Cost-effectiveness analysis of bevacizumab combined with lomustine in the treatment of progressive glioblastoma using a Markov model simulation analysis.

Background: Progressive glioblastoma (GBM) is a malignancy with extremely poor prognosis. Chemotherapy is one of the approved systemic treatment modalities. The aim of this study is to assess the cost-effectiveness of using bevacizumab (BEV) in combination with lomustine (LOM) regimen for the treatment of progressive glioblastoma in China. Methods: The estimation results are derived from a multicenter randomized phase III trial, which demonstrated improved survival in GBM patients receiving BEV+LOM combination therapy. To calculate the incremental cost-effectiveness ratio (ICER) from the perspective of Chinese society, a Markov model was established. Univariate deterministic analysis and probabilistic sensitivity analysis were employed to address the uncertainties within the model. Results: Compared to LOM monotherapy, the total treatment cost for BEV+LOM combination therapy increased from $2,646.70 to $23,650.98. The health-adjusted life years (QALYs) for BEV+LOM combination therapy increased from 0.26 QALYs to 0.51 QALYs, representing an increment of 0.25 QALYs. The incremental cost-effectiveness ratio (ICER) was $84,071.12. The cost-effectiveness curve indicates that within the willingness-to-pay (WTP) range of $35,906 per QALY, BEV+LOM combination therapy is not a cost-effective treatment option for unresectable malignant pleural mesothelioma patients. Conclusions: Taken as a whole, the findings of this study suggest that, from the perspective of payers in China, BEV+LOM combination therapy as a first-line treatment for GBM is not a cost-effective option. However, considering the survival advantages this regimen may offer for this rare disease, it may still be one of the clinical treatment options for this patient population.

Assessment of silver nanoparticles' antitumor effects: Insights into cell number, viability, and morphology of glioblastoma and prostate cancer cells.

Silver nanoparticles (AgNPs) hold promise for cancer therapy. This study aimed to evaluate their impact on tumor and non-tumor cell number, viability, and morphology. Antitumor activity was tested on U-87MG (glioblastoma) and DU-145 (prostate cancer) cell lines. Treatment with AgNPs notably reached a reduction of U-87MG and DU-145 cell growth by 89.30% and 79.74%, respectively, resulting in slower growth rates. AgNPs induced DNA damage, evidenced by reduced nuclear area and DNA content via fluorescent image-based analyses. Conversely, HFF-1 non-tumor cells displayed no significant changes post-AgNPs exposure. Viability assays revealed substantial reductions in U-87MG and DU-145 cells (79% and 63% in MTT assays, 30% and 52.2% in high-content analyses), while HFF-1 cells exhibited lower sensitivity. Tumor cells had notably lower IC50 values than non-tumor cells, indicating selective susceptibility. Transmission electron microscopy (TEM) showed morphological changes post-AgNPs administration, including increased vacuoles, myelin figures, membrane ghosts, cellular extravasation, and membrane projections. The findings suggest the potential of AgNPs against glioblastoma and prostate cancer, necessitating further exploration across other cancer cell lines.

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.

Image-based assessment of natural killer cell activity against glioblastoma stem cells.

Glioblastoma (GBM) poses a significant challenge in oncology and stands as the most aggressive form of brain cancer. A primary contributor to its relentless nature is the stem-like cancer cells, called glioblastoma stem cells (GSCs). GSCs have the capacity for self-renewal and tumorigenesis, leading to frequent GBM recurrences and complicating treatment modalities. While natural killer (NK) cells exhibit potential in targeting and eliminating stem-like cancer cells, their efficacy within the GBM microenvironment is limited due to constrained infiltration and function. To address this limitation, novel investigations focusing on boosting NK cell activity against GSCs are imperative. This study presents two streamlined image-based assays assessing NK cell migration and cytotoxicity towards GSCs. It details protocols and explores the strengths and limitations of these methods. These assays could aid in identifying novel targets to enhance NK cell activity towards GSCs, facilitating the development of NK cell-based immunotherapy for improved GBM treatment.

