Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma?

Pleomorphic Xanthoastrocytoma: Multi-Institutional Evaluation of Stereotactic Radiosurgery

BACKGROUND AND OBJECTIVES

Pleomorphic xanthoastrocytoma (PXA) is a rare low-grade glial tumor primarily affecting young individuals. Surgery is the primary treatment option; however, managing residual/recurrent tumors remains uncertain. This international multi-institutional study retrospectively assessed the use of stereotactic radiosurgery (SRS) for PXA.

METHODS

A total of 36 PXA patients (53 tumors) treated at 11 institutions between 1996 and 2023 were analyzed. Data included demographics, clinical variables, SRS parameters, tumor control, and clinical outcomes. Kaplan-Meier estimates summarized the local control (LC), progression-free survival, and overall survival (OS). Secondary end points addressed adverse radiation effects and the risk of malignant transformation. Cox regression analysis was used.

RESULTS

A total of 38 tumors were grade 2, and 15 tumors were grade 3. Nine patients underwent initial gross total resection, and 10 received adjuvant therapy. The main reason for SRS was residual tumors (41.5%). The median follow-up was 34 months (range, 2-324 months). LC was achieved in 77.4% of tumors, with 6-month, 1-year, and 2-year LC estimates at 86.7%, 82.3%, and 77.8%, respectively. Younger age at SRS (hazard ratios [HR] 3.164), absence of peritumoral edema (HR 4.685), and higher marginal dose (HR 6.190) were significantly associated with better LC. OS estimates at 1, 2, and 5 years were 86%, 74%, and 49.3%, respectively, with a median OS of 44 months. Four patients died due to disease progression. Radiological adverse radiation effects included edema (n = 8) and hemorrhagic change (n = 1). One grade 3 PXA transformed into glioblastoma 13 months after SRS.

CONCLUSION

SRS offers promising outcomes for PXA management, providing effective LC, reasonable progression-free survival, and minimal adverse events.

Diffusion-weighted imaging-based radiomics model using automatic machine learning to differentiate cerebral cystic metastases from brain abscesses

OBJECTIVES

To develop a radiomics model based on diffusion-weighted imaging (DWI) utilizing automated machine learning method to differentiate cerebral cystic metastases from brain abscesses.

MATERIALS AND METHODS

A total of 186 patients with cerebral cystic metastases (n = 98) and brain abscesses (n = 88) from two clinical institutions were retrospectively included. The datasets (129 from institution A) were randomly portioned into separate 75% training and 25% internal testing sets. Radiomics features were extracted from DWI images using two subregions of the lesion (cystic core and solid wall). A thorough image preprocessing method was applied to DWI images to ensure the robustness of radiomics features before feature extraction. Then the Tree-based Pipeline Optimization Tool (TPOT) was utilized to search for the best optimized machine learning pipeline, using a fivefold cross-validation in the training set. The external test set (57 from institution B) was used to evaluate the model's performance.

RESULTS

Seven distinct TPOT models were optimized to distinguish between cerebral cystic metastases and abscesses either based on different features combination or using wavelet transform. The optimal model demonstrated an AUC of 1.00, an accuracy of 0.97, sensitivity of 1.00, and specificity of 0.93 in the internal test set, based on the combination of cystic core and solid wall radiomics signature using wavelet transform. In the external test set, this model reached 1.00 AUC, 0.96 accuracy, 1.00 sensitivity, and 0.93 specificity.

CONCLUSION

The DWI-based radiomics model established by TPOT exhibits a promising predictive capacity in distinguishing cerebral cystic metastases from abscesses.

Bevacizumab in the Treatment of Refractory Brain Edema in High-grade Glioma

We report the case of a 14-year-old boy with a steroid-dependent refractory tumor whose longstanding dexamethasone treatment was successfully discontinued after a course of bevacizumab. The use of bevacizumab despite the absence of clear evidence of radionecrosis allowed a significant decrease in the amount of the brain edema.

Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer

AIM

To investigate the value of multiparametric magnetic resonance imaging (MRI)-based radiomics nomograms for predicting the hormone receptor (HR) status of HER2-positive breast cancer.

MATERIALS AND METHODS

Patients with HER2-positive invasive breast cancer were divided randomly into training (68 patients) and validation (30 patients) sets. All were classified as either HR-positive (HR+) or negative (HR-) at histopathology. Two radiologists outlined the three-dimensional (3D) volumetric regions of interest (VOI) on the MRI images. Features (n=1,096) were extracted from the T2-weighted imaging (WI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images separately. Dimensionality was reduced using feature screening. Binary radiomics prediction models were established using a logistic regression classifier and were validated in the validation set. To construct a nomogram, independent predictors were identified using multivariate logistic regression analysis. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

Ten radiomics features were obtained after feature dimensionality reduction based on the merged T2WI, ADC, and DCE images. The diagnostic efficacy of the radiomics signature using the three sequences was better than that of any single sequence (training set AUC: 0.797; validation set AUC: 0.75). Using multivariate logistic regression analysis, the independent predictors for identifying HR status were combined radiomics signature and peritumoural oedema. Nomograms constructed by combining the radiomics signature and peritumoural oedema showed good discrimination in both the training and validation sets (AUC: 0.815 and 0. 805, respectively).

CONCLUSION

A multiparametric MRI-based nomogram incorporating the radiomics signature and peritumoural oedema can assess the HR status of HER2-positive breast cancer. The resulting model can improve diagnostic accuracy, improving patient outcomes.

Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model

Background

The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation.

Methods

One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans.

Results

The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans.

Conclusions

Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.

Unlocking Bevacizumab's Potential: rCBVmax as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients

BACKGROUND

Aberrant vascular architecture and angiogenesis are hallmarks of glioblastoma IDH-wildtype, suggesting that these tumors are suitable for antiangiogenic therapy. Bevacizumab was FDA-approved in 2009 following promising results in two clinical trials. However, its use for recurrent glioblastomas remains a subject of debate, as it does not universally improve patient survival.

PURPOSES

In this study, we aimed to analyze the influence of tumor vascularity on the benefit provided by BVZ and propose preoperative rCBVmax at the high angiogenic tumor habitat as a predictive biomarker to select patients who can benefit the most.

METHODS

Clinical and MRI data from 106 patients with glioblastoma IDH-wildtype have been analyzed. Thirty-nine of them received BVZ, and the remaining sixty-seven did not receive a second-line treatment. The ONCOhabitats method was used to automatically calculate rCBV.

RESULTS

We found a median survival from progression of 305 days longer for patients with moderate vascular tumors who received BVZ than those who did not receive any second-line treatment. This contrasts with patients with high-vascular tumors who only presented a median survival of 173 days longer when receiving BVZ. Furthermore, better responses to BVZ were found for the moderate-vascular group with a higher proportion of patients alive at 6, 12, 18, and 24 months after progression.

CONCLUSIONS

We propose rCBVmax as a potential biomarker to select patients who can benefit more from BVZ after tumor progression. In addition, we propose a threshold of 7.5 to stratify patients into moderate- and high-vascular groups to select the optimal second-line treatment.

