Evaluation of pre-radiotherapy apparent diffusion coefficient (ADC): patterns of recurrence and survival outcomes analysis in patients treated for glioblastoma multiforme

Association of ADC of hyperintense lesions on FLAIR images with TERT promoter mutation status in glioblastoma IDH wild type

Background

Although mutations in telomerase reverse transcriptase (TERT) promoter (TERTp) are the most common alterations in glioblastoma (GBM), predicting TERTp mutation status by preoperative imaging is difficult. We determined whether tumour-surrounding hyperintense lesions on fluid-attenuated inversion recovery (FLAIR) were superior to those of contrast-enhanced lesions (CELs) in assessing TERTp mutation status using magnetic resonance imaging (MRI).

Methods

This retrospective study included 114 consecutive patients with primary isocitrate dehydrogenase (IDH)-wild-type GBM. The apparent diffusion coefficient (ADC) and volume of CELs and FLAIR hyperintense lesions (FHLs) were determined, and the correlation between MRI features and TERTp mutation status was analyzed. In a subset of cases, FHLs were histopathologically analyzed to determine the correlation between tumor cell density and ADC.

Results

TERTp mutations were present in 77 (67.5%) patients. The minimum ADC of FHLs was significantly lower in the TERTp-mutant group than in the TERTp-wild-type group (mean, 958.9 × 10-3 and 1092.1 × 10-3 mm2/s, respectively, P < 0.01). However, other MRI features, such as CEL and FHL volumes, minimum ADC of CELs, and FHL/CEL ratio, were not significantly different between the two groups. Histopathologic analysis indicated high tumor cell density in FHLs with low ADC.

Conclusion

The ADC of FHLs was significantly lower in IDH-wild-type GBM with TERTp mutations, suggesting that determining the ADC of FHLs on preoperative MRI might be helpful in predicting TERTp mutation status and surgical planning.

Relationship between apparent diffusion coefficient and survival as a function of distance from gross tumor volume on radiation planning MRI in newly diagnosed glioblastoma

PURPOSE

To investigate the changes in apparent diffusion coefficient (ADC) within incrementally-increased margins beyond the gross tumor volume (GTV) on post-operative radiation planning MRI and their prognostic utility in glioblastoma.

METHODS

Radiation planning MRIs of adult patients with newly diagnosed glioblastoma from 2017 to 2020 were assessed. The ADC values were normalized to contralateral normal white matter (nADC). Using 1 mm isotropic incremental margin increases from the GTV, the nADC values were calculated at each increment. Age, ECOG performance status, extent of resection and MGMT promoter methylation status were obtained from medical records. Using univariate and multivariable Cox regression analysis, association of nADC to progression-free and overall survival (PFS, OS) was assessed at each increment.

RESULTS

Seventy consecutive patients with mean age of 53.6 ± 10.3 years, were evaluated. The MGMT promoter was methylated in 31 (44.3%), unmethylated in 36 (51.6%) and unknown in 3 (4.3%) patients. 11 (16%) underwent biopsy, 41 (44%) subtotal resection and 18 (26%) gross total resection. For each 1 mm increase in distance from GTV, the nADC decreased by 0.16% (p < 0.0001). At 1-5 mm increment, the nADC was associated with OS (p < 0.01). From 6 to 11 mm increment the nADC was associated with OS with the p-value gradually increasing from 0.018 to 0.046. nADC was not associated with PFS.

CONCLUSION

The nADC values at 1-11 mm increments from the GTV margin were associated with OS. Future prospective multicenter studies are needed to validate the findings and to pave the way for the utilization of ADC for margin reduction in radiation planning.

The potential of advanced MR techniques for precision radiotherapy of glioblastoma

As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO₂), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO₂ to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.

