Skip to main content
Egyptian Journal of Radiology and Nuclear Medicine
Download PDF

The value of adding DWI and FLAIR signal changes in the resection cavity on the diagnostic performance of BT-RADS category 3 for tumor progression prediction in post-treated glioma patients: a prospective pilot study

Egyptian Journal of Radiology and Nuclear Medicine volume 54, Article number: 52 (2023) Cite this article

Abstract

Background

BT-RADS is a structured reporting system of post-treatment glioma. BT-RADS category 3 carries a probability of recurrent malignancy versus treatment-related changes. The aim of this study is to evaluate the additive value of DWI and resection cavity FLAIR signal changes to BT-RADS category 3 in the prediction of tumor progression. We prospectively evaluated follow-up contrast-enhanced MR imaging where 27 post-treated glioma patients were assigned BT-RADS category 3. In all images, FLAIR signal, enhancement component, mass effect, and ADCmean were assessed. We used imaging follow-up from the second stage of the study as the gold standard for comparing the diagnostic performance of BT-RADS category 3 for predicting tumor recurrence before and after the addition of DWI and resection cavity FLAIR signal changes. ROC curves analyses were assessed and compared using the Delong test.

Results

48.1% of patients had tumor recurrence and 51.9% of patients had treatment-related changes. There was significant difference between ADCmean in recurrent and non-recurrent groups measuring 0.9 and 1.15 × 10−3mm2/s, respectively (p value < 0.001). BT-RADS, BT-RADS added DWI, and BT-RADS added DWI and resection cavity FLAIR signal had a specificity of 64.3, 71.4, and 71.4%, sensitivity of 76.9, 84.6, and 92.3%, and accuracy of 70.5, 77.8, and 81.5%, with improved AUC from 0.706 (95% CI of 0.50–0.86) to 0.78 (95% CI of 0.58–0.92) to 0.819 (95% CI of 0.64–0.94), respectively.

Conclusions

Adding DWI and resection cavity FLAIR signal alteration improves the diagnostic performance of BT-RADS category 3.

Background

Glioma accounts for about 80% of all malignant primary tumors of the brain [1] with the highest incidence for glioblastoma multiforme (GBM), representing about 60% of adult primary brain tumors [2]. High-grade primary brain tumors are characterized by aggressive behavior without significant difference in incidence rate between females and males [3]. GBM carries the poorest prognosis with a 40% survival rate during the first year post-diagnosis [4]. The standard treatment protocol for glioma is the resection with concomitant chemoradiotherapy followed by adjuvant chemotherapy [5]. Unfortunately, the prognosis of GBM is still poor with no improvements in outcome since the combined treatment with chemoradiotherapy in 2005 [6]. The radiation therapy oncology group had held a trial to compare survival between conventional adjuvant and dense-dose temozolomide; however, no significant difference regarding the survival improvement between both groups was concluded. The incorporation of Bevacizumab in management protocol yielded improvement in progression-free survival without change in overall survival [7, 8]. Meanwhile, a recent meta-analysis detected an association between prolonged temozolomide and elongated survival in comparison to standard six cycles of temozolomide therapy [9].

One of the recognized complications of radiotherapy is post-radiation brain necrosis. It can occur 6 to 36 months post-radiation, and cause radiological changes at the radiation zone that look similar to a recurrent tumor [10].

Differentiation between radiation-induced changes and recurrent brain tumors is sometimes a challenging matter for radiologists, so several series of response evaluation criteria applied to conventional MRI were held for the precise assessment of the post-treated glioma response and differentiation between radiation-induced changes and recurrent brain tumors. Lastly, these trials concluded with Brain Tumor Reporting and Data System (BT-RADS) which categorizes post-treated glioma response into 4 groups according to the enhanced component and the non-enhanced FLAIR signal abnormality, and mass effect in correlation with patients’ clinical states and received treatment [11]. However, BT-RADS neglects the effect of FLAIR signal changes within the resection cavity which has been proved by another research [12] to be an early and specific sign in predicting tumor recurrence in operated high-grade glioma.

