Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis. Moreover, taking advantage from the available benchmarks of MRI, algorithms should be trained on large amounts of data. Additionally, the segmentation of postoperative imaging should be further addressed to limit the inaccuracies previously observed for this task. Indeed, molecular information has been promisingly integrated in the most recent DL tools, providing useful prognostic and therapeutic information. Finally, ethical concerns should be carefully addressed and standardized to allow for data protection. DL has provided reliable results for GBM assessment concerning MRI analysis and segmentation, but the routine clinical application is still limited. The current limitations could be prospectively addressed, giving particular attention to data collection, introducing new technical advancements, and carefully regulating ethical issues.

Pediatric-type diffuse low-grade glioma with T2-FLAIR mismatch sign: a case report and literature review

INTRODUCTION

Pediatric-type diffuse low-grade gliomas are a new entity that was introduced in the fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System, which was published in 2021. Notably, the information regarding the radiophenotypes of this new entity is limited.

OBJECTIVE

T2-FLAIR mismatch sign has been mostly studied in adult-type diffuse gliomas so far. We aimed to present more pediatric cases for future research about T2-FLAIR mismatch signs in pediatric-type diffuse low-grade gliomas.

CASE PRESENTATION

The current study presents a case of a 2-year-old boy who has a subcortical tumor at the right precentral frontal region. This tumor exhibited a T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign that was identified as specific for isocitrate dehydrogenase (IDH)-mutant 1p/19q non-co-deleted astrocytomas. The tumor was pathologically identified as pediatric-type diffuse low-grade gliomas, and it tested negative for IDH-1 immunohistochemistry. The whole-exome sequencing of tumor tissue revealed negative results for IDH mutation, 1p/19q co-deletion, MYB rearrangement, and all other potential pathogenic mutations.

CONCLUSION

The T2-FLAIR mismatch sign may not be 100% specific for IDH-mutant gliomas, especially in children, and researchers must further investigate the pathophysiology of the T2-FLAIR mismatch sign in brain tumors and the radiophenotypes of entities of pediatric brain tumors.

Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells' microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients.

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan-Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 × 10 - 4 , 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 × 10 - 4 , for T2w, and .992 ± 2.63 × 10 - 4 , 2.49 ± 6.89 × 10 - 2 , 40.51 ± 0.22, and 0.993 ± 3.40 × 10 - 4 for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient's outcome prediction with multi-contrast images.

Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities

BACKGROUND

While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival.

METHODS

A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership.

RESULTS

Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each).

CONCLUSIONS

Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.

Radiogenomics-Based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.

PDZK1 is correlated with DCE-MRI perfusion parameters in high-grade glioma

OBJECTIVE

This study investigated the relationship between PDZK1 expression and Dynamic Contrast-Enhanced MRI (DCE-MRI) perfusion parameters in High-Grade Glioma (HGG).

METHODS

Preoperative DCE-MRI scanning was performed on 80 patients with HGG to obtain DCE perfusion transfer coefficient (Ktrans), vascular plasma volume fraction (vp), extracellular volume fraction (ve), and reverse transfer constant (kep). PDZK1 in HGG patients was detected, and its correlation with DCE-MRI perfusion parameters was assessed by the Pearson method. An analysis of Cox regression was performed to determine the risk factors affecting survival, while Kaplan-Meier and log-rank tests to evaluate PDZK1's prognostic significance, and ROC curve analysis to assess its diagnostic value.

RESULTS

PDZK1 was upregulated in HGG patients and predicted poor overall survival and progression-free survival. Moreover, PDZK1 expression distinguished grade III from grade IV HGG. PDZK1 expression was positively correlated with Ktrans 90, and ve_90, and negatively correlated with kep_max, and kep_90.

CONCLUSION

PDZK1 is upregulated in HGG, predicts poor survival, and differentiates tumor grading in HGG patients. PDZK1 expression is correlated with DCE-MRI perfusion parameters.

Correlation between rCBV Delineation Similarity and Overall Survival in a Prospective Cohort of High-Grade Gliomas Patients: The Hidden Value of Multimodal MRI?

PURPOSE

The accuracy of target delineation in radiation treatment planning of high-grade gliomas (HGGs) is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Magnetic resonance imaging (MRI) represents the standard imaging modality for delineation of gliomas with inherent limitations in accurately determining the microscopic extent of tumors. The purpose of this study was to assess the survival impact of multi-observer delineation variability of multiparametric MRI (mpMRI) and [18F]-FET PET/CT.

MATERIALS AND METHODS

Thirty prospectively included patients with histologically confirmed HGGs underwent a PET/CT and mpMRI including diffusion-weighted imaging (DWI: b0, b1000, ADC), contrast-enhanced T1-weighted imaging (T1-Gado), T2-weighted fluid-attenuated inversion recovery (T2Flair), and perfusion-weighted imaging with computation of relative cerebral blood volume (rCBV) and K2 maps. Nine radiation oncologists delineated the PET/CT and MRI sequences. Spatial similarity (Dice similarity coefficient: DSC) was calculated between the readers for each sequence. Impact of the DSC on progression-free survival (PFS) and overall survival (OS) was assessed using Kaplan-Meier curves and the log-rank test.

RESULTS

The highest DSC mean values were reached for morphological sequences, ranging from 0.71 +/- 0.18 to 0.84 +/- 0.09 for T2Flair and T1Gado, respectively, while metabolic volumes defined by PET/CT achieved a mean DSC of 0.75 +/- 0.11. rCBV variability (mean DSC0.32 +/- 0.20) significantly impacted PFS (p = 0.02) and OS (p = 0.002).

CONCLUSIONS

Our data suggest that the T1-Gado and T2Flair sequences were the most reproducible sequences, followed by PET/CT. Reproducibility for functional sequences was low, but rCBV inter-reader similarity significantly impacted PFS and OS.

Rethinking the potential role of dose painting in personalized ultra-fractionated stereotactic adaptive radiotherapy

Fractionated radiotherapy was established in the 1920s based upon two principles: (1) delivering daily treatments of equal quantity, unless the clinical situation requires adjustment, and (2) defining a specific treatment period to deliver a total dosage. Modern fractionated radiotherapy continues to adhere to these century-old principles, despite significant advancements in our understanding of radiobiology. At UT Southwestern, we are exploring a novel treatment approach called PULSAR (Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy). This method involves administering tumoricidal doses in a pulse mode with extended intervals, typically spanning weeks or even a month. Extended intervals permit substantial recovery of normal tissues and afford the tumor and tumor microenvironment ample time to undergo significant changes, enabling more meaningful adaptation in response to the evolving characteristics of the tumor. The notion of dose painting in the realm of radiation therapy has long been a subject of contention. The debate primarily revolves around its clinical effectiveness and optimal methods of implementation. In this perspective, we discuss two facets concerning the potential integration of dose painting with PULSAR, along with several practical considerations. If successful, the combination of the two may not only provide another level of personal adaptation ("adaptive dose painting"), but also contribute to the establishment of a timely feedback loop throughout the treatment process. To substantiate our perspective, we conducted a fundamental modeling study focusing on PET-guided dose painting, incorporating tumor heterogeneity and tumor control probability (TCP).

