Standardized MRI assessment of high-grade glioma response: a review of the essential elements and pitfalls of the RANO criteria

Results from a first-in-human phase I safety trial to evaluate the use of a vascularized pericranial/temporoparietal fascial flap to line the resection cavity following resection of newly diagnosed glioblastoma

PURPOSE

The efficacy of systemic therapies for glioblastoma (GBM) remains limited due to the constraints of systemic toxicity and blood-brain barrier (BBB) permeability. Temporoparietal fascial flaps (TPFFs) and vascularized peri cranial flaps (PCF) are not restricted by the blood-brain barrier (BBB), as they derive their vascular supply from branches of the external carotid artery. Transposition of a vascularized TPFF or PCF along a GBM resection cavity may bring autologous tissue not restricted by the BBB in close vicinity to the tumor bed microenvironment, permit ingrowth of vascular channels fed by the external circulation, and offer a mechanism of bypassing the BBB. In addition, circulating immune cells in the vascularized flap may have better access to tumor-associated antigens (TAA) within the tumor microenvironment. We conducted a first-in-human Phase I trial assessing the safety of lining the resection cavity with autologous TPFF/PCF of newly diagnosed patients with GBM.

METHODS

12 patients underwent safe, maximal surgical resection of newly diagnosed GBMs, followed by lining of the resection cavity with a pedicled, autologous TPFF or PCF. Safety was assessed by monitoring adverse events. Secondary analysis of efficacy was examined as the proportion of patients experiencing progression-free disease (PFS) as indicated by response assessment in neuro-oncology (RANO) criteria and overall survival (OS). The study was powered to determine whether a Phase II study was warranted based on these early results. For this analysis, subjects who were alive and had not progressed as of the date of the last follow-up were considered censored and all living patients who were alive as of the date of last follow-up were considered censored for overall survival. For simplicity, we assumed that a 70% PFS rate at 6 months would be considered an encouraging response and would make an argument for further investigation of the procedure.

RESULTS

Median age of included patients was 57 years (range 46-69 years). All patients were Isocitrate dehydrogenase (IDH) wildtype. Average tumor volume was 56.6 cm3 (range 14-145 cm3). Resection was qualified as gross total resection (GTR) of all of the enhancing diseases in all patients. Grade III or above adverse events were encountered in 3 patients. No Grade IV or V serious adverse events occurred in the immediate post-operative period including seizure, infection, stroke, or tumor growing along the flap. Disease progression at the site of the original tumor was identified in only 4 (33%) patients (median 23 months, range 8-25 months), 3 of whom underwent re-operation. Histopathological analyses of those implanted flaps and tumor bed biopsy at repeat surgery demonstrated robust immune infiltrates within the transplanted flap. Importantly, no patient demonstrated evidence of tumor infiltration into the implanted flap. At the time of this manuscript preparation, only 4/12 (33%) of patients have died. Based on the statistical considerations above and including all 12 patients 10/12 (83.3%) had 6-month PFS. The median PFS was 9.10 months, and the OS was 17.6 months. 4/12 (33%) of patients have been alive for more than two years and our longest surviving patient currently is alive at 60 months.

CONCLUSIONS

This pilot study suggests that insertion of pedicled autologous TPFF/PCF along a GBM resection cavity is safe and feasible. Based on the encouraging response rate in 6-month PFS and OS, larger phase II studies are warranted to assess and reproduce safety, feasibility, and efficacy. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: ClinicalTrials.gov ID NCT03630289, dated: 08/02/2018.

Effect of tumor genetics, pathology, and location on fMRI of language reorganization in brain tumor patients

OBJECTIVES

Language reorganization may follow tumor invasion of the dominant hemisphere. Tumor location, grade, and genetics influence the communication between eloquent areas and tumor growth dynamics, which are drivers of language plasticity. We evaluated tumor-induced language reorganization studying the relationship of fMRI language laterality to tumor-related variables (grade, genetics, location), and patient-related variables (age, sex, handedness).

METHODS

The study was retrospective cross-sectional. We included patients with left-hemispheric tumors (study group) and right-hemispheric tumors (controls). We calculated five fMRI laterality indexes (LI): hemispheric, temporal lobe, frontal lobe, Broca's area (BA), Wernicke's area (WA). We defined LI ≥ 0.2 as left-lateralized (LL) and LI < 0.2 as atypical lateralized (AL). Chi-square test (p < 0.05) was employed to identify the relationship between LI and tumor/patient variables in the study group. For those variables having significant results, confounding factors were evaluated in a multinomial logistic regression model.

