Usefulness of perfusion- and diffusion-weighted imaging to differentiate between pilocytic astrocytomas and high-grade gliomas: a multicenter study in Japan

Hypothalamic Hemangioma-like Pilocytic Astrocytoma in an Adult Patient: A Systematic Review with a Focus on Differential Diagnosis and Neurological Presentation

Background: Pilocytic astrocytoma (PCA) are commonly observed as slow-growing noncancerous brain tumors in pediatric populations, but they can also occur in adults, albeit rarely. When located in diencephalic regions, particularly in the hypothalamus, they present unique diagnostic and management challenges due to their rarity and overlapping clinical and radiological features with other intracranial pathologies. This systematic review aims to provide a comprehensive understanding of hypothalamic PCA in adults, focusing on their differential diagnosis, neurological presentation, diagnostic modalities, treatment strategies. A case illustration is also described in order to better underline all the difficulties related to the diagnostic process. Material and methods: A systematic literature search was conducted in the PubMed/MEDLINE, Embase, and Scopus databases up to November 2023 to identify studies. Results: The systematic literature search identified a total of 214 articles. Following screening by title and abstract and full-text review, 12 studies were deemed eligible and are included here. Conclusions: Adult-onset PCA in diencephalic regions pose diagnostic challenges due to their rarity and overlapping features with other intracranial lesions. Advanced imaging techniques play a crucial role in diagnosis, while surgery remains the cornerstone of treatment. Multidisciplinary collaboration is essential for the optimal management and long-term follow-up of these patients.

Normal Brain and Brain Tumor ADC: Changes Resulting From Variation of Diffusion Time and/or Echo Time in Pulsed-Gradient Spin Echo Diffusion Imaging

OBJECTIVES

Increasing gradient performance on modern magnetic resonance imaging scanners has profoundly reduced the attainable diffusion and echo times for clinically available pulsed-gradient spin echo (PGSE) sequences. This study investigated how this may impact the measured apparent diffusion coefficient (ADC), which is considered an important diagnostic marker for differentiation between normal and abnormal brain tissue and for therapeutic follow-up.

MATERIALS AND METHODS

Diffusion time and echo time dependence of the ADC were evaluated on a high-performance 3 T magnetic resonance imaging scanner. Diffusion PGSE brain scans were performed in 10 healthy volunteers and in 10 brain tumor patients using diffusion times of 16, 40, and 70 ms, echo times of 60, 75, and 104 ms at 3 b-values (0, 100, and 1000 s/mm 2 ), and a maximum gradient amplitude of 68 mT/m. A low gradient performance system was also emulated by reducing the diffusion encoding gradient amplitude to 19 mT/m. In healthy subjects, the ADC was measured in 6 deep gray matter regions and in 6 white matter regions. In patients, the ADC was measured in the solid part of the tumor.

RESULTS

With increasing diffusion time, a small but significant ADC increase of up to 2.5% was observed for 6 aggregate deep gray matter structures. With increasing echo time or reduced gradient performance, a small but significant ADC decrease of up to 2.6% was observed for 6 aggregate white matter structures. In tumors, diffusion time-related ADC changes were inconsistent without clear trend. For tumors with diffusivity above 1.0 μm 2 /ms, with prolonged echo time, there was a pronounced ADC increase of up to 12%. Meanwhile, for tumors with diffusivity at or below 1.0 μm 2 /ms, no change or a reduction was observed. Similar results were observed for gradient performance reduction, with an increase of up to 21%. The coefficient of variation determined in repeat experiments was 2.4%.

CONCLUSIONS

For PGSE and the explored parameter range, normal tissue ADC changes seem negligible. Meanwhile, observed tumor ADC changes can be relevant if ADC is used as a quantitative biomarker and not merely assessed by visual inspection. This highlights the importance of reporting all pertinent timing parameters in ADC studies and of considering these effects when building scan protocols for use in multicenter investigations.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI

BACKGROUND AND PURPOSE

Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T₁ perfusion MRI parameters combined under a machine learning framework.

MATERIALS AND METHODS

This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T₁ perfusion and DWI data along with conventional MRI images. Analysis of T₁ perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T₁ perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T₁ perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter.

