OBJECTIVE: It remains unclear whether combining carmustine wafer (CW) implantation with the standard treatment for adult-type diffuse gliomas is safe and has a prognostic impact. This study aimed to investigate the prognostic value and safety of CW implantation.
METHODS: Adult patients with IDH-wild-type and -mutant gliomas, grades 3-4 treated with surgical resection, radiotherapy, and temozolomide chemotherapy between 2013 and 2023 were surveyed. CWs were implanted except in cases of intraoperative wide ventricle opening or marked preoperative brain swelling. For survival analyses, a case-matched dataset based on propensity score matching (PSM), including multiple factors (patient background, diagnosis, and extent of resection) was generated. Progression-free survival (PFS), overall survival (OS), and frequency of complications of CW implantation (brain edema, infection, and cerebrospinal fluid leakage) were compared between the CW and non-use groups.
RESULTS: In total, 127 patients (75 in the CW use group and 52 in the non-use group) were enrolled. Regardless of stratification, no significant differences in PFS and OS were observed between the CW use and non-use groups. The frequency of postoperative brain edema was significantly higher in the CW use group than in the non-use group. An adjusted dataset containing 41 patients in the CW use and nonuse groups was generated. Even after PSM, CW implantation had no prognostic effect.
CONCLUSIONS: CW implantation with standard treatment demonstrated little beneficial effect for the present strategy of CW use.

To clarify the relationships between 11C-methionine (MET) positron emission tomography (PET) metrics and the histology, genetics, and prognosis of adult-type diffuse glioma (ADG) based on the World Health Organization (WHO) 2021 classification.
A total of 125 newly diagnosed patients with ADG were enrolled. We compared the maximum standardized uptake value (SUVmax), tumor-to-normal background ratio (TNR), metabolic tumor volume (MTV), and total lesion methionine uptake (TLMU) to the histology and genetics of the patients with ADG. We also evaluated the prognoses of the 93 surgically treated patients.
The patients with isocitrate dehydrogenase wild ADG showed significantly higher MET-PET metrics (P < 0.05 for all parameters), significantly shorter overall survival and progression-free survival (P < 0.0001 for both) than those of the patients with isocitrate dehydrogenase mutant (IDHm) ADG. In the IDHm ADG group, the SUVmax, MTV, and TLMU values were significantly higher in patients with IDHm grade (G) 4 astrocytoma than patients with IDHm G2/3 astrocytoma (P < 0.05 for all), but not than patients with G2-3 oligodendroglioma. The progression-free survival was significantly shorter in the patients with G4 astrocytoma versus the patients with G2/3 astrocytoma and G3 oligodendroglioma (P < 0.05 for both). The SUVmax and TNR values were significantly higher in recurrent patients than nonrecurrent patients (P < 0.01 for both), but no significant differences were found in MTV or TLMU values.
MET-PET metrics well reflect the histological subtype, WHO grade and prognosis of ADG based on the 2021 WHO classification, with the exception of oligodendroglial tumors. Volumetric parameters were not significantly associated with recurrence, unlike the SUVmax and TNR.

In 2021, the World Health Organization published a new classification system for central nervous system tumors. This study reclassified the adult diffuse glioma (ADG) into astrocytoma, oligodendroglioma, and glioblastoma (GBM) according to the new tumor classification.
The association of TERT promoter (pTERT) mutation, MGMT methylation, and CD47/TIGIT expression with patient prognosis was investigated.
Immunohistochemical analysis showed that the expression levels of CD47 and TIGIT in tumor tissues were significantly higher than those in normal brain tissues. CD47 levels were higher in GBM and grade 4 astrocytoma tissues. TIGIT expression was also higher in patients with GBM. The high expressions of CD47, TIGIT, and CD47/TIGIT were positively correlated with MGMT unmethylation but not pTERT mutation. Moreover, MGMT unmethylation was associated with poor overall survival in astrocytoma. High CD47, TIGIT, and CD47/TIGIT levels were associated with significantly reduced survival in ADG and GBM. GBM, MGMT unmethylation, and high CD47 expression were independent prognostic factors for overall survival in ADG.
Collectively, these results showed that the MGMT unmethylation and high levels of CD47 and TIGIT are associated with a poor prognosis in ADG. Patients with high CD47 and TIGIT expression may benefit from anti-CD47 and TIGIT immunotherapy.