Maximal cardiopulmonary exercise testing in glioblastoma patients undergoing chemotherapy: assessment of feasibility, safety, and physical fitness status.

PURPOSE: Maximal cardiopulmonary exercise testing (max. CPET) provides the most accurate measurement of cardiorespiratory fitness. However, glioblastoma (GBM) patients often undergo less intensive tests, e.g., 6-min walk test or self-rating scales. This study aims to demonstrate feasibility and safety of max. CPET in GBM patients, concurrently evaluating their physical fitness status. METHODS: Newly diagnosed GBM patients undergoing adjuvant chemotherapy were offered participation in an exercise program. At baseline, max. CPET assessed cardiorespiratory fitness including peak oxygen consumption (VO2peak), peak workload, and physical work capacity (PWC) at 75% of age-adjusted maximal heart rate (HR). Criteria for peak workload were predefined based on threshold values in HR, respiratory quotient, respiratory equivalent, lactate, and rate of perceived effort. Data were compared to normative values. Adverse events were categorized according to standardized international criteria. Further, self-reported exercise data pre- and post-diagnosis were gathered. RESULTS: All 36 patients (median-aged 60; 21 men) met the predefined criteria for peak workload. Mean absolute VO2peak was 1750 ± 529 ml/min, peak workload averaged 130 ± 43 W, and mean PWC was 0.99 ± 0.38 W/kg BW, all clinically meaningful lower than age- and sex-predicted normative values (87%, 79%, 90%, resp.). Only once (3%) a minor, transient side effect occurred (post-test dizziness, no intervention needed). Self-reported exercise decreased from 15.8 MET-h/week pre-diagnosis to 7.2 MET-h/week post-diagnosis. CONCLUSION: Max. CPET in this well-defined population proved feasible and safe. GBM patients exhibit reduced cardiorespiratory fitness, indicating the need for tailored exercise to enhance health and quality of life. CPET could be essential in establishing precise exercise guidelines.

Sex differences in adverse events in Medicare individuals ≥ 66 years of age post glioblastoma treatment.

PURPOSE: Glioblastoma (GB) is the most common primary malignant brain tumor with the highest incidence occurring in older adults with a median age at diagnosis of 64 years old. While treatment often improves survival it brings toxicities and adverse events (AE). Here we identify sex differences in treatment patterns and AE in individuals ≥ 66 years at diagnosis with GB. METHODS: Using the SEER-Medicare dataset sex differences in adverse events were assessed using multivariable logistic regression performed to calculate the male/female odds ratio (M/F OR) and 95% confidence intervals [95% CI] of experiencing an AE adjusted for demographic variables and Elixhauser comorbidity score. RESULTS: Males with GB were more likely to receive standard of care (SOC; Surgery with concurrent radio-chemotherapy) [20%] compared to females [17%], whereas females were more likely to receive no treatment [26%] compared to males [21%]. Females with GB receiving SOC were more likely to develop gastrointestinal disorders (M/F OR = 0.76; 95% CI,0.64-0.91, p = 0.002) or blood and lymphatic system disorders (M/F OR = 0.79; 95% CI,0.66-0.95, p = 0.012). Males with GB receiving SOC were more likely to develop cardiac disorders (M/F OR = 1.21; 95% CI,1.02-1.44, p = 0.029) and renal disorders (M/F OR = 1.65; 95% CI,1.37-2.01, p < 0.001). CONCLUSIONS: Sex differences for individuals, 66 years and older, diagnosed with GB exist in treatment received and adverse events developed across different treatment modalities.

Personalized prognosis stratification of newly diagnosed glioblastoma applying a statistical decision tree model.