Albeit restricted signal on the diffusion-weighted image (DWI) characterizes high cellular tumor tissue [13], DWI was not included within the criteria of BT-RADS categorization.

As BT-RADS category 3 has an intermediate probability of recurrent neoplastic activity [11], so in our study, we evaluated the added value of DWI and resection cavity FLAIR signal changes on the diagnostic performance of BT-RADS category 3 for a more precise distinction between post-treatment changes and recurrent tumor in post-treated glioma.

Methods

Ethical considerations

Approval was procured from the Institutional review board and research ethics committee. A written informed consent was taken from the included patients in the study.

Study design and population

Our pilot prospective study dealt with imaging of post-treated glioma patients. It consisted of two stages, the first one was concerned with the collection of data and classification of two consecutive post-treated glioma MR imaging acquired between May 2019 and December 2020 according to BT-RADS categorization. The second stage was concerned with the evaluation of first stage patients’ imaging follow-up and it was held during the period from January 2021 to December 2021 and served as the reference standard for the given BT-RADS category. A preliminary sample included 63 patients. Inclusion and exclusion criteria are shown in the flowchart of the recruited patient (Fig. 1). The final sample entailed 27 patients.

Fig. 1
figure 1

Flowchart of the recruited patients. BT-RADS = brain tumor reporting and data system; MR = magnetic resonance; n = numbers

Full size image

MR image acquisition

All contrast-enhanced MR imaging were obtained on GE Healthcare and Siemens 1.5T MR machine using 8 channels head coil. The brain tumor MR protocol included axial T1weighted-image (WI), axial and coronal T2WIs, and axial FLAIR. By intravenous injection of 0.1 mmol/kg of gadopentetate dimeglumine at a rate of 2 ml/s, post-contrast axial, sagittal, and coronal T1WIs were acquired. DWI was done at b value (0 and 1000) with automatically calculated ADC map by the software. Table 1 shows the parameters of acquired MR sequences.

Table 1 Parameters of acquired MRI sequences in post-treated glioma imaging
Full size table

Image analysis and BT-RADS implementation

All first-stage MR studies were independently interpreted by two neuroradiologists (who were already implementing BT-RADS in their daily practice) on a PACS system (Paxera Ultima-Paxeramed) according to the BT-RADS scoring system, and their results were compared and the final BT-RADS category was confirmed consensually in cases with different ratings. They were informed about all patients' clinical states and previous treatment regimens. BT-RADS categorization was based on the tumors’ FLAIR/T2 component, mass effect, and enhanced component [11]. Studies were classified into BT-RADS 3a, 3b, and 3c as following:

  • BT-RADS 3a (image worsening favoring treatment effect): within 3 months following completed chemoradiotherapy with increase T2/FLAIR component or increase enhancing component < 25%, and increase mass effect in a clinically stable patient.

  • BT-RADS 3b (indeterminate): beyond 3 months following completed chemoradiotherapy with increase T2/FLAIR component or increase enhancing component < 25%, and increase mass effect in a clinically stable patient.

  • BI-RADS 3c (image worsening favoring tumor progression): beyond 3 months following completed chemoradiotherapy with increase T2/FLAIR component, increase enhancing component < 25%, and increase mass effect in a clinically worse patient and/or demonstration of a new indeterminate lesion outside the zone of radiation.

Adding DWI and resection cavity FLAIR signal to BT-RADS categorization

After interpretation of ADCmean, lowering ADCmean upgraded the BT-RADS categorization, whereas stable or increasing ADCmean stabilized or downgraded the BT-RADS categorization. Also, resection cavity FLAIR signal change into a higher signal upgraded BT-RADS categorization.

Reference study

The imaging of each enrolled patient in the first stage was followed in the second stage of the study. The second stage follow-up studies were used to determine the diagnostic performance of previous BT-RADS categorization and the additive value of ADCmean and resection cavity FLAIR signal change on the diagnostic performance of BT-RADS category 3.

