Stemness, invasion, and immunosuppression modulation in recurrent glioblastoma using nanotherapy.

The recurrent nature of glioblastoma negatively impacts conventional treatment strategies leading to a growing need for nanomedicine. Nanotherapeutics, an approach designed to deliver drugs to specific sites, is experiencing rapid growth and gaining immense popularity. Having potential in reaching the hard-to-reach disease sites, this field has the potential to show high efficacy in combatting glioblastoma progression. The presence of glioblastoma stem cells (GSCs) is a major factor behind the poor prognosis of glioblastoma multiforme (GBM). Stemness potential, heterogeneity, and self-renewal capacity, are some of the properties that make GSCs invade across the distant regions of the brain. Despite advances in medical technology and MRI-guided maximal surgical resection, not all GSCs residing in the brain can be removed, leading to recurrent disease. The aggressiveness of GBM is often correlated with immune suppression, where the T-cells are unable to infiltrate the cancer initiating GSCs. Standard of care therapies, including surgery and chemotherapy in combination with radiation therapy, have failed to tackle all the challenges of the GSCs, making it increasingly important for researchers to develop strategies to tackle their growth and proliferation and reduce the recurrence of GBM. Here, we will focus on the advancements in the field of nanomedicine that has the potential to show positive impact in managing glioblastoma tumor microenvironment. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction.

Objective.We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction.Approach.57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed.Main results.The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVMPE), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method.Significance. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.

Porphyrin-Based Organic Nanoparticles with NIR-IIa Fluorescence for Orthotopic Glioblastoma Theranostics.

The development of efficient theranostic nanoagents for the precise diagnosis and targeted therapy of glioblastoma (GBM) remains a big challenge. Herein, we designed and developed porphyrin-based organic nanoparticles (PNP NPs) with strong emission in the near-infrared IIa window (NIR-IIa) for orthotopic GBM theranostics. PNP NPs possess favorable photoacoustic and photothermal properties, high photostability, and low toxicity. After modification with the RGD peptide, the obtained PNPD NPs exhibited enhanced blood-brain barrier (BBB) penetration capability and GBM targeting ability. NIR-IIa imaging was employed to monitor the in vivo biodistribution and accumulation of the nanoparticles, revealing a significant enhancement in penetration depth and signal-to-noise ratio. Both in vitro and in vivo results demonstrated that PNPD NPs effectively inhibited the proliferation of tumor cells and induced negligible side effects in normal brain tissues. In general, the work presented a kind of brain-targeted porphyrin-based NPs with NIR-IIa fluorescence for orthotopic glioblastoma theranostics, showing promising prospects for clinical translation.

Iron gallic acid biomimetic nanoparticles for targeted magnetic resonance imaging.

Developing T1-weighted magnetic resonance imaging (MRI) contrast agents with enhanced biocompatibility and targeting capabilities is crucial owing to concerns over current agents' potential toxicity and suboptimal performance. Drawing inspiration from "biomimetic camouflage," we isolated cell membranes (CMs) from human glioblastoma (T98G) cell lines via the extrusion method to facilitate homotypic glioma targeting. At an 8:1 mass ratio of ferric chloride hexahydrate to gallic acid (GA), the resulting iron (Fe)-GA nanoparticles (NPs) proved effective as a T1-weighted MRI contrast agent. T98G CM-coated Fe-GA NPs demonstrated improved homotypic glioma targeting, validated through Prussian blue staining and in vitro MRI. This biomimetic camouflage strategy holds promise for the development of targeted theranostic agents in a safe and effective manner.

The Role of Fibroblast Activation Protein in Glioblastoma and Gliosarcoma: A Comparison of Tissue, 68Ga-FAPI-46 PET Data, and Survival Data.