Integrating multi-modal imaging in radiation treatments for glioblastoma

Advances in diagnostic and treatment technology along with rapid developments in translational research may now allow the realization of precision radiotherapy. Integration of biologically informed multimodality imaging to address the spatial and temporal heterogeneity underlying treatment resistance in glioblastoma is now possible for patient care, with evidence of safety and potential benefit. Beyond their diagnostic utility, several candidate imaging biomarkers have emerged in recent early-phase clinical trials of biologically based radiotherapy, and their definitive assessment in multicenter prospective trials is already in development. In this review, the rationale for clinical implementation of candidate advanced magnetic resonance imaging and positron emission tomography imaging biomarkers to guide personalized radiotherapy, the current landscape, and future directions for integrating imaging biomarkers into radiotherapy for glioblastoma are summarized. Moving forward, response-adaptive radiotherapy using biologically informed imaging biomarkers to address emerging treatment resistance in rational combination with novel systemic therapies may ultimately permit improvements in glioblastoma outcomes and true individualization of patient care.

Prognostic value of brain perfusion by MRI in the initial study of high grade gliomas

OBJECTIVES

To evaluate if the tumour perfusion at the initial MRI scan is a marker of prognosis for survival in patients diagnosed with High Grade Gliomas (HGG). To analyse the risk factors which influence on the mortality from HGG to quantify the overall survival to be expected in patients.

PATIENTS AND METHODS

The patients diagnosed with HGG through a MRI scan in a third-level hospital between 2017 and 2019 were selected. Clinical and tumour variables were collected. The survival analysis was used to determine the association between the tumour perfusion and the survival time. The relation between the collected variables and the survival period was assessed through Wald's statistical method, measuring the relationship via Cox's regression model. Finally, the type of relationship that exists between the tumour perfusion and the survival was analysed through the Lineal Regression method.Those statistical analysis were carried out using the software SPSS v.17.

RESULTS

38 patients were included (average age: 61.1 years old). The general average survival period was 20.6 months. A relationship between the tumour perfusion at the MRI scan and the overall survival has been identified, in detail, a group with intratumor values of relative cerebral blood volume (rCBV)>3.0 has shown a significant decline in the average survival period with regard to the average survival period of the group with values <3.0 (14.6 months vs. 22.8 months, p = 0.046). It has also been proved that variables like Karnofsky's scale and the response time since the intervention significantly influence on the survival period.

CONCLUSIONS

It has become evident that the tumour perfusion via MRI scan has a prognostic value in the initial analysis of HGG. The average survival period of patients with rCBV less than or equal to 3.0 is significantly higher than those patients whose values are higher, which allows to be more precise with the prognosis of each patient.

Practical guidance to identify and troubleshoot suboptimal DSC-MRI results

Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.

Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors

This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.

Experimenting with ASL-based arterialized cerebral blood volume as a novel imaging biomarker in grading glial neoplasms

BACKGROUND

Perfusion imaging is one of the methods used to grade glial neoplasms, and in this study we evaluated the role of ASL perfusion in grading brain glioma.

PURPOSE

The aim is to evaluate the role of arterialized cerebral blood volume (aCBV) of multi-delay ASL perfusion for grading glial neoplasm.

MATERIALS AND METHODS

This study is a prospective observational study of 56 patients with glial neoplasms of the brain who underwent surgery, and only cases with positive diagnosis of glioma are included to evaluate the novel diagnostic parameter.

RESULTS

In the study, ASL-derived normalized aCBV (naCBV) and T2*DSC-derived normalized CBV (nCBV) are showing very high correlation (Pearson's correlation coefficient value of 0.94) in grading glial neoplasms. naCBV and nCBF are also showing very high correlation (Pearson's correlation coefficient value of 0.876). The study also provides cutoff values for differentiating LGG from HGG for normalized aCBV(naCBV) of ASL, normalized CBV (nCBV), and normalized nCBF derived from T2* DCS as 1.12, 1.254, and 1.31, respectively. ASL-derived aCBV also shows better diagnostic accuracy than ASL-derived CBF.

CONCLUSION

This study is one of its kind to the best of our knowledge where multi-delay ASL perfusion-derived aCBV is used as a novel imaging biomarker for grading glial neoplasms, and it has shown high statistical correlation with T2* DSC-derived perfusion parameters.

Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles

BACKGROUND

Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy.

PURPOSE

To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients.

MATERIALS AND METHODS

Multi-contrast MRI, comprising T1-weighted, T1-weighted post-contrast, T2-weighted, T2 fluid-attenuated-inversion-recovery, and pathology images from glioma patients (n = 160) were retrospectively collected (1983-2008) from TCGA alongside age and sex. Phenotypic profiling of tumors was performed by quantifying the radiographic and histopathologic descriptors extracted from the delineated region-of-interest in MRI and PATH images. A Cox proportional hazard model was trained with the MRI and PATH features, IDH mutation status, and basic demographic variables (age and sex) to predict OS. The performance was evaluated in a split-train-test configuration using the concordance-index, computed between the predicted risk score and observed OS.

RESULTS

The average age of patients was 51.2years (women: n = 77, age-range=18-84years; men: n = 83, age-range=21-80years). The median OS of the participants was 494.5 (range,3-4752), 481 (range,7-4752), and 524.5 days (range,3-2869), respectively, in complete dataset, training, and test datasets. The addition of MRI or PATH features improved prediction of OS when compared to models based on age, sex, and mutation status alone or their combination (p < 0.001). The full multi-omics model integrated MRI, PATH, clinical, and genetic profiles and predicted the OS best (c-index= 0.87).

CONCLUSION

The combination of imaging, genetic, and clinical profiles leads to a more accurate prognosis than the clinical and/or mutation status.

