Relationship between Apparent Diffusion Coefficients and MR Spectroscopy Findings in High-Grade Gliomas

The promise of metabolic imaging in diffuse midline glioma

Recent insights into histopathological and molecular subgroups of glioma have revolutionized the field of neuro-oncology by refining diagnostic categories. An emblematic example in pediatric neuro-oncology is the newly defined diffuse midline glioma (DMG), H3 K27-altered. DMG represents a rare tumor with a dismal prognosis. The diagnosis of DMG is largely based on clinical presentation and characteristic features on conventional magnetic resonance imaging (MRI), with biopsy limited by its delicate neuroanatomic location. Standard MRI remains limited in its ability to characterize tumor biology. Advanced MRI and positron emission tomography (PET) imaging offer additional value as they enable non-invasive evaluation of molecular and metabolic features of brain tumors. These techniques have been widely used for tumor detection, metabolic characterization and treatment response monitoring of brain tumors. However, their role in the realm of pediatric DMG is nascent. By summarizing DMG metabolic pathways in conjunction with their imaging surrogates, we aim to elucidate the untapped potential of such imaging techniques in this devastating disease.

Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO₂) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO₂; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO₂ and mitoPO₂ values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.

Spinal changes after 5-day dry immersion as shown by magnetic resonance imaging (DI-5-CUFFS)

Astronauts frequently report microgravity-induced back pain, which is generally more pronounced in the beginning of a spaceflight. The dry immersion (DI) model reproduces the early effects of microgravity in terms of global support unloading and fluidshift, both of which are involved in back pain pathogenesis. Here, we assessed spinal changes induced by exposure to 5 days of strict DI in 18 healthy men (25-43 years old) with (n = 9) or without (n = 9) thigh cuffs countermeasure. Intervertebral disc (IVD) height, spinal cord position, and apparent diffusion coefficient (ADC; reflecting global water motion) were measured using magnetic resonance imaging before and after DI. After DI, IVD height increased in thoracic (+3.3 ± 0.8 mm; C₇-T₁₂) and lumbar (+4.5 ± 0.4 mm; T₁₂-L₅) regions but not in the cervical region (C₂-C₇) of the spine. An increase in ADC after DI was observed at the L₁ (~6% increase, from 3.2 to 3.4 × 10^-3 mm²/s; p < 0.001) and L₂ (~3% increase, from 3.4 to 3.5 × 10^-3 mm²/s; p = 0.005) levels. There was no effect of thigh cuffs on spinal parameters. This change in IVD after DI follows the same "gradient" pattern of height increase from the cervical to the lumbar region as observed after bedrest and spaceflight. The increase in ADC at L₁ level positively correlated with reported back pain. These findings emphasize the utility of the DI model for studying early spinal changes observed in microgravity.

AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?

Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ensemble learning, which is known to boost performance, and comparative analyses are lacking in the literature. We aimed to compare ML classifiers to predict clinically relevant tasks for HGG: overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor vIII (EGFR) amplification, and Ki-67 expression, based on radiomic features from conventional and advanced magnetic resonance imaging (MRI). Our objective was to identify the best algorithm for each task. One hundred fifty-six adult patients with pathologic diagnosis of HGG were included. Three tumoral regions were manually segmented: contrast-enhancing tumor, necrosis, and non-enhancing tumor. Radiomic features were extracted with a custom version of Pyradiomics and selected through Boruta algorithm. A Grid Search algorithm was applied when computing ten times K-fold cross-validation (K=10) to get the highest mean and lowest spread of accuracy. Model performance was assessed as AUC-ROC curve mean values with 95% confidence intervals (CI). Extreme Gradient Boosting (xGB) obtained highest accuracy for OS (74,5%), Adaboost (AB) for IDH mutation (87.5%), MGMT methylation (70,8%), Ki-67 expression (86%), and EGFR amplification (81%). Ensemble classifiers showed the best performance across tasks. High-scoring radiomic features shed light on possible correlations between MRI and tumor histology.

Glioblastoma radiomics to predict survival: Diffusion characteristics of surrounding nonenhancing tissue to select patients for extensive resection

BACKGROUND AND PURPOSE

Glioblastoma (GBM) is an aggressive primary CNS neoplasm with poor overall survival (OS) despite standard of care. On MRI, GBM is usually characterized by an enhancing portion (CET) (surgery target) and a nonenhancing surrounding (NET). Extent of resection is a long debated issue in GBM, with recent evidence suggesting that both CET and NET should be resected in <65 years old patients, regardless of other risk factors (i.e., molecular biomarkers). Our aim was to test a radiomic model for patient survival stratification in <65 years old patients, by analyzing MRI features of NET, to aid tumor resection.

