Recently, Ultra-High Field MRI (UHF-MRI) has become more available and one of the best tools to study the brain. One common step in quantitative neuroimaging is the brain segmentation. However, the differences between UHF-MRI and 1.5-3T images are such that the automatic segmentation techniques optimized at these field strengths usually produce unsatisfactory segmentation results for UHF images. It has been particularly challenging to perform quantitative analyses as typically done with 1.5-3T data, considerably limiting the potential of UHF-MRI. Hence, we propose a novel Deep Learning (DL)-based segmentation technique called GOUHFI: Generalized and Optimized segmentation tool for Ultra-High Field Images, designed to segment UHF images of various contrasts and resolutions. For training, we used a total of 206 label maps from four datasets acquired at 3T, 7T and 9.4T. In contrast to most DL strategies, we used a previously proposed domain randomization approach, where synthetic images generated from the label maps were used for training a 3D U-Net. GOUHFI was tested on seven different datasets and compared to techniques like FastSurferVINN and CEREBRUM-7T. GOUHFI was able to the segment six contrasts and seven resolutions tested at 3T, 7T and 9.4T. Average Dice-Sorensen Similarity Coefficient (DSC) scores of 0.87, 0.84, 0.91 were computed against the ground truth segmentations at 3T, 7T and 9.4T. Moreover, GOUHFI demonstrated impressive resistance to the typical inhomogeneities observed at UHF-MRI, making it a new powerful segmentation tool that allows to apply the usual quantitative analysis pipelines also at UHF. Ultimately, GOUHFI is a promising new segmentation tool, being the first of its kind proposing a contrast- and resolution-agnostic alternative for UHF-MRI, making it the forthcoming alternative for neuroscientists working with UHF-MRI or even lower field strengths.

This study aimed to develop and validate a clinical-radiomics deep learning (DL) model based on MRI for endometrial cancer (EC) molecular subtypes classification. This multicenter retrospective study included EC patients undergoing surgery, MRI, and molecular pathology diagnosis across three institutions from January 2020 to March 2024. Patients were divided into training, internal, and external validation cohorts. A total of 386 handcrafted radiomics features were extracted from each MR sequence, and MoCo-v2 was employed for contrastive self-supervised learning to extract 2048 DL features per patient. Feature selection integrated selected features into 12 machine learning methods. Model performance was evaluated with the AUC. A total of 526 patients were included (mean age, 55.01 ± 11.07). The radiomics model and clinical model demonstrated comparable performance across the internal and external validation cohorts, with macro-average AUCs of 0.70 vs 0.69 and 0.70 vs 0.67 (p = 0.51), respectively. The radiomics DL model, compared to the radiomics model, improved AUCs for POLEmut (0.68 vs 0.79), NSMP (0.71 vs 0.74), and p53abn (0.76 vs 0.78) in the internal validation (p = 0.08). The clinical-radiomics DL Model outperformed both the clinical model and radiomics DL model (macro-average AUC = 0.79 vs 0.69 and 0.73, in the internal validation [p = 0.02], 0.74 vs 0.67 and 0.69 in the external validation [p = 0.04]). The clinical-radiomics DL model based on MRI effectively distinguished EC molecular subtypes and demonstrated strong potential, with robust validation across multiple centers. Future research should explore larger datasets to further uncover DL's potential. Our clinical-radiomics DL model based on MRI has the potential to distinguish EC molecular subtypes. This insight aids in guiding clinicians in tailoring individualized treatments for EC patients. Accurate classification of EC molecular subtypes is crucial for prognostic risk assessment. The clinical-radiomics DL model outperformed both the clinical model and the radiomics DL model. The MRI features exhibited better diagnostic performance for POLEmut and p53abn.

