Regional Edema Research Articles

Radiomic Fingerprinting of the Peritumoral Edema in Brain Tumors

Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role in tumor behavior and progression. While most studies focus on the radiomic features of the tumor core, this work investigates whether peritumoral edema exhibits distinct radiomic fingerprints specific to glioma (GLI), meningioma (MEN), and metastasis (MET). By analyzing these patterns, we aim to deepen our understanding of the tumor microenvironment’s role in tumor development and progression. Methods: Radiomic features were extracted from peritumoral edema regions in T1-weighted (T1), post-gadolinium T1-weighted (T1-c), T2-weighted (T2), and T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) sequences. Three classification tasks using those features were then conducted: differentiating between Low-Grade Glioma (LGG) and High-Grade Glioma (HGG), distinguishing GLI from MET and MEN, and examining all four tumor types, i.e., LGG, HGG, MET, and MEN, to observe how tumor-specific signatures manifest in peritumoral edema. Model performance was assessed using balanced accuracy derived from 10-fold cross-validation. Results: The radiomic fingerprints specific to tumor types were more distinct in the peritumoral regions of T1-c images compared to other modalities. The best models, utilizing all features extracted from the peritumoral regions of T1-c images, achieved balanced accuracies of 0.86, 0.81, and 0.76 for the LGG-HGG, GLI-MET-MEN, and LGG-HGG-MET-MEN tasks, respectively. Conclusions: This study demonstrates that peritumoral edema, as characterized by radiomic features extracted from MRIs, contains fingerprints specific to tumor type, providing a non-invasive approach to understanding tumor-brain interactions. The results of this study hold the potential for predicting recurrence, distinguishing progression from pseudo-progression, and assessing treatment-induced changes, particularly in gliomas.

Standard b-value DWI-derived stiffness index analysis may provide a way to evaluate the development of intracerebral hematoma

Background and purposeThe development of intracerebral hemorrhage (ICH) is closely related to mechanical forces. However, noninvasively evaluating mechanical forces for ICH patients in the current clinical setting is challenging. In this study, we aimed to build an easily accessible stiffness index (STI) and evaluate the stiffness of the perihematomal edema (PHE) region in ICH patients.Materials and methodsIn this retrospective study, two cohorts of 57 patients were included. One cohort (the exploratory cohort) comprised patients with both standard b-value diffusion-weighted imaging (sDWI) (b-values of 0 and 1,000 s/mm2, b0 and b1000) and higher b-value diffusion-weighted imaging (hDWI) (b-values of 200 and 1,500 s/mm2). Another cohort (the hemorrhage cohort) consisted of patients who were diagnosed with ICH and who underwent sDWI within 48 h from onset. The hDWI-based virtual shear modulus (μdiff) was calculated and correlated with the sDWI data in the exploratory cohort. In the hemorrhage cohort, STI maps that were used to estimate μdiff were generated. The mean STI (mSTI) and coefficient of variation (COV) of the STI were computed on the basis of the STI maps in the whole and largest-slice PHE regions.ResultsThe STI could be calculated with the Equation 0.047697*S1000-0.022944*S0 + 5.359883, where S1000 and S0 represent the signal intensities of the b1000 and b0 images, respectively. In the whole and largest-slice PHE regions, both the mSTI and COV were correlated with the hematoma volume (p < 0.01), but neither were correlated with the time from onset.ConclusionThe standard b-value DWI-derived stiffness index analysis may provide a noninvasive and easily accessible way to evaluate the development of ICH.

Amyloid-related imaging abnormalities: manifestations, metrics and mechanisms.