PURPOSE: Glioblastoma (GBM) is the most frequent glioma in adults with a high treatment resistance resulting into limited survival. The individual prognosis varies depending on individual prognostic factors, that must be considered while counseling patients with newly diagnosed GBM. The aim of this study was to elaborate a risk stratification algorithm based on reliable prognostic factors to facilitate a personalized prognosis estimation early on after diagnosis. METHODS: A consecutive patient cohort with confirmed GBM treated between 2010 and 2021 was retrospectively analyzed. Clinical, radiological, and molecular parameters were assessed and included in the analysis. Overall survival (OS) was the primary outcome parameter. After identifying the strongest prognostic factors, a risk stratification algorithm was elaborated with estimated odds of survival. RESULTS: A total of 462 GBM patients were analyzed. The strongest prognostic factors were Charlson Comorbidity Index (CCI), extent of tumor resection, and adjuvant treatment. Patients with CCI ≤ 1 receiving tumor resection had the highest survival odds (88% for 10 months). On the contrary, patients with CCI > 3 receiving no adjuvant treatment had the lowest survival odds (0% for 10 months). The 10-months survival rate in patients with CCI > 3 receiving adjuvant treatment was 56% for patients younger than 70 years and 22% for patients older than 70 years. CONCLUSION: A risk stratification algorithm based on significant prognostic factors allowed a personalized early prognosis estimation at the time of GBM diagnosis, that can contribute to a more personalized patient counseling.

Real-world cost- effectiveness analysis: Tumor Treating Fields for newly diagnosed glioblastoma in China.

BACKGROUND: Glioblastoma (GBM) stands as the most aggressive and prevalent primary brain malignancy. Tumor Treating Fields (TTFields), an innovative therapy complementing chemotherapy for GBM treatment, which can significantly enhance overall survival, disease progression-free survival, and patient's quality of life. However, there is a dearth of health economics evaluation on TTFields therapy both domestically and internationally. OBJECTIVE: The study aims to assess the cost-effectiveness of TTFields + temozolomide (TMZ) in comparison to TMZ alone for newly diagnosed GBM patients. The intent is to provide robust economic evidence to serve as a foundation for policymaking and decision-making processes in GBM treatment. METHODS: We estimated outcomes for newly diagnosed GBM patients over a lifetime horizon using a partitioned survival model with three states: Progression-Free Survival, Progression Disease, and Death. The survival model was derived from a real-world study in China, with long-term survival data drawn from GBM epidemiology literature. Adverse event rates were sourced from the EF-14 trial data. Cost data, validated by expert consultation, was obtained from public literature and databases. Utility values were extracted from published literature. Using Microsoft Excel, we calculated expected costs and quality-adjusted life years (QALYs) over 15 years from a health system perspective. The willingness-to-pay threshold was set at three times the Chinese per capita Gross Domestic Product (GDP) in 2022, amounting to CN¥242,928 (US$37,655) /QALY. A 5% discount rate was applied to costs and utilities. Results underwent analysis through single factor and probability sensitivity analyses. RESULTS: TTFields + TMZ demonstrated a mean increase in cost by CN¥389,326 (US$57,859) and an increase of 2.46 QALYs compared to TMZ alone. The incremental cost-effectiveness ratio (ICER) was CN¥157,979 (US$23,474) per QALY gained. The model exhibited heightened sensitivity to changes in the discount rate. Probability sensitivity analysis indicates that, under the existing threshold, the probability of TTFields + TMZ being economical is 95.60%. CONCLUSIONS: This cost-effectiveness analysis affirms that incorporating TTFields into TMZ treatment proves to be cost-effective, given a threshold three times the Chinese per capita GDP.

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme.

In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration.

Real-time glioblastoma tumor microenvironment assessment by SpiderMass for improved patient management.

Glioblastoma is a highly heterogeneous and infiltrative form of brain cancer associated with a poor outcome and limited therapeutic effectiveness. The extent of the surgery is related to survival. Reaching an accurate diagnosis and prognosis assessment by the time of the initial surgery is therefore paramount in the management of glioblastoma. To this end, we are studying the performance of SpiderMass, an ambient ionization mass spectrometry technology that can be used in vivo without invasiveness, coupled to our recently established artificial intelligence pipeline. We demonstrate that we can both stratify isocitrate dehydrogenase (IDH)-wild-type glioblastoma patients into molecular sub-groups and achieve an accurate diagnosis with over 90% accuracy after cross-validation. Interestingly, the developed method offers the same accuracy for prognosis. In addition, we are testing the potential of an immunoscoring strategy based on SpiderMass fingerprints, showing the association between prognosis and immune cell infiltration, to predict patient outcome.