RESULTS

We included 405 patients (235 M, mean age: 51 years old) and 49 controls (36 M, mean age: 51 years old). Contralateral language reorganization was more common in patients than controls. The statistical analysis demonstrated significant association between BA LI and patient sex (p = 0.005); frontal LI, BA LI, and tumor location in BA (p < 0.001); hemispheric LI and fibroblast growth factor receptor (FGFR) mutation (p = 0.019); WA LI and O6-methylguanine-DNA methyltransferase promoter (MGMT) methylation in high-grade gliomas (p = 0.016).

CONCLUSIONS

Tumor genetics, pathology, and location influence language laterality, possibly due to cortical plasticity. Increased fMRI activation in the right hemisphere was seen in patients with tumors in the frontal lobe, BA and WA, FGFR mutation, and MGMT promoter methylation.

KEY POINTS

• Patients harboring left-hemispheric tumors present with contralateral translocation of language function. Influential variables for this phenomenon included frontal tumor location, BA location, WA location, sex, MGMT promoter methylation, and FGFR mutation. • Tumor location, grade, and genetics may influence language plasticity, thereby affecting both communication between eloquent areas and tumor growth dynamics. • In this retrospective cross-sectional study, we evaluated language reorganization in 405 brain tumor patients by studying the relationship of fMRI language laterality to tumor-related variables (grade, genetics, location), and patient-related variables (age, sex, handedness).

Prognostic analysis and nomogram construction for older patients with IDH-wild-type glioblastoma

As many countries face an ageing population, the number of older patients with glioblastoma (GB) is increasing. Thus, there is an urgent need for prognostic models to aid in treatment decision-making and life planning. A total of 98 patients with isocitrate dehydrogenase (IDH)-wild-type GB aged ≥65 years were analysed from January 2012 to January 2020. Independent prognostic factors were identified by prognostic analysis. Using the independent prognostic factors for overall survival (OS), a nomogram was constructed by R software to predict the prognosis of older patients with IDH-wild-type GB. The concordance index (C-index) and receiver operating characteristic (ROC) curve were used to assess model discrimination, and the calibration curve was used to assess model calibration. Prognostic analysis showed that the extent of resection (EOR), adjusted Charlson comorbidity index (ACCI), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, postoperative radiotherapy, and postoperative temozolomide (TMZ) chemotherapy were independent prognostic factors for OS. MGMT methylation status and subventricular zone (SVZ) involvement were independent prognostic factors for progression-free survival (PFS). A nomogram was constructed based on EOR, ACCI, MGMT methylation status, postoperative radiotherapy and postoperative TMZ chemotherapy to predict the 6-month, 12-month and 18-month OS of older patients with IDH-wild-type GB. The C-index of the nomogram was 0.72, and the ROC curves showed that the areas under the curve (AUCs) at 6, 12 and 18 months were 0.874, 0.739 and 0.779, respectively. The calibration plots showed that the nomogram was in good agreement with the actual observations in predicting the OS of older patients with IDH-wild-type GB. Older patients with IDH-wild-type GB can benefit from gross total resection (GTR), postoperative radiotherapy and postoperative TMZ chemotherapy. A high ACCI score and MGMT nonmethylation are poor prognostic factors. We constructed a nomogram including the ACCI to facilitate clinical decision-making and follow-up interval selection.

The complementary role of MRI and FET PET in high-grade gliomas to differentiate recurrence from radionecrosis

Introduction

Conventional magnetic resonance imaging (MRI) has limitations in differentiating tumor recurrence (TR) from radionecrosis (RN) in high-grade gliomas (HGG), which can present with morphologically similar appearances. Multiparametric advanced MR sequences and Positron Emission Tomography (PET) with amino acid tracers can aid in diagnosing tumor metabolism. The role of both modalities on an individual basis and combined performances were investigated in the current study.

Materials and Methods

Patients with HGG with MRI and PET within three weeks were included in the retrospective analysis. The multiparametric MRI included T1-contrast, T2-weighted sequences, perfusion, diffusion, and spectroscopy. MRI was interpreted by a neuroradiologist without using information from PET imaging. 18F-Fluoroethyl-Tyrosine (FET) uptake was calculated from the areas of maximum enhancement/suspicion, which was assessed by a nuclear medicine physician (having access to MRI to determine tumor-to-white matter ratio over a specific region). A definitive diagnosis of TR or RN was made based on the combination of multidisciplinary joint clinic decisions, histopathological examination, and clinic-radiological follow-up as applicable.

