade gliomas.

The main limitation of our case series was the small cohort size. However, this reduction in patient numbers was due to our stringent inclusion criteria, which aimed to ensure data accuracy and relevance. Patients were excluded for reasons such as incomplete follow-up data, lack of consistent imaging, and non-compliance with the WHO 2021 classification criteria. While this exclusion could suggest a selection bias, it was necessary to maintain the integrity and reliability of the study. Nonetheless, the consistency and pertinence of our findings offer a substantial contribution to the existing literature.

While the exclusion of advanced imaging sequences might be viewed as another constraint, it’s noteworthy that such sequences have not demonstrated significant diagnostic strength, particularly within the crucial initial three months of follow-up [12].

Our focus, therefore, was deliberately tailored to assess the role of inflammatory indices and volumetric analysis as per the framework outlined in La Fevre’s review [7], [8].

In the literature, in line with our study, the few papers that considered the neutrophil/lymphocyte ratio (NLR) to distinguish between PD and PsP had promising results, suggesting its potential as a reliable marker [22]. While studies analyzing the role of the systemic inflammation response index (SIRI) and systemic immune-inflammation index (SII) post-radiotherapy are lacking, significant results have been observed at other timings. For instance, a low SIRI preoperatively is associated with better survival [15], and a high SII at diagnosis correlates with worse survival [17], [21]. These findings suggest that inflammatory indices hold prognostic value and could potentially aid in early post-treatment assessments.

Our results also focus on the volume of enhancing tissue on post-contrast T1 weighted (T1WCE), measured in cm3 on the MRI post-RT, in line with what was hypothesized in a part of the phase III SpectroGlio trial (NCT01507506) [5] and is particularly relevant in light of the new RANO 2.0 classification [41]. According to these updated guidelines, the incidence of pseudoprogression is significantly high in the first 12 weeks post-chemoradiotherapy for glioblastomas [12], [38], [41] and may extend beyond 3 months for IDH-mutated gliomas and other glial tumours [42]. Especially during the first 12 weeks for glioblastomas, the correlation between radiological changes and true progression, as well as survival, is poorly defined. Consequently, RANO 2.0 proposes that for clinically stable patients showing signs of radiological progression, MRI should be repeated at 4- or 8-week intervals to confirm progression prior to any significant changes in the patient’s treatment plan [38], [41].

This recommendation resonates with our observation that the relationship between pre-existing volumes and volumes assessed after radiotherapy may not be as crucial as previously thought.

Our results indeed indirectly confirm that the first post-RT MRI can serve as an independent and reliable basis for response assessment, regardless of the initial disease presentation [38].

The timing and aim of our study are therefore highly relevant and underline the need for continued research and validation of diagnostic markers that can help to define radiological response in the first 3–6 months after the end of radiotherapy.

5 Conclusion

Our study underscores the potential of systemic inflammation markers and volume of enhancing tissue on post-contrast T1 weighted (T1WCE) MRI sequences, cost-effective tools for differentiating between TTP and PsP in the early post-radiotherapy period.

By integrating these markers into the clinical decision-making process, we can enhance the accuracy of early treatment assessments, thereby improving patient management and outcomes in HGGs cases. However, it is important to note that our results should be confirmed with a broader casuistry to solidify these findings and ensure their applicability in diverse clinical settings.

Funding information

Network Programme All-Ages Malignant Glioma: Holistic Management In The Personalised Minimally-Invasive Medicine Era – From Lab To Rehab – GLI-HOPE.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Italian Ministry of Health for funds allocated to the GLI-HOPE study.
==== Refs
References