Role of Permeability Surface Area Product in Grading of Brain Gliomas using CT Perfusion

Purpose  The aim of this study was to evaluate the role of permeability surface area product in grading brain gliomas using computed tomography (CT) perfusion Materials and Methods  CT perfusion was performed on 33 patients with brain glioma diagnosed on magnetic resonance imaging. Of these, 19 had high-grade glioma and 14 had low-grade glioma on histopathological follow-up. CT perfusion values were obtained and first compared between the tumor region and normal brain parenchyma. Then the relative values of perfusion parameters were compared between high- and low-grade gliomas. Cut-off values, sensitivity, specificity, and strength of agreement for each parameter were calculated and compared subsequently. A conjoint factor (permeability surface area product + cerebral blood volume) was also evaluated since permeability surface area product and cerebral blood volume are considered complimentary factors for tumor vascularity. Results  All five perfusion parameters namely permeability surface area product, cerebral blood volume, cerebral blood flow, mean transit time, and time to peak were found significantly higher in the tumor region than normal brain parenchyma. Among these perfusion parameters, only relative permeability surface area product and relative cerebral blood volume were found significant in differentiating high- and low-grade glioma. Moreover, relative permeability surface area product was significantly better than all other perfusion parameters with highest sensitivity and specificity (97.74 and 100%, respectively, at a cut-off of 9.0065). Relative permeability surface area product had a very good agreement with the histopathology grade. The conjoint factor did not yield any significant diagnostic advantage over permeability surface area product. Conclusion  Relative permeability surface area product and relative cerebral blood volume were helpful in differentiating high- and low-grade glioma; however, relative permeability surface area product was significantly better than all other perfusion parameters. Grading brain gliomas using relative permeability surface area product can add crucial value in their management and prognostication; hence, it should be evaluated in the routine CT perfusion imaging protocol.

Relative cerebral blood volume as response predictor in the treatment of recurrent glioblastoma with anti-angiogenic therapy

BACKGROUND

Glioblastoma is one of the most common brain tumors in adult populations, usually carrying a poor prognosis. While several studies have researched the impact of anti-angiogenic therapies, especially anti-VEFG treatments in glioblastoma, few have attempted to assess its progress using imaging studies.

PURPOSE

We attempted to analyze whether relative cerebral blood volume (rCBV) from dynamic susceptibility-weighted contrast-enhanced MRI (DSC-MRI) could predict response in patients with glioblastoma undergoing Bevacizumab (BVZ) treatment.

METHODS

We performed a retrospective study evaluating patients with recurrent glioblastoma receiving anti-angiogenic therapy with BVZ between 2012 and 2017 in our institution. Patients were scheduled for routine MRIs at baseline and first-month follow-up visits. Studies were processed for DSC-MRI, cT1, and FLAIR images, from which relative cerebral blood volume measurements were obtained. We assessed patient response using the Response Assessment in Neuro-Oncology (RANO) working group criteria and overall survival.

RESULTS

40 patients were included in the study and were classified as Bevacizumab responders and non-responders. The average rCBV before treatment was 4.5 for both groups, and average rCBV was 2.5 for responders and 5.4 for non-responders. ROC curve set a cutoff point of 3.7 for rCBV predictive of response to BVZ. Cox Multivariate analysis only showed rCBV as a predictive factor of OS.

CONCLUSION

A statistically significant difference was found in rCBV between patients who responded and those who did not respond to BVZ treatment. rCBV may be a low-cost and effective marker to assess response to Bevacizumab treatment in GBM.

Tumour status prediction by means of carbon-ion beam irradiation: comparison of washout rates between in-beam PET and DCE-MRI in rats

Objective.Tumour response to radiation therapy appears as changes in tumour vascular condition. There are several methods for analysing tumour blood circulatory changes one of which is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but there is no method that can observe the tumour vascular condition and physiological changes at the site of radiation therapy. Positron emission tomography (PET) has been applied for treatment verification in charged particle therapy, which is based on the detection of positron emitters produced through nuclear fragmentation reactions in a patient's body. However, the produced positron emitters are washed out biologically depending on the tumour vascular condition. This means that measuring the biological washout rate may allow evaluation of the tumour radiation response, in a similar manner to DCE-MRI. Therefore, this study compared the washout rates in rats between in-beam PET during12C ion beam irradiation and DCE-MRI.Approach.Different vascular conditions of the tumour model were prepared for six nude rats. The tumour of each nude rat was irradiated by a12C ion beam with simultaneous in-beam PET measurement. In 10-12 h, the DCE-MRI experiment was performed for the same six nude rats. The biological washout rate of the produced positron emitters (k2,1st) and the MRI contrast agent (k2a) were derived using the single tissue compartment model.Main results.A linear correlation was observed betweenk2,1standk2a, and they were inversely related to fractional necrotic volume.Significance.This is the first animal study which confirmed the biological washout rate of in-beam PET correlates closely with tumour vascular condition measured with the MRI contrast agent administrated intravenously.

Association of Relative Cerebral Blood Volume from Dynamic Susceptibility Contrast-Enhanced Perfusion MR with HER2 Status in Breast Cancer Brain Metastases

RATIONALE AND OBJECTIVES

With the development of HER2-directed therapies, identifying non-invasive imaging biomarkers of HER2 expression in breast cancer brain metastases has become increasingly important. The purpose of this study was to investigate whether relative cerebral blood volume (rCBV) from dynamic susceptibility contrast-enhanced (DSC) perfusion MR could help identify the HER2 status of breast cancer brain metastases.

MATERIALS AND METHODS

With IRB approval for this HIPAA-compliant cross-sectional study and a waiver of informed consent, we queried our institution's electronic medical record to derive a cohort of 14 histologically proven breast cancer brain metastases with preoperative DSC perfusion MR and HER2 analyses of the resected/biopsied brain specimens from 2011-2021. The rCBV of the lesions was measured and compared using Mann-Whitney tests. Receiver operating characteristic analyses were performed to evaluate the performance of rCBV in identifying HER2 status.

RESULTS

The study cohort was comprised of 14 women with a mean age of 56 years (range: 32-81 years) with a total of 14 distinct lesions. The rCBV of HER2-positive breast cancer brain metastases was significantly greater than the rCBV of HER2-negative lesions (8.02 vs 3.97, U=48.00, p=0.001). rCBV differentiated HER2-positive lesions from HER2-negative lesions with an area under the curve of 0.98 (standard error=0.032, p<0.001). The accuracy-maximizing rCBV threshold (4.8) was associated with an accuracy of 93% (13/14), a sensitivity of 100% (7/7), and a specificity of 86% (6/7).

CONCLUSION

rCBV may assist in identifying the HER2 status of breast cancer brain metastases, if validated in a large prospective trial.