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.

Very high-energy electron therapy as light-particle alternative to transmission proton FLASH therapy - An evaluation of dosimetric performances.

PURPOSE: Clinical translation of FLASH-radiotherapy (RT) to deep-seated tumours is still a technological challenge. One proposed solution consists of using ultra-high dose rate transmission proton (TP) beams of about 200-250 MeV to irradiate the tumour with the flat entrance of the proton depth-dose profile. This work evaluates the dosimetric performance of very high-energy electron (VHEE)-based RT (50-250 MeV) as a potential alternative to TP-based RT for the clinical transfer of the FLASH effect. METHODS: Basic physics characteristics of VHEE and TP beams were compared utilizing Monte Carlo simulations in water. A VHEE-enabled research treatment planning system was used to evaluate the plan quality achievable with VHEE beams of different energies, compared to 250 MeV TP beams for a glioblastoma, an oesophagus, and a prostate cancer case. RESULTS: Like TP, VHEE above 100 MeV can treat targets with roughly flat (within ± 20 %) depth-dose distributions. The achievable dosimetric target conformity and adjacent organs-at-risk (OAR) sparing is consequently driven for both modalities by their lateral beam penumbrae. Electron beams of 400[500] MeV match the penumbra of 200[250] MeV TP beams and penumbra is increased for lower electron energies. For the investigated patient cases, VHEE plans with energies of 150 MeV and above achieved a dosimetric plan quality comparable to that of 250 MeV TP plans. For the glioblastoma and the oesophagus case, although having a decreased conformity, even 100 MeV VHEE plans provided a similar target coverage and OAR sparing compared to TP. CONCLUSIONS: VHEE-based FLASH-RT using sufficiently high beam energies may provide a lighter-particle alternative to TP-based FLASH-RT with comparable dosimetric plan quality.

68 Ga-Pentixafor PET/CT for In Vivo Imaging of CXCR4 Receptors in Glioma Demonstrating a Potential for Response Assessment to Radiochemotherapy: Preliminary Results.

PURPOSE: The aim of this study was to evaluate the diagnostic potential of 68 Ga-pentixafor PET/CT for in vivo CXCR4 receptors imaging in glioma and its possible role in response assessment to radiochemotherapy (R-CT). METHODS: Nineteen (12 men, 7 women) patients with glioblastoma multiforme (GBM) underwent 68 Ga-pentixafor PET/CT, contrast-enhanced MR, and MR spectroscopy. Patients were divided in to 2 groups, that is, group I was the presurgical (n = 9) group in which the scanning was done before surgery, and PET findings were correlated with CXCR4 receptors' density. The group II was the postsurgical (n = 10) group in which the scanning was done before and after R-CT and used for treatment response evaluation. The quantitative analysis of 68 Ga-pentixafor PET/CT evaluated the mean SUV max , SUV mean , SUV peak , and T/B values. MR spectroscopy data evaluated the ratios of tumor metabolites (choline, NAA, creatine). RESULTS: 68 Ga-Pentixafor uptake was noted in all (n = 19) the patients. In the group I, the mean SUV max , SUV mean , SUV peak , and T/B values were found to be 4.5 ± 1.6, 0.60 ± 0.26, 1.95 ± 0.8, and 6.9 ± 4.6, respectively. A significant correlation ( P < 0.005) was found between SUV mean and choline/NAA ratio. Immunohistochemistry performed in 7/9 showed CXCR4 receptors' positivity (intensity 3 + ; stained cells >50.0%). In the group II, the mean SUV max at baseline was 4.6 ± 2.1 and did not differ (4.4 ± 1.6) significantly from the value noted at post-R-CT follow-up PET/CT imaging. At 6 months' clinical follow-up, 4 patients showed stable disease. SUV max and T/B ratios at follow-up imaging were lower (3.70 ± 0.90, 2.64 ± 1.35) than the corresponding values (4.40 ± 2.8; 2.91 ± 0.93) noted at baseline. Six (6/10) patients showed disease progression, and the mean SUV max , and T/B ratio in these patients were significantly ( P < 0.05) higher than the corresponding values at baseline and also higher than that noted in the stable patients. CONCLUSIONS: 68 Ga-Pentixafor PET/CT can be used for in vivo mapping of CXCR4 receptors in GBM. The technique after validation in a large cohort of patients may have added diagnostic value for the early detection of GBM recurrence and for treatment response evaluation.