The World Health Organization Classification of Tumors of the Central Nervous System recently incorporated histological features, immunophenotypes, and molecular characteristics to improve the accuracy of glioblastoma (GBM) diagnosis. FGFR3::TACC3 (F3T3) fusion has been identified as an oncogenic driver in IDH-wildtype GBMs. Recent studies have demonstrated the potential of using FGFR inhibitors in clinical trials and TACC3-targeting agents in preclinical models for GBM treatment. However, there is limited information on the clinicopathological and genetic features of IDH-wildtype GBMs with F3T3 fusion. The aim of this study was to comprehensively investigate the clinical manifestations, histological features, and mutational profiles of F3T3-positive GBMs. Between September 2017 and February 2023, 25 consecutive cases (5.0%) of F3T3-positive GBM were extracted from 504 cases of IDH-wildtype GBM. Clinicopathological information and targeted sequencing results obtained from 25 primary and 4 recurrent F3T3-positive GBMs were evaluated and compared with those from F3T3-negative GBMs. The provisional grades determined by histology only were distributed as follows: 4 (26/29; 89.7%), 3 (2/29; 6.9%), and 2 (1/29; 3.4%). Grade 2-3 tumors were ultimately diagnosed as grade 4 GBMs based on the identification of the TERT promoter mutation and the combined gain of chromosome 7 and loss of chromosome 10 (7+/10-). F3T3-positive GBMs predominantly affected women (2.6 females per male). The mean age of patients with an F3T3-positive GBM at initial diagnosis was 62 years. F3T3-positive GBMs occurred more frequently in the cortical locations compared to F3T3-negative GBMs. Imaging studies revealed that more than one-third (12/29; 41.4%) of F3T3-positive GBMs displayed a circumscribed tumor border. Seven of the seventeen patients (41.2%) whose follow-up periods exceeded 20 months died of the disease. Histologically, F3T3-positive GBMs more frequently showed curvilinear capillary proliferation, palisading nuclei, and calcification compared to F3T3-negative GBMs. Molecularly, the most common alterations observed in F3T3-positive GBMs were TERT promoter mutations and 7+/10-, whereas amplifications of EGFR, PDGFRA, and KIT were not detected at all. Other genetic alterations included CDKN2A/B deletion, PTEN mutation, TP53 mutation, CDK4 amplification, and MDM2 amplification. Our observations suggest that F3T3-positive GBM is a distinct molecular subgroup of the IDH-wildtype GBM. Both clinicians and pathologists should consider this rare entity in the differential diagnosis of diffuse astrocytic glioma to make an accurate diagnosis and to ensure appropriate therapeutic management.

OBJECTIVE: Accurate preoperative radiological staging of adult-type diffuse glioma is crucial for effective prognostic stratification and selection of appropriate therapeutic interventions. The purpose of this study was to compare the effectiveness of apparent diffusion coefficient (ADC) maps generated from ultrahigh b-value diffusion-weighted imaging (DWI) for molecular grading with that for histological grading of adult-type diffuse glioma, and to evaluate the correlation between these ADC maps and molecular and histological biomarkers.
METHODS: This study retrospectively enrolled forty adult-type diffuse glioma patients, diagnosed using the 2021 WHO classification criteria. Preoperative imaging data, including multiple b-value DWI and conventional magnetic resonance imaging, were collected. Tumors were graded using both histological and molecular criteria. Histogram analysis was conducted to generate 14 parameters for each tumor. Receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate tumor grading and molecular status differentiation. Analysis of histological biomarkers was performed by calculating the Pearson and Spearman correlation coefficients of continuous and hierarchical variables, respectively.
RESULTS: The intensity-related parameters for molecular grading were found to be superior to those for histological grading for the identification of WHO grade 4 (WHO4) adult-type diffuse glioma. The AUC of both grading systems increased with increasing b-values, with ADC8000-based histogram parameters showing the best results (molecular grading, square root: AUC = 0.897; histological grading, median: AUC = 0.737). The intensity-related parameters could also differentiate molecular WHO4 gliomas from histologically lower-grade gliomas (ADC8000-based square root: AUC = 0.919), and different ADC8000-based kurtosis was observed between molecular and histological WHO4 gliomas (AUC = 0.833). Significant correlations between the Ki-67 index and molecular status prediction for IDH, CDKN2A, and EGFR were also demonstrated.
CONCLUSIONS: The histogram parameters derived from high b-value ADC maps were found to be more effective for differentiating molecular grades of WHO4 adult-type diffuse glioma than for differentiating histological grades.