Results

62 patients were included in the study between July 2018 and August 2021. The histology during initial diagnosis was glioblastoma, oligodendroglioma, and astrocytoma in 43, 7, and 6 patients, respectively, while in 6, no definitive histological characterization was available. The median time from radiation (RT) was 23 months. 46 and 16 patients had TR and RN recurrence, respectively. Sensitivity, specificity, and accuracy using MRI were 98, 77, and 94%, respectively. Using PET imaging with T/W cut-off of 2.65, sensitivity, specificity, and accuracy were 79, 84, and 80%, respectively. The best results were obtained using both imaging combined with sensitivity, specificity, and accuracy of 98, 100, and 98%, respectively.

Conclusion

Combined imaging with MRI and FET-PET offers multiparametric assessment of glioma recurrence that is correlative and complimentary, with higher accuracy and clinical value.

Prognostic and predictive impact of abnormal signal volume evolution early after chemoradiotherapy in glioblastoma

INTRODUCTION

This study was designed to explore the feasibility of semiautomatic measurement of abnormal signal volume (ASV) in glioblastoma (GBM) patients, and the predictive value of ASV evolution for the survival prognosis after chemoradiotherapy (CRT).

METHODS

This retrospective trial included 110 consecutive patients with GBM. MRI metrics, including the orthogonal diameter (OD) of the abnormal signal lesions, the pre-radiation enhancement volume (PRRCE), the volume change rate of enhancement (rCE), and fluid attenuated inversion recovery (rFLAIR) before and after CRT were analyzed. Semi-automatic measurements of ASV were done through the Slicer software.

RESULTS

In logistic regression analysis, age (HR = 2.185, p = 0.012), PRRCE (HR = 0.373, p < 0.001), post CE volume (HR = 4.261, p = 0.001), rCE_1m (HR = 0.519, p = 0.046) were the significant independent predictors of short overall survival (OS) (< 15.43 months). The areas under the receiver operating characteristic curve (AUCs) for predicting short OS with rFLAIR_3m and rCE_1m were 0.646 and 0.771, respectively. The AUCs of Model 1 (clinical), Model 2 (clinical + conventional MRI), Model 3 (volume parameters), Model 4 (volume parameters + conventional MRI), and Model 5 (clinical + conventional MRI + volume parameters) for predicting short OS were 0.690, 0.723, 0.877, 0.879, 0.898, respectively.

CONCLUSION

Semi-automatic measurement of ASV in GBM patients is feasible. The early evolution of ASV after CRT was beneficial in improving the survival evaluation after CRT. The efficacy of rCE_1m was better than that of rFLAIR_3m in this evaluation.

Brain Functional Connectivity in Low- and High-Grade Gliomas: Differences in Network Dynamics Associated with Tumor Grade and Location

Brain tumors lead to modifications of brain networks. Graph theory plays an important role in clarifying the principles of brain connectivity. Our objective was to investigate network modifications related to tumor grade and location using resting-state functional magnetic resonance imaging (fMRI) and graph theory. We retrospectively studied 30 low-grade (LGG), 30 high-grade (HGG) left-hemispheric glioma patients and 20 healthy controls (HC) with rs-fMRI. Tumor location was labeled as: frontal, temporal, parietal, insular or occipital. We collected patients’ clinical data from records. We analyzed whole-brain and hemispheric networks in all patients and HC. Subsequently, we studied lobar networks in subgroups of patients divided by tumor location. Seven graph-theoretical metrics were calculated (FDR p < 0.05). Connectograms were computed for significant nodes. The two-tailed Student t-test or Mann−Whitney U-test (p < 0.05) were used to compare graph metrics and clinical data. The hemispheric network analysis showed increased ipsilateral connectivity for LGG (global efficiency p = 0.03) and decreased contralateral connectivity for HGG (degree/cost p = 0.028). Frontal and temporal tumors showed bilateral modifications; parietal and insular tumors showed only local effects. Temporal tumors led to a bilateral decrease in all graph metrics. Tumor grade and location influence the pattern of network reorganization. LGG may show more favorable network changes than HGG, reflecting fewer clinical deficits.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models

We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.

Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis

Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, a morphological measure of the complexity of pixel arrangement, have shown relationships with outcome across a variety of cancers. However, the application of these metrics to glioblastoma (GBM), a very aggressive primary brain tumor, has not been fully explored. In this project, we computed lacunarity and fractal dimension values for GBM-induced abnormalities on clinically standard magnetic resonance imaging (MRI). In our patient cohort (n = 402), we connect these morphological metrics calculated on pretreatment MRI with the survival of patients with GBM. We calculated lacunarity and fractal dimension on necrotic regions (n = 390), all abnormalities present on T1Gd MRI (n = 402), and abnormalities present on T2/FLAIR MRI (n = 257). We also explored the relationship between these metrics and age at diagnosis, as well as abnormality volume. We found statistically significant relationships to outcome for all three imaging regions that we tested, with the shape of T2/FLAIR abnormalities that are typically associated with edema showing the strongest relationship with overall survival. This link between morphological and survival metrics could be driven by underlying biological phenomena, tumor location or microenvironmental factors that should be further explored.

A systematic review of the utility of amino acid PET in assessing treatment response to bevacizumab in recurrent high-grade glioma

Background. Currently, bevacizumab (BEV), an antiangiogenic agent, is used as an adjunctive therapy to re-irradiation and surgery in patients with recurrent high-grade gliomas (rHGG). BEV has shown to decrease enhancement on MRI, but it is often unclear if these changes are due to tumor response to BEV or treatment-induced changes in the blood brain barrier. Preliminary studies show that amino acid PET can aid in distinguishing these changes on MRI.

Methods. The authors performed a systematic review of PubMed and Embase through July 2020 with the search terms ‘bevacizumab’ or ‘Avastin’ and ‘recurrent glioma’ and ‘PET,’ yielding 38 papers, with 14 meeting inclusion criteria.

Results. Thirteen out of fourteen studies included in this review used static PET and three studies used dynamic PET to evaluate the use of BEV in rHGG. Six studies used the amino acid tracer [18F]FET, four studies used [11C]MET, and four studies used [18F]FDOPA.

Conclusion. [18F]FET, [11C]MET, and [18F]FDOPA PET in combination with MRI have shown promising results for improving accuracy in diagnosing tumor recurrence, detecting early treatment failure, and distinguishing between tumor progression and treatment-induced changes in patients with rHGG treated with BEV.

Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

Purpose

To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [¹⁸F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma.

Material and methods

At suspected tumor progression, MRI and [¹⁸F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [¹⁸F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments.

Results

In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [¹⁸F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [¹⁸F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities.

Conclusion

Automated, joint image analysis of [¹⁸F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05427-8.

Refinement of response assessment in neuro-oncology (RANO) using non-enhancing lesion type and contrast enhancement evolution pattern in IDH wild-type glioblastomas

Background

Updated response assessment in neuro-oncology (RANO) does not consider peritumoral non-enhancing lesion (NEL) and baseline (residual) contrast enhancement (CE) volume. The objective of this study is to explore helpful imaging characteristics to refine RANO for assessing early treatment response (pseudoprogression and time-to-progression [TTP]) in patients with IDH wild-type glioblastoma.

Methods

This retrospective study enrolled 86 patients with IDH wild-type glioblastoma who underwent consecutive MRI examinations before and after concurrent chemoradiotherapy (CCRT). NEL was classified as edema- or tumor-dominant type on pre-CCRT MRI. CE evolution was categorized into 4 patterns based on post-operative residual CE (measurable vs. non-measurable) and CE volume change (same criteria with RANO) during CCRT. Multivariable logistic regression, including clinical parameters, NEL type, and CE evolution pattern, was used to analyze pseudoprogression rate. TTP and OS according to NEL type and CE evolution pattern was analyzed by the Kaplan–Meier method.

Results

Pseudoprogression rate was significantly lower (chi-square test, P = .047) and TTP was significantly shorter (hazard ratio [HR] = 2.03, P = .005) for tumor-dominant type than edema-dominant type of NEL. NEL type was the only predictive marker of pseudoprogression on multivariate analysis (odds ratio = 0.26, P = .046). Among CE evolution patterns, TTP and OS was shortest in patients with residual CE compared with those exhibiting new CE (HR = 4.33, P < 0.001 and HR = 3.71, P = .009, respectively). In edema-dominant NEL type, both TTP and OS was stratified by CE evolution pattern (log-rank, P = .001), whereas it was not in tumor-dominant NEL.