Comparative assessment of direct and indirect cold atmospheric plasma effects, based on helium and argon, on human glioblastoma: an in vitro and in vivo study.

Recent research has highlighted the promising potential of cold atmospheric plasma (CAP) in cancer therapy. However, variations in study outcomes are attributed to differences in CAP devices and plasma parameters, which lead to diverse compositions of plasma products, including electrons, charged particles, reactive species, UV light, and heat. This study aimed to evaluate and compare the optimal exposure time, duration, and direction-dependent cellular effects of two CAPs, based on argon and helium gases, on glioblastoma U-87 MG cancer cells and an animal model of GBM. Two plasma jets were used as low-temperature plasma sources in which helium or argon gas was ionized by high voltage (4.5 kV) and frequency (20 kHz). In vitro assessments on human GBM and normal astrocyte cell lines, using MTT assays, flow cytometry analysis, wound healing assays, and immunocytochemistry for Caspase3 and P53 proteins, demonstrated that all studied plasma jets, especially indirect argon CAP, selectively induced apoptosis, hindered tumor cell growth, and inhibited migration. These effects occurred concurrently with increased intracellular levels of reactive oxygen species and decreased total antioxidant capacity in the cells. In vivo results further supported these findings, indicating that single indirect argon and direct helium CAP therapy, equal to high dose Temozolomide treatment, induced tumor cell death in a rat model of GBM. This was concurrent with a reduction in tumor size observed through PET-CT scan imaging and a significant increase in the survival rate. Additionally, there was a decrease in GFAP protein levels, a significant GBM tumor marker, and an increase in P53 protein expression based on immunohistochemical analyses. Furthermore, Ledge beam test analysis revealed general motor function improvement after indirect argon CAP therapy, similar to Temozolomide treatment. Taken together, these results suggest that CAP therapy, using indirect argon and direct helium jets, holds great promise for clinical applications in GBM treatment.

[PET/CT with 11C-methionine in assessment of brain glioma metabolism]. / Pozitronnaya emissionnaya tomografiya v sochetanii s komp'yuternoi tomografiei i 11S-metioninom v otsenke metabolizma gliom golovnogo mozga.

OBJECTIVE: To study 11C-methionine (MET) metabolism in gliomas using CNS tumor biobank imaging data. MATERIAL AND METHODS: MRI and 11C-MET PET/CT were performed in 225 patients (49±14 years, M/F=84/101) according to standard protocols with analysis of 11C-MET accumulation index and volumetric parameters (V_FLAIR, V_PET and V_PET/FLAIR). These results were compared with molecular genetic testing and 2-year overall survival. RESULTS: We examined 225 patients with gliomas (97 glioblastomas, 70 astrocytomas, 58 oligodendrogliomas). Accumulation index and volume of 11C-MET in glioblastomas were significantly higher in the general group (AI=2.90, Se 69%, Sp 76%, AUC 0.76; V_PET=24.3 cm3, Se 67%, Sp 60%, AUC 0.65; V_PET/FLAIR 0.46, Se 60%, Sp 69%, AUC 0.67) and within the group of astrocytomas (AI=2.93, Se 68%, Sp 89%, AUC 0.84; V_PET=8.06 cm3, Se 91%, Sp 35%, AUC 0.66; V_PET/FLAIR 0.27, Se 77%, Sp 60%, AUC 0.71). The median 2-year overall survival in patients with glioblastomas was 13 months that was significantly lower compared to IDH «+¼ gliomas (p<0.0001). There was a relationship between high accumulation index of 11C-MET and shorter overall survival in patients with glioblastomas. Significantly higher AI >3.59 (Se 89%, Sp 67%, AUC 0.79) was additionally obtained in subgroup of patients with glioblastomas >50 years (n=34) for EGFR «+¼ tumors. CONCLUSION: We found variable 11C-MET metabolism in WHO 2021 gliomas and confirmed significant difference in metabolic activity and volume of 11C-MET accumulation in glioblastomas compared to IDH «+¼ gliomas. Moreover, we revealed the relationship between high accumulation index and shorter survival. Analysis of 11C-MET metabolism in patients over 50 years old revealed higher accumulation index in the EGFR «+¼ group. Further comparison of these imaging methods and assessment of other significant mutations are necessary to identify the anatomical and metabolic patterns of IDH «+¼ gliomas.