Statistical analysis

Analyses were done using SPSS version 28.0 (IBM, Armonk, NY). The normality of distribution was assessed using the Kolmogorov–Smirnov test. Normally distributed data were presented as mean and standard deviation (SD), and categorical data as frequency and percentage. The difference between groups was analyzed by using Chi-square and Fisher exact tests (for categorical data). The correlation was done to detect the linear relationship between ADC value and tumor recurrence using the Pearson correlation coefficient. The accuracy, sensitivity, specificity, PPV and NPP before and after addition of ADCmean and resection cavity FLAIR signal change were calculated. ROC curve analyses were done to determine the diagnostic performance of BT-RADS implementation before and after addition of ADCmean and resection cavity FLAIR signal change in detection of tumor recurrence. Comparison of AUC was interpreted by Delong test. A statistical difference was significant as long as the P-value ≤ 0.05.

Results

Patient demographic and clinical characteristics

Twenty-seven patients (male, 62.9%, mean age ± SD, 50.1 ± 3.5; female, 37.1%, mean age ± SD, 35 ± 3.6) were included in our study. Table 2 shows the demographic and clinical data of the included patients. The primary tumor was resected either totally in 55.6% or partially in 44.4%, the rate of tumor recurrence was insignificantly related to the surgical tumor resection pattern measuring 46.2% and 53.8% in both groups, respectively. On resection cavity FLAIR signal assessment, all the five cases (38.5%) who developed a higher signal in the resection cavity on follow-up study proved to have tumor progression (Table 3).

Table 2 Demographic and clinical data of the included patients
Full size table
Table 3 Distribution of BT-RADS category 3 and ADCmean among recurrent and non-recurrent glioma patients
Full size table

Diagnostic performance of BT-RADS category 3

Twenty-seven patients were classified as BT-RADS category 3 using the BT-RADS classification system. Recurrent glioma was found in 9/20 BT-RADS 3b cases and 4/5 BT-RADS 3c cases. On follow-up studies, the two BT-RADS 3a cases showed regression, with no evidence of recurrent glioma (Table 2). BT-RADS category 3 could truly detect tumor recurrence in 10 out of 13 cases, and to eliminate tumor recurrence in 9 out of 14 cases reaching 76.9% sensitivity, 64.3% specificity, and 70.4% accuracy (Table 4).

Table 4 Diagnostic performance of BT-RADS category 3 before and after addition of DWI and FLAIR signal within resection cavity
Full size table

Relation between ADC value and tumor recurrence

The recurrence group had a significantly lower ADCmean (0.9 × 10−3mm2/s) than the non-recurrence group (1.15 × 10−3mm2/s). A significant correlation existed between ADC value and tumor recurrence (p˂ 0.001) (Table 3).

Additive value of DWI to the diagnostic performance of BT-RADS category 3

After incorporation of ADCmean to BT-RADS category 3, the diagnostic performance for differentiating tumor recurrence and non-recurrence increased from 64.3 to 71.4%, 76.9 to 84.6%, 66.7 to 73.3%, 79.5 to 83.3%, and 70.4 to 77.8% in terms of specificity, sensitivity, PPV, NPV, and Accuracy, respectively, with increased AUC from 0.706 (95% CI: 50–86%) to 0.780 (95% CI: 58–92%) (Table 4).

Additive value of DWI and resection cavity FLAIR signal to the diagnostic performance of BT-RADS category 3

After integration of both ADCmean and resection cavity FLAIR signal change to BT-RADS category 3, AUC increased to 0.819 (95% CI: 62–94%) with increased specificity, sensitivity, PPV, NPV and accuracy to 71.4%, 92.3%, 75%, 90.9%, and 81.5%, respectively (Table 4).

Figure 2 displays the comparison between Roc curves.

Fig. 2
figure 2

Comparison of The ROC curves regarding the AUC for BT-RADS, BT-RADS added DWI, and BT-RADS added DWI and FLAIR signal alteration within the resection cavity. ROC = receiver operating characteristic curve; BT-RADS = brain tumor reporting and data system; AUC = area under curve; DWI = diffusion-weighted image; FLAIR = fluid attenuation inversion recovery

Full size image

Figures 3, 4, 5 and 6 represent the cases of our study.