Clinical Value of a Nomogram Model Based on Apparent Diffusion Coefficient Values Within 1 cm of the Tumor Cavity to Predict Postoperative Progression of Glioma

OBJECTIVE

To explore the clinical value of constructing a nomogram model based on apparent diffusion coefficient values within 1 cm of the residual tumor cavity to predict the postoperative progression of gliomas.

METHODS

Clinical data of patients with glioma who underwent surgery were retrospectively retrieved from the First Hospital of Qinhuangdao. The mean apparent diffusion coefficient (mADC) was measured using a picture archiving and communication system. The Kaplan-Meier survival curve was constructed with the optimal mADC threshold determined by the X-tile. A nomogram was developed based on the independent risk factors determined using the Cox proportional hazards model (Cox regression model) to predict the progression of postoperative glioma. A receiver operating characteristic curve was drawn to evaluate the prediction accuracy of the model, and decision curve analysis was performed to assess the clinical value of the nomogram.

RESULTS

There was good agreement between the mADC values of the 2 repeated measurements before and after, with a consistency correlation coefficient of 0.83. Multivariate Cox regression analysis showed that peritumoral mADC values, degree of peritumoral enhancement, age, pathological grading, and degree of tumor resection were independent risk factors for predicting postoperative progression of glioma (all P < 0.05). The receiver operating characteristic curves of the nomogram predicting 1, 2, and 3 years postoperative progression were 0.86, 0.82, and 0.91, respectively. The calibration curve showed good consistency between the observed and predicted values in the model. The curve showed that the nomogram model has a good clinical application value.

CONCLUSIONS

The peritumoral mADC values, degree of peritumoral enhancement, age, pathological grade, and degree of tumor resection were independent factors affecting the postoperative progression of glioma. The nomogram model established for the first time based on mADC values within 1 cm of the tumor can predict the postoperative condition of patients with glioma intuitively and comprehensively. It can provide a relatively accurate prediction tool for neurosurgeons to individualize the evaluation of survival and prognosis, and formulate treatment plans for patients.

Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images

OBJECTIVES

Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs).

METHODS

In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs.

RESULTS

The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05).

CONCLUSIONS

Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification.

CLINICAL RELEVANCE STATEMENT

Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure.

KEY POINTS

• The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.

Prognostic analysis of stereotactic radiosurgery for brain metastases: a single-center retrospective study

OBJECTIVES

Brain metastasis (BM) is a common event during the development of many cancers, and is also one of the main causes of death of patients. Stereotactic radiosurgery (SRS) is an effective treatment for BM. The prognostic effects of various clinical factors on local control (LC) and overall survival (OS) after SRS treatment are still unclear. The purpose of this study is to retrospectively analyze the intracranial progression free survival (iPFS) and OS of patients receiving SRS treatment, and explore the relationship between various clinical characteristics and patient prognosis.

MATERIALS AND METHODS

We collected the clinical information of patients who were diagnosed with BM and received SRS treatment in our center between 2018 and 2021. Univariate and multivariate Cox regression analysis and KM analysis for iPFS and OS were conducted in R software to investigate the prognostic effects of clinical characteristics.

RESULTS

In total, 183 patients that received SRS in our center were enrolled in the cohort. The median iPFS for all patients was 8.87 months (95% CI 6.9-10.6), and the median OS was 16.5 months (95% CI 12.9-20.7). BM number >  = 5 (HR 1.965 [95% CI 1.381-2.796], p < 0.001, FDR-corrected p < 0.001) was found to be strong predictor for shorter iPFS and OS. Subgroup analysis showed that patients with cumulative intracranial tumor volume (CITV) >  = 2.14 cm3 and number >  = 5 had shortest iPFS (P < 0.001) and OS (P = 0.007), compared with other subgroups. For patients with more than 5 BMs, SRS plus whole brain radiotherapy (WBRT) could achieve better local control, compared with SRS alone group (P = 0.0357). Peripheral blood inflammation indicators were associated with the prognosis of BM patients in univariate Cox analysis, but not in multivariate Cox analysis.

CONCLUSIONS

BM number is an independent prognostic factor for BM patients. The prognosis of patients in the subgroup with larger CITV and more BM is the worst. For patients with more than 5 BM, the combination of SRS and WBRT can improve the local control, but cannot prolong the OS.

Stereotactic Radiosurgery for Contrast-Enhancing Satellite Nodules in Recurrent Glioblastoma: A Rare Case Series From a Single Institution

Introduction Glioblastoma (GBM) is the most common malignant adult brain tumor and is invariably fatal. The standard treatment for GBM involves resection where possible, followed by chemoradiation per Stupp's protocol. We frequently use stereotactic radiosurgery (SRS) as a single-fraction treatment for small (volume ≤ 1cc) nodular recurrent GBM to the contrast-enhancing target on T1 MRI scan. In this paper, we aimed to evaluate the safety and efficacy of SRS for patients with contrast-enhancing satellite nodules in recurrent GBM. Methods This retrospective study analyzed the clinical and radiological outcomes of five patients who underwent CyberKnife (Accuray Inc., Sunnyvale, California) SRS at the institute between 2013 and 2022. Results From 96 patients receiving SRS for GBM, five (four males, one female; median age 53) had nine distinct new satellite lesions on MRI, separate from their primary tumor beds. Those nine lesions were treated with a median margin dose of 20 Gy in a single fraction. The three-, six, and 12-month local tumor control rates were 77.8%, 66.7%, and 26.7%, respectively. Median progression-free survival (PFS) was seven months, median overall survival following SRS was 10 months, and median overall survival (OS) was 35 months. Interestingly, the only lesion that did not show radiological progression was separate from the T2-fluid attenuated inversion recovery (FLAIR) signal of the main tumor. Conclusion Our SRS treatment outcomes for recurrent GBM satellite lesions are consistent with existing findings. However, in a unique case, a satellite nodule distinct from the primary tumor's T2-FLAIR signal and treated with an enlarged target volume showed promising control until the patient's demise. This observation suggests potential research avenues, given the limited strategies for 'multicentric' GBM lesions.

Tumor-related epilepsy and post-surgical outcomes: tertiary hospital experience in Vietnam

Seizures have a significant impact on the quality of life of those who suffer. This study aimed to evaluate the variables that influence the incidence of seizures during the perioperative period and effective measures to enhance epilepsy outcomes among individuals undergoing surgical resection of brain tumors. The authors carried out a prospective observational analysis of all patients who experienced seizures before their brain tumor surgery at UMC, HCMC between 2020 and 2022. 54 cases presented with seizures were enrolled for the study, generalized seizure was the most prevalent seizure type (61.1%), followed by focal seizure (29.6%). The majority of patients presented with seizures are those who were diagnosed with glioma. Low-grade gliomas and frontotemporal lobe tumors increase the postoperative risk of seizure. Other predictive factors are a prolonged history of seizure, especially resistant epilepsy and major peritumoral edema. In contrast, gross total resection reduces postoperative seizure incidence. There was correlation between Ki67 proliferation index and seizure incidence in both low-grade and high-grade gliomas. ECoG made insubstantial difference in enhancing the epilepsy surgery outcome. Overall, 88.9% of patients were seizure-free at 6 months of follow-up (Engel Class I), 7.4% were almost seizure-free (Class II), and 3.7% had significant improvement (Class III), figures for 12-month follow-up were 87.0%, 9.3%, and 3.7% respectively. A shorter history of seizure and gross-total resection appear to be associated with a favorable prognosis for seizure control.

Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart

OBJECTIVE

To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas.

METHODS

Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma.

RESULTS

There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618).

CONCLUSIONS

Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed.

The peritumoral brain zone in glioblastoma: where we are and where we are going

Glioblastoma (GBM) is the most aggressive and invasive primary brain tumor. Current therapies are not curative, and patients' outcomes remain poor with an overall survival of 20.9 months after surgery. The typical growing pattern of GBM develops by infiltrating the surrounding apparent normal brain tissue within which the recurrence is expected to appear in the majority of cases. Thus, in the last decades, an increased interest has developed to investigate the cellular and molecular interactions between GBM and the peritumoral brain zone (PBZ) bordering the tumor tissue. The aim of this review is to provide up-to-date knowledge about the oncogenic properties of the PBZ to highlight possible druggable targets for more effective treatment of GBM by limiting the formation of recurrence, which is almost inevitable in the majority of patients. Starting from the description of the cellular components, passing through the illustration of the molecular profiles, we finally focused on more clinical aspects, represented by imaging and radiological details. The complete picture that emerges from this review could provide new input for future investigations aimed at identifying new effective strategies to eradicate this still incurable tumor.

Multiparametric study for glioma grading with FLAIR, ADC map, eADC map, T1 map, and SWI images

PURPOSE

This paper is a preliminary attempt to compare the diagnostic efficiency and performance of Fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC) map, exponential ADC (eADC) map, T₁ map, and Susceptibility-weighted image (SWI) for glioma grading and combine these image data pairs to compare the diagnostic performance of different image data pairs for glioma grading.

MATERIAL AND METHODS

Fifty-nine patients underwent FLAIR, ADC map, eADC map, Variable flip-angle (VFA) spoiled gradient recalled echo (SPGR) method, and SWI MRI imaging. The T₁ map was reconstructed by the VFA-SPGR method. The average Relative Signal Contrast (RSC) and receiver operating characteristic curve (ROC) was calculated in a different image. The multivariate binary logistic regression model combined different image data pairs.

RESULTS

The average RSC of SWI and ADC maps in high-grade glioma is significantly lower than RSCs in low-grade. The average RSC of the eADC map and T₁ maps increased with glioma grade. No significant difference was detected between low and high-grade glioma on FLAIR images. The AUC for low and high-grade glioma differentiation on ADC maps, eADC maps, T₁ map, and SWI were calculated 0.781, 0.864, 0.942, and 0.904, respectively. Also, by adding different image data, diagnostic performance was increased.

CONCLUSION

Interestingly, the T₁ map and SWI image have the potential to use in the clinic for glioma grading purposes due to their high performance. Also, the eADC map+T₁ map and T₁ map+SWI image weights have the highest diagnostic performance for glioma grading.

PIEZO1-Related Physiological and Pathological Processes in CNS: Focus on the Gliomas

PIEZO1 is ubiquitously expressed in cells in different kinds of tissues throughout the body, which can sense physical or mechanical stimuli and translate them into intracellular electrochemical signals to regulate organism functions. In particular, PIEZO1 appears in complex interactive regulatory networks as a central node, governing normal and pathological functions in the body. However, the effect and mechanism of the activation or expression of PIEZO1 in diseases of the central nervous system (CNS) remain unclear. On one hand, in CNS diseases, pathophysiological processes in neurons and glial are often accompanied by variations in the mechanical properties of the cellular and extracellular matrix stiffness. The expression of PIEZO1 can therefore be upregulated, in responding to mechanical stimulation, to drive the biological process in cells, which in turns indirectly affects the cellular microenvironment, resulting in alterations of the cellular status. On the other hand, it may have contradictory effects with the change of active patterns and/or subcellular location. This review highlights the biological processes involved with PIEZO1 in CNS cells, with special emphasis on its multiple roles in glioma-associated phenotypes. In conclusion, PIEZO1 can be used as an indicator to assess the malignancy and prognosis of patients with gliomas, as well as a therapeutic target for clinical application following fully exploring the potential mechanism of PIEZO1 in CNS diseases.

Observation of the delineation of the target volume of radiotherapy in adult-type diffuse gliomas after temozolomide-based chemoradiotherapy: analysis of recurrence patterns and predictive factors

BACKGROUND

Radiation therapy is the cornerstone of treatment for adult-type diffuse gliomas, but recurrences are inevitable. Our study assessed the prognosis and recurrence pattern of different radiotherapy volumes after temozolomide-based chemoradiation in our institution.

METHODS

The treatment plans were classified into two groups, the plan 1 intentionally involved the entire edema area while plan 2 did not. Retrospectively investigate the differences in outcomes of 118 adult-type diffuse gliomas patients between these two treatment plans. Then, patients who underwent relapse were selected to analyze their recurrence patterns. Continuous dynamic magnetic resonance images (MRI) were collected to categorized the recurrence patterns into central, in-field, marginal, distant, and cerebrospinal fluid dissemination (CSF-d) recurrence. Finally, the clinical and molecular characteristics which influenced progression were analyzed.

RESULTS

Plan 1 (n = 63) showed a median progression-free survival (PFS) and overall survival (OS) of 9.5 and 26.4 months while plan 2 (n = 55) showed a median PFS and OS of 9.4 and 36.5 months (p = 0.418; p = 0.388). Treatment target volume had no effect on the outcome in patients with adult-type diffuse gliomas. And there was no difference in radiation toxicity (p = 0.388). Among the 90 relapsed patients, a total of 58 (64.4%) patients had central recurrence, 10 (11.1%) patients had in-field recurrence, 3 (3.3%) patients had marginal recurrence, 11 (12.2.%) patients had distant recurrence, and 8 (8.9%) patients had CSF-d recurrence. By treatment plans, the recurrence patterns were similar and there was no significant difference in survival. Reclassifying the progression pattern into local and non-local groups, we observed that oligodendroglioma (n = 10) all relapsed in local and no difference in PFS and OS between the two groups (p > 0.05). Multivariable analysis showed that subventricular zone (SVZ) involvement was the independent risk factor for non-local recurrence in patients with GBM (p < 0.05).

CONCLUSION

In our study, deliberately including or not the entire edema had no impact on prognosis and recurrence. Patients with varied recurrence patterns had diverse clinical and genetic features.

MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme

Background and purpose

As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management.

Materials and methods

The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value.

Results

Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM.

Conclusions

Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.

Evaluation of diffuse glioma grade and proliferation activity by different diffusion-weighted-imaging models including diffusion kurtosis imaging (DKI) and mean apparent propagator (MAP) MRI

PURPOSE

To evaluate two advanced diffusion models, diffusion kurtosis imaging and the newly proposed mean apparent propagation factor-magnetic resonance imaging, in the grading of gliomas and the assessing of their proliferative activity.

METHODS

Fifty-nine patients with clinically diagnosed and pathologically proven gliomas were enrolled in this retrospective study. All patients underwent DKI and MAP-MRI scans. Manually outline the ROI of the tumour parenchyma. After delineation, the imaging parameters were extracted using only the data from within the ROI including mean diffusion kurtosis (MK), return-to-origin probability (RTOP), Q-space inverse variance (QIV) and non-Gaussian index (NG), and the differences in each parameter in the classification of glioma were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of these parameters.