Conclusions

NEL type improves prediction of pseudoprogression and, together with CE evolution pattern, further stratifies TTP and OS in patients with IDH wild-type glioblastoma and may become a helpful biomarker for refining RANO.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08414-2.

Combined 18F-FET PET and diffusion kurtosis MRI in posttreatment glioblastoma: differentiation of true progression from treatment-related changes

Background

Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine (¹⁸F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma.

Methods

Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased ¹⁸F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered ¹⁸F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression.

Results

Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of ¹⁸F-FET uptake and diffusion MRI kurtosis metrics.

Conclusions

The combined use of ¹⁸F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation.

A Quantitative Assessment of Pre-Operative MRI Reports in Glioma Patients: Report Metrics and IDH Prediction Ability

Objectives

To measure the metrics of glioma pre-operative MRI reports and build IDH prediction models.

Methods

Pre-operative MRI reports of 144 glioma patients in a single institution were collected retrospectively. Words were transformed to lowercase letters. White spaces, punctuations, and stop words were removed. Stemming was performed. A word cloud method applied to processed text matrix visualized language behavior. Spearman's rank correlation assessed the correlation between the subjective descriptions of the enhancement pattern. The T1-contrast images associated with enhancement descriptions were selected. The keywords associated with IDH status were evaluated by χ2 value ranking. Random forest, k-nearest neighbors and Support Vector Machine algorithms were used to train models based on report features and age. All statistical analysis used two-tailed test with significance at p <.05.

Results

Longer word counts occurred in reports of older patients, higher grade gliomas, and wild type IDH gliomas. We identified 30 glioma enhancement descriptions, eight of which were commonly used: peripheral, heterogeneous, irregular, nodular, thick, rim, large, and ring. Five of eight patterns were correlated. IDH mutant tumors were characterized by words related to normal, symmetric or negative findings. IDH wild type tumors were characterized words by related to pathological MR findings like enhancement, necrosis and FLAIR foci. An integrated KNN model based on report features and age demonstrated high-performance (AUC: 0.89, 95% CI: 0.88-0.90).

Conclusion

Report length depended on age, glioma grade, and IDH status. Description of glioma enhancement was varied. Report descriptions differed for IDH wild and mutant gliomas. Report features can be used to predict glioma IDH status.

Theranostic Design of Angiopep-2 Conjugated Hyaluronic Acid Nanoparticles (Thera-ANG-cHANPs) for Dual Targeting and Boosted Imaging of Glioma Cells

Simple Summary

Glioblastoma multiforme is the most aggressive malignant brain tumor with poor patient prognosis. The presence of the blood-brain barrier and the complex tumor microenvironment impair the efficient accumulation of drugs and contrast agents, causing late diagnosis, inefficient treatment and monitoring. Functionalized theranostic nanoparticles are a valuable tool to modulate biodistribution of active agents, promoting their active delivery and selective accumulation for an earlier diagnosis and effective treatment, and provide simultaneous therapy and imaging for improved evaluation of treatment efficacy. In this work, we developed angiopep-2 functionalized crosslinked hyaluronic acid nanoparticles encapsulating gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) and irinotecan (Thera-ANG-cHANPs) that were shown to boost relaxometric properties of Gd-DTPA by the effect of Hydrodenticity, improve the uptake of nanoparticles by the exploitation of angiopep-2 improved transport properties, and accelerate the therapeutic effect of Irinotecan.

Abstract

Glioblastoma multiforme (GBM) has a mean survival of only 15 months. Tumour heterogeneity and blood-brain barrier (BBB) mainly hinder the transport of active agents, leading to late diagnosis, ineffective therapy and inaccurate follow-up. The use of hydrogel nanoparticles, particularly hyaluronic acid as naturally occurring polymer of the extracellular matrix (ECM), has great potential in improving the transport of drug molecules and, furthermore, in facilitatating the early diagnosis by the effect of hydrodenticity enabling the T₁ boosting of Gadolinium chelates for MRI. Here, crosslinked hyaluronic acid nanoparticles encapsulating gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) and the chemotherapeutic agent irinotecan (Thera-cHANPs) are proposed as theranostic nanovectors, with improved MRI capacities. Irinotecan was selected since currently repurposed as an alternative compound to the poorly effective temozolomide (TMZ), generally approved as the gold standard in GBM clinical care. Also, active crossing and targeting are achieved by theranostic cHANPs decorated with angiopep-2 (Thera-ANG-cHANPs), a dual-targeting peptide interacting with low density lipoprotein receptor related protein-1(LRP-1) receptors overexpressed by both endothelial cells of the BBB and glioma cells. Results showed preserving the hydrodenticity effect in the advanced formulation and internalization by the active peptide-mediated uptake of Thera-cHANPs in U87 and GS-102 cells. Moreover, Thera-ANG-cHANPs proved to reduce ironotecan time response, showing a significant cytotoxic effect in 24 h instead of 48 h.