Fig. 3
figure 3

A 44-year-old clinically-stable male patient with an operated right parieto-frontal grade III astrocytoma finished chemoradiotherapy 6 months ago. a and b Axial FLAIR images of the first and second studies, respectively show stationary size of the FLAIR component with development of a higher signal in the resection cavity in the follow-up study. No significant changes in the surrounding mass effect. c and d Axial post-contrast T1 WIs of the first and second studies, respectively show an increase in the enhancing component by less than 25% in the follow-up study. e and f Coronal post-contrast T1 WIs of the first and second studies, respectively show an increase in the enhancing component. g and h Axial ADC maps show a decrease of ADCmean in the enhancing resection cavity margin (1.1 × 10−3mm2/s) and (0.9 × 10−3mm2/s) in the first and second studies, respectively. BT-RADS category 3b was given with an intermediate probability of tumor recurrence. Decrease ADCmean and higher FLAIR signal within resection cavity favor the possibility of tumor recurrence. Tumor progression was proved in the second stage follow-up study

Full size image
Fig. 4
figure 4

A 49-year-old clinically-stable male patient with bi-frontal grade IV glioblastoma multiforme finished chemoradiotherapy 7 months ago. a and b Axial FLAIR images of the first and second studies, respectively show an increase of the FLAIR component by less than 25%. No changes in the surrounding mass effect. c and d Axial post-contrast T1 WIs of the first and second studies, respectively show an increase in the enhancing component by less than 25% in the follow-up study. e and f Axial DWI and g and h Axial ADC maps show a decrease of ADCmean in the enhancing resection cavity margin (1.3 × 10−3mm2/s) and (0.99 × 10−3mm2/s) in the first and second studies, respectively. BT-RADS category 3b was given with an intermediate probability of tumor recurrence. Decrease ADCmean favor the possibility of tumor recurrence. Tumor progression was proved in the second stage follow-up study

Full size image
Fig. 5
figure 5

A 47-year-old clinically-deteriorated male patient with left temporo-parietal grade IV glioblastoma multiforme finished chemoradiotherapy 9 months ago. a and b Axial FLAIR images of the first and second studies, respectively show an increase of the FLAIR component by less than 25%. Increase mass effect on the second follow-up study. c and d Axial post-contrast T1 WIs of the first and second studies, respectively show an increase in the enhancing component by less than 25% in the follow-up study. e and f Axial DWI and (g and h) Axial ADC maps show a decrease of ADCmean in the heterogeneously enhancing component (0.99 × 10−3mm2/s) and (0.75 × 10−3mm2/s) in the first and second studies, respectively. BT-RADS category 3c was given that favors tumor progression. Decrease ADCmean ascertains the possibility of tumor recurrence. Tumor progression was proved in the second stage follow-up study

Full size image
Fig. 6
figure 6

A 48-year-old clinically-deteriorated male patient with an operated left frontal grade III astrocytoma finished chemoradiotherapy 20 months ago. a and b Axial FLAIR images of the first and second studies, respectively show demonstration of new FLAIR abnormal signal intensity component that displays iso intense signal with surrounding high SI in fronto-parietal brain parenchyma. Increase mass effect on ipsilateral ventricular system. c and d Axial DWI and e and f show area of restricted diffusion corresponding to the new enhanced area with a decrease in ADCmean measures (1.6 × 10−3mm2/s) and (0.8 × 10−3mm2/s) in the first and second studies, respectively. It was categorized as BT-RADS category 3c that favors tumor progression. Decrease ADCmean supported tumor progression. Tumor progression was proved in the second stage follow-up study

Full size image

Discussion

The interpretation of post-treated glioma is daily encountered in routine neuroimaging practice. Despite advances in treatment of high-grade glioma, it still has a tendency for recurrence. Moreover, it is sometimes hardly differentiated form treatment-related effect. Structured reporting of post-treated brain tumors imaging underwent several trials that ended with BT-RADS classification. BT-RADS classified the probability of tumor recurrence in post-treated glioma into 4 categories tied to suggested management strategy according to MR imaging features, clinical states of the patients, and the time elapsed since last received chemoradiotherapy [11]. However, BT-RADS category 3 had the intermediate probability of tumor recurrence and treatment-related changes. We concluded that adding ADCmean and resection cavity FLAIR signal changes could improve the diagnostic performance of BT-RADS category 3in the prediction capability of tumor recurrence.