RESULTS

MK, NG, RTOP and QIV were significantly different amongst the different grades of glioma. MK, NG and RTOP had excellent diagnostic value in differentiating high-grade from low-grade glioma, with largest areas under the curve (AUCs; 0.929, 0.933 and 0.819, respectively; P < 0.01). MK and NG had the largest AUCs (0.912 and 0.904) when differentiating grade II tumours from III tumours (P < 0.01) and large AUCs (0.791 and 0.786) when differentiating grade III from grade IV tumours. Correlation analysis of tumour proliferation activity showed that MK, NG and QIV were strongly correlated with the Ki-67 LI (P < 0.001).

CONCLUSION

MK, RTOP and NG can effectively represent the microstructure of these altered tumours. Multimodal diffusion-weighted imaging is valuable for the preoperative evaluation of glioma grade and tumour proliferative activity.

The combination of radiomics features and VASARI standard to predict glioma grade

Background and Purpose

Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods.

Materials and Methods

Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation.

Results

Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966.

Conclusion

The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

Factors Involved in Preoperative Edema in High-Grade Gliomas

Background Expansion of preoperative edema (PE) is an independent poor prognostic factor in high-grade gliomas. Evaluation of PE provides important information that can be readily obtained from magnetic resonance imaging (MRI), but there are few reports on factors associated with PE. The goal of this study was to identify factors contributing to PE in Grade 3 (G3) and Grade 4 (G4) gliomas. Methodology PE was measured in 141 pathologically proven G3 and G4 gliomas, and factors with a potential relationship with PE were examined in univariate and multivariate analyses. The following eight explanatory variables were used: age, sex, Karnofsky performance status (KPS), location of the glioma, tumor diameter, pathological grade, isocitrate dehydrogenase (IDH)-1-R132H status, and Ki-67 index. Overall survival (OS) and progression-free survival (PFS) were calculated in groups divided by PE (<1 vs. ≥1 cm) and by factors with a significant correlation with PE in multivariate analysis. Results In univariate analysis, age (p = 0.013), KPS (p = 0.012), pathology grade (p = 0.004), and IDH1-R132H status (p = 0.0003) were significantly correlated with PE. In multivariate analysis, only IDH1-R132H status showed a significant correlation (p = 0.036), with a regression coefficient of -0.42. The median follow-up period in survivors was 38.9 months (range: 1.2-131.7 months). The one-, two-, and three-year OS rates for PE <1 vs. ≥1 cm were 77% vs. 68%, 67% vs. 44%, and 63% vs. 24% (p = 0.0001), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 67%, 85% vs. 40%, and 81% vs. 21% (p < 0.0001), respectively. The one-, two-, and three-year PFS rates for PE <1 vs. ≥1 cm were 77% vs. 49%, 64% vs. 24%, and 50% vs. 18% (p = 0.0002), respectively, and those for IDH1-R132H mutated vs. wild-type cases were 85% vs. 48%, 77% vs. 23%, and 73% vs. 14% (p < 0.0001), respectively. Conclusions IDH1-R132H status was found to be a significant contributor to PE. Cases with PE <1 cm and those with the IDH1-R132H mutation clearly had a better prognosis.

Presence of peritumoral edema on T2w MRI: a poor non-invasive prognostic marker in breast cancer patients

Background

The purpose of the study was to assess the correlation between peritumoral edema (PE) seen on magnetic resonance imaging (MRI) in breast cancer and the established pathological prognostic factors like tumor histology and molecular subtype, grade, Ki67 index, lymphovascular invasion (LVI) and nodal stage. The breast MRI and pathological data of post-surgery specimen of 126 breast cancer patients over a period of 18 months were retrospectively studied. Those who received neoadjuvant therapy, had non-invasive, locally advanced, inflammatory and bilateral breast cancers were excluded. Patients were divided into two groups based on finding of peritumoral edema on T2w MRI images: Group A with PE (n = 88) and Group B without PE (n = 38). Pathological results for the two groups were analyzed and compared using Chi square test. p values of < .05 were considered as significant.

Results

Statistically significant correlation was found between the PE and molecular subtype (p value of < .01), high grade (p value of .001) and High Ki-67 index (p value of .001). No significant correlation was present for the histological type and LVI pathological nodal stage (pN).

Conclusions

We concluded that presence of PE on MRI is associated with poor pathological prognostic factors in breast cancer. It can serve as an additional non-invasive marker to assess prognosis in breast cancer patients especially in those receiving neoadjuvant therapy where the whole tumor may not be available for pathological analysis post-therapy.

Quantitative Indicators of Retraction Phenomenon on an Automated Breast Volume Scanner: Initial Study in the Diagnosis and Prognostic Prediction of Breast Tumors

Retraction phenomenon is a unique sign on an automated breast volume scanner coronal plane image and has high specificity in differentiating benign lesions from malignant breast cancer. The purpose of this study was to quantify the retraction phenomenon by setting different rules to describe connected regions from different dimensions. In total, six quantitative indicators (F_Ω1,F_Γ,F_S,F_Ω2,F_Ω3and F_L) were obtained. F_Ω1, F_Ω2 and F_Ω3 represent the relative areas of the connected region under different rules. F_ΓandF_S represent the number ratio and absolute area of the connected region, respectively. F_L represents the ratio of edge numbers. Two hundred fourteen patients with 214 lesions (90 benign and 124 malignant) were enrolled in this study. All quantitative indicators in the malignant group were significantly higher than those in the benign group (all p values <0.001). The indicator F_Γ achieved the highest area under the receiver operating characteristic curve (AUC) (0.701, 95% confidence interval: 0.631-0.771). Both F_Γ and F_S had significant associations with axillary lymph node metastasis (p = 0.023 and 0.049). Compared with the classic texture feature gray-level co-occurrence matrix, retraction phenomenon quantization improved the AUC by 8.3%. The results indicate that retraction phenomenon quantitative indicators have certain value in distinguishing benign and malignant breast lesions and seem to be associated with axillary lymph node metastasis.

Pro-inflammatory cytokines in cystic glioblastoma: A quantitative study with a comparison with bacterial brain abscesses. With an MRI investigation of displacement and destruction of the brain tissue surrounding a glioblastoma

Cystic glioblastomas are aggressive primary brain tumors that may both destroy and displace the surrounding brain tissue as they grow. The mechanisms underlying these tumors’ destructive effect could include exposure of brain tissue to tumor-derived cytokines, but quantitative cytokine data are lacking. Here, we provide quantitative data on leukocyte markers and cytokines in the cyst fluid from 21 cystic glioblastomas, which we compare to values in 13 brain abscess pus samples. The concentration of macrophage/microglia markers sCD163 and MCP-1 was higher in glioblastoma cyst fluid than in brain abscess pus; lymphocyte marker sCD25 was similar in cyst fluid and pus, whereas neutrophil marker myeloperoxidase was higher in pus. Median cytokine levels in glioblastoma cyst fluid were high (pg/mL): TNF-α: 32, IL-6: 1064, IL-8: 23585, tissue factor: 28, the chemokine CXCL1: 639. These values were not significantly different from values in pus, pointing to a highly pro-inflammatory glioblastoma environment. In contrast, levels of IFN-γ, IL-1β, IL-2, IL-4, IL-10, IL-12, and IL-13 were higher in pus than in glioblastoma cyst fluid. Based on the quantitative data, we show for the first time that the concentrations of cytokines in glioblastoma cyst fluid correlate with blood leukocyte levels, suggesting an important interaction between glioblastomas and the circulation. Preoperative MRI of the cystic glioblastomas confirmed both destruction and displacement of brain tissue, but none of the cytokine levels correlated with degree of brain tissue displacement or peri-tumoral edema, as could be assessed by MRI. We conclude that cystic glioblastomas are highly pro-inflammatory environments that interact with the circulation and that they both displace and destroy brain tissue. These observations point to the need for neuroprotective strategies in glioblastoma therapy, which could include an anti-inflammatory approach.

Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods

Glioma peritumoral brain edema (GPTBE) is a frequent complication in patients with glioma. The severity of peritumoral edema endangers patients’ life and prognosis. However, there are still questions concerning the process of GPTBE formation and evolution. In this study, the patients were split into two groups based on edema scoring findings in the cancer imaging archive (TCIA) comprising 186 TCGA-LGG patients. Using mRNA sequencing data, differential gene (DEG) expression analysis was performed, comparing the two groups to find the key genes affecting GPTBE. A functional enrichment analysis of differentially expressed genes was performed. Then, a protein–protein interaction (PPI) network was established, and important genes were screened. Gene set variation analysis (GSVA) scores were calculated for major gene sets and comparatively correlated with immune cell infiltration. Overall survival (OS) was analyzed using the Kaplan–Meier curve. A total of 59 DEGs were found, with 10 of them appearing as important genes. DEGs were shown to be closely linked to inflammatory reactions. According to the network score, IL10 was in the middle of the network. The presence of the IL10 protein in glioma tissues was verified using the human protein atlas (HPA). Furthermore, the gene sets’ GSVA scores were favorably linked with immune infiltration, particularly, with macrophages. The high-edema group had higher GSVA scores than the low-edema group. Finally, Kaplan–Meier analysis revealed no differences in OS between the two groups, and eight genes were found to be related to prognosis, whereas two genes were not. GPTBE is linked to the expression of inflammatory genes.

Glioblastoma Stem-like Cell Detection Using Perfusion and Diffusion MRI

Simple Summary

Glioblastoma stem-like cells (GSCs) are known to be aggressive and radio-resistant and proliferate heterogeneously in preferred environments. Additionally, quantitative diffusion and perfusion MRI biomarkers provide insight into the tissue micro-environment. This study assessed the sensitivity of these imaging biomarkers to GSCs in the hyperintensities-FLAIR region, where relapses may occur. A total of 16 patients underwent an MRI session and biopsies were extracted to study the GSCs. In vivo and in vitro biomarkers were compared and both Apparent Diffusion Coefficient (ADC) and relative Cerebral Blood Volume (rCBV) MRI metrics were found to be good predictors of GSCs presence and aggressiveness.

Abstract

Purpose: With current gold standard treatment, which associates maximum safe surgery and chemo-radiation, the large majority of glioblastoma patients relapse within a year in the peritumoral non contrast-enhanced region (NCE). A subpopulation of glioblastoma stem-like cells (GSC) are known to be particularly radio-resistant and aggressive, and are thus suspected to be the cause of these relapses. Previous studies have shown that their distribution is heterogeneous in the NCE compartment, but no study exists on the sensitivity of medical imaging for localizing these cells. In this work, we propose to study the magnetic resonance (MR) signature of these infiltrative cells. Methods: In the context of a clinical trial on 16 glioblastoma patients, relative Cerebral Blood Volume (rCBV) and Apparent Diffusion Coefficient (ADC) were measured in a preoperative diffusion and perfusion MRI examination. During surgery, two biopsies were extracted using image-guidance in the hyperintensities-FLAIR region. GSC subpopulation was quantified within the biopsies and then cultivated in selective conditions to determine their density and aggressiveness. Results: Low ADC was found to be a good predictor of the time to GSC neurospheres formation in vitro. In addition, GSCs were found in higher concentrations in areas with high rCBV. Conclusions: This study confirms that GSCs have a critical role for glioblastoma aggressiveness and supports the idea that peritumoral sites with low ADC or high rCBV should be preferably removed when possible during surgery and targeted by radiotherapy.

Prognostic and Predictive Markers of Limited (1-4) Brain Metastases in Patients with Lung Adenocarcinoma After Stereotactic Radiosurgery: A Retrospective Analysis

BACKGROUND

Owing to the complexities of brain metastases (BMs), accurate and reliable prognostic and predictive factors remain critical roadblocks in patients with lung adenocarcinoma (LUAD) BMs who undergo stereotactic radiosurgery (SRS).

METHODS

In total, 132 patients with LUAD BMs who underwent SRS were retrospectively analyzed; Cox proportional hazards analysis of imaging and clinical characteristics was used to identify independent predictors related to overall survival (OS) and progression-free survival (PFS).

RESULTS

Our data indicated that initial brain metastasis velocity (iBMV), Karnofsky performance score (KPS), and Rvol (the sum of peritumoral edema volume/cumulative intracranial tumor volume) could potentially be independent prognostic factors for OS. iBMV ≥2 (P = 0.000), KPS <80 (P = 0.042), and Rvol ≥5.7 (P = 0.017) were strongly associated with unsatisfactory OS. The KPS and BM contrast enhancement were also identified as independent prognostic factors for PFS. A higher KPS (P = 0.004) and homogeneous BM contrast enhancement (P = 0.026) were strongly associated with longer PFS.

CONCLUSIONS

Collectively, iBMV and Rvol are highly related to OS and could be used as potential prognostic indices in patients with LUAD BMs who underwent SRS. Furthermore, we also revealed that the KPS and BM contrast enhancement could be potential indices of PFS in LUAD BMs.

Nomogram for predicting early recurrence in patients with high-grade gliomas

OBJECTIVE

To develop a nomogram to predict early recurrence of high-grade glioma (HGG) based on clinical pathology, genetic factors and MRI parameters.

METHODS

154 patients with HGG were classified into recurrence and non-recurrence groups based on the pathological diagnosis and RANO criteria. Clinical pathology information included age, sex, preoperative Karnofsky performance status (KPS) scores,grade, and cell proliferation index (Ki-67). Gene information included P53, IDH1, MGMT, and TERT expression status. All patients underwent baseline MRIs before treatment, including T1WI, T2WI, T1C, Flair, and DWI examinations. Tumor location, single/multiple tumors, tumor diameter, peritumoral edema, necrotic cyst, hemorrhage, average apparent diffusion coefficient(ADC) value, and minimum ADC values were evaluated. Univariate and multivariate logistic regression analyses were used to determine the predictors of early recurrence and build nomogram.