METHODS

Sixty-eight <65 years old GBM patients, with extensive CET resection, were selected. Resection was evaluated by manually segmenting CET on volumetric T1-weighted MRI pre and postsurgery (within 72 h). All patients underwent the same treatment protocol including chemoradiation. NET radiomic features were extracted with a custom version of Pyradiomics. Feature selection was performed with principal component analysis (PCA) and its effect on survival tested with Cox regression model. Twelve months OS discrimination was tested by t-test followed by logistic regression. Statistical significance was set at p<0.05. The most relevant features were identified from the component matrix.

RESULTS

Five PCA components (PC1-5) explained 90% of the variance. PC5 resulted significant in the Cox model (p = 0.002; exp(B) = 0.686), at t-test (p = 0.002) and logistic regression analysis (p = 0.006). Apparent diffusion coefficient (ADC)-based features were the most significant for patient survival stratification.

CONCLUSIONS

ADC radiomic features on NET predict survival after standard therapy and could be used to improve patient selection for more extensive surgery.

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

SFCE-RAPIRI Phase I Study of Rapamycin Plus Irinotecan: A New Way to Target Intra-Tumor Hypoxia in Pediatric Refractory Cancers

Simple Summary

More and more relapsing or refractory pediatric cancers are described to present hypoxic features linked to a worse outcome. Therefore, the aim of our phase I study RAPIRI was the targeting of the central node mTor/HIF-1α with rapamycin plus irinotecan and determine the appropriated dose of this combination. As expected, the tolerance was optimal across all dose levels and no maximum tolerated dose of both drugs was reached. The pharmacokinetics (PK) helped us to refine the doses to use in the future phase II trial and the importance of PK follow-up in such combination. We also confirmed in almost half of the interpretable patients for tumor response a non-progressive disease. All those observations additionally to the ancillary’s studies provide strong evidence to propose a next trial focusing on brain tumors and sarcomas and using biweekly 125 mg/m² irinotecan dose with a PK follow-up and a rapamycin dose of 1.5 mg/m²/day, reaching a blood concentration above 10 µg/L.

Abstract

Hypoxic environment is a prognostic factor linked in pediatric cancers to a worse outcome, favoring tumor progression and resistance to treatments. The activation of mechanistic Target Of Rapamycin (mTor)/hypoxia inducible factor (HIF)-1α pathway can be targeted by rapamycin and irinotecan, respectively. Therefore, we designed a phase I trial associating both drugs in pediatric refractory/relapsing solid tumors. Patients were enrolled according to a 3 + 3 escalation design with ten levels, aiming to determine the MTD (maximum tolerated dose) of rapamycin plus irinotecan. Rapamycin was administered orally once daily in a 28-day cycle (1 to 2.5 mg/m²/day), associating biweekly intravenous irinotecan (125 to 240 mg/m²/dose). Toxicities, pharmacokinetics, efficacy analyses, and pharmacodynamics were evaluated. Forty-two patients, aged from 2 to 18 years, were included. No MTD was reached. Adverse events were mild to moderate. Only rapamycin doses of 1.5 mg/m²/day reached over time clinically active plasma concentrations. Tumor responses and prolonged stable disease were associated with a mean irinotecan area under the curve of more than 400 min.mg/L. Fourteen out of 31 (45.1%) patients had a non-progressive disease at 8 weeks. Most of them were sarcomas and brain tumors. For the phase II trial, we can then propose biweekly 125 mg/m² irinotecan dose with a pharmacokinetic (PK) follow-up and a rapamycin dose of 1.5 mg/m²/day, reaching a blood concentration above 10 µg/L.

Quantitative biparametric analysis of hybrid 18F-FET PET/MR-neuroimaging for differentiation between treatment response and recurrent glioma

We investigated the diagnostic potential of simultaneous ¹⁸F-FET PET/MR-imaging for differentiation between recurrent glioma and post-treatment related effects (PTRE) using quantitative volumetric (3D-VOI) lesion analysis. In this retrospective study, a total of 42 patients including 32 patients with histologically proven glioma relapse and 10 patients with PTRE (histopathologic follow-up, n = 4, serial imaging follow-up, n = 6) were evaluated regarding recurrence. PET/MR-imaging was semi-automatically analysed based on FET tracer uptake using conservative SUV thresholding (isocontour 80%) with emphasis on the metabolically most active regions. Mean (relative) apparent diffusion coefficient (ADCmean, rADCmean), standardised-uptake-value (SUV) including target-to-background (TBR) ratio were determined. Glioma relapse presented higher ADCmean (MD ± SE, 284 ± 91, p = 0.003) and TBRmax (MD ± SE, 1.10 ± 0.45, p = 0.02) values than treatment-related changes. Both ADCmean (AUC ± SE = 0.82 ± 0.07, p-value < 0.001) and TBRmax (AUC ± SE = 0.81 ± 0.08, p-value < 0.001) achieved reliable diagnostic performance in differentiating glioma recurrence from PTRE. Bivariate analysis based on a combination of ADCmean and TBRmax demonstrated highest diagnostic accuracy (AUC ± SE = 0.90 ± 0.05, p-value < 0.001), improving clinical (false negative and false positive) classification. In conclusion, biparametric analysis using DWI and FET PET, both providing distinct information regarding the underlying pathophysiology, presented best diagnostic accuracy and clinical benefit in differentiating recurrent glioma from treatment-related changes.