DWI represents visual and quantitative assessment of water diffusion in extracellular space through creation of DWI (typically at b-value 0 and 1000) and ADC map, so high cellular tumors impair diffusivity of water resulting in decrease ADCmean and diffusion restriction [13]. Similar to Yang et al. study [14] who reported statistically significant difference of ADCmean between recurrent (0.8, IQR: 0.7–1.0 × 10–3 mm2/s) and non-recurrent groups (1.3, IQR: 1.2–1.4 × 10–3 mm2/s), we reported statistically significant difference of ADCmean in recurrent (0.9, IQR: 0.7–1.4) × 10–3 mm2/s) and non-recurrent groups (1.15, IQR: 1.04–1.6 × 10–3 mm2/s).

In Yang et al. study [14], specificity and accuracy of BT-RADS increased after adding DWI from 0.55 to 0.83%, and 0.7 to 0.8%, respectively, while sensitivity decreased from 0.88 to 0.78%.

It is worth noting that even after incorporating DWI into the classification system in our study, three cases classified as BT-RADS 3b and one case classified as BT-RADS 3c were falsely positive interpreted as tumor progression while was confirmed on follow-up to be delayed radiation necrosis, this was attributed to the inflammation accompanied radiation-induced brain injury, which can cause alteration in diffusion signal and restriction [15]. Moreover, radiation-induced changes are not limited to 3 months post-treatment, but radiation-induced brain injury has wide time range to occur either from days to weeks or weeks to months or months to years ascribed as acute, early delayed, and late delayed effects, respectively [16].

Previously reported that progressive contrast enhancement occurred within 2 months of chemoradiotherapy in approximately 21% of high-grade glioma patients, which was defined as pseudoprogression and resulted in imaging that mimicked tumor progression [17, 18]. This was explained by the treatment-induced local inflammation, glial and white matter damage, and vascular injury caused by a temporary increase of the blood brain barrier permeability at tumor bed [19]. In comparison to viable tumor progression, this phenomenon was described radiologically as thick peripheral fluffy-like enhancement with facilitated diffusion [16]. The late effects of radiation caused parenchymal and vascular injury and resulted in radiation necrosis of normal brain tissue or tumor [20] can occur from 2 to 32 months after radiotherapy with up to 85% of cases occur after 2 years of completed radiation [16]. Also, Shah et al. [21] concluded that imaging worsening occurred 3 years after completed radiation was a combined effect of tumor progression and radiation necrosis. Remarkably, pseudoprogression was associated with improved survival and better prognosis, while late radiation necrosis required further treatment with higher morbidity [14, 22, 23].

In our study, DWI could not detect the tumor progression in two BT-RADS 3b cases, this was attributed to the variable recurrent tumor cellularity and mixed effect of tumor recurrence and necrosis [15].

Yang et al. [14] found that BT-RADS category 3 alone had sensitivity, specificity, and accuracy of 88%, 55% and 74% with AUC 0.76 (95% CI 0.66–0.84), while adding DWI improved diagnostic performance of BT-RADS to 78%, 83%, and 80%, respectively with AUC 0.88 (95% CI 0.80–0.94). Similarly, by adding DWI to BT-RADS category 3 in our study, an improvement of the diagnostic performance of BT-RADS category 3 was noted with increasing sensitivity, specificity and accuracy from 76.9 to 84.6%, 64.3 to 71.4%, and 70.4 to 77.8%, respectively.