RESULTS

Univariate analysis showed that the number of tumors (OR, 0.258; 95% CI: 0.104, 0.639; P = 0.003) and peritumoral edema (OR, 0.965; 95% CI 0.942, 0.988; P = 0.003; mean in the recurrence group 22.04±17.21 mm; mean in the non-recurrence group 14.22±12.84 mm) were statistically significantly different in patients with early recurrence. Genetic factors associated with early recurrence included IDH1 (OR, 4.405; 95% CI 1.874, 10.353; P= 0.001), and MGMT (OR, 2.389; 95% CI 1.234, 4.628; P= 0.010). Multivariate logistic regression analysis revealed that the number of tumors (OR, 0.227; 95% CI 0.084, 0.616; P = 0.004), peritumoral edema (OR, 0.969; 95% CI 0.945, 0.993; P = 0.013), and IDH1 (OR, 4.200; 95% CI 1.602, 10.013; P= 0.004) were independent risk factors for early recurrence. The nomogram showed the highest net benefit when the threshold probability was less than 60%.

CONCLUSION

A nomogram prediction model can effectively aid in clinical treatment decisions for patients with newly diagnosed HGG .

Impact of Levetiracetam Treatment on 5-Aminolevulinic Acid Fluorescence Expression in IDH1 Wild-Type Glioblastoma

Simple Summary

The amino acid 5-aminolevulinic acid (5-ALA) is the benchmark regarding intraoperative imaging tools for glioblastoma (GB) surgery, and is known to facilitate the extent of resection, which results in an enhanced 6 month progression-free survival rate. Recent in vitro studies suggest that antiepileptic drugs (AEDs) result in a reduction in the fluorescence quality in gliomas. To date, there is no large clinical series investigating this issue in a homogeneous cohort. Approximately 25% of all GB patients have a symptomatic epilepsy as the initial symptom at presentation. Hence, this potential dilemma is of paramount importance. We found that the preoperative intake of levetiracetam is a significant risk factor for reduced intraoperative fluorescence in IDH1 wild-type GBs. We believe that this issue must be considered in future external validations, and physicians must carefully evaluate the indication of levetiracetam and avoid a prophylactic levetiracetam treatment in terms of the suspected diagnosis of glioblastoma.

Abstract

The amino acid 5-aminolevulinic acid (5-ALA) is the most established neurosurgical fluorescent dye and facilitates the achievement of gross total resection. In vitro studies raised concerns that antiepileptic drugs (AED) reduce the quality of fluorescence. Between 2013 and 2018, 175 IDH1 wild-type glioblastoma (GB) patients underwent 5-ALA guided surgery. Patients’ data were retrospectively reviewed regarding demographics, comorbidities, medications, tumor morphology, neuropathological characteristics, and their association with intraoperative 5-ALA fluorescence. The fluorescence of 5-ALA was graded in a three point scaling system (grade 0 = no; grade 1 = weak; grade 2 = strong). Univariable analysis shows that the intake of dexamethasone or levetiracetam, and larger preoperative tumor area significantly reduce the intraoperative fluorescence activity (fluorescence grade: 0 + 1). Multivariable binary logistic regression analysis demonstrates the preoperative intake of levetiracetam (adjusted odds ratio: 12.05, 95% confidence interval: 3.91–37.16, p = 0.001) as the only independent and significant risk factor for reduced fluorescence quality. Preoperative levetiracetam intake significantly reduced intraoperative fluorescence. The indication for levetiracetam in suspected GB should be carefully reviewed and prophylactic treatment avoided for this tumor entity. Future comparative trials of neurosurgical fluorescent dyes need a special focus on the influence of levetiracetam on fluorescence intensity. Further trials must validate our findings.

A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients

PURPOSE

This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features.

MATERIALS AND METHODS

Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances.

RESULTS

Patients with cortical involvement had shorter survival times in the training set (P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized.

CONCLUSIONS

The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.

Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning

Background

Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia.

Methods

This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization.

Results

The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors.

Conclusions

This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively.

Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI

In recent years, the development of advanced magnetic resonance imaging (MRI) technology and machine learning (ML) have created new tools for evaluating treatment response and prognosis of patients with high-grade gliomas (HGG); however, patient prognosis has not improved significantly. This is mainly due to the heterogeneity between and within HGG tumors, resulting in standard treatment methods not benefitting all patients. Moreover, the survival of patients with HGG is not only related to tumor cells, but also to noncancer cells in the tumor microenvironment (TME). Therefore, during preoperative diagnosis and follow-up treatment of patients with HGG, noninvasive imaging markers are needed to characterize intratumoral heterogeneity, and then to evaluate treatment response and predict prognosis, timeously adjust treatment strategies, and achieve individualized diagnosis and treatment. In this review, we summarize the research progress of conventional MRI, advanced MRI technology, and ML in evaluation of treatment response and prognosis of patients with HGG. We further discuss the significance of the TME in the prognosis of HGG patients, associate imaging features with the TME, indirectly reflecting the heterogeneity within the tumor, and shifting treatment strategies from tumor cells alone to systemic therapy of the TME, which may be a major development direction in the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 4.

Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer

RATIONALE AND OBJECTIVES

Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes.

MATERIALS AND METHODS

This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%).

RESULTS

For this specific application, the accuracy in the validation set when using the two radiologists' ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement.

CONCLUSION

This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.

Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients

Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.

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

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

Laser interstitial thermal therapy for newly diagnosed glioblastoma

Gliomas are the most frequent primary brain tumor in adults. Patients with glioblastoma (GBM) tumors deemed inoperable with open surgical techniques and treated only with chemo/radiation have a median overall survival of less than 9 months. Laser interstitial thermal therapy (LITT) has emerged as a cytoreductive alternative to surgery for these patients. The present study describes the outcomes of twenty patients with newly diagnosed, IDH wild-type glioblastoma treated with LITT. We retrospectively reviewed patients with newly diagnosed, unresectable GBM who underwent LITT at our institution. Progression-free survival (PFS) was the primary endpoint measured in our study, defined as time from LITT to disease progression. Results Twenty patients were identified with newly diagnosed, inoperable GBM lesions who underwent LITT. The overall median PFS was 4 months (95% CI = 2 - N/A, upper limit not reached). The median progression-free survival (PFS) for patients with less than 1 cm 3 residual tumor (gross total ablation, GTA) was 7 months (95% CI = 6 - N/A, upper limit not reached), compared to 2 months (95% CI = 1 - upper limit not reached) for patients with a lower GTA (p = .0019). The median overall survival was 11 months (95% CI = 6 - upper limit not reached). Preoperative Karnofsky performance score (KPS) less than or equal to 80 and deep-seated tumor location were significantly associated with decreased PFS (HR, .18, p = .03; HR, .08, p = .03, respectively). At the end of 1 month, only 4 patients (20%) experienced persistent motor deficits. LITT is a safe and effective treatment for patients with unresectable, untreated GBM with rates of survival and local recurrence comparable to patients with surgically accessible lesions treated with conventional resection. Careful patient selection is needed to determine if GTA is attainable.

Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost

Background

Glioblastoma peritumoral edema (PE) extent is associated with survival and progression pattern after tumor resection and radiotherapy (RT). To increase tumor control, proton beam was adopted to give high-dose boost (> 90 Gy). However, the correlation between PE extent and prognosis of glioblastoma after postoperative high-dose proton boost (HDPB) therapy stays unknown. We intend to utilize the PE status to classify the survival and progression patterns.