Three monoclonal antibodies directed against specific forms of the amyloid-β (Aβ) peptide have been granted accelerated or traditional approval by the FDA as treatments for Alzheimer disease, representing the first step towards bringing disease-modifying treatments for this disease into clinical practice. Here, we review the detection, underlying pathophysiological mechanisms and clinical implications of amyloid-related imaging abnormalities (ARIA), the most impactful adverse effect of anti-Aβ immunotherapy. ARIA appears as regions of oedema or effusions (ARIA-E) in brain parenchyma or sulci or as haemorrhagic lesions (ARIA-H) in the form of cerebral microbleeds, convexity subarachnoid haemorrhage, cortical superficial siderosis or intracerebral haemorrhage. Analysis of the radiographic appearance of ARIA, its clinical risk factors and underlying neuropathology, and results from animal models point to a central role for cerebral amyloid angiopathy - a condition characterized by cerebrovascular Aβ deposits - as a key component, either as a direct target for antibody-mediated inflammation or as recipient of Aβ mobilized from plaques in the Alzheimer brain parenchyma. The great majority of ARIA occurrences are associated with mild or no clinical symptoms. However, ~5% of all ARIA events are severe enough to result in hospitalization, permanent disability or death and thus raise challenging clinical questions regarding patient selection and use of concomitant agents. Therefore, identifying novel approaches to predicting, modelling, preventing and treating ARIA remains a key step towards allowing safe use of anti-Aβ immunotherapy for the world's rapidly ageing population.

Segmentation of glioblastomas via 3D FusionNet.

This study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors. The MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD). When applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth. FusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance.

NIMG-62. SEX-DISTINCT TRANSCRIPTOMIC SIGNATURES UNDERLY MRI-DEFINED EDEMA PATTERNS IN HUMAN GLIOMAS

Abstract Magnetic resonance imaging (MRI) is key to clinically managing brain tumor patients, however connecting the biology to imaging remains challenging. We previously developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Using this model, we previously identified the fatty acid metabolism (FAM) and oxidative phosphorylation (OxPhos) pathways as sex-distinct, with both pathways amplified for high edema in males and low edema in females. The purpose of this project was to further delineate sex-distinct biology associated with MRI-estimated brain tumor edema abundance. We analyzed 179 multiregional samples (Female: 75; Male: 104) from 55 high grade glioma patients (Female: 21; Male: 34) for bulk RNA-Seq. Patients’ pre-surgical multiparametric MRIs were preprocessed and segmented for abnormal regions and normal tissue. Utilizing the segmentations and preprocessed images we estimated the relative fractions of extracellular and intracellular space based on the edema mathematical model. Samples were characterized by their edema scores, which were analyzed using differential expression for high and low edema, gene set enrichment analysis (GSEA) using MSigDB hallmarks, and leading edge interpretation. Based on transcriptomic leading edge analyses of high and low edema samples from sex-separated cohorts, we identified common and sex-distinct enrichment of the FAM and OxPhos pathways underlying edema patterns. Of the OxPhos pathway leading edge genes, 45 were common, 36 were unique to females, and 66 were unique to males. Of the FAM pathway leading edge genes, 8 were common, 39 were unique to females, and 29 were unique to males. Notably, expression of both IDH1 and IDH2 were increased for males in regions of high edema in the OxPhos pathway. IDH3a was decreased for females in regions of low edema in the OxPhos pathway. These data suggest that there may be sex-distinct metabolism underlying MRI measurable edema formation.

NIMG-79. A RADIOMIC-RISK SCORE FOR SURVIVAL PREDICTION IN GLIOMA: A LARGE MULTI-INSTITUTIONAL RETROSPECTIVE VALIDATION STUDY

Abstract Gliomas have dismal survival. Recently, radiomics has demonstrated success in developing non-invasive image-based biomarkers for survival prediction in gliomas. However, clinical applicability of radiomics-based biomarkers will ultimately require rigorous validation on large, multi-institutional data. This work attempts to evaluate a radiomic-based survival risk-assessment prognostic score on a multi-institutional cohort of Glioma patients. Our rationale is that optimizing and validating radiomic features that capture intensity-based textural variations and morphology-based changes (e.g., local