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review

Background and purpose

Improving the accuracy of brain tumour radiotherapy (RT) treatment planning is important to optimise patient outcomes. This systematic review investigates primary studies providing clinical evidence for the integration of quantitative magnetic resonance imaging (qMRI) biomarkers and MRI radiomics to optimise brain tumour RT planning.

Materials and methods

PubMed, Scopus, Embase and Web of Science databases were searched for all years until June 21, 2022. The search identified original articles demonstrating clinical evidence for the use of qMRI biomarkers and MRI radiomics for the optimization of brain cancer RT planning. Relevant information was extracted and tabulated, including qMRI metrics and techniques, impact on RT plan optimization and changes in target and normal tissue contouring and dose distribution.

Results

Nineteen articles met the inclusion criteria. Studies were grouped according to the qMRI biomarkers into: 1) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI; five studies); 2) diffusion tensor imaging (DTI; seven studies); and 3) MR spectroscopic imaging (MRSI; seven studies). No relevant MRI-based radiomics studies were identified. Integration of DTI maps offers the potential for improved organs at risk (OAR) sparing. MRSI metabolic maps are a promising technique for improving delineation accuracy in terms of heterogeneity and infiltration, with OAR sparing. No firm conclusions could be drawn regarding the integration of DWI metrics and PWI maps.

Conclusions

Integration of qMRI metrics into RT planning offers the potential to improve delineation and OAR sparing. Clinical trials and consensus guidelines are required to demonstrate the clinical benefits of such approaches.

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma

BACKGROUND AND PURPOSE

An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase (IDH) wild-type glioblastoma.

MATERIALS AND METHODS

Preoperative dynamic susceptibility contrast MR images were obtained from 272 patients with IDH wild-type glioblastoma (training and validation, 183 and 89 patients, respectively). The autoencoder was applied to the dynamic susceptibility contrast MR imaging time-signal intensity curves of tumor and peritumoral areas. Representative perfusion patterns were defined by voxelwise K-means clustering using autoencoder latent features. Perfusion patterns were labeled by comparing parameters with anatomic reference tissues for baseline, signal drop, and percentage recovery. In the validation set (n = 89), a survival model was created from representative patterns and clinical predictors using Cox proportional hazard regression analysis, and its performance was calculated using the Harrell C-index.

RESULTS

Eighty-nine patients were enrolled. Five representative perfusion patterns were used to characterize tissues as high angiogenic tumor, low angiogenic/cellular tumor, perinecrotic lesion, infiltrated edema, and vasogenic edema. Of these, the low angiogenic/cellular tumor (hazard ratio, 2.18; P = .047) and infiltrated edema patterns (hazard ratio, 1.88; P = .009) in peritumoral areas showed significant prognostic value. The combined perfusion patterns and clinical predictors (C-index, 0.72) improved prognostication when added to clinical predictors (C-index, 0.55).

CONCLUSIONS

The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.

Assessment and prediction of glioblastoma therapy response: challenges and opportunities

Glioblastoma is the most aggressive type of primary adult brain tumour. The median survival of patients with glioblastoma remains approximately 15 months, and the 5-year survival rate is <10%. Current treatment options are limited, and the standard of care has remained relatively constant since 2011. Over the last decade, a range of different treatment regimens have been investigated with very limited success. Tumour recurrence is almost inevitable with the current treatment strategies, as glioblastoma tumours are highly heterogeneous and invasive. Additionally, another challenging issue facing patients with glioblastoma is how to distinguish between tumour progression and treatment effects, especially when relying on routine diagnostic imaging techniques in the clinic. The specificity of routine imaging for identifying tumour progression early or in a timely manner is poor due to the appearance similarity of post-treatment effects. Here, we concisely describe the current status and challenges in the assessment and early prediction of therapy response and the early detection of tumour progression or recurrence. We also summarize and discuss studies of advanced approaches such as quantitative imaging, liquid biomarker discovery and machine intelligence that hold exceptional potential to aid in the therapy monitoring of this malignancy and early prediction of therapy response, which may decisively transform the conventional detection methods in the era of precision medicine.

DSC MRI in the human brain using deoxyhemoglobin and gadolinium-Simulations and validations at 3T

INTRODUCTION

Dynamic susceptibility contrast (DSC) MRI allows clinicians to determine perfusion parameters in the brain, such as cerebral blood flow, cerebral blood volume, and mean transit time. To enable quantification, susceptibility changes can be induced using gadolinium (Gd) or deoxyhemoglobin (dOHb), the latter just recently introduced as a contrast agent in DSC. Previous investigations found that experimental parameters and analysis choices, such as the susceptibility amplitude and partial volume, affect perfusion quantification. However, the accuracy and precision of DSC MRI has not been systematically investigated, particularly in the lower susceptibility range.

METHODS

In this study, we compared perfusion values determined using Gd with values determined using a contrast agent with a lower susceptibility-dOHb-under different physiological conditions, such as varying the baseline blood oxygenation and/or magnitude of hypoxic bolus, by utilizing numerical simulations and conducting experiments on healthy subjects at 3T. The simulation framework we developed for DSC incorporates MRI signal contributions from intravascular and extravascular proton spins in arterial, venous, and cerebral tissue voxels. This framework allowed us to model the MRI signal in response to both Gd and dOHb.

RESULTS AND DISCUSSION

We found, both in the experimental results and simulations, that a reduced intravascular volume of the selected arterial voxel, reduced baseline oxygen saturation, greater susceptibility of applied contrast agent (Gd vs. dOHb), and/or larger magnitude of applied hypoxic bolus reduces the overestimation and increases precision of cerebral blood volume and flow. As well, we found that normalizing tissue to venous rather than arterial signal increases the accuracy of perfusion quantification across experimental paradigms. Furthermore, we found that shortening the bolus duration increases the accuracy and reduces the calculated values of mean transit time. In summary, we experimentally uncovered an array of perfusion quantification dependencies, which agreed with the simulation framework predictions, using a wider range of susceptibility values than previously investigated. We argue for caution when comparing absolute and relative perfusion values within and across subjects obtained from a standard DSC MRI analysis, particularly when employing different experimental paradigms and contrast agents.