Simulating photodynamic therapy for the treatment of glioblastoma using Monte Carlo radiative transport.

Significance: Glioblastoma (GBM) is a rare but deadly form of brain tumor with a low median survival rate of 14.6 months, due to its resistance to treatment. An independent simulation of the INtraoperative photoDYnamic therapy for GliOblastoma (INDYGO) trial, a clinical trial aiming to treat the GBM resection cavity with photodynamic therapy (PDT) via a laser coupled balloon device, is demonstrated. Aim: To develop a framework providing increased understanding for the PDT treatment, its parameters, and their impact on the clinical outcome. Approach: We use Monte Carlo radiative transport techniques within a computational brain model containing a GBM to simulate light path and PDT effects. Treatment parameters (laser power, photosensitizer concentration, and irradiation time) are considered, as well as PDT's impact on brain tissue temperature. Results: The simulation suggests that 39% of post-resection GBM cells are killed at the end of treatment when using the standard INDYGO trial protocol (light fluence = 200 J/cm2 at balloon wall) and assuming an initial photosensitizer concentration of 5 µM. Increases in treatment time and light power (light fluence = 400 J/cm2 at balloon wall) result in further cell kill but increase brain cell temperature, which potentially affects treatment safety. Increasing the p hotosensitizer concentration produces the most significant increase in cell kill, with 61% of GBM cells killed when doubling concentration to 10 µM and keeping the treatment time and power the same. According to these simulations, the standard trial protocol is reasonably well optimized with improvements in cell kill difficult to achieve without potentially dangerous increases in temperature. To improve treatment outcome, focus should be placed on improving the photosensitizer. Conclusions: With further development and optimization, the simulation could have potential clinical benefit and be used to help plan and optimize intraoperative PDT treatment for GBM.

Updated Response Assessment in Neuro-Oncology (RANO) for Gliomas.

PURPOSE OF REVIEW: The response assessment in Neuro-Oncology (RANO) criteria and its versions were developed by expert opinion consensus to standardize response evaluation in glioma clinical trials. New patient-based data informed the development of updated response assessment criteria, RANO 2.0. RECENT FINDINGS: In a recent study of patients with glioblastoma, the post-radiation brain MRI was a superior baseline MRI compared to the pretreatment MRI, and confirmation scans were only beneficial within the first 12 weeks of completion of radiation in newly diagnosed disease. Nonenhancing disease evaluation did not improve the correlation between progression-free survival and overall survival in newly diagnosed and recurrent settings. RANO 2.0 recommends a single common response criteria for high- and low-grade gliomas, regardless of the treatment modality being evaluated. It also provides guidance on the evaluation of nonenhancing tumors and tumors with both enhancing and nonenhancing components.

MRI Treatment Response Assessment Maps (TRAMs) for differentiating recurrent glioblastoma from radiation necrosis.