Despite their unique histologic features, gliosarcomas belong to the group of glioblastomas and are treated according to the same standards. Fibroblast activation protein (FAP) is a component of a tumor-specific subpopulation of fibroblasts that plays a critical role in tumor growth and invasion. Some case studies suggest an elevated expression of FAP in glioblastoma and a particularly strong expression in gliosarcoma attributed to traits of predominant mesenchymal differentiation. However, the prognostic impact of FAP and its diagnostic and therapeutic potential remain unclear. Here, we investigate the clinical relevance of FAP expression in gliosarcoma and glioblastoma and how it correlates with 68Ga-FAP inhibitor (FAPI)-46 PET uptake. Methods: Patients diagnosed with gliosarcoma or glioblastoma without sarcomatous differentiation with an overall survival of less than 2.5 y were enrolled. Histologic examination included immunohistochemistry and semiquantitative scoring of FAP (0-3, with higher values indicating stronger expression). Additionally, 68Ga-FAPI-46 PET scans were performed in a subset of glioblastomas without sarcomatous differentiation patients. The clinical SUVs were correlated with FAP expression levels in surgically derived tumor tissue and relevant prognostic factors. Results: Of the 61 patients who were enrolled, 13 of them had gliosarcoma. Immunohistochemistry revealed significantly more FAP in gliosarcomas than in glioblastomas without sarcomatous differentiation of tumor tissue (P < 0.0001). In the latter, FAP expression was confined to the perivascular space, whereas neoplastic cells additionally expressed FAP in gliosarcoma. A significant correlation of immunohistochemical FAP with SUVmean and SUVpeak of 68Ga-FAPI-46 PET indicates that clinical tracer uptake represents FAP expression of the tumor. Although gliosarcomas express higher levels of FAP than do glioblastomas without sarcomatous differentiation, overall survival does not significantly differ between the groups. Conclusion: The analysis reveals a significant correlation between SUVmean and SUVpeak in 68Ga-FAPI-46 PET and immunohistochemical FAP expression. This study indicates that FAP expression is much more abundant in the gliosarcoma subgroup of glioblastomas. This could open not only a diagnostic but also a therapeutic gap, since FAP could be explored as a theranostic target to enhance survival in a distinct subgroup of high-risk brain tumor patients with poor survival prognosis.

Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status.

Background.Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis.O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients.Purpose.To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.Methods.Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.Results.The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.Conclusions.SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.

Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis.

O6-methylguanine-DNA methyltransferase (MGMT) has been demonstrated to be an important prognostic and predictive marker in glioblastoma (GBM). To establish a reliable radiomics model based on MRI data to predict the MGMT promoter methylation status of GBM. A total of 183 patients with glioblastoma were included in this retrospective study. The visually accessible Rembrandt images (VASARI) features were extracted for each patient, and a total of 14676 multi-region features were extracted from enhanced, necrotic, "non-enhanced, and edematous" areas on their multiparametric MRI. Twelve individual radiomics models were constructed based on the radiomics features from different subregions and different sequences. Four single-sequence models, three single-region models and the combined radiomics model combining all individual models were constructed. Finally, the predictive performance of adding clinical factors and VASARI characteristics was evaluated. The ComRad model combining all individual radiomics models exhibited the best performance in test set 1 and test set 2, with the area under the receiver operating characteristic curve (AUC) of 0.839 (0.709-0.963) and 0.739 (0.581-0.897), respectively. The results indicated that the radiomics model combining multi-region and multi-parametric MRI features has exhibited promising performance in predicting MGMT methylation status in GBM. The Modeling scheme that combining all individual radiomics models showed best performance among all constructed moels.

11C-methionine in the diagnostics and management of glioblastoma patients with rapid early progression: nonrandomized, open label, prospective clinical trial (GlioMET).

BACKGROUND: Glioblastoma (GBM) is the most common and aggressive primary brain cancer. The treatment of GBM consists of a combination of surgery and subsequent oncological therapy, i.e., radiotherapy, chemotherapy, or their combination. If postoperative oncological therapy involves irradiation, magnetic resonance imaging (MRI) is used for radiotherapy treatment planning. Unfortunately, in some cases, a very early worsening (progression) or return (recurrence) of the disease is observed several weeks after the surgery and is called rapid early progression (REP). Radiotherapy planning is currently based on MRI for target volumes definitions in many radiotherapy facilities. However, patients with REP may benefit from targeting radiotherapy with other imaging modalities. The purpose of the presented clinical trial is to evaluate the utility of 11C-methionine in optimizing radiotherapy for glioblastoma patients with REP. METHODS: This study is a nonrandomized, open-label, parallel-setting, prospective, monocentric clinical trial. The main aim of this study was to refine the diagnosis in patients with GBM with REP and to optimize subsequent radiotherapy planning. Glioblastoma patients who develop REP within approximately 6 weeks after surgery will undergo 11C-methionine positron emission tomography (PET/CT) examinations. Target volumes for radiotherapy are defined using both standard planning T1-weighted contrast-enhanced MRI and PET/CT. The primary outcome is progression-free survival defined using RANO criteria and compared to a historical cohort with REP treated without PET/CT optimization of radiotherapy. DISCUSSION: PET is one of the most modern methods of molecular imaging. 11C-Methionine is an example of a radiolabelled (carbon 11) amino acid commonly used in the diagnosis of brain tumors and in the evaluation of response to treatment. Optimized radiotherapy may also have the potential to cover those regions with a high risk of subsequent progression, which would not be identified using standard-of-care MRI for radiotherapy planning. This is one of the first study focused on radiotherapy optimization for subgroup of patinets with REP. TRIAL REGISTRATION: NCT05608395, registered on 8.11.2022 in clinicaltrials.gov; EudraCT Number: 2020-000640-64, registered on 26.5.2020 in clinicaltrialsregister.eu. Protocol ID: MOU-2020-01, version 3.2, date 18.09.2020.