1 Ostrom Q.T. Cioffi G. Waite K. Kruchko C. Barnholtz-Sloan J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–2018 Neuro-Oncology 23 2021 10.1093/neuonc/noab200 iii1–105
2 Louis D.N. Perry A. Wesseling P. Brat D.J. Cree I.A. Figarella-Branger D. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary Neuro Oncol 23 2021 1231 1251 10.1093/neuonc/noab106 34185076
3 Karschnia P. Young J.S. Dono A. Häni L. Sciortino T. Bruno F. Prognostic validation of a new classification system for extent of resection in glioblastoma: A report of the RANO resect group Neuro Oncol 25 2023 940 954 10.1093/neuonc/noac193 35961053
4 Stupp R. Weller M. Belanger K. Bogdahn U. Ludwin S.K. Lacombe D. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma N Engl J Med 2005
5 Sidibe I. Tensaouti F. Gilhodes J. Cabarrou B. Filleron T. Desmoulin F. Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis Radiother Oncol 181 2023 109486 10.1016/j.radonc.2023.109486
6 Hygino da Cruz L.C. Rodriguez I. Domingues R.C. Gasparetto E.L. Sorensen A.G. Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma AJNR Am J Neuroradiol 32 2011 1978 1985 10.3174/ajnr.A2397 21393407
7 Le Fèvre C. Constans J.-M. Chambrelant I. Antoni D. Bund C. Leroy-Freschini B. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review. Part 2 – Radiological features and metric markers Crit Rev Oncol/Hematol 159 2021 10.1016/j.critrevonc.2021.103230 103230
8 Le Fèvre C. Lhermitte B. Ahle G. Chambrelant I. Cebula H. Antoni D. Pseudoprogression versus true progression in glioblastoma patients: A multiapproach literature review Crit Rev Oncol Hematol 157 2021 103188 10.1016/j.critrevonc.2020.103188
9 Brandsma D. Stalpers L. Taal W. Sminia P. Van Den Bent M.J. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas Lancet Oncol 9 2008 453 461 10.1016/S1470-2045(08)70125-6 18452856
10 Taylor C. Ekert J.O. Sefcikova V. Fersht N. Samandouras G. Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis Sci Rep 12 2022 13258 10.1038/s41598-022-16726-x 35918373
11 Tsakiris C. Siempis T. Alexiou G.A. Zikou A. Sioka C. Voulgaris S. Differentiation between true tumor progression of glioblastoma and pseudoprogression using diffusion-weighted imaging and perfusion-weighted imaging: systematic review and meta-analysis World Neurosurg 144 2020 e100 e109 10.1016/j.wneu.2020.07.218 32777397
12 Leone R. Meredig H. Foltyn-Dumitru M. Sahm F. Hamelmann S. Kurz F. Assessing the added value of apparent diffusion coefficient, cerebral blood volume, and radiomic magnetic resonance features for differentiation of pseudoprogression versus true tumor progression in patients with glioblastoma Neuro-Oncol Adv 5 2023 vdad016 10.1093/noajnl/vdad016
13 Zanier O. Da Mutten R. Vieli M. Regli L. Serra C. Staartjes V.E. DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning Acta Neurochir 165 2022 555 566 10.1007/s00701-022-05446-w 36529785
14 Motegi H. Kamoshima Y. Terasaka S. Kobayashi H. Yamaguchi S. Tanino M. IDH1 mutation as a potential novel biomarker for distinguishing pseudoprogression from true progression in patients with glioblastoma treated with temozolomide and radiotherapy Brain Tumor Pathol 30 2013 67 72 10.1007/s10014-012-0109-x 22752663
15 He Q. Li L. Ren Q. The prognostic value of preoperative systemic inflammatory response index (SIRI) in patients with high-grade glioma and the establishment of a nomogram Front Oncol 11 2021 671811 10.3389/fonc.2021.671811
16 Topkan E. Besen A.A. Ozdemir Y. Kucuk A. Mertsoylu H. Pehlivan B. Prognostic value of pretreatment systemic immune-inflammation index in glioblastoma multiforme patients undergoing postneurosurgical radiotherapy plus concurrent and adjuvant temozolomide Mediators Inflamm 2020 2020 1 9 10.1155/2020/4392189
17 Wang D. Kang K. Lin Q. Hai J. Prognostic significance of preoperative systemic cellular inflammatory markers in gliomas: a systematic review and meta-analysis Clin Transl Sci 13 2020 179 188 10.1111/cts.12700 31550075
18 Yang C. Li Z.-Q. Wang J. Association between systemic immune-inflammation index (SII) and survival outcome in patients with primary glioblastoma Medicine 102 2023 e33050 36800573
19 Gomes dos Santos A. de Carvalho R.F. de Morais A.N.L.R. Silva T.M. Baylão V.M.R. Azevedo M. Role of neutrophil-lymphocyte ratio as a predictive factor of glioma tumor grade: A systematic review Crit Rev Oncol Hematol 163 2021 10.1016/j.critrevonc.2021.103372
20 Pasqualetti F. Giampietro C. Montemurro N. Giannini N. Gadducci G. Orlandi P. Old and new systemic immune-inflammation indexes are associated with overall survival of glioblastoma patients treated with radio-chemotherapy Genes 13 2022 1054 10.3390/genes13061054 35741816
21 Yang C. Hu B.-W. Tang F. Zhang Q. Quan W. Wang J. Prognostic value of systemic immune-inflammation index (SII) in patients with glioblastoma: a comprehensive study based on meta-analysis and retrospective single-center analysis JCM 11 2022 7514 10.3390/jcm11247514 36556130