RESULTS

T₁ perfusion parameter values were significantly different between the two groups. The combination of T₁ perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error.

CONCLUSION

The approach of using quantitative T₁ perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

A multi-reader comparison of normal-appearing white matter normalization techniques for perfusion and diffusion MRI in brain tumors

PURPOSE

There remains no consensus normal-appearing white matter (NAWM) normalization method to compute normalized relative cerebral blood volume (nrCBV) and apparent diffusion coefficient (nADC) in brain tumors. This reader study explored nrCBV and nADC differences using different NAWM normalization methods.

METHODS

Thirty-five newly diagnosed glioma patients were studied. For each patient, two readers created four NAWM regions of interests: (1) a single plane in the centrum semiovale (CSOp), (2) 3 spheres in the centrum semiovale (CSOs), (3) a single plane in the slice of the tumor center (TUMp), and (4) 3 spheres in the slice of the tumor center (TUMs). Readers repeated NAWM segmentations 1 month later. Differences in nrCBV and nADC of the FLAIR hyperintense tumor, inter-/intra-reader variability, and time to segment NAWM were assessed. As a validation step, the diagnostic performance of each method for IDH-status prediction was evaluated.

RESULTS

Both readers obtained significantly different nrCBV (P < .001), nADC (P < .001), and time to segment NAWM (P < .001) between the four normalization methods. nrCBV and nADC were significantly different between CSO and TUM methods, but not between planar and spherical methods in the same NAWM region. Broadly, CSO methods were quicker than TUM methods, and spherical methods were quicker than planar methods. For all normalization techniques, inter-reader reproducibility and intra-reader repeatability were excellent (intraclass correlation coefficient > 0.9), and the IDH-status predictive performance remained similar.

CONCLUSION

The selected NAWM region significantly impacts nrCBV and nADC values. CSO methods, particularly CSOs, may be preferred because of time reduction, similar reader variability, and similar diagnostic performance compared to TUM methods.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading

Diffuse gliomas are the most common primary malignant intracranial neoplasms. Aside from the challenges pertaining to their treatment-glioblastomas, in particular, have a dismal prognosis and are currently incurable-their pre-operative assessment using standard neuroimaging has several drawbacks, including broad differentials diagnosis, imprecise characterization of tumor subtype and definition of its infiltration in the surrounding brain parenchyma for accurate resection planning. As the pathophysiological alterations of tumor tissue are tightly linked to an aberrant vascularization, advanced hemodynamic imaging, in addition to other innovative approaches, has attracted considerable interest as a means to improve diffuse glioma characterization. In the present part A of our two-review series, the fundamental concepts, techniques and parameters of hemodynamic imaging are discussed in conjunction with their potential role in the differential diagnosis and grading of diffuse gliomas. In particular, recent evidence on dynamic susceptibility contrast, dynamic contrast-enhanced and arterial spin labeling magnetic resonance imaging are reviewed together with perfusion-computed tomography. While these techniques have provided encouraging results in terms of their sensitivity and specificity, the limitations deriving from a lack of standardized acquisition and processing have prevented their widespread clinical adoption, with current efforts aimed at overcoming the existing barriers.

Advanced magnetic resonance imaging findings of cerebellar hemangioblastomas: A report of three cases and a literature review

On conventional magnetic resonance imaging (MRI), hemangioblastomas typically appear as mural nodules with an adjacent surrounding cyst or a solid mass in the cerebellum. However, hemangioblastomas sometimes cannot be reliably distinguished using this imaging technique from other tumors, especially pilocytic astrocytomas and metastatic tumors, because of their similar imaging findings and locations. Herein, we report three cases of cerebellar hemangioblastomas and review their findings on conventional and advanced MRI, including diffusion-weighted imaging (DWI), dynamic susceptibility-weighted contrast-enhanced perfusion-weighted imaging (DSC-PWI), and magnetic resonance spectroscopy (MRS). Solid contrast-enhanced lesions of hemangioblastomas showed increased apparent diffusion coefficient values on DWI, increased relative cerebral blood volume ratio on DSC-PWI, and high lipid/lactate peak on MRS. Therefore, advanced MRI techniques can be helpful in understanding the pathological and metabolic changes of hemangioblastomas and may be useful for their characterization.