We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classification of high-grade glioma (HGG) and determined the optimal machine learning (ML) approach. This retrospective analysis included 184 patients (59 grade III lesions and 125 grade IV lesions). Radiomics features were extracted from MRI with T1-weighted imaging (T1WI). The least absolute shrinkage and selection operator (LASSO) feature selection method and seven classification methods including logistic regression, XGBoost, Decision Tree, Random Forest (RF), Adaboost, Gradient Boosting Decision Tree, and Stacking fusion model were used to differentiate HGG. Performance was compared on AUC, sensitivity, accuracy, precision and specificity. In the non-fusion models, the best performance was achieved by using the XGBoost classifier, and using SMOTE to deal with the data imbalance to improve the performance of all the classifiers. The Stacking fusion model performed the best, with an AUC = 0.95 (sensitivity of 0.84; accuracy of 0.85; F1 score of 0.85). MRI-based quantitative radiomics features have good performance in identifying the classification of HGG. The XGBoost method outperforms the classifiers in the non-fusion model and the Stacking fusion model outperforms the non-fusion model.

Glioblastoma (GBM) is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. We developed a highly reproducible, personalized prognostication, and clinical subgrouping system using machine learning (ML) on routine clinical data, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.

We conducted a systematic review and meta-analysis to evaluate the performance of magnetic resonance imaging (MRI)-derived deep learning (DL) models in predicting 1p/19q codeletion status in glioma patients. The literature search was performed in four databases: PubMed, Web of Science, Embase, and Scopus. We included the studies that evaluated the performance of end-to-end DL models in predicting the status of glioma 1p/19q codeletion. The quality of the included studies was assessed by the Quality assessment of diagnostic accuracy studies-2 (QUADAS-2) METhodological RadiomICs Score (METRICS). We calculated diagnostic pooled estimates and heterogeneity was evaluated using I². Subgroup analysis and sensitivity analysis were conducted to explore sources of heterogeneity. Publication bias was evaluated by Deeks' funnel plots. Twenty studies were included in the systematic review. Only two studies had a low quality. A meta-analysis of the ten studies demonstrated a pooled sensitivity of 0.77 (95% CI: 0.63-0.87), a specificity of 0.85 (95% CI: 0.74-0.92), a positive diagnostic likelihood ratio (DLR) of 5.34 (95% CI: 2.88-9.89), a negative DLR of 0.26 (95% CI: 0.16-0.45), a diagnostic odds ratio of 20.24 (95% CI: 8.19-50.02), and an area under the curve of 0.89 (95% CI: 0.86-0.91). The subgroup analysis identified a significant difference between groups depending on the segmentation method used. DL models can predict glioma 1p/19q codeletion status with high accuracy and may enhance non-invasive tumor characterization and aid in the selection of optimal therapeutic strategies.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogenous macromolecules. CEST contrast highly depends on radiofrequency irradiation B₁ level. Spatial inhomogeneity of B₁ field would bias CEST measurement. Conventional interpolation-based B₁ correction method required CEST dataset acquisition under multiple B₁ levels, substantially prolonging scan time. The recently proposed supervised deep learning approach reconstructed B₁ inhomogeneity corrected CEST effect at the identical B₁ as of the training data, hindering its generalization to other B₁ levels. In this study, we proposed a Conditional Variational Autoencoder (CVAE)-based generative model to generate B₁ inhomogeneity corrected Z spectra from single CEST acquisition. The model was trained from pixel-wise source-target paired Z spectra under multiple B₁ with target B₁ as a conditional variable. Numerical simulation and healthy human brain imaging at 5 T were respectively performed to evaluate the performance of proposed model in B₁ inhomogeneity corrected CEST MRI. Results showed that the generated B₁-corrected Z spectra agreed well with the reference averaged from regions with subtle B₁ inhomogeneity. Moreover, the performance of the proposed model in correcting B₁ inhomogeneity in APT CEST effect, as measured by both MTR_asym and [Formula: see text] at 3.5 ppm, were superior over conventional Z/contrast-B₁-interpolation and other deep learning methods, especially when target B₁ were not included in sampling or training dataset. In summary, the proposed model allows generalized B₁ inhomogeneity correction, benefiting quantitative CEST MRI in clinical routines.

Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks have also significantly enhanced the accuracy of vascular parameter estimation. This review provides an overview of key vascular MRF studies, emphasizing advancements in geometrical models for vascular simulations, novel sequences, and state-of-the-art reconstruction techniques incorporating machine learning and deep learning algorithms. Both pre-clinical and clinical applications are discussed. Based on these findings, we outline future directions and development areas that need to be addressed to facilitate their clinical translation. EVIDENCE LEVEL: N/A. TECHNICAL EFFICACY: Stage 1.

Local recurrence and distant metastasis were a common manifestation of locoregionally advanced nasopharyngeal carcinoma (LA-NPC) after neoadjuvant chemoradiotherapy (NACT). To validate the clinical value of MRI radiomic models based on deep learning for predicting the recurrence of LA-NPC patients. A total of 328 NPC patients from four hospitals were retrospectively included and divided into the training(n = 229) and validation (n = 99) cohorts randomly. Extracting 975 traditional radiomic features and 1000 deep radiomic features from contrast enhanced T1-weighted (T1WI + C) and T2-weighted (T2WI) sequences, respectively. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning classifiers were conducted to develop three models for LA-NPC prediction in training cohort, namely Model I: traditional radiomic features, Model II: combined the deep radiomic features with Model I, and Model III: combined Model II with clinical features. The predictive performance of these models were evaluated by receive operating characteristic (ROC) curve analysis, area under the curve (AUC), accuracy, sensitivity and specificity in both cohorts. The clinical characteristics in two cohorts showed no significant differences. Choosing 15 radiomic features and 6 deep radiomic features from T1WI + C. Choosing 9 radiomic features and 6 deep radiomic features from T2WI. In T2WI, the Model II based on Random forest (RF) (AUC = 0.87) performed best compared with other models in validation cohort. Traditional radiomic model combined with deep radiomic features shows excellent predictive performance. It could be used assist clinical doctors to predict curative effect for LA-NPC patients after NACT.

In this study, we attempted to identify the subtypes of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region. We performed principal component analysis independently on the volume, thickness, surface area, and mean curvature features and identified the top 10 features. Further, we applied k-means clustering on these top 10 features and validated the number of clusters using Elbow and Silhouette method. Our study identified two clusters in the dataset which significantly shows the existence of two subtypes in ASD. We identified the features such as volume of scaled lh_G_front middle, thickness of scaled rh_S_temporal transverse, area of scaled lh_S_temporal sup, and mean curvature of scaled lh_G_precentral as the significant features discriminating the two clusters with statistically significant p-value (p<0.05). Thus, our proposed method is effective for the identification of ASD subtypes and can also be useful for the screening of other similar neurological disorders.

Pediatric bipolar disorder (PBD) is a highly debilitating condition, characterized by alternating episodes of mania and depression, with intervening periods of remission. Limited information is available about the functional and structural abnormalities in PBD, particularly when comparing type I with type II subtypes. Resting-state brain activity and structural grey matter, assessed through MRI, may provide insight into the neurobiological biomarkers of this disorder. In this study, Resting state Regional Homogeneity (ReHo) and grey matter concentration (GMC) data of 58 PBD patients, and 21 healthy controls matched for age, gender, education and IQ, were analyzed in a data fusion unsupervised machine learning approach known as transposed Independent Vector Analysis. Two networks significantly differed between BPD and HC. The first network included fronto- medial regions, such as the medial and superior frontal gyrus, the cingulate, and displayed higher ReHo and GMC values in PBD compared to HC. The second network included temporo-posterior regions, as well as the insula, the caudate and the precuneus and displayed lower ReHo and GMC values in PBD compared to HC. Additionally, two networks differ between type-I vs type-II in PBD: an occipito-cerebellar network with increased ReHo and GMC in type-I compared to type-II, and a fronto-parietal network with decreased ReHo and GMC in type-I compared to type-II. Of note, the first network positively correlated with depression scores. These findings shed new light on the functional and structural abnormalities displayed by pediatric bipolar patients.