Imaging Recommendations for the Diagnosis, Staging, and Management of Adult Brain Tumors

Neuroimaging plays a pivotal role in the clinical practice of brain tumors aiding in the diagnosis, genotype prediction, preoperative planning, and prognostication. The brain tumors most commonly seen in adults are extra-axial lesions like meningioma, intra-axial lesions like gliomas and lesions of the pituitary gland. Clinical features may be localizing like partial seizures, weakness, and sensory disturbances or nonspecific like a headache. On clinical suspicion of a brain tumor, the primary investigative workup should focus on imaging. Other investigations like fundoscopy and electroencephalography may be performed depending on the clinical presentation. Obtaining a tissue sample after identifying a brain tumor on imaging is crucial for confirming the diagnosis and planning further treatment. Tissue sample may be obtained by techniques such as stereotactic biopsy or upfront surgery. The magnetic resonance (MR) imaging protocol needs to be standardized and includes conventional sequences like T1-weighted (T1W) imaging with and without contrast, T2w imaging, fluid-attenuated axial inversion recovery, diffusion-weighted imaging (DWI), susceptibility-weighted imaging, and advanced imaging sequences like MR perfusion and MR spectroscopy. Various tumor characteristics in each of these sequences can help us narrow down the differential diagnosis and also predict the grade of the tumor. Multidisciplinary co-ordination is needed for proper management and care of brain tumor patients. Treatment protocols need to be adapted and individualized for each patient depending on the age, general condition of the patient, histopathological characteristics, and genotype of the tumor. Treatment options include surgery, radiotherapy, and chemotherapy. Imaging also plays a vital role in post-treatment follow-up. Sequences like DWI, MR perfusion, and MR spectroscopy are useful to distinguish post-treatment effects like radiation necrosis and pseudoprogression from true recurrence. Radiological reporting of brain tumor images should follow a structured format to include all the elements that could have an impact on the treatment decisions in patients.

Clinical, Imaging and Neurogenetic Features of Patients with Gliomatosis Cerebri Referred to a Tertiary Neuro-Oncology Centre

INTRODUCTION

Gliomatosis cerebri describes a rare growth pattern of diffusely infiltrating glioma. The treatment options are limited and clinical outcomes remain poor. To characterise this population of patients, we examined referrals to a specialist brain tumour centre.

METHODS

We analysed demographic data, presenting symptoms, imaging, histology and genetics, and survival in individuals referred to a multidisciplinary team meeting over a 10-year period.

RESULTS

In total, 29 patients fulfilled the inclusion criteria with a median age of 64 years. The most common presenting symptoms were neuropsychiatric (31%), seizure (24%) or headache (21%). Of 20 patients with molecular data, 15 had IDH wild-type glioblastoma, with an IDH1 mutation most common in the remainder (5/20). The median length of survival from MDT referral to death was 48 weeks (IQR 23 to 70 weeks). Contrast enhancement patterns varied between and within tumours. In eight patients who had DSC perfusion studies, five (63%) had a measurable region of increased tumour perfusion with rCBV values ranging from 2.8 to 5.7. A minority of patients underwent MR spectroscopy with 2/3 (66.6%) false-negative results.

CONCLUSIONS

Gliomatosis imaging, histological and genetic findings are heterogeneous. Advanced imaging, including MR perfusion, could identify biopsy targets. Negative MR spectroscopy does not exclude the diagnosis of glioma.

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Identification of single-dose, dual-echo based CBV threshold for fractional tumor burden mapping in recurrent glioblastoma

Background

Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is widely used to distinguish high grade glioma recurrence from post treatment radiation effects (PTRE). Application of rCBV thresholds yield maps to distinguish between regional tumor burden and PTRE, a biomarker termed the fractional tumor burden (FTB). FTB is generally measured using conventional double-dose, single-echo DSC-MRI protocols; recently, a single-dose, dual-echo DSC-MRI protocol was clinically validated by direct comparison to the conventional double-dose, single-echo protocol. As the single-dose, dual-echo acquisition enables reduction in the contrast agent dose and provides greater pulse sequence parameter flexibility, there is a compelling need to establish dual-echo DSC-MRI based FTB mapping. In this study, we determine the optimum standardized rCBV threshold for the single-dose, dual-echo protocol to generate FTB maps that best match those derived from the reference standard, double-dose, single-echo protocol.

Methods

The study consisted of 23 high grade glioma patients undergoing perfusion scans to confirm suspected tumor recurrence. We sequentially acquired single dose, dual-echo and double dose, single-echo DSC-MRI data. For both protocols, we generated leakage-corrected standardized rCBV maps. Standardized rCBV (sRCBV) thresholds of 1.0 and 1.75 were used to compute single-echo FTB maps as the reference for delineating PTRE (sRCBV < 1.0), tumor with moderate angiogenesis (1.0 < sRCBV < 1.75), and tumor with high angiogenesis (sRCBV > 1.75) regions. To assess the sRCBV agreement between acquisition protocols, the concordance correlation coefficient (CCC) was computed between the mean tumor sRCBV values across the patients. A receiver operating characteristics (ROC) analysis was performed to determine the optimum dual-echo sRCBV threshold. The sensitivity, specificity, and accuracy were compared between the obtained optimized threshold (1.64) and the standard reference threshold (1.75) for the dual-echo sRCBV threshold.

Results

The mean tumor sRCBV values across the patients showed a strong correlation (CCC = 0.96) between the two protocols. The ROC analysis showed maximum accuracy at thresholds of 1.0 (delineate PTRE from tumor) and 1.64 (differentiate aggressive tumors). The reference threshold (1.75) and the obtained optimized threshold (1.64) yielded similar accuracy, with slight differences in sensitivity and specificity which were not statistically significant (1.75 threshold: Sensitivity = 81.94%; Specificity: 87.23%; Accuracy: 84.58% and 1.64 threshold: Sensitivity = 84.48%; Specificity: 84.97%; Accuracy: 84.73%).

Conclusions

The optimal sRCBV threshold for single-dose, dual-echo protocol was found to be 1.0 and 1.64 for distinguishing tumor recurrence from PTRE; however, minimal differences were observed when using the standard threshold (1.75) as the upper threshold, suggesting that the standard threshold could be used for both protocols. While the prior study validated the agreement of the mean sRCBV values between the protocols, this study confirmed that their voxel-wise agreement is suitable for reliable FTB mapping. Dual-echo DSC-MRI acquisitions enable robust single-dose sRCBV and FTB mapping, provide pulse sequence parameter flexibility and should improve reproducibility by mitigating variations in preload dose and incubation time.

High-resolution dynamic susceptibility contrast perfusion imaging using higher-order temporal smoothness regularization

PURPOSE

To improve image quality and resolution of dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) by developing acquisition and reconstruction methods exploiting the temporal regularity property of DSC-PWI signal.