Preoperative Apparent Diffusion Coefficient of Peritumoral Lesion Associate with Recurrence in Patients with Glioblastoma

Additional resection beyond contrast enhanced lesion on MRI is recently considered to prolong survival in glioblastoma. Prediction of future recurrent site in the peritumoral lesion on preoperative MRI could be useful for surgical planning. The objective of this study was to determine if the preoperative ADC value was associated with the site of future recurrence in patients with glioblastoma. We retrospectively analyzed 21 patients with primary GBM. The ADC value on MRI were analyzed before and after operation and at recurrence. The region of interests (ROIs) were set to cover almost the FLAIR high-signal lesion surrounding contrast enhanced lesion. We determined whether the value of ADC on MRI was correlated with the spot of future recurrence. Among 1844 ROIs determined in the FLAIR high-signal lesion on preoperative MRI, new enhanced lesions occurred in 186 sites. The other 1258 sites showed no change or decrease in size on follow up MRI, and the other 400 sites were removed in first operation. The pre-operative ADC values of sites corresponding to future recurrence were significantly lower than that of non-recurrent sites (p <0.001). We suggest that a low ADC values in FLAIR high-signal lesion is corresponding to recurrence, and useful for predicting recurrence of the lesion in cases of GBM. These results will be helpful for planning of surgery or radiation therapy and facilitate future prospective studies on GBM.

Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype glioblastoma

Background:

Prediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome.

Methods:

38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADC_T1C, ADC_T2-FLAIR) and perfusion fraction (f_T1C, f_T2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated.

Results:

Higher ADC_T2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01–1.15, p = 0.025], lower f_T2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04–4.27, p = 0.018), and lack of increase in ADC_T2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02–1.22, p = 0.02) were associated with early progression. Combining ADC_T2-FLAIR at baseline, f_T2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher f_T2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56–0.95, p = 0.018) was associated with longer PFS.

Conclusion:

ADC_T2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or f_T2-FLAIR at fraction 10 significantly predicted early progression. f_T2-FLAIR at fraction 10 was associated with PFS.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Background

Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.

Methods

This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.

Results

Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.

Conclusion

Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

Molecular and Clinical Insights into the Invasive Capacity of Glioblastoma Cells

The invasive capacity of GBM is one of the key tumoral features associated with treatment resistance, recurrence, and poor overall survival. The molecular machinery underlying GBM invasiveness comprises an intricate network of signaling pathways and interactions with the extracellular matrix and host cells. Among them, PI3k/Akt, Wnt, Hedgehog, and NFkB play a crucial role in the cellular processes related to invasion. A better understanding of these pathways could potentially help in developing new therapeutic approaches with better outcomes. Nevertheless, despite significant advances made over the last decade on these molecular and cellular mechanisms, they have not been translated into the clinical practice. Moreover, targeting the infiltrative tumor and its significance regarding outcome is still a major clinical challenge. For instance, the pre- and intraoperative methods used to identify the infiltrative tumor are limited when trying to accurately define the tumor boundaries and the burden of tumor cells in the infiltrated parenchyma. Besides, the impact of treating the infiltrative tumor remains unclear. Here we aim to highlight the molecular and clinical hallmarks of invasion in GBM.

Bright Edge Sign on High b-Value Diffusion-Weighted Imaging as a New Imaging Biomarker to Predict Poor Prognosis in Glioma Patients: A Retrospective Pilot Study

Purpose: To investigate the prognostic value of bright edge sign observed on high b-value diffusion-weighted imaging (DWI) map in glioma patients. Methods: We retrospectively reviewed our prospectively collected database for gliomas. Bright edge sign was defined as the presence of extremely high signal in tumor margin on high b-value DWI map (b = 3,000 s/mm²) with the signal intensity higher than those in contralateral normal white matter and tumor central region. Extremely poor prognosis was defined as overall survival time < 9 months. Survival analyses were conducted by using the Cox regression for both the univariable and multivariable analyses. Results: A total of 52 patients were enrolled (WHO IV, 25; WHO III, 13; WHO II, 14). Bright edge sign presented in 10 (19.2%) patients (WHO IV, 5; WHO III, 3; WHO II, 2). Nine (90.0%) patients with bright edge sign had extremely poor prognosis, while only 1 (2.4 %) patient without bright edge sign had extremely poor prognosis. The sensitivity and specificity of bright edge sign in determining extremely poor prognosis were 90 and 97.7%, respectively. Bright edge sign (HR [95% CI] = 25.11 [7.26-86.81], p < 0.001) was an independent predictor of poor prognosis after adjustment. Conclusion: Bright edge sign on high b-value DWI may be an accurate predictor of extremely poor prognosis in glioma patients, regardless of pathologic grades.