A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from glioblastomas

OBJECTIVES

To develop a fully automatic radiomics model to differentiate adult pilocytic astrocytomas (PA) from high-grade gliomas (HGGs).

METHODS

This retrospective study included 302 adult patients with PA (n = 62) or HGG (n = 240). The patients were randomly divided into training (n = 211) and test (n = 91) sets. Clinical data were obtained, and radiomic features (n = 372) were extracted from multiparametric MRI with automatic tumour segmentation. After feature selection with F-score, a Light Gradient Boosting Machine classifier with subsampling was trained to develop three models: (1) clinical model, (2) radiomics model, and (3) combined clinical and radiomics model. Human performance was also assessed. The performance of the classifier was validated in the test set. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model.

RESULTS

A total of 15 radiomic features were selected. In the test set, the combined clinical and radiomics model (area under the curve [AUC], 0.93) showed a significantly higher performance than the clinical model (AUC, 0.79, p = 0.037) and had a similar performance to the radiomics model (AUC, 0.92, p = 0.828). The combined clinical and radiomics model also showed a significantly higher performance than humans (AUC, 0.76-0.81, p < 0.05). The model explanation by SHAP suggested that lower intratumoural heterogeneity from T2-weighted images was highly associated with PA diagnosis.

CONCLUSIONS

The fully automatic combined clinical and radiomics model may be helpful for differentiating adult PAs from HGGs.

KEY POINTS

• Differentiating adult PAs from HGGs is challenging because PAs may manifest a large spectrum of imaging presentations, often including aggressive imaging features. • The fully automatic combined clinical and radiomics model showed a significantly higher performance than the clinical model or humans. • The model explanation by SHAP suggested that second-order features from T2-weighted imaging were important in diagnosis and might reflect the underlying pathophysiology that PAs have lesser tissue heterogeneity than HGGs.

Dynamic susceptibility contrast and diffusion-weighted MRI in posterior fossa pilocytic astrocytoma and medulloblastoma

BACKGROUND AND PURPOSE

The utility of perfusion MRI in distinguishing between pilocytic astrocytoma (PA) and medulloblastoma (MB) is unclear. This study aimed to evaluate the diagnostic and prognostic performance of dynamic susceptibility contrast (DSC)-MRI parameters and apparent diffusion coefficient (ADC) values between PA and MB.

METHODS

Between January 2012 and August 2021, 49 (median, 7 years [range, 1-28 years]; 28 females) and 35 (median, 8 years [1-24 years]; 12 females) patients with pathologically confirmed PA and MB, respectively, were included. The normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and mean and minimal normalized ADC (nADCmean and nADCmin) values were calculated using volume-of-interest analyses. Diagnostic performance and Pearson's correlation with progression-free survival were also evaluated.

RESULTS

The MB group showed a significantly higher nrCBV and nrCBF (nrCBV: 1.69 [0.93-4.23] vs. 0.95 [range, 0.37-2.28], p = .0032; nrCBF: 1.62 [0.93-3.16] vs. 1.07 [0.46-2.26], p = .0084) and significantly lower nADCmean and nADCmin (nADCmean: 0.97 [0.70-1.68] vs. 2.21 [1.44-2.80], p < .001; nADCmin: 0.50 [0.19-0.89] vs. 1.42 [0.89-2.20], p < .001) than the PA group. All parameters exhibited good diagnostic ability (accuracy >0.80) with nADCmin achieving the highest score (accuracy = 1). A moderate correlation was found between nADCmean and progression-free survival for MB (r = 0.44, p = .0084).

CONCLUSIONS

DSC-MRI parameters and ADC values were useful for distinguishing between PA and MB. A lower ADC indicated an unfavorable MB prognosis, but the DSC-MRI parameters did not correlate with progression-free survival in either group.

Differentiation between supratentorial pilocytic astrocytoma and extraventricular ependymoma using multiparametric MRI

BACKGROUND

The differentiation of supratentorial pilocytic astrocytomas (STPAs) and supratentorial extraventricular ependymomas (STEEs) is clinically pivotal because of distinct therapeutic management and prognosis, which is sometimes challenging to both neuroradiologists and pathologists.

PURPOSE

To explore and compare the conventional and advanced magnetic resonance imaging (MRI) features between STPA and STEE.