BACKGROUND: MRI treatment response assessment maps (TRAMs) were introduced to distinguish recurrent malignant glioma from therapy related changes. TRAMs are calculated with two contrast-enhanced T1-weighted sequences and reflect the "late" wash-out (or contrast clearance) and wash-in of gadolinium. Vital tumor cells are assumed to produce a wash-out because of their high turnover rate and the associated hypervascularization, whereas contrast medium slowly accumulates in scar tissue. To examine the real value of this method, we compared TRAMs with the pathology findings obtained after a second biopsy or surgery when recurrence was suspected. METHODS: We retrospectively evaluated TRAMs in adult patients with histologically demonstrated glioblastoma, contrast-enhancing tissue and a pre-operative MRI between January 1, 2017, and December 31, 2022. Only patients with a second biopsy or surgery were evaluated. Volumes of the residual tumor, contrast clearance and contrast accumulation before the second surgery were analyzed. RESULTS: Among 339 patients with mGBM who underwent MRI, we identified 29 repeated surgeries/biopsies in 27 patients 59 ± 12 (mean ± standard deviation) years of age. Twenty-eight biopsies were from patients with recurrent glioblastoma histology, and only one was from a patient with radiation necrosis. We volumetrically evaluated the 29 pre-surgery TRAMs. In recurrent glioblastoma, the ratio of wash-out volume to tumor volume was 36 ± 17% (range 1-73%), and the ratio of the wash-out volume to the sum of wash-out and wash-in volumes was 48 ± 21% (range 22-92%). For the one biopsy with radiation necrosis, the ratios were 42% and 54%, respectively. CONCLUSIONS: Typical recurrent glioblastoma shows a > 20%ratio of the wash-out volume to the sum of wash-out and wash-in volumes. The one biopsy with radiation necrosis indicated that such necrosis can also produce high wash-out in individual cases. Nevertheless, the additional information provided by TRAMs increases the reliability of diagnosis.

High-volume facilities are not always low risk: comparing risk-standardized mortality rates versus facility volume as quality measures in surgical neuro-oncology.

OBJECTIVE: Risk-standardized mortality rates (RSMRs) have recently been shown to outperform facility case volume as a proxy for surgical quality in lung and gastrointestinal cancer. The aim of this study was to investigate RSMR as a surgical quality metric in primary CNS cancer. METHODS: This retrospective observational cohort study used data from the National Cancer Database, a population-based oncology outcomes database sourced from more than 1500 institutions in the United States, and included adult patients 18 years of age and older who were diagnosed with glioblastoma, pituitary adenoma, or meningioma and were treated with surgery. For each group, RSMR quintiles and annual volume were calculated in a training set (2009-2013) and these thresholds were applied to the validation set (2014-2018). In this paper, the authors compared the effectiveness and efficiency of facility volume-based versus RSMR-based hospital centralization models and evaluated the overlap between the two systems. A patterns-of-care analysis was also performed to explore socioeconomic predictors of being treated at better-performing treating facilities. RESULTS: A total of 37,838 meningioma, 21,189 pituitary adenoma, and 30,788 glioblastoma patients were surgically treated from 2014 to 2018. There were substantial differences between RSMR and facility volume classification schemes among all tumor types. In an RSMR-based centralization model, an average of 36 patients undergoing glioblastoma surgery would need to relocate to a low-mortality hospital to prevent one 30-day mortality following surgery, whereas 46 would need to relocate to a high-volume hospital. For pituitary adenoma and meningioma, both metrics were inefficient in centralizing care to reduce surgical mortality. Additionally, overall survival for glioblastoma patients was better modeled in an RSMR classification scheme. Analyses to investigate the impact of care disparities found that Black and Hispanic patients, patients earning less than $38,000, and uninsured patients were more likely to be treated at high-mortality hospitals. CONCLUSIONS: RSMR is more effective and efficient than a traditional volume-based approach for preventing early postoperative death in glioblastoma surgery. These data have important implications for future quality-related studies in neurosurgical oncology and may be relevant for healthcare/insurance payments, hospital evaluation assessments, healthcare disparities, and the standardization of care across hospitals.