Rapid Glutathione Analysis with SERS Microneedles for Deep Glioblastoma Tissue Differentiation.

Rapid tissue differentiation at the molecular level is a prerequisite for precise surgical resection, which is of special value for the treatment of malignant tumors, such as glioblastoma (GBM). Herein, a SERS-active microneedle is prepared by modifying glutathione (GSH)-responsive molecules, 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB), on the surface of Au@Ag substrates for the distinction of different GBM tissues. Since the Raman signals on the surface of the DTNB@Au@Ag microneedle can be collected by both portable and benchtop Raman spectrometers, the distribution of GSH in different tissues at centimeter scale can be displayed through Raman spectroscopy and Raman imaging, and the entire analysis process can be accomplished within 12 min. Accordingly, in vivo brain tissues of orthotopic GBM xenograft mice and ex vivo tissues of GBM patients are accurately differentiated with the microneedle, and the results are well consistent with tissue staining and postoperative pathological reports. In addition, the outline of tumor, peritumoral, and normal tissues can be indicated by the DTNB@Au@Ag microneedle for at least 56 days. Considering that the tumor tissues are quickly discriminated at the molecular level without the restriction of depth, the DTNB@Au@Ag microneedle is promising to be a powerful intraoperative diagnostic tool for surgery navigation.

A Computational Framework for the Administration of 5-Aminovulinic Acid Before Glioblastoma Surgery.

5-Aminolevulinic Acid (5-ALA) is the only fluorophore approved by the FDA as an intraoperative optical imaging agent for fluorescence-guided surgery in patients with glioblastoma. The dosing regimen is based on rodent tests where a maximum signal occurs around 6 h after drug administration. Here, we construct a computational framework to simulate the transport of 5-ALA through the stomach, blood, and brain, and the subsequent conversion to the fluorescent agent protoporphyrin IX at the tumor site. The framework combines compartmental models with spatially-resolved partial differential equations, enabling one to address questions regarding quantity and timing of 5-ALA administration before surgery. Numerical tests in two spatial dimensions indicate that, for tumors exceeding the detection threshold, the time to peak fluorescent concentration is 2-7 h, broadly consistent with the current surgical guidelines. Moreover, the framework enables one to examine the specific effects of tumor size and location on the required dose and timing of 5-ALA administration before glioblastoma surgery.

Live chicken egg embryos as an alternative in vivo tumour model for deep surface enhanced Raman spectroscopy.

Live chicken egg embryos offer new opportunities for evaluation and continuous monitoring of tumour growth for in vivo studies compared to traditional rodent models. Here, we report the first use of surface enhanced Raman scattering (SERS) mapping and surface enhanced spatially offset Raman scattering (SESORS) for the detection and localisation of targeted gold nanoparticles in live chicken egg embryos bearing a glioblastoma tumour.

In vivo biodistribution and tumor uptake of [64Cu]-FAU nanozeolite via positron emission tomography Imaging.

Nanoparticles have emerged as promising theranostic tools for biomedical applications, notably in the treatment of cancers. However, to fully exploit their potential, a thorough understanding of their biodistribution is imperative. In this context, we prepared radioactive [64Cu]-exchanged faujasite nanosized zeolite ([64Cu]-FAU) to conduct positron emission tomography (PET) imaging tracking in preclinical glioblastoma models. In vivo results revealed a rapid and gradual accumulation over time of intravenously injected [64Cu]-FAU zeolite nanocrystals within the brain tumor, while no uptake in the healthy brain was observed. Although a specific tumor targeting was observed in the brain, the kinetics of uptake into tumor tissue was found to be dependent on the glioblastoma model. Indeed, our results showed a rapid uptake in U87-MG model while in U251-MG glioblastoma model tumor uptake was gradual over the time. Interestingly, a [64Cu] activity, decreasing over time, was also observed in organs of elimination such as kidney and liver without showing a difference in activity between both glioblastoma models. Ex vivo analyses confirmed the presence of zeolite nanocrystals in brain tumor with detection of both Si and Al elements originated from them. This radiolabelling strategy, performed for the first time using nanozeolites, enables precise tracking through PET imaging and confirms their accumulation within the glioblastoma. These findings further bolster the potential use of zeolite nanocrystals as valuable theranostic tools.