The Role of Standard and Advanced Imaging for the Management of Brain Malignancies From a Radiation Oncology Standpoint

Radiation therapy (RT) plays a critical role in the overall management of many central nervous system (CNS) tumors. Advances in RT treatment planning, with techniques such as intensity modulated radiation therapy, volumetric modulated arc therapy, and stereotactic radiosurgery, now allow the delivery of highly conformal dose with great precision. These techniques rely on high-resolution 3-dimensional anatomical imaging modalities such as computed tomography or magnetic resonance imaging (MRI) scans to accurately and reliably define CNS targets and normal tissue avoidance structures. The integration of cross-sectional imaging into radiation oncology has directly translated into improvements in the therapeutic window of RT, and the union between radiation oncology and imaging is only expected to grow stronger. In addition, advanced imaging modalities including diffusion, perfusion, and spectroscopic MRIs as well as positron emission tomography (PET) scans with novel tracers are being utilized to provide additional insight into tumor biology and behavior beyond anatomy. Together, these standard and advanced imaging modalities hold significant potential to improve future RT delivery and response assessment. In this review, we will discuss the current utilization of standard/advanced imaging for CNS tumors from a radiation oncology perspective as well as the implications of novel MRI and PET modalities currently under investigation.

Neuroimaging classification of progression patterns in glioblastoma: a systematic review

BACKGROUND

Our primary objective was to report the current neuroimaging classification systems of spatial patterns of progression in glioblastoma. In addition, we aimed to report the terminology used to describe 'progression' and to assess the compliance with the Response Assessment in Neuro-Oncology (RANO) Criteria.

METHODS

We conducted a systematic review to identify all neuroimaging studies of glioblastoma that have employed a categorical classification system of spatial progression patterns. Our review was registered with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) registry.

RESULTS

From the included 157 results, we identified 129 studies that used labels of spatial progression patterns that were not based on radiation volumes (Group 1) and 50 studies that used labels that were based on radiation volumes (Group 2). In Group 1, we found 113 individual labels and the most frequent were: local/localised (58%), distant/distal (51%), diffuse (20%), multifocal (15%) and subependymal/subventricular zone (15%). We identified 13 different labels used to refer to 'progression', of which the most frequent were 'recurrence' (99%) and 'progression' (92%). We identified that 37% (n = 33/90) of the studies published following the release of the RANO classification were adherent compliant with the RANO criteria.

CONCLUSIONS

Our review reports significant heterogeneity in the published systems used to classify glioblastoma spatial progression patterns. Standardization of terminology and classification systems used in studying progression would increase the efficiency of our research in our attempts to more successfully treat glioblastoma.

Glioma infiltration sign on high b-value diffusion-weighted imaging in gliomas and its prognostic value

BACKGROUND

Glioma cells may infiltrate beyond the tumor margins revealed on conventional structural images.

PURPOSE

To investigate whether the presence of a glioma infiltration sign on high b-value diffusion-weighted imaging (DWI) can predict the prognosis of gliomas.

STUDY TYPE

Retrospective cohort.

SUBJECTS

Fifty-two patients with gliomas (14 WHO grade II; 13 WHO grade III; 25 WHO grade IV).

FIELD STRENGTH/SEQUENCE

3.0T, including a T1 -weighted contrast-enhanced (T1 w-CE) sequence, contrast-enhanced T2 -flair sequence, and a DWI sequence.

ASSESSMENT

T1 w-CE images and contrast-enhanced T2 -flair images were used for identifying the tumor region for enhancing and nonenhancing gliomas, respectively. The glioma infiltration sign was defined as the presence of a peritumoral abnormal high signal region on DWI map, which was adjacent to the tumor region and had higher signal than surrounding areas. This sign was assessed on a high b-value DWI map with b = 3000 s/mm2 . For patients with glioma infiltration sign, DWI3000max , DWI1000max , ADC3000min , and ADC1000min were measured by drawing a region of interest over the peritumoral abnormal high signal region.

STATISTICAL TESTS

Survival analysis was conducted by using Cox regression.

RESULTS

Glioma infiltration sign was observed in 28 (53.8%) patients. The occurrence rate of this sign was 92.0% in grade IV gliomas, 30.8% in grade III gliomas, and 7.1% in grade II gliomas. The glioma infiltration sign could independently predict both the progression-free survival (hazard ratio [HR], 95% confidence interval [CI] = 8.58 [3.19-23.03], P < 0.001) and overall survival (HR, 95% CI = 11.90 [3.41-41.55], P < 0.001) after adjustment. For patients with glioma infiltration sign, DWI3000max (P = 0.005) and ADC3000min (P = 0.008) were both independent predictors of overall survival after adjustment, while DWI1000max and ADC1000min were not.

