Yan Tan

Background: This study aims to achieve accurate prediction of high‐risk molecular subtypes of gliomas through a cross‐scale Combined model, matching the model's classification metrics with patient risk stratification and exploring the underlying biological mechanisms. Methods: This study retrospectively collected preoperative MRI, postoperative whole‐slide pathological images, molecular markers, and clinical data from 456 adult diffuse glioma patients. We separately constructed an MRI habitat prediction model, a WSI Transformer‐based deep learning pathomics (PDL) model, and a Combined model. A dynamic nomogram web page for predicting high‐risk molecular subtypes was developed based on the Combined model. Patients were stratified into risk groups according to the output scores of the Combined model, and Kaplan–Meier survival analysis and the Log‐rank test were employed to evaluate survival differences between the groups. Additionally, differential expression and GO/KEGG enrichment analyses were further performed in the test set with available RNA‐seq data to explore transcriptional features and biological processes associated with model‐based risk stratification. Results: The Combined model demonstrated the highest AUC (Training set: 0.888, Test set: 0.836) compared to the Habitat model (Training set: 0.832, Test set: 0.798) and the PDL model (Training set: 0.852, Test set: 0.821). The high‐risk and low‐risk groups, stratified based on the cutoff value derived from the Combined model output scores, exhibited significant survival differences. Exploratory transcriptomic analysis showed that differentially expressed genes between the high‐ and low‐risk groups were mainly enriched in biological processes and pathways related to the extracellular matrix and cell–matrix interactions. Conclusion: The cross‐scale Combined model not only enabled identification of high‐risk molecular subtypes and risk stratification but also showed associations with biologically relevant transcriptional features.

IDH wild-type glioblastoma (IDH-wt GBM) exhibits substantial intratumoral heterogeneity, which contributes to treatment resistance, disease recurrence, and variable clinical outcomes. This study aimed to identify radiomics-based intratumoral heterogeneity (RITH) subtypes in IDH-wt GBM and to assess their prognostic relevance, associated pathomic differences, and transcriptomic associations. We enrolled 333 patients with IDH-wt GBM across three cohorts. Radiomic features were extracted from CE-T1 and T2-FLAIR images, and partitioning around medoids (PAM) clustering was applied to identify RITH subtypes. Survival analyses included Kaplan-Meier analysis, log-rank tests, and univariable and multivariable Cox proportional hazards regression. Pathomic features extracted via CellProfiler were used to construct the PathScore to assess RITH subtype-associated pathomic differences. RNA-seq data from Cohort 3 were used for exploratory transcriptomic analysis, including candidate differentially expressed gene (DEG) identification, protein-protein interaction (PPI) network construction, and enrichment analysis. PAM clustering identified two RITH subtypes with significantly different overall survival (OS). The High RITH subtype was associated with shorter OS and remained independently associated with worse OS after adjustment for age, sex, hospital, MGMT promoter methylation status, and treatment category. The PathScore differed significantly between RITH subtypes, indicating associated histopathological differences. Exploratory transcriptomic analysis identified eleven candidate hub genes (CCL4, IL6, CSF2, HGF, IFNG, SERPINE1, CCR2, CXCR3, CXCL13, CCL20, and IL1B). Enrichment analyses suggested that these candidate hub genes were mainly associated with immune-inflammatory processes and pathways. The High RITH subtype was associated with poorer survival and showed corresponding pathomic and exploratory transcriptomic associations. These findings suggest that RITH subtypes may serve as non-invasive imaging phenotypes linked to prognosis and biological heterogeneity in IDH-wt GBM.

Occupational aluminum (Al) exposure is linked to mild cognitive impairment (MCI), yet its neuroanatomical mechanisms remain unclear. We investigated gray matter volume (GMV) alterations specifically associated with Al-induced MCI. This study enrolled 86 participants: 30 Al-exposed MCI (Al-MCI), 26 Al-exposed cognitively normal (Al-HC), and 30 non-exposed controls (Non-Al-HC). Structural MRI quantified whole-brain GMV. Plasma Al concentrations, Montreal Cognitive Assessment (MoCA), the Rey Auditory Verbal Learning Test (AVLT), and the Trail Making Test (TMT) were assessed in Al-exposed individuals. Intergroup GMV differences were analyzed via ANCOVA. Partial correlations and mediation analyses explored cognition-GMV relationships. We found that Al-MCI and Al-HC showed comparable plasma Al concentrations (42.74 ± 26.00 vs. 32.52 ± 30.86 µg/L, p = 0.184). A graded pattern of bilateral caudate nucleus (CAU) atrophy was observed: most severe atrophy in the Al-MCI group, intermediate atrophy in the Al-HC group, and the largest volumes in the Non-Al-HC group (p < 0.05 for all pairwise comparisons). In Al-exposed individuals, left CAU volume correlated positively with abstraction (r = 0.294, p = 0.028), language (r = 0.321, p = 0.016), and AVLT-immediate recall (r = 0.289, p = 0.030). Right CAU volume correlated negatively with TMT-A error responses (r=-0.306, p = 0.022). In conclusions, CAU atrophy was significantly associated with cognitive decline and may represent a specific neuroimaging feature of occupational Al-induced MCI.