MATERIAL AND METHODS

A total of 23 patients with STPAs and 23 patients with STEEs were reviewed in this study. All patients performed conventional MRI, susceptibility-weighted imaging (SWI), and diffusion-weighted imaging (DWI), and 34 patients (17 with STPAs and 17 with STEEs) examined dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) in addition. Clinical data, conventional MRI features, minimum relative apparent diffusion coefficient ratio (rADC_min), and maximum relative cerebral blood volume ratio (rCBV_max) were compared between the two groups and subgroups. The optimal cutoff values of rADC_min and rCBV_max with sensitivity and specificity were calculated.

RESULTS

STPA manifested similar to STEE as a solid-cystic mass but more frequently presented with a marked enhancing deep nodule (P = 0.02), no peritumoral edema (P = 0.036), higher rADC_min value (2.0 ± 0.5 vs. 0.9 ± 0.2; P < 0.001), and lower rCBV_max value (2.1 ± 0.4 vs. 14.4 ± 5.5; P < 0.001). The cutoff value of >1.39 for rADC_min and ≤ 2.81 for rCBV_max produced a high sensitivity of 95.65% and 100.0%, respectively, and all produced a specificity of 100.0% in differentiating STPAs from STEEs.

CONCLUSION

Multiparametric MRI techniques including conventional MRI, DWI, and DSC-PWI contribute to the differential diagnosis of STPA and STEE.

A diagnostic tree for differentiation of adult pilocytic astrocytomas from high-grade gliomas

BACKGROUND

To develop a diagnostic tree analysis (DTA) model based on demographical information and conventional MRI for differential diagnosis of adult pilocytic astrocytomas (PAs) and high-grade gliomas (HGGs; World Health Organization grade III-IV).

METHODS

A total of 357 adult patients with pathologically confirmed PA (n = 65) and HGGs (n = 292) who underwent conventional MRI were included. The patients were randomly divided into training (n = 250) and validation (n = 107) datasets to assess the diagnostic performance of the DTA model. The DTA model was created using a classification and regression tree algorithm on the basis of demographical and MRI findings.

RESULTS

In the DTA model, tumor location (on cerebellum, brainstem, hypothalamus, optic nerve, or ventricle), cystic mass with mural nodule appearance, presence of infiltrative growth, and major axis (cutoff value, 2.9 cm) were significant predictors for differential diagnosis of adult PAs and HGGs. The AUC, accuracy, sensitivity, and specificity were 0.94 (95% confidence interval 0.86-1.00), 96.2%, 89.5%, and 97.7%, respectively, in the test set. The accuracy of the DTA model was significantly higher than the no-information rate in the test (96.2 % vs 85.0%, P < 0.001) set.

CONCLUSION

The DTA model based on MRI findings may be useful for differential diagnosis of adult PA and HGGs.

Peri-operative prognostic factors for primary skull base chordomas: results from a single-center cohort

BACKGROUND

Skull base chordomas (SBC) are rare malignant tumors and few factors have been found to be reliable markers for clinical decision making and survival prognostication. The aim of the present work was to identify specific prognostic factors potentially useful for the management of SBC patients.

METHODS

A retrospective review of all the patients diagnosed and treated for SBC at the Fondazione IRCCS Istituto Neurologico "Carlo Besta" between January 1992 and December 2017 has been performed. Survival analysis was performed and a logistic regression model was used. Statistically significant predictors were rated based on their log odds in order to preliminarily build a personalized grading scale-the Peri-Operative Chordoma Scale (POCS).

RESULTS

Fifty-nine primary chordoma patients were included. The average follow-up from the first treatment was 82.6 months (95% CI, 65.5-99.7). POCS was built over PFS and MR contrast enhancement (intense vs mild/no, value 4), preoperative motor deficit (yes vs no, value 3), and the development of any postoperative complications (yes vs no, value 2). POCS ranges between 0 and 9, with higher scores being associated with reduced likelihood of survival and progression-free state.

CONCLUSIONS

Our results show that preoperative clinical symptoms (motor deficits), surgical features (extent of tumor resection and surgeon's experience), development of postoperative complications, and KPS decline represent significant prognostic factors. The degree of MR contrast enhancement significantly correlated to both OS and PFS. We also preliminarily developed the POCS as a prognostic grading scale which may help neurosurgeons in the personalized management of patients undergoing potential adjuvant therapies.