THEORY AND METHODS

A novel regularized reconstruction is proposed that recovers DSC-PWI series from interleaved segmented spiral k-space acquisition using higher order temporal smoothness (HOTS) properties of the DSC-PWI signal. The HOTS regularization is designed to tackle representational insufficiency of the standard first-order temporal regularizations for supporting higher accelerations. The higher accelerations allow for k-space coverage with shorter spiral interleaves resulting in improved acquisition point spread function, and acquisition of images at multiple TEs for more accurate DSC-PWI analysis.

RESULTS

The methods were evaluated in simulated and in-vivo studies. HOTS regularization provided increasingly more accurate models for DSC-PWI than the standard first-order methods with either quadratic or robust norms at the expense of increased noise. HOTS DSC-PWI optimized for noise and accuracy demonstrated significant advantages over both spiral DSC-PWI without temporal regularization and traditional echo-planar DSC-PWI, improving resolution and mitigating image artifacts associated with long readout, including blurring and geometric distortions. In context of multi-echo DSC-PWI, the novel methods allowed ∼4.3× decrease of voxel volume, providing 2× number of TEs compared to the previously published results.

CONCLUSIONS

Proposed HOTS reconstruction combined with dynamic spiral sampling represents a valid mechanism for improving image quality and resolution of DSC-PWI significantly beyond those available with established fast imaging techniques.

Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI

OBJECTIVES

To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients.

METHODS

Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum.

RESULTS

Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS.

CONCLUSION

Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences.

ADVANCES IN KNOWLEDGE

The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis

Gliomas are the most common primary malignant brain tumors in adults. The fifth edition of the WHO Classification of Tumors of the Central Nervous System, published in 2021, provided molecular and practical approaches to CNS tumor taxonomy. Currently, molecular features are essential for differentiating the histological subtypes of gliomas, and recent studies have emphasized the importance of isocitrate dehydrogenase (IDH) mutations in stratifying biologically distinct subgroups of gliomas. IDH plays a significant role in gliomagenesis, and the association of IDH status with prognosis is very clear. Recently, there has been much progress in conventional MR imaging (cMRI), advanced MR imaging (aMRI), and radiomics, which are widely used in the study of gliomas. These advances have resulted in an improved correlation between MR signs and IDH mutation status, which will complement the prediction of the IDH phenotype. Although imaging cannot currently substitute for genetic tests, imaging findings have shown promising signs of diagnosing glioma subtypes and evaluating the efficacy and prognosis of individualized molecular targeted therapy. This review focuses on the correlation between MRI and MRI-based radiomics and IDH gene-phenotype prediction, discussing the value and application of these techniques in the diagnosis and evaluation of the prognosis of gliomas.

Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiating Recurrence From Radiation Injury in Postoperative Glioma: A Meta-analysis

PURPOSE

It is important to differentiate between radiation injury (RI) and tumor recurrence (TR) in patients with glioma after surgery and radiotherapy. Our objective was to evaluate the use of dynamic susceptibility contrast-enhanced perfusion-weighted imaging to distinguish between TR and RI in patients with glioma.

METHODS

Relevant studies published until October 2021 were identified in the PubMed, Embase, and Cochrane Library databases. Stata v12.0 and RevMan 5.3 were used for meta-analysis.

RESULTS

In total, the meta-analysis incorporated 13 retrospective studies that included 513 patients with 522 lesions. Among the 522 lesions, 329 lesions were TRs and 193 lesions were RIs. The pooled relative cerebral blood volume value was significantly greater in the TR group (P < 0.00001) with significant heterogeneity (I2 = 88%). The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 83% (95 confidence interval [CI], 77%-88%), 85% (95 CI, 77%-91%), 5.60 (95 CI, 3.61-8.70), and 0.20 (95% CI, 0.14-0.27), respectively. The heterogeneity of sensitivity (I2 = 33.18%), specificity (I2 = 24.01%), PLR (I2 = 0.00%), and NLR (I2 = 6.68%) is not significant. The area under the receiver operating characteristic curve was 0.91 (95% CI, 0.88-0.93). The 3.0 T magnetic resonance imaging, high-grade glioma, and Europe/America patient subgroups showed PLR greater than 5 and NLR less than 0.2. There was no significant indication of publication bias in the analysis (P = 0.496).

CONCLUSIONS

It is concluded that dynamic susceptibility contrast-enhanced perfusion-weighted imaging is effective for the diagnostic differentiation between TR and RI in patients with glioma.

Volumetric Measurement of Relative CBV Using T1-Perfusion-Weighted MRI with High Temporal Resolution Compared with Traditional T2*-Perfusion-Weighted MRI in Postoperative Patients with High-Grade Gliomas

BACKGROUND AND PURPOSE

T1-PWI with high temporal resolution may provide a reliable relative CBV value as a valid alternative to T2*-PWI under increased susceptibility. The purpose of this study was to assess the technical and clinical performance of T1-relative CBV in patients with postoperative high-grade gliomas.

MATERIALS AND METHODS

Forty-five MRIs of 34 patients with proved high-grade gliomas were included. In all MRIs, T1- and T2*-PWIs were both acquired and processed semiautomatically to generate relative CBV maps using a released commercial software. Lesion masks were overlaid on the relative CBV maps, followed by a histogram of the whole VOI. The intraclass correlation coefficient and Bland-Altman plots were used for quantitative and qualitative comparisons. Signal loss from both methods was compared using the Wilcoxon signed-rank test of zero voxel percentage. The MRIs were divided into a progression group (n = 20) and a nonprogression group (n = 14) for receiver operating characteristic curve analysis.

RESULTS

Fair intertechnique consistency was observed between the 90th percentiles of the T1- and T2*-relative CBV values (intraclass correlation coefficient = 0.558, P < .001). T2*-PWI revealed a significantly higher percentage of near-zero voxels than T1-PWI (17.7% versus 3.1%, P < .001). There was no statistically significant difference between the area under the curve of T1- and T2*-relative CBV (0.811 versus 0.793, P = .835). T1-relative CBV showed 100% sensitivity and 57.1% specificity for the detection of progressive lesions.

CONCLUSIONS

T1-relative CBV demonstrated exquisite diagnostic performance for detecting progressive lesions in postoperative patients with high-grade gliomas, suggesting the potential role of T1-PWI as a valid alternative to the traditional T2*-PWI.

Grading of IDH-mutant astrocytoma using diffusion, susceptibility and perfusion-weighted imaging

Background

The accurate grading of IDH-mutant astrocytoma is essential to make therapeutic strategies and assess the prognosis of patients. The purpose of this study was to investigate the usefulness of DWI, SWI and DSC-PWI in grading IDH-mutant astrocytoma.