Adult-type diffuse gliomas are divided into Astrocytoma, IDH-mutant, Oligodendroglioma, IDH-mutant and 1p/19q-codeleted and Glioblastoma, IDH-wildtype based on the IDH mutation, and 1p/19q codeletion status. To determine the treatment strategy for these tumors, pre-operative prediction of IDH mutation and 1p/19q codeletion status might be effective. Computer-aided diagnosis (CADx) systems using machine learning have been noted as innovative diagnostic methods. However, it is difficult to promote the clinical application of machine learning systems at each institute because the support of various specialists is essential. In this study, we established an easy-to-use computer-aided diagnosis system using Microsoft Azure Machine Learning Studio (MAMLS) to predict these statuses. We constructed an analysis model using 258 adult-type diffuse glioma cases from The Cancer Genome Atlas (TCGA) cohort. Using MRI T2-weighted images, the overall accuracy, sensitivity, and specificity for the prediction of IDH mutation and 1p/19q codeletion were 86.9%, 80.9%, and 92.0%, and 94.7%, 94.1%, and 95.1%, respectively. We also constructed an reliable analysis model for the prediction of IDH mutation and 1p/19q codeletion using an independent Nagoya cohort including 202 cases. These analysis models were established within 30 min. This easy-to-use CADx system might be useful for the clinical application of CADx in various institutes.

BACKGROUND: The World Health Organization (WHO) classification of central nervous system (CNS) tumors necessitates testing of isocitrate dehydrogenase (IDH) 1/2 gene mutation in patients with adult-type diffuse glioma (ADG) for better disease management. In clinical practice, the testing of IDH1 is primarily achieved using immunohistochemistry (IHC) specific to IDH1-R132, which carries a sensitivity of 80% and specificity of 100%. However, in some cases, non-specific background staining or regional heterogeneity in the protein expression of IDH1 may necessitate confirmatory genetic analysis. Robust and reliable assays are needed for IDH1/2 mutation testing. The aim of the current study was to detect IDH1 mutation in cfDNA and tissue of adult-type diffuse glioma with allele-specific qPCR.
METHODS: In the current study, IDH1-R132H mutation was analyzed in tumor tissue with paired cell-free DNA (cfDNA) in patients with ADG (n = 45) using IHC and competitive allele-specific Taqman PCR (CAST-PCR). Genomic DNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue and matched serum for cfDNA using commercially available kits. CAST-PCR with IHC for the detection of IDH1-R132H mutation was also compared.
RESULTS: The IDH1-R132H mutation was detected in 46.67% (21/45) cases and 57.78% (26/45) cases using IHC and allele-specific CAST-PCR. In cfDNA of matched IDH1-mutant FFPE tissue DNA, IDH1-R132H mutation was detected in 11.54% (3/26) using CAST-PCR. The concordance rate for IDH1-R132Hmutation between IHC and CAST-PCR was 80.77% (21/26).
CONCLUSIONS: The CAST-PCR assay is more precise and sensitive for  IDH1-R132Hdetection than traditional IHC, and IDH1-R132H mutation detection using cfDNA may add to the current methods of glioma genomic characterization.