Methods

Patients receiving HDPB (96.6 GyE) were retrospectively evaluated. Limited peritumoral edema (LPE) was defined as PE extent < 3 cm with a ratio of PE extent to tumor maximum diameter of < 0.75. Extended progressive disease (EPD) was defined as progression of tumors extending > 1 cm from the tumor bed edge.

Results

After long-term follow-up (median 88.7, range 63.6–113.8 months) for surviving patients with (n = 13) and without (n = 32) LPE, the median overall survival (OS) and progression-free survival (PFS) were 77.2 vs. 16.7 months (p = 0.004) and 13.6 vs. 8.6 months (p = 0.02), respectively. In multivariate analyses combined with factors of performance, age, tumor maximum diameter, and tumor resection extent, LPE remained a significant factor for favorable OS and PFS. The rates of 5-year complete response, EPD, and distant metastasis with and without LPE were 38.5% vs. 3.2% (p = 0.005), 7.7% vs. 40.6% (p = 0.04), and 0% vs. 34.4% (p = 0.02), respectively.

Conclusions

The LPE status effectively identified patients with relative long-term control and specific progression patterns after postoperative HDPB for glioblastoma.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-021-03765-6.

Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

Objective

To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma.

Materials and Methods

One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (K^trans), fractional volume of vascular plasma space (V_p), and fractional volume of extravascular extracellular space (V_e) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the “radiomics risk score” groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates.

Results

16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively).

Conclusion

We developed and validated the “radiomics risk score” from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

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

BACKGROUND AND PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mean Platelet Volume/Platelet Count Ratio and Risk of Progression in Glioblastoma

Objective

The mean platelet volume/platelet count (MPV/PC) ratio is an emerging biomarker in selected types of cancer. The objective of this study is to analyze the association of MPV/PC ratio with progression and survival in glioblastoma (GB) patients, with consideration of patient demographics, tumor morphology, extent of resection, molecular pathology, and oncological therapy.

Methods

One hundred ninety-one patients with newly diagnosed GB were analyzed retrospectively. MPV/PC ratio groups (≤ or >0.0575) were dichotomized into low-MPV/PC ratio (≤0.0575) and high-MPV/PC ratio (>0.0575) groups according to the most significant split in the log-rank test.

Results

A two-sided Fisher's exact test showed no significant differences in the confounders between the low- and high-MPV/PC ratio groups. The median progression-free survival (PFS) was 9.0 months (95% CI=8.0-10.0) in the low-MPV/PC ratio group (n=164) and 6.0 months (95% CI=3.0-8.9) in the high-MPV/PC group (n=28) (p=0.013). Multivariate Cox regression analysis including the O-6-methylguanine-DNA methyltransferase (MGMT) status, age (≤/>65 years), baseline Karnofsky Performance Status (KPS), and MPV/PC ratio showed high-MPV/PC ratio as a predictor of progression (p =0.04, HR=1.61, 95% CI=1.01-2.57). In the subgroup of IDH1 wild-type GBs, high MPV/PC ratio was still a significant predictor for shortened PFS (p=0.042, HR=1.60, 95% CI=1.02-2.52). MPV/PC ratio showed no significant effect in the overall survival (OS) analysis. Median OS was 15.0 months in the high-MPV/PC ratio group and 21.0 months in the low-MPV/PC ratio group (p=0.22).

Conclusion

MPV/PC ratio may independently predict the progression-free survival rates of patients with glioblastoma multiforme.

The Prognostic Role of Peritumoral Edema in Patients with Newly Diagnosed Glioblastoma: A Retrospective Analysis

OBJECTIVE

Previous studies on glioblastomas (GBMs) have not reached a consensus on peritumoral edema (PTE)'s influence on survival. This study evaluated the PTE index's prognostic role in newly diagnosed GBMs using a well-designed method.

METHODS

Selected patients were reviewed after a rigorous screening process. Their general information was obtained from electronic medical records. The imaging metrics (MTD, TTM, TTE) representing tumor diameter, laterality, and PTE extent were obtained by manual measurement in Syngo FastView software. The PTE index was a ratio of TTE to MTD. Multiple variables were evaluated using analysis of variance and Cox regression model.

RESULTS

Of 143 patients, 62 were included in this study. MGMT promoter methylation and tumor laterality were both independent prognostic factors (p = 0.020, 0.042; HR = 0.272, 2.630). The lateral tumors' index was higher than that of the medial tumors (57.7% vs. 42.6%, p = 0.027). Low-index tumors were located in relatively medial positions compared with high-index tumors (TTM, 4.9 vs. 12.8, p = 0.032). This finding indicated that the PTE index tended to increase with tumor laterality. Moreover, the patients with low-index tumors had a significant survival disadvantage in the univariate analysis but not in the multivariate analysis (p = 0.023, 0.220). However, further analysis found that the combination of tumor laterality and PTE statistically stratified the survival outcome. The patients with lateral high-index tumors survived significantly longer (p = 0.022, HR = 1.927).

CONCLUSIONS

In contrast with the previous studies, this study recommends combining PTE and tumor laterality for survival stratification in newly diagnosed GBMs.

Prognostic Prediction Based on Dynamic Contrast-Enhanced MRI and Dynamic Susceptibility Contrast-Enhanced MRI Parameters from Non-Enhancing, T2-High-Signal-Intensity Lesions in Patients with Glioblastoma

Objective

Few attempts have been made to investigate the prognostic value of dynamic contrast-enhanced (DCE) MRI or dynamic susceptibility contrast (DSC) MRI of non-enhancing, T2-high-signal-intensity (T2-HSI) lesions of glioblastoma multiforme (GBM) in newly diagnosed patients. This study aimed to investigate the prognostic values of DCE MRI and DSC MRI parameters from non-enhancing, T2-HSI lesions of GBM.

Materials and Methods

A total of 76 patients with GBM who underwent preoperative DCE MRI and DSC MRI and standard treatment were retrospectively included. Six months after surgery, the patients were categorized into early progression (n = 15) and non-early progression (n = 61) groups. We extracted and analyzed the permeability and perfusion parameters of both modalities for the non-enhancing, T2-HSI lesions of the tumors. The optimal percentiles of the respective parameters obtained from cumulative histograms were determined using receiver operating characteristic (ROC) curve and univariable Cox regression analyses. The results were compared using multivariable Cox proportional hazards regression analysis of progression-free survival.

Results

The 95th percentile value (PV) of Ktrans, mean Ktrans, and median Ve were significant predictors of early progression as identified by the ROC curve analysis (area under the ROC curve [AUC] = 0.704, p = 0.005; AUC = 0.684, p = 0.021; and AUC = 0.670, p = 0.0325, respectively). Univariable Cox regression analysis of the above three parametric values showed that the 95th PV of Ktrans and the mean Ktrans were significant predictors of early progression (hazard ratio [HR] = 1.06, p = 0.009; HR = 1.25, p = 0.017, respectively). Multivariable Cox regression analysis, which also incorporated clinical parameters, revealed that the 95th PV of Ktrans was the sole significant independent predictor of early progression (HR = 1.062, p < 0.009).

Conclusion

The 95th PV of Ktrans from the non-enhancing, T2-HSI lesions of GBM is a potential prognostic marker for disease progression.