Methods

One hundred and seven patients with IDH-mutant astrocytoma who underwent DWI, SWI and DSC-PWI were retrospectively reviewed. Minimum apparent diffusion coefficient (ADC_min), intratumoral susceptibility signal intensity(ITSS) and maximum relative cerebral blood volume (rCBV_max) values were assessed. ADC_min, ITSS and rCBV_max values were compared between grade 2 vs. grade 3, grade 3 vs. grade 4 and grade 2 + 3 vs. grade 4 tumors. Logistic regression, tenfold cross-validation,and receiver operating characteristic (ROC) curve analyses were used to assess their diagnostic performances.

Results

Grade 4 IDH-mutant astrocytomas showed significantly lower ADC_min and higher rCBV_max as compared to grade 3 tumors (adjusted P < 0.001). IDH-mutant grade 3 astrocytomas showed significantly lower ITSS levels as compared with grade 4 tumors (adjusted P < 0.001). ITSS levels between IDH-mutant grade 2 and grade 3 astrocytomas were significantly different (adjusted P = 0.002). Combined the ADC_min, ITSS and rCBV_max resulted in the highest AUC for differentiation grade 2 and grade 3 tumors from grade 4 tumors.

Conclusion

ADC_min, rCBV_max and ITSS can be used for grading the IDH-mutant astrocytomas. The combination of ADC_min, ITSS and rCBV_max could improve the diagnostic performance in grading of IDH-mutant astrocytoma.

Standard clinical approaches and emerging modalities for glioblastoma imaging

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

Combined Diagnostic Accuracy of Diffusion and Perfusion MR Imaging to Differentiate Radiation-Induced Necrosis from Recurrence in Glioblastoma

We aimed to use quantitative values derived from perfusion and diffusion-weighted MR imaging (PWI and DWI) to differentiate radiation-induced necrosis (RIN) from tumor recurrence in Glioblastoma (GBM) and investigate the best parameters for improved diagnostic accuracy and clinical decision-making. Methods: A retrospective analysis of follow-up MRI with new enhancing observations was performed in histopathologically confirmed subjects of post-treated GBM, who underwent re-surgical exploration. Quantitative estimation of rCBV (relative cerebral blood volume) from PWI and three methods of apparent diffusion coefficient (ADC) estimation were performed, namely ADC R1 (whole cross-sectional area of tumor), ADC R2 (only solid enhancing lesion), and ADC R3 (central necrosis). ROC curve and logistic regression analysis was completed. A confusion matrix table created using Excel provided the best combination parameters to ameliorate false-positive and false-negative results. Results: Forty-four subjects with a mean age of 46 years (range, 19−70 years) underwent re-surgical exploration with RIN in 28 (67%) and recurrent tumor in 16 (33%) on histopathology. rCBV threshold of >3.4 had the best diagnostic accuracy (AUC = 0.93, 81% sensitivity and 89% specificity). A multiple logistic regression model showed significant contributions from rCBV (p < 0.001) and ADC R3 (p = 0.001). After analysis of confusion matrix ADC R3 > 2032 × 10−6 mm2 achieved 100% specificity with gain in sensitivity (94% vs. 56%). Conclusions: A combination of parameters had better diagnostic performance, and a stepwise combination of rCBV and ADC R3 obviated unnecessary biopsies in 10% (3/28), leading to improved clinical decision-making.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

3D pCASL-perfusion in preoperative assessment of brain gliomas in large cohort of patients

The aim of the study was to evaluate the role of pseudocontinuous arterial spin labeling perfusion (pCASL-perfusion) in preoperative assessment of cerebral glioma grades. The study group consisted of 253 patients, aged 7-78 years with supratentorial gliomas (65 low-grade gliomas (LGG), 188 high-grade gliomas (HGG)). We used 3D pCASL-perfusion for each patient in order to calculate the tumor blood flow (TBF). We obtained maximal tumor blood flow (maxTBF) in small regions of interest (30 ± 10 mm²) and then normalized absolute maximum tumor blood flow (nTBF) to that of the contralateral normal-appearing white matter of the centrum semiovale. MaxTBF and nTBF values significantly differed between HGG and LGG groups (p < 0.001), as well as between patient groups separated by the grades (grade II vs. grade III) (p < 0.001). Moreover, we performed ROC-analysis which demonstrated high sensitivity and specificity in differentiating between HGG and LGG. We found significant differences for maxTBF and nTBF between grade III and IV gliomas, however, ROC-analysis showed low sensitivity and specificity. We did not observe a significant difference in TBF for astrocytomas and oligodendrogliomas. Our study demonstrates that 3D pCASL-perfusion as an effective diagnostic tool for preoperative differentiation of glioma grades.

Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge

OBJECTIVES

Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this feasibility study, we employed blood oxygenation level-dependent (BOLD) MRI with standardized and precise carbon dioxide (CO₂) and oxygen (O₂) modulation to investigate specific tumor tissue response patterns in patients with newly diagnosed glioblastoma.

MATERIALS AND METHODS

Seven newly diagnosed untreated patients with suspected glioblastoma were prospectively included to undergo a BOLD study with combined CO₂ and O₂ standardized protocol. %BOLD signal change/mmHg during hypercapnic, hypoxic, and hyperoxic stimulus was calculated in the whole brain, tumor lesion and segmented volumes of interest (VOI) [contrast-enhancing (CE) - tumor, necrosis and edema] to analyze their tissue response patterns.

RESULTS

Quantification of BOLD signal change after gas challenges can be used to identify specific responses to standardized stimuli in glioblastoma patients. Integration of this approach with automatic VOI segmentation grants improved characterization of tumor subzones and edema. Magnitude of BOLD signal change during the 3 stimuli can be visualized at voxel precision through color-coded maps overlayed onto whole brain and identified VOIs.

CONCLUSIONS

Our preliminary investigation shows good feasibility of BOLD with standardized and precise CO₂ and O₂ modulation as an emerging physiologic imaging technique to detail specific glioblastoma characteristics. The unique tissue response patterns generated can be further investigated to better detail glioblastoma lesions and gauge treatment response.

Diffusion and perfusion imaging biomarkers of H3 K27M mutation status in diffuse midline gliomas

PURPOSE

H3K27M-mutant diffuse midline gliomas (M-DMGs) exhibit a clinically aggressive course. We studied diffusion-weighted imaging (DWI) and perfusion (PWI) MRI features of DMG with the hypothesis that DWI-PWI metrics can serve as biomarkers for the prediction of the H3K27M mutation status in DMGs.