Radiological Recurrence Patterns after Bevacizumab Treatment of Recurrent High-Grade Glioma: A Systematic Review and Meta-Analysis

Objective

To categorize the radiological patterns of recurrence after bevacizumab treatment and to derive the pooled proportions of patients with recurrent malignant glioma showing the different radiological patterns.

Materials and Methods

A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed to identify studies reporting radiological recurrence patterns in patients with recurrent malignant glioma after bevacizumab treatment failure until April 10, 2019. The pooled proportions according to radiological recurrence patterns (geographically local versus non-local recurrence) and predominant tumor portions (enhancing tumor versus non-enhancing tumor) after bevacizumab treatment were calculated. Subgroup and meta-regression analyses were also performed.

Results

The systematic review and meta-analysis included 17 articles. The pooled proportions were 38.3% (95% confidence interval [CI], 30.6–46.1%) for a geographical radiologic pattern of non-local recurrence and 34.2% (95% CI, 27.3–41.5%) for a non-enhancing tumor-predominant recurrence pattern. In the subgroup analysis, the pooled proportion of non-local recurrence in the patients treated with bevacizumab only was slightly higher than that in patients treated with the combination with cytotoxic chemotherapy (34.9% [95% CI, 22.8–49.4%] versus 22.5% [95% CI, 9.5–44.6%]).

Conclusion

A substantial proportion of high-grade glioma patients show non-local or non-enhancing radiologic patterns of recurrence after bevacizumab treatment, which may provide insight into surrogate endpoints for treatment failure in clinical trials of recurrent high-grade glioma.

Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep learning approaches, along with various advanced physiologic imaging parameters, hold great potential for aiding radiological assessments in neuro-oncology. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. However, none of the potential neuro-oncological applications for radiomics and deep learning has yet been realized in clinical practice. In this review, we summarize the current applications of radiomics and deep learning in neuro-oncology and discuss challenges in relation to evidence-based medicine and reporting guidelines, as well as potential applications in clinical workflows and routine clinical practice.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

Earliest radiological progression in glioblastoma by multidisciplinary consensus review

BACKGROUND

Detection of glioblastoma progression is important for clinical decision-making on cessation or initiation of therapy, for enrollment in clinical trials, and for response measurement in time and location. The RANO-criteria are considered standard for the timing of progression. To evaluate local treatment, we aim to find the most accurate progression location. We determined the differences in progression free survival (PFS) and in tumor volumes at progression (Vprog) by three definitions of progression.

METHODS

In a consecutive cohort of 73 patients with newly-diagnosed glioblastoma between 1/1/2012 and 31/12/2013, progression was established according to three definitions. We determined (1) earliest radiological progression (ERP) by retrospective multidisciplinary consensus review using all available imaging and follow-up, (2) clinical practice progression (CPP) from multidisciplinary tumor board conclusions, and (3) progression by the RANO-criteria.

RESULTS

ERP was established in 63 (86%), CPP in 64 (88%), RANO progression in 42 (58%). Of the 63 patients who had died, 37 (59%) did with prior RANO-progression, compared to 57 (90%) for both ERP and CPP. The median overall survival was 15.3 months. The median PFS was 8.8 months for ERP, 9.5 months for CPP, and 11.8 months for RANO. The PFS by ERP was shorter than CPP (HR 0.57, 95% CI 0.38-0.84, p = 0.004) and RANO-progression (HR 0.29, 95% CI 0.19-0.43, p < 0.001). The Vprog were significantly smaller for ERP (median 8.8 mL), than for CPP (17 mL) and RANO (22 mL).

CONCLUSION

PFS and Vprog vary considerably between progression definitions. Earliest radiological progression by retrospective consensus review should be considered to accurately localize progression and to address confounding of lead time bias in clinical trial enrollment.