There is variability in overall survival among 2021 World Health Organization isocitrate dehydrogenase wild type glioblastoma (IDH-wt GBM) patients. The aim of the study was to develop a combined model for stratifying survival risk in IDH-wt GBM and explore the biological foundation. A total of 369 IDH-wt GBM patients were retrospectively collected: 273 patients from three local hospitals (training set: n = 192, testing set: n = 81) and 96 patients from the TCIA database (validation set). Radiomics features from tumor and peritumoral edema in preoperative CE-T1WI and T2FLAIR were extracted. Univariate and least absolute shrinkage and selection operator Cox regression analyses selected significant radiomics features to construct radiomics model, while univariable and multivariable analyses identified clinical risk factors for the clinical model. High-risk and low-risk patients from radiomics and clinical model underwent subgroup analysis. The combined model was constructed using the Random Survival Forest model. Additionally, differentially expressed genes between combined high-risk and low-risk groups were identified, with enrichment analyses exploring their biological mechanisms. The radiomics model categorizes patients into high-risk and low-risk groups with superior performance (C-index: 0.762/0.715/0.690 for training/testing/validation sets) compared to the clinical model (C-index: 0.700/0.656/0.643). The combined model demonstrates the highest value in survival risk stratification (C-index: training/testing/validation sets: 0.788/0.725/0.709). The activation of Gamma-aminobutyric acid (GABA) receptor-related pathways is closely associated with malignant progression and prognosis of IDH-wt GBM. The radiomics model might be a new prognostic biomarker for IDH-wt GBM. The combined model shows an approximately 12.57% improvement of stratification ability over the clinical model. Additionally, the significant activation of GABA receptor-related pathways may be a biological feature of combined high-risk IDH-wt GBM.

Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment. To develop and validate a machine learning (ML) model using multiparametric MRI for the preoperative differentiation of astrocytoma, CNS WHO grade 4, and glioblastoma (GBM), isocitrate dehydrogenase-wild-type (IDH-wt) (WHO 2021) (Task 1:grade 4 vs. GBM); and to stratify astrocytoma, CNS WHO grade 4, by distinguish astrocytoma, IDH-mutant (IDH-mut), CNS WHO grade 4 from astrocytoma, IDH-wild-type (IDH-wt), CNS WHO grade 4 (Task 2:IDH-mut grade 4 vs. IDH-wt grade 4). Additionally, to evaluate the model’s prognostic value. We retrospectively analyzed 320 glioma patients from three hospitals (training/testing, 7:3 ratio) and 99 patients from ‌The Cancer Genome Atlas (TCGA) database for external validation‌. Radiomic features were extracted from tumor and edema on contrast-enhanced T1-weighted imaging (CE-T1WI) and T2 fluid-attenuated inversion recovery (T2-FLAIR). Extreme gradient boosting (XGBoost) was utilized for constructing the ML, clinical, and combined models. Model performance was evaluated with receiver operating characteristic (ROC) curves, decision curves, and calibration curves. Stability was evaluated using six additional classifiers. Kaplan-Meier (KM) survival analysis and the log-rank test assessed the model’s prognostic value. In Task 1 and Task 2, the combined model (AUC = 0.907, 0.852 and 0.830 for Task 1; AUC = 0.899, 0.895 and 0.792 for Task 2) and the optimal ML model (AUC = 0.902, 0.854 and 0.832 for Task 1; AUC = 0.904, 0.899 and 0.783 for Task 2) significantly outperformed the clinical model (AUC = 0.671, 0.656, and 0.543 for Task 1; AUC = 0.619, 0.605 and 0.400 for Task 2) in both the training, testing and validation sets. Survival analysis showed the combined model performed similarly to molecular subtype in both tasks (p = 0.964 and p = 0.746). The multiparametric MRI ML model effectively distinguished astrocytoma, CNS WHO grade 4 from GBM, IDH-wt (WHO 2021) and differentiated astrocytoma, IDH-mut from astrocytoma, IDH-wt, CNS WHO grade 4. Additionally, the model provided reliable survival stratification for glioma patients across different molecular subtypes.

The first case of primary extragastrointestinal stromal tumor of the kidney: a case description

To develop radiomics models based on multi-sequence MRI from two centers for the preoperative prediction of the WHO/ISUP grade of Clear Cell Renal Cell Carcinoma (ccRCC). This retrospective study included 334 ccRCC patients from two centers. Significant clinical factors were identified through univariate and multivariate analyses. MRI sequences included Dynamic contrast-enhanced MRI, axial fat-suppressed T2-weighted imaging, diffusion-weighted imaging, and in-phase/out-of-phase images. Feature selection methods and logistic regression (LR) were used to construct clinical and radiomics models, and a combined model was developed using the Rad-score and significant clinical factors. Additionally, seven classifiers were used to construct the combined model and different folds LR was used to construct the combined model to evaluate its performance. Models were evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), and decision curve analysis (DCA). The Delong test compared ROC performance, with p < 0.050 considered significant. Multivariate analysis identified intra-tumoral vessels as an independent predictor of high-grade ccRCC. In the external validation set, the radiomics model (AUC = 0.834) outperformed the clinical model (AUC = 0.762), with the combined model achieving the highest AUC (0.855) and significantly outperforming the clinical model (p = 0.003). DCA showed that the combined model had a higher net benefit within the 0.04–0.54 risk threshold range than clinical model. Additionally, the combined model constructed using logistic regression has a higher priority compared to other classifiers. Additionally, 10-fold cross-validation with LR for the combined model showed consistent AUC values (0.849–0.856) across different folds. The radiomics models based on multi-sequence MRI might be a noninvasive and effective tool, demonstrating good efficacy in preoperatively predicting the WHO/ISUP grade of ccRCC.