Association of Isocitrate Dehydrogenase (IDH) Status With Edema to Tumor Ratio and Its Correlation With Immune Infiltration in Glioblastoma

Purpose

The extent of preoperative peritumoral edema in glioblastoma (GBM) has been negatively correlated with patient outcome. As several ongoing studies are investigating T-cell based immunotherapy in GBM, we conducted this study to assess whether peritumoral edema with potentially increased intracranial pressure, disrupted tissue homeostasis and reduced local blood flow has influence on immune infiltration and affects survival.

Methods

A volumetric analysis of preoperative imaging (gadolinium enhanced T1 weighted MRI sequences for tumor size and T2 weighted sequences for extent of edema (including the infiltrative zone, gliosis etc.) was conducted in 144 patients using the Brainlab® software. Immunohistochemical staining was analyzed for lymphocytic- (CD 3+) and myelocytic (CD15+) tumor infiltration. A retrospective analysis of patient-, surgical-, and molecular characteristics was performed using medical records.

Results

The edema to tumor ratio was neither associated with progression-free nor overall survival (p=0.90, p=0.74). However, GBM patients displaying IDH-1 wildtype had significantly higher edema to tumor ratio than patients displaying an IDH-1 mutation (p=0.01). Immunohistopathological analysis did not show significant differences in lymphocytic or myelocytic tumor infiltration (p=0.78, p=0.74) between these groups.

Conclusion

In our cohort, edema to tumor ratio had no significant correlation with immune infiltration and outcome. However, patients with an IDH-1wildtype GBM had a significantly higher edema to tumor ratio compared to their IDH-1 mutated peer group. Further studies are necessary to elucidate the underlying mechanisms.

Baseline Serum C-Reactive Protein and Plasma Fibrinogen-Based Score in the Prediction of Survival in Glioblastoma

Objective: The present study investigates a score based on baseline C-reactive protein (CRP) and fibrinogen values (FC score) in 173 consecutive glioblastoma (GBM) patients.

Methods: The optimal cut-off value for fibrinogen and CRP was defined as 3.5 g/dl and 3.0 mg/L, respectively, according to previous reports. Patients with elevated CRP and fibrinogen were classified with a score of 2, those with an elevation of only one of these parameters were allocated a score of 1, and those without any abnormalities were assigned a score of 0.

Results: No significant differences in age, gender, tumor area, molecular pathology, physical status, or extent of resection were identified among the three groups defined by this score. Univariate survival analysis demonstrated that a high baseline FC score (≥1) is significantly associated with a shortened overall survival (OS) (HR: 1.52, 95% CI: 1.05–2.20, p = 0.027). A multivariate Cox regression analysis considering age (>65/≤65), extent of resection (GTR/STR), MGMT promoter status (hypermethylated/non-hypermethylated), and FC score (0/≥1) confirmed that an elevated FC score (≥1) is an independent predictor of shortened OS (HR: 1.71, 95% CI: 1.16–2.51, p = 0.006).

Conclusions: The baseline fibrinogen and CRP score thus serves as an independent predictor of OS in GBM. Further investigations of the role of inflammation in the prediction of a prognosis are needed.

Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma

Simple Summary

Glioblastoma (GBM) is the most malignant primary brain tumor, for which improving patient outcome is limited by a substantial amount of tumor heterogeneity. Magnetic resonance imaging (MRI) in combination with machine learning offers the possibility to collect qualitative and quantitative imaging features which can be used to predict patient prognosis and relevant tumor markers which can aid in selecting the right treatment. This study showed that combining these MRI features with clinical features has the highest prognostic value for GBM patients; this model performed similarly in an independent GBM cohort, showing its reproducibility. The prediction of tumor markers showed promising results in the training set but not could be validated in the independent dataset. This study shows the potential of using MRI to predict prognosis and tumor markers, but further optimization and prospective studies are warranted.

Abstract

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.

Peri-tumoral brain edema associated with glioblastoma correlates with tumor recurrence

Glioblastoma is the most common malignant tumor of the brain. Despite advances in treatment, the prognosis for the condition has remained poor. Glioblastoma is often associated with peritumoral brain edema (PTBE), which can result in increased intracranial pressure and devastating neurological sequelae if left untreated. Surgery is the main treatment for glioblastoma, however current international surgical guidelines do not specify whether glioblastoma-induced PTBE tissue should be resected. In this study, we analyzed treatment outcomes of PTBE using surgical resection. We performed a retrospective analysis of 255 cases of glioblastoma between 2014 and 2016, and found that a significant proportion of patients had a degree of PTBE. We found that surgical resection led to reduction in midline shift that had resulted from edema, however, postoperative complications and KPS scores were not significantly different in the two conditions. We also observed a delay in glioblastoma recurrence in patients undergoing PTBE tissue resection vs patients without resection of PTBE tissue. Interestingly, there was an abnormal expression of tumor associated genes in PTBE, which has not been previously been found. Taken together, this study indicates that glioblastoma-induced PTBE should be investigated further particularly as the tumor microenvironment is a known therapeutic target and therefore interactions between the microenvironment and PTBE should be explored. This study also highlights the importance of resection of PTBE tissue to not only reduce the mechanical obstruction associated with edema but also to delay recurrence of glioblastoma.

Peritumoral edema in preoperative magnetic resonance imaging is an independent prognostic factor for hepatocellular carcinoma

BACKGROUND

Peritumoral edema is an independent prognostic risk factor for malignant tumors. Therefore, assessment of peritumoral edema in preoperative magnetic resonance imaging (MRI) may provide better prognostic information in patients with hepatocellular carcinoma (HCC).

AIM

To determine whether peritumoral edema in preoperative MRI is a prognostic factor for HCC.

METHODS

A retrospective analysis of 90 patients with HCC confirmed by surgical pathology was performed. All patients' peritumoral edema in preoperative MRI was reviewed by two radiologists. The association of disease recurrence with peritumoral edema and clinicopathological features was assessed using the Cox proportional hazards model. Interobserver agreement for evaluating peritumoral edema was determined using Cohen's κ coefficient.

RESULTS

Recurrence and non-recurrence after an average 20.8 month follow-up was 25.6% (23/90) and 74.4% (67/90), respectively. The ratio of peritumoral edema of 90 patients with HCC in preoperative MRI was 35.6% (32/90). In univariate Cox regression analysis, peritumoral edema [hazard ratio (HR) 11.08, P < 0.001], tumor diameter (HR 4.12, P = 0.001), microvascular invasion (HR 2.78, P = 0.020), gender (HR 0.29, P = 0.006), cirrhosis (HR 2.45, P = 0.049), ascites syndrome (HR 2.83, P = 0.022), aspartate aminotransferase(AST)/alanine aminotransferase(ALT) (HR 5.07, P = 0.003) were indicators for HCC recurrence. In multivariate Cox regression analysis, the tumor diameter (HR 2.53, P = 0.032) and peritumoral edema (HR 8.71, P < 0.001) were independent prognostic factors of HCC. The sensitivity, specificity, positive predictive value and negative predictive value of peritumoral edema and tumor diameter were 82.6%&60.9%, 80.6%&77.6%, 59.4%&48.3%, and 93.1%&85.3%, respectively.

CONCLUSION

Peritumoral edema in preoperative MRI may be considered as a biomarker of prognostic information for patients with HCC.