Metastatic glioblastoma to the lungs: a case report and literature review.

Glioblastoma is the most common malignant primary brain tumor. Despite its infiltrative nature, extra-cranial glioblastoma metastases are rare. We present a case of a 63-year-old woman with metastatic glioblastoma in the lungs. Sarcomatous histology, a reported risk factor for disseminated disease, was found. Genomic alterations of TP53 mutation, TERT mutation, PTEN mutation, and +7/-10 were also uncovered. Early evidence suggests these molecular aberrations are common in metastatic glioblastoma. Treatment with third-line lenvatinib resulted in a mixed response. This case contributes to the growing body of evidence for the role of genomic alterations in predictive risk in metastatic glioblastoma. There remains an unmet need for treatment of metastatic glioblastoma.

Glioblastoma is the most common malignant primary brain tumor. Glioblastoma can spread into healthy tissue, but metastases beyond the brain are rare. We present a case of a 63-year-old woman with metastatic glioblastoma in the lungs. We identified risk factors associated with spread beyond the brain, including factors related to tissue structure and specific molecular alterations. Treatment with third-line lenvatinib resulted in a mixed response. This case adds to the limited existing data for the use of molecular alterations to serve as risk factors for metastatic glioblastoma. Treatment options are needed for this devastating disease.

Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis.

To evaluate the utility of magnetic resonance imaging (MRI) histogram parameters in predicting O(6)-methylguanine-DNA methyltransferase promoter (pMGMT) methylation status in IDH-wildtype glioblastoma (GBM). From November 2021 to July 2023, forty-six IDH-wildtype GBM patients with known pMGMT methylation status (25 unmethylated and 21 methylated) were enrolled in this retrospective study. Conventional MRI signs (including location, across the midline, margin, necrosis/cystic changes, hemorrhage, and enhancement pattern) were assessed and recorded. Histogram parameters were extracted and calculated by Firevoxel software based on contrast-enhanced T1-weighted images (CET1). Differences and diagnostic performance of conventional MRI signs and histogram parameters between the pMGMT-unmethylated and pMGMT-methylated groups were analyzed and compared. No differences were observed in the conventional MRI signs between pMGMT-unmethylated and pMGMT-methylated groups (all p > 0.05). Compared with the pMGMT-methylated group, pMGMT-unmethylated showed a higher minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, and coefficient of variation (CV) (all p < 0.05). Among all significant CET1 histogram parameters, minimum achieved the best distinguishing performance, with an area under the curve of 0.836. CET1 histogram parameters could provide additional value in predicting pMGMT methylation status in patients with IDH-wildtype GBM, with minimum being the most promising parameter.

DSC-PWI presurgical differentiation of grade 4 astrocytoma and glioblastoma in young adults: rCBV percentile analysis across enhancing and non-enhancing regions.

PURPOSE: The presurgical discrimination of IDH-mutant astrocytoma grade 4 from IDH-wildtype glioblastoma is crucial for patient management, especially in younger adults, aiding in prognostic assessment, guiding molecular diagnostics and surgical planning, and identifying candidates for IDH-targeted trials. Despite its potential, the full capabilities of DSC-PWI remain underexplored. This research evaluates the differentiation ability of relative-cerebral-blood-volume (rCBV) percentile values for the enhancing and non-enhancing tumor regions compared to the more commonly used mean or maximum preselected rCBV values. METHODS: This retrospective study, spanning 2016-2023, included patients under 55 years (age threshold based on World Health Organization recommendations) with grade 4 astrocytic tumors and known IDH status, who underwent presurgical MR with DSC-PWI. Enhancing and non-enhancing regions were 3D-segmented to calculate voxel-level rCBV, deriving mean, maximum, and percentile values. Statistical analyses were conducted using the Mann-Whitney U test and AUC-ROC. RESULTS: The cohort consisted of 59 patients (mean age 46; 34 male): 11 astrocytoma-4 and 48 glioblastoma. While glioblastoma showed higher rCBV in enhancing regions, the differences were not significant. However, non-enhancing astrocytoma-4 regions displayed notably higher rCBV, particularly in lower percentiles. The 30th rCBV percentile for non-enhancing regions was 0.705 in astrocytoma-4, compared to 0.458 in glioblastoma (p = 0.001, AUC-ROC = 0.811), outperforming standard mean and maximum values. CONCLUSION: Employing an automated percentile-based approach for rCBV selection enhances differentiation capabilities, with non-enhancing regions providing more insightful data. Elevated rCBV in lower percentiles of non-enhancing astrocytoma-4 is the most distinguishable characteristic and may indicate lowly vascularized infiltrated edema, contrasting with glioblastoma's pure edema.