DATA CONCLUSION

The glioma infiltration sign on high b-value DWI is an independent predictor of poor prognosis in glioma patients. High b-value DWI might be a convenient method to detect glioma infiltration.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Multiparametric MRI and [18F]Fluorodeoxyglucose Positron Emission Tomography Imaging Is a Potential Prognostic Imaging Biomarker in Recurrent Glioblastoma

Purpose/objectives

Multiparametric advanced MR and [¹⁸F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes.

Methods/materials

We retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria.

Results

17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5 months [interquartile range (IQR) 3.7–12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10 months (IQR 7.2–13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUV_max to the normal contralateral brain (SUV_max/NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ≤1,400 × 10⁻⁶ mm²/s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUV_max/NB index >1.5 and ADC ≤1,400 × 10⁻⁶ mm²/s defined high-risk patients (n = 7), SUV_max/NB index ≤1.5 and ADC >1,400 × 10⁻⁶ mm²/s defined low-risk patients (n = 11), and intermediate-risk (n = 12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8 months (p < 0.01). Median PFS were 10.0, 4.4, and 1.9 months (p = 0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% (p = 0.04).

Conclusion

Recurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence.

Brain Tumor Imaging

Modern imaging techniques, particularly functional imaging techniques that interrogate some specific aspect of underlying tumor biology, have enormous potential in neuro-oncology for disease detection, grading, and tumor delineation to guide biopsy and resection; monitoring treatment response; and targeting radiotherapy. This brief review considers the role of magnetic resonance imaging and spectroscopy, and positron emission tomography in these areas and discusses the factors that limit translation of new techniques to the clinic, in particular, the cost and difficulties associated with validation in multicenter clinical trials.

A state-of-the-art review and guidelines for tumor treating fields treatment planning and patient follow-up in glioblastoma

Tumor treating fields (TTFields) are an integral treatment modality in the management of glioblastoma and extend overall survival when combined with maintenance temozolomide in newly diagnosed patients. Complexities exist regarding correct selection of imaging sequences with which to perform TTFields treatment planning. Guidelines are warranted first, to facilitate treatment planning standardization across medical disciplines and institutions, to ensure optimal TTFields delivery to the tumor and peritumoral brain zone while maximizing patient safety, and also to mitigate the risk of premature cessation of a potentially beneficial treatment. This summary guideline outlines methods for starting patients on TTFields, for monitoring patient response to therapy and provides a framework for evaluating when therapy should be re-planned, based on the extent of sequential imaging changes.

Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab

BACKGROUND

Bevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab.

METHODS

The aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model.

RESULTS

Using standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively.

CONCLUSION

With the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.

Mean apparent diffusion coefficient values in defining radiotherapy planning target volumes in glioblastoma

BACKGROUND

To evaluate mean apparent diffusion coefficient (ADC) values on pre-radiotherapy magnetic resonance (MR) at sites that gave rise to glioblastoma (GBM) recurrence compared to similar surrounding background tissue that did not progress to tumor.

METHODS

Twenty out of 110 consecutive patients with pathology proven GBM treated at our institution from 1/1/2009 to 5/31/2012 had definitive recurrence 6 months following radiotherapy. In this single-center retrospective cohort study, pre- and post-radiotherapy MR brain exams were evaluated. Sites of tumor recurrence on post-therapy exams were co-localized to pre-therapy exams and the background tissue type which gave rise to tumor was noted (i.e., T2 hyperintensity, normal appearing white or gray matter). Similar surrounding background tissue not progressing to tumor was also selected. Two radiologists compared mean ADC values on pre-radiotherapy MR for sites which gave rise to future tumor recurrence and sites of similar background tissue.

RESULTS

Pre-radiotherapy mean ADC values were significantly lower in regions of future tumor recurrence than in regions of surrounding background tissue not progressing to tumor (P=0.003). There were no significant quantitative differences on T1-weighted pre contrast (P=0.50) or T2-weighted (P=0.10) sequences between sites. There was strong interobserver agreement with an intraclass correlation of 0.867 for ADC values at sites of future tumor recurrence and background tissue.

CONCLUSIONS

Mean ADC values may help predict sites of future gross tumor recurrence in GBM, which could be helpful in radiation therapy planning.