Reappraisal of haemorrhagic suprasellar pilocytic astrocytoma during adulthood

Pilocytic astrocytomas comprise the most common central nervous system tumour during childhood and have an excellent response to surgical treatment in this population. The tumour incidence decreases with age, whereas more aggressive behaviour tends to increase. Haemorrhage as a presenting feature of pilocytic astrocytomas is a rare phenomenon, especially in the adult population. We present a case of a 55-year-old patient with progressive headaches and dizziness. MRI confirmed a sellar and predominantly retrochiasmal suprasellar lesion with heterogeneous signal, enhancement and blood products. Management via transsphenoidal approach was performed, and histopathology revealed the unexpected diagnosis of haemorrhagic pilocytic astrocytoma. Haemorrhagic pilocytic astrocytoma is an infrequent entity in the adult population and it is essential to recognise the peculiarities regarding diagnostic evaluation and management, which differ from the paediatric population. During adulthood, this tumour carries an overall unfavourable prognosis, with higher rates of progression and recurrence.

The combined role of MR spectroscopy and perfusion imaging in preoperative differentiation between high- and low-grade gliomas

Background

Brain tumors are an important health problem. The preoperative classification of gliomas by non-invasive techniques is a significant problem. Relative cerebral blood volume and spectroscopy have the ability to sample the entire lesion non-invasively. The present study aims to evaluate the combined role of dynamic susceptibility perfusion and spectroscopy in the classification of primary brain tumors. The combination of both provides overall diagnostic accuracy (100%). Relative cerebral blood volume in peritumoral region plays an important additional role in this regard.

Results

On the basis of histopathology, among 50 patients with brain tumors, high-grade gliomas accounted for 58%, while low-grade gliomas accounted for 42%. The relative cerebral blood volume in the tumor had the best sensitivity, specificity, and accuracy of 96.8%, 95.3%, and 96, respectively. The use of relative cerebral blood volume and choline/N-acetyl Aspartate increased diagnostic accuracy by 100%.

Conclusion

The combination of magnetic resonance spectroscopy and perfusion can increase sensitivity and positive predictive value to define the degree of glioma.

Oncosuppressive Role of RUNX3 in Human Astrocytomas

Background

Gliomas are the most common and aggressive among primary malignant brain tumours with significant inter- and intratumour heterogeneity in histology, molecular profile, and patient outcome. However, molecular targets that could provide reliable diagnostic and prognostic information on this type of cancer are currently unknown. Recent studies show that certain phenotypes of gliomas such as malignancy, resistance to therapy, and relapses are associated with the epigenetic alterations of tumour-specific genes. Runt-related transcription factor 3 (RUNX3) is feasible tumour suppressor gene since its inactivation was shown to be related to carcinogenesis.

Aim

The aim of the study was to elucidate RUNX3 changes in different regulation levels of molecular biology starting from epigenetics to function in particular cases of astrocytic origin tumours of different grade evaluating significance of molecular changes of RUNX3 for patient clinical characteristics as well as evaluate RUNX3 reexpression effect to GBM cells.

Methods

The methylation status and protein expression levels of RUNX3 were measured by methylation-specific PCR and Western blot in 136 and 72 different malignancy grade glioma tissues, respectively. Lipotransfection and MTT were applied for proliferation assessment in U87-MG cells.

Results

We found that RUNX3 was highly methylated and downregulated in GBM. RUNX3 promoter methylation was detected in 69.4% of GBM (n=49) as compared to 0 to 17.2% in I-III grade astrocytomas (n=87). Weighty lower RUNX3 protein level was observed in GMB specimens compared to grade II-III astrocytomas. Correlation test revealed a weak but significant link among Runx3 methylation and protein level. Kaplan-Meier analysis showed that increased RUNX3 methylation and low protein level were both associated with shorter patient survival (p<0.05). Reexpression of RUNX3 in U87-MG cells significantly reduced glioma cell viability compared to control transfection.

Conclusions

The results demonstrate that RUNX3 gene methylation and protein expression downregulation are glioma malignancy dependent and contribute to tumour progression.