Notably, increased or decreased FLAIR component size and development of new signal outside radiation zone were the main concern in BT-RADS categorization [11] neglecting FLAIR signal changes within the resection cavity, whereas the development of hyperintense fluid signal in the resection cavity on follow-up FLAIR sequence was previously reported to be allied with a poorer prognosis and disease progression in high-grade glioma with a positive predictive value 90.6% (95% CI, 79.3–96.3%), and it was higher for grade IV glioma approaching 93.2% (95% CI, 87.3–99.1%) [24]. Bette et al. [12] confirmed the significance of FLAIR signal change assessment within the resection cavity that could differentiate between post-radiation changes and tumor recurrence in operated high-grade glioma with 80% specificity and 96.3% positive predictive value.

In our study, Integration of resection cavity FLAIR signal alteration and DWI to BT-RADS improved AUC with enhanced diagnostic performance.

In our study, we faced limitations. First, since re-surgical evaluation of post-treated glioma patients is not frequently performed in daily clinical practice, we depended on the second stage imaging follow-up as a reference standard in conjunction with clinical data. Second, we did not investigate the utility of adding perfusion weighted image to BT-RADS since it is not regularly acquired in radiological centers and has different image acquisition techniques. Third, our sample size is relatively small, this was explained by the nature of our study design, inclusion and exclusion criteria.

Conclusions

Diagnostic performance of BT-RADS category 3 for tumor recurrence prediction improved whenever it is combined with DWI and FLAIR signal assessment within the resection cavity. We recommend BT-RADS category 3 modification to avoid the necessity for treatment protocol adjustment and the hazards of unnecessary re-surgical interference.

Availability of data and materials

All data are available on a software system owned by each of the authors and the corresponding author has the authority to respond if there is any query.

Abbreviations

BT-RADS:

Brain tumor reporting and data system

DWI:

Diffusion weighted image

ADCmean :

Mean apparent diffusion coefficient

FLAIR:

Fluid attenuation inversion recovery

GBM:

Glioblastoma multiforme

ROC:

Receiver operating characteristics

AUC:

Area under the curve

PPV:

Positive predicative value

NPV:

Negative predicative value

References

  1. Messali A et al (2014) A review of the economic burden of glioblastoma and the cost effectiveness of pharmacologic treatments. Pharmacoeconomics 32(12):1201–1212

    Article  PubMed  Google Scholar 

  2. Rock K et al (2012) A clinical review of treatment outcomes in glioblastoma multiforme—the validation in a non-trial population of the results of a randomised phase III clinical trial: has a more radical approach improved survival? Br J Radiol 85(1017):729–733

    Article  Google Scholar 

  3. Nilsson J, Holgersson G et al (2017) Incidence trends in high-grade primary brain tumors in males and females. Oncol Lett 13(4):2831–2837

    Article  PubMed  PubMed Central  Google Scholar 

  4. Tamimi AF, Juweid M (2017) Epidemiology and outcome of glioblastoma. Exon Publications, Brisbane City, pp 143–153

    Book  Google Scholar 

  5. Ma BO et al (2016) Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas. J Magn Reson Imaging 44(2):456–462

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chinot OL et al (2014) Bevacizumab plus radiotherapy–temozolomide for newly diagnosed glioblastoma. N Engl J Med 370(8):709–722

    Article  CAS  PubMed  Google Scholar 

  7. Gilbert MR, Wang M, Aldape KD, Stupp R, Hegi ME, Jaeckle KA, Armstrong TS, Wefel JS, Won M, Blumenthal DT et al (2013) Dose-dense temozolomide for newly diagnosed glioblastoma: a randomized phase III clinical trial. J Clin Oncol 31:4085–4091

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fabian D, Eibl MDPGP, Alnahhas I, Sebastian N, Giglio P, Puduvalli V, Gonzalez J, Palmer JD (2019) Treatment of glioblastoma (GBM) with the addition of tumor-treating fields (TTF): a review. Cancers 11(2):174

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Alimohammadi E, Bagheri SR, Taheri S, Dayani M, Abdi A (2020) The impact of extended adjuvant temozolomide in newly diagnosed glioblastoma multiforme: a meta-analysis and systematic review. Oncol Rev 14(1):461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cox JD, Ang KK (2009) Radiation oncology E-book: rationale, technique, results. Elsevier Health Science, Amsterdam, pp 835–871