Clinical value of magnetic resonance imaging in identifying multiple cerebral gliomas from primary central nervous system lymphoma

Clinical value of magnetic resonance imaging (MRI) in identifying and diagnosing multiple cerebral glioma (MCG) from primary central nervous system lymphoma (PCNSL) was evaluated. A total of 21 patients with MCG diagnosed clinically and pathologically in Zhangzhou Municipal Hospital from March 2016 to April 2017 were selected as group A, and 30 patients with PCNSL diagnosed in Zhangzhou Affiliated Hospital of Fujian Medical University during the same period as group B. Plain MRI, enhanced MRI and diffusion weighted imaging (DWI) were performed in all patients, the apparent diffusion coefficient (ADC) value of lesions was measured, and the diagnostic efficacy of ADC for MCG and PCNSL was evaluated by receiver operating characteristic (ROC) curve. The incidence of hippocampus lesions, patchy and cystic lesions, and the heterogeneous signal of plain scan in group A was significantly higher than that in group B (P<0.05), and the incidence of basal ganglia lesions was significantly lower than that in group B (P<0.05). Mass lesions in group A were significantly less than those in group B (P<0.05). The ADC value of lesions in group A was significantly higher than that in contralateral normal white matter (P<0.05), the ADC value in group B was significantly lower than that in normal contralateral white matter (P<0.05), so the ADC value in group A was significantly higher than that in group B (P<0.05). The location, lesion shape and signal characteristic of MCG and PCNSL have their own specificity; there are significant differences in DWI signal and ADC color map signal intensity of the lesions; ADC has certain diagnostic value for MCG and PCNSL; the differential diagnosis of MCG from PCNSL by MRI is of great significance.

Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position - A quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging

PURPOSE

To propose an MRI quality assurance procedure that can be used for routine controls and multi-centre comparison of different MR-scanners for quantitative diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

44 MR-scanners with different field strengths (1 T, 1.5 T and 3 T) were included in the study. DWI acquisitions (b-value range 0-1000 s/mm²), with three different orthogonal diffusion gradient directions, were performed for each MR-scanner. All DWI acquisitions were performed by using a standard spherical plastic doped water phantom. Phantom solution ADC value and its dependence with temperature was measured using a DOSY sequence on a 600 MHz NMR spectrometer. Apparent diffusion coefficient (ADC) along each diffusion gradient direction and mean ADC were estimated, both at magnet isocentre and in six different position 50 mm away from isocentre, along positive and negative AP, RL and HF directions.

RESULTS

A good agreement was found between the nominal and measured mean ADC at isocentre: more than 90% of mean ADC measurements were within 5% from the nominal value, and the highest deviation was 11.3%. Away from isocentre, the effect of the diffusion gradient direction on ADC estimation was larger than 5% in 47% of included scanners and a spatial non uniformity larger than 5% was reported in 13% of centres.

CONCLUSION

ADC accuracy and spatial uniformity can vary appreciably depending on MR scanner model, sequence implementation (i.e. gradient diffusion direction) and hardware characteristics. The DWI quality assurance protocol proposed in this study can be employed in order to assess the accuracy and spatial uniformity of estimated ADC values, in single- as well as multi-centre studies.

Insular gliomas: a surgical reappraisal based on a systematic review of the literature

INTRODUCTION

Insular gliomas are heterogeneous lesions whose management presents multiple challenges for their tendency to affect young patients in good neurological and cognitive conditions, their deep anatomic location and proximity with critical functional and vascular structures. The appropriate management of insular gliomas requires a multidisciplinary evidence-centred teamwork grounded on the best anatomic, neurophysiological and oncological knowledge. The present study provides a reappraisal of the management of insular gliomas based on a systematic review of the literature with the aim of guiding clinicians in the management of such tumors.

EVIDENCE ACQUISITION

A systematic review of the literature from the Medline, Embase and Cochrane Central databases was performed. From 2006 to 2016, all articles meeting specific inclusion criteria were included.

EVIDENCE SYNTHESIS

The present work summarizes the most relevant evidence about insular gliomas management. The anatomy and physiology of the insula, the new WHO 2016 classification and clinico-radiological presentation of insular gliomas are reviewed. Surgical pearls of insular gliomas resection as well as oncologic and functional outcomes after insular gliomas treatment are discussed.

CONCLUSIONS

Management of insular gliomas remains challenging despite improvement in surgical and oncological techniques. However, the literature review supports a growing evidence that recent developments in the multidisciplinary care account for constant improvements of survival and quality of life.