METHODS

A retrospective review of the institutional database (imaging and histopathology) of patients with DMG (July 2016 to July 2020) was performed. Tumoral apparent diffusion coefficient (ADC) and peritumoral ADC (PT ADC) values and their normalized values (nADC and nPT ADC) were computed. Perfusion data were analyzed with manual arterial input function (AIF) and leakage correction (LC) Boxerman-Weiskoff models. Normalized maximum relative CBV (rCBV) was evaluated. Intergroup analysis of the imaging variables was done between M-DMGs and wild-type (WT-DMGs) groups.

RESULTS

Ninety-four cases (M-DMGs-n = 48 (51%) and WT-DMGs-n = 46(49%)) were included. Significantly lower PT ADC (mutant-1.1 ± 0.33, WT-1.23 ± 0.34; P = 0.033) and nPT ADC (mutant-1.64 ± 0.48, WT-1.83 ± 0.54; P = 0.040) were noted in the M-DMGs. The rCBV (mutant-25.17 ± 27.76, WT-13.73 ± 14.83; P = 0.018) and nrCBV (mutant-3.44 ± 2.16, WT-2.39 ± 1.25; P = 0.049) were significantly higher in the M-DMGs group. Among thalamic DMGs, the min ADC, PT ADC, and nADC and nPT ADC were lower in M-DMGs while nrCBV (corrected and uncorrected) was significantly higher. Receiver operator characteristic curve analysis demonstrated that PT ADC (cut-off-1.245), nPT ADC (cut-off-1.853), and nrCBV (cut-off-1.83) were significant independent predictors of H3K27M mutational status in DMGs.

CONCLUSION

DWI and PWI features hold value in preoperative prediction of H3K27M-mutation status in DMGs.

Current understanding of gliomagenesis: from model to mechanism

Glioma, a kind of central nervous system (CNS) tumor, is hard to cure and accounts for 32% of all CNS tumors. Establishing a stable glioma model is critically important to investigate the underlying molecular mechanisms involved in tumorigenesis and tumor progression. Various core signaling pathways have been identified in gliomagenesis, such as RTK/RAS/PI3K, TP53, and RB1. Traditional methods of establishing glioma animal models have included chemical induction, xenotransplantation, and genetic modifications (RCAS/t-va system, Cre-loxP, and TALENs). Recently, CRISPR/Cas9 has emerged as an efficient gene editing tool with high germline transmission and has extended the scope of stable and efficient glioma models that can be generated. Therefore, this review will highlight the documented evidence about the molecular characteristics, critical genetic markers, and signaling pathways responsible for gliomagenesis and progression. Moreover, methods of establishing glioma models using gene editing techniques and therapeutic aspects will be discussed. Finally, the prospect of applying gene editing in glioma by using CRISPR/Cas9 strategy and future research directions to establish a stable glioma model are also included in this review. In-depth knowledge of glioma signaling pathways and use of CRISPR/Cas9 can greatly assist in the development of a stable, efficient, and spontaneous glioma model, which can ultimately improve the effectiveness of therapeutic responses and cure glioma patients.

[Radiological biomarkers of brain gliomas]

The most important objective of modern neuroimaging is comparison of data on genotype and phenotype of brain gliomas. Radiogenomics as a new branch of science is devoted to searching for such relationships based on MRI and PET/CT parameters. The 2021 WHO classification of tumors of the central nervous system poses the most important tasks for physicians in assessment of biological behavior of tumors and their response to combined treatment. The review demonstrates the possibilities and prospects of preoperative MRI and PET/CT with amino acids in assessing the genetic profile of brain gliomas.

Prediction of the effects of radiation therapy in esophageal cancer using diffusion and perfusion MRI

Chemoradiation therapy (CRT) of locally advanced esophageal cancer (LAEC), although improving outcomes of patients, still results in 50% of local failure. An early prediction could identify patients at high risk of poor response for individualized adaptive treatment. We aimed to investigate physiological changes in LAEC using diffusion and perfusion magnetic resonance imaging (MRI) for early prediction of treatment response. In the study, 115 LAEC patients treated with CRT were enrolled (67 in the discovery cohort and 48 in the validation cohort). MRI scans were performed before radiotherapy (pre‐RT) and at week 3 during RT (mid‐RT). Gross tumor volume (GTV) of primary tumor was delineated on T2‐weighted images. Within the GTV, the hypercellularity volume (V_HC) and high blood volume (V_HBV) were defined based on the analysis of ADC and fractional plasma volume (Vp) histogram distributions within the tumors in the discovery cohort. The median GTVs were 28 cc ± 2.2 cc at pre‐RT and 16.7 cc ± 1.5 cc at mid‐RT. Respectively, V_HC and V_HBV decreased from 4.7 cc ± 0.7 cc and 5.7 cc ± 0.7 cc at pre‐RT to 2.8 cc ± 0.4 cc and 3.5 cc ± 0.5 cc at mid‐RT. Smaller V_HC at mid‐RT (area under the curve [AUC] = 0.67, P = .05; AUC = 0.66, P = .05) and further decrease in V_HC at mid‐RT (AUC = 0.7, P = .01; AUC = 0.69, P = .03) were associated with longer progression‐free survival (PFS) in both discovery and validation cohort. No significant predictive effects were shown in GTV and V_HBV at any time point. In conclusion, we demonstrated that V_HC represents aggressive subvolumes in LAEC. Further analysis will be carried out to confirm the correlations between the changes in image‐phenotype subvolumes and local failure to determine the radiation‐resistant tumor subvolumes, which may be useful for dose escalation.

Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment

Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.

Precision Radiotherapy for Gliomas: Implementing Novel Imaging Biomarkers to Improve Outcomes With Patient-Specific Therapy

ABSTRACT

Gliomas are the most common primary brain cancer, yet are extraordinarily challenging to treat because they can be aggressive and infiltrative, locally recurrent, and resistant to standard treatments. Furthermore, the treatments themselves, including radiation therapy, can affect patients' neurocognitive function and quality of life. Noninvasive imaging is the standard of care for primary brain tumors, including diagnosis, treatment planning, and monitoring for treatment response. This article explores the ways in which advanced imaging has and will continue to transform radiation treatment for patients with gliomas, with a focus on cognitive preservation and novel biomarkers, as well as precision radiotherapy and treatment adaptation. Advances in novel imaging techniques continue to push the field forward, to more precisely guided treatment planning, radiation dose escalation, measurement of therapeutic response, and understanding of radiation-associated injury.