    Google Scholar 

  11. Weinberg BD et al (2018) Management-based structured reporting of posttreatment glioma response with the brain tumor reporting and data system. J Am Coll Radiol 15(5):767–771

    Article  PubMed  Google Scholar 

  12. Bette S, Gempt J et al (2017) FLAIR signal increase of the fluid within the resection cavity after glioma surgery: generally valid as early recurrence marker? J Neurosurg 127(2):417–422

    Article  PubMed  Google Scholar 

  13. Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. Am J Neuroradiol 27(3):475–487

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang Y, Wu X et al (2020) Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas. J Neurooncol 146(2):363–371

    Article  PubMed  Google Scholar 

  15. Mamlouk MD, Handwerker J et al (2013) Neuroimaging findings of the post-treatment effects of radiation and chemotherapy of malignant primary glial neoplasms. Neuroradiol J 26(4):396–412

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kessler AT, Bhatt AA (2018) Brain tumour post-treatment imaging and treatment-related complications. Insights Imaging 9(6):1057–1075

    Article  PubMed  PubMed Central  Google Scholar 

  17. da Cruz LCH, Rodrigues I et al (2011) Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 32(11):1978–1985

    Article  Google Scholar 

  18. Taal W et al (2008) Incidence of early pseudo-progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide. Cancer 113(2):405–410

    Article  CAS  PubMed  Google Scholar 

  19. Brandsma D et al (2008) Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 9(5):453–461

    Article  PubMed  Google Scholar 

  20. Asao C, Korogi Y et al (2005) Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol 26(6):1455–1460

    PubMed  PubMed Central  Google Scholar 

  21. Shah R, Vattoth S et al (2012) Radiation necrosis in the brain: imaging features and differentiation from tumor recurrence. Radiographics 32(5):1343–1359

    Article  PubMed  Google Scholar 

  22. Kong D-S et al (2011) Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status. Am J Neuroradiol 32(2):382–387

    Article  PubMed  PubMed Central  Google Scholar 

  23. Prager AJ et al (2015) Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. Am J Neuroradiol 36(5):877–885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sarbu N, Oleaga L et al (2016) Increased signal intensity in FLAIR sequences in the resection cavity can predict progression and progression-free survival in gliomas. Neurocirugia 27(6):269–276

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Special thanks and hope for cure to all patients incorporated in our study.

Funding

The authors declare that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radio-diagnosis, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt

    Maha Ibrahim Metwally, Faten Fawzy Mohammad Hafez, Ahmed Ali Morsy & Nesma Adel Zeed

  2. Radio-Diagnosis Department, Chest Hospital, Almansura, Egypt

    Sara Abdulrhman Ibrahim

  3. Department of Neurosurgery, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt

    Maha Ibrahim Metwally

Authors
  1. Maha Ibrahim Metwally
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Faten Fawzy Mohammad Hafez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara Abdulrhman Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed Ali Morsy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nesma Adel Zeed
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Guarantor of integrity of the entire study—MM. Study concepts and design—MM, FF and NZ. Literature research—MZ and MM. Clinical studies— SI, AM, MM, FF. Experimental studies/data analysis—MM, AM and NZ. Statistical analysis—MZ and MM. Manuscript preparation—MM. Manuscript editing—FF and MM. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Maha Ibrahim Metwally.

Ethics declarations

Ethics approval and consent to participate

Zagazig University Institutional review board approval was obtained. Approval number is # 6184-14-6-2020. Written informed consent was obtained from all patients.

Consent for publication

All patients included in this research gave written informed consent to publish the data contained within the study.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Metwally, M.I., Hafez, F.F.M., Ibrahim, S.A. et al. The value of adding DWI and FLAIR signal changes in the resection cavity on the diagnostic performance of BT-RADS category 3 for tumor progression prediction in post-treated glioma patients: a prospective pilot study. Egypt J Radiol Nucl Med 54, 52 (2023). https://doi.org/10.1186/s43055-023-00993-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43055-023-00993-3

Keywords