The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has emerged as a favorable independent prognostic and predictive biomarker in glioma. This study aimed to build a radiomics signature based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) for noninvasive measurement of the MGMT promoter methylation status in glioma. One hundred and seven pathology-confirmed primary diffuse glioma patients were retrospectively included and randomly assigned to the primary (n = 71) or validation cohort (n = 36). The MGMT promoter methylation status was measured by pyrosequencing. A total of 1561 radiomics features were extracted from the three-dimensional region of interest (ROI) on the standard uptake value (SUV) maps that were generated from the original 18F-FDG PET data. A radiomics signature, a clinical signature and a fusion signature that combined the clinical and radiomics features together were generated. The performance of the three signatures was evaluated by receiver operating characteristic (ROC) curve analysis, and the patient prognosis was stratified based on the MGMT promoter methylation status and the signature with the best performance. Five radiomics features were selected to construct the radiomics signature, and displayed the best performance with area under the receiver operating characteristic (ROC) curve (AUC) reaching 0.94 and 0.86 in the primary and validation cohorts, respectively, which outweigh the performances of clinical signature and fusion signature. With a median follow-up time of 32.4 months, the radiomics signature stratified the glioma patients into two risk groups with significantly different prognoses (p = 0.04). 18F-FDG-PET-based radiomics is a promising approach for preoperatively evaluating the MGMT promoter methylation status in glioma and predicting the prognosis of glioma patients noninvasively.

Background: To explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively. Methods: One hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values. Results: The values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P < 0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC = 0.607 for nADC and 0.617 for omADC) and MGMT status (AUC = 0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values. Conclusion: DWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

Background: Osteogenesis imperfecta (OI) is marked by clinical and genetic heterogeneity, and the genotype–phenotype correlation remains not very clear. We conducted a clinical and genetic study in a Chinese OI cohort to determine the spectra of phenotypes and pathogenic variants. Methods: In this study, 298 Chinese families were recruited from 2019 to 2024. Clinical phenotypes including fractures, short stature, skeletal deformities, blue sclera, dentinogenesis imperfecta, and hearing loss were recorded and analyzed. Next-generation sequencing combined with PCR-based techniques was used to detect candidate pathogenic variants. Variant pathogenicity was evaluated via conservation analysis, bioinformatics analysis, and functional studies at the cellular level. In this OI cohort, the spectra of pathogenic variants, clinical phenotypes, and genotype–phenotype correlations were analyzed. Results: Our OI cohort included 71 type I (23.83%), 122 type III (40.94%), 90 type IV (30.20%), and 15 type V (5.03%) probands. The cohort consisted of 196 children (65.77%) and 102 adults (34.23%). For the first time, phenotypic differences between different age groups were confirmed. In total, we identified 231 variants, including 47 novel pathogenic variants. Notable variants include two atypical splicing variants, one small deletion, two small duplications, one gross deletion, and one gross duplication. New genotype–phenotype correlations were observed: patients with SERPINF1 variants had the highest fracture frequency, followed by those with WNT1 variants, compared to patients with other gene variants. Conclusions: We performed the clinical and genetic analysis in a large Chinese OI cohort. The expanded spectra of genetic variants and clinical phenotypes were constructed by identifying 47 novel pathogenic variants and summarizing the skeletal and extra-skeletal manifestations. The current paper will provide important evidence for the precise diagnosis of the disease.