Characterizing the relationship between MRI radiomics and AHR expression and deriving a predictive model for prognostic assessment in glioblastoma.

PURPOSE: Aryl hydrocarbon receptor (AHR), a crucial molecular marker associated with glioma, is a potential therapeutic target. We aimed to establish a non-invasive predictive model for AHR through radiomics. METHODS: Contrast-enhanced T1-weighted (T1W) MRI and the corresponding and clinical variables of glioblastoma patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were obtained for analysis. KM curves and Cox regression analyses were used to assess the prognostic value of AHR expression. The radiomics features were screened by Max-Relevance and Min-Redundancy (mRMR) and recursive feature elimination (RFE), followed by the construction of two predictive models using logistic regression (LR) and a support vector machine (SVM). RESULTS: The expression levels of AHR in tumour patients were significantly higher than those in the control group, and higher AHR expression was associated with worse prognosis (P<0.05). AHR remained a risk factor for poor prognosis in glioblastoma after multivariate adjustment (HR: 1.61, 95% CI: 1.085-2.39, P<0.05). The radiomics models constructed using LR and SVM based on three selected features achieved area under the curve (AUC) values of 0.887 and 0.872, respectively. Radiomics score emerged as a key factor influencing overall survival (OS) after multivariate adjustment in the Cox model (HR: 3.931, 95% CI: 1.272-12.148, P < 0.05). CONCLUSION: The radiomics models could effectively distinguish the expression levels of AHR and predict prognosis in patients with glioblastoma, which may serve as a powerful tool to assist clinical assessment and precision treatment.

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.

Dosimetric evaluation and treatment planning considerations for GammaTile permanent brain implants - a pilot, institutional experience.

PURPOSE: GammaTile® (GT) is a brachytherapy platform that received Federal Drug Administration (FDA) approval as brain tumor therapy in late 2018. Here, we reviewed our institutional experience with GT as treatment for recurrent glioblastomas and characterized dosimetric parameter and associated clinical outcome. METHODS AND MATERIALS: A total of 20 consecutive patients with 21 (n = 21) diagnosis of recurrent glioblastoma underwent resection followed by intraoperative GT implant between 01/2019 and 12/2020. Data on gross tumor volume (GTV), number of GT units implanted, dose coverage for the high-risk clinical target volume (HR-CTV), measured by D90 or dose received by 90% of the HR-CTV, dose to organs at risk, and six months local control were collected. RESULTS: The median D90 to HR-CTV was 56.0 Gy (31.7-98.7 Gy). The brainstem, optic chiasm, ipsilateral optic nerve, and ipsilateral hippocampus median Dmax were 11.2, 5.4, 6.4, and 10.0 Gy, respectively. None of the patients in this study cohort suffered from radiation necrosis or adverse events attributable to the GT. Correlation was found between pre-op GTV, the volume of the resection cavity, and the number of GT units implanted. Of the resection cavities, 7/21 (33%) of the cavity experienced shrinkage, 3/21 (14%) remained stable, and 11/21 (52%) of the cavities expanded on the 3-months post-resection/GT implant MRIs. D90 to HR-CTV was found to be associated with local recurrence at 6-month post GT implant, suggesting a dose response relationship (p = 0.026). The median local recurrence-free survival was 366.5 days (64-1,098 days), and a trend towards improved local recurrence-free survival was seen in patients with D90 to HR-CTV ≥ 56 Gy (p = 0.048). CONCLUSIONS: Our pilot, institutional experience provides clinical outcome, dosimetric considerations, and offer technical guidance in the clinical implementation of GT brachytherapy.