22 Huang Y. Ding H. Wu Q. Li Z. Li H. Li S. Neutrophil–lymphocyte ratio dynamics are useful for distinguishing between recurrence and pseudoprogression in high-grade gliomas CMAR 11 2019 6003 6009 10.2147/CMAR.S202546
23 Raza I.J. Tingate C.A. Gkolia P. Romero L. Tee J.W. Hunn M.K. Blood biomarkers of glioma in response assessment including pseudoprogression and other treatment effects: a systematic review Front Oncol 10 2020 1191 10.3389/fonc.2020.01191 32923382
24 Weller M. van den Bent M. Preusser M. Le Rhun E. Tonn J.C. Minniti G. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood Nat Rev Clin Oncol 18 2021 170 186 10.1038/s41571-020-00447-z 33293629
25 Li Y. Zhang Z.-X. Huang G.-H. Xiang Y. Yang L. Pei Y.-C. A systematic review of multifocal and multicentric glioblastoma J Clin Neurosci 83 2021 71 76 10.1016/j.jocn.2020.11.025 33358091
26 Bjorland L.S. Dæhli Kurz K. Fluge Ø. Gilje B. Mahesparan R. Sætran H. Butterfly glioblastoma: Clinical characteristics, treatment strategies and outcomes in a population-based cohort Neuro-Oncol Adv 4 2022 vdac102 10.1093/noajnl/vdac102
27 Ismail M. Hill V. Statsevych V. Mason E. Correa R. Prasanna P. Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression vs. tumor recurrence in glioblastoma?—A feasibility study Front Comput Neurosci 14 2020 10.3389/fncom.2020.563439 563439
28 Stockhammer F. Misch M. Helms H.-J. Lengler U. Prall F. Von Deimling A. IDH1/2 mutations in WHO grade II astrocytomas associated with localization and seizure as the initial symptom Seizure 21 2012 194 197 10.1016/j.seizure.2011.12.007 22217666
29 Ellingson B.M. Cloughesy T.F. Pope W.B. Zaw T.M. Phillips H. Lalezari S. Anatomic localization of O6-methylguanine DNA methyltransferase (MGMT) promoter methylated and unmethylated tumors: A radiographic study in 358 de novo human glioblastomas Neuroimage 59 2012 908 916 10.1016/j.neuroimage.2011.09.076 22001163
30 Kim Y. Kim K.H. Park J. Yoon H.I. Sung W. Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model Radiother Oncol 183 2023 109617 10.1016/j.radonc.2023.109617
31 Liang T.-H.-K. Kuo S.-H. Wang C.-W. Chen W.-Y. Hsu C.-Y. Lai S.-F. Adverse prognosis and distinct progression patterns after concurrent chemoradiotherapy for glioblastoma with synchronous subventricular zone and corpus callosum invasion Radiother Oncol 118 2016 16 23 10.1016/j.radonc.2015.11.017 26678342
32 Wen P.Y. Chang S.M. Van den Bent M.J. Vogelbaum M.A. Macdonald D.R. Lee E.Q. Response assessment in neuro-oncology clinical trials JCO 35 2017 2439 2449 10.1200/JCO.2017.72.7511
33 Hanna C. Lawrie T.A. Rogozińska E. Kernohan A. Jefferies S. Bulbeck H. Treatment of newly diagnosed glioblastoma in the elderly: a network meta-analysis Cochrane Database Syst Rev 2020 2020 10.1002/14651858.CD013261.pub2
34 Perry J.R. Laperriere N. O’Callaghan C.J. Brandes A.A. Menten J. Phillips C. Short-course radiation plus temozolomide in elderly patients with glioblastoma N Engl J Med 376 2017 1027 1037 10.1056/NEJMoa1611977 28296618
35 Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, et al., Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge, 2019. https://doi.org/10.48550/arXiv.1811.02629.
36 Menze B.H. Jakab A. Bauer S. Kalpathy-Cramer J. Farahani K. Kirby J. The multimodal brain tumor image segmentation benchmark (BRATS) IEEE Trans Med Imag 34 2015 1993 2024 10.1109/TMI.2014.2377694
37 Ellingson B.M. Wen P.Y. Cloughesy T.F. Modified criteria for radiographic response assessment in glioblastoma clinical trials Neurotherapeutics 14 2017 307 320 10.1007/s13311-016-0507-6 28108885
38 Youssef G. Wen P.Y. Updated response assessment in neuro-oncology (RANO) for gliomas Curr Neurol Neurosci Rep 2024 10.1007/s11910-023-01329-4
39 Wen P.Y. Macdonald D.R. Reardon D.A. Cloughesy T.F. Sorensen A.G. Galanis E. updated response assessment criteria for high-grade gliomas: response assessment in Neuro-Oncology Working Group JCO 28 2010 1963 1972 10.1200/JCO.2009.26.3541
40 Ellingson B.M. Wen P.Y. Chang S.M. Van Den Bent M. Vogelbaum M.A. Li G. Objective response rate targets for recurrent glioblastoma clinical trials based on the historic association between objective response rate and median overall survival Neuro Oncol 25 2023 1017 1028 10.1093/neuonc/noad002 36617262
41 Wen P.Y. Van Den Bent M. Youssef G. Cloughesy T.F. Ellingson B.M. Weller M. RANO 2.0: update to the response assessment in neuro-oncology criteria for high- and low-grade gliomas in adults JCO 41 2023 5187 5199 10.1200/JCO.23.01059
42 Van West S.E. De Bruin H.G. Van De Langerijt B. Swaak-Kragten A.T. Van Den Bent M.J. Taal W. Incidence of pseudoprogression in low-grade gliomas treated with radiotherapy NEUONC 2016 10.1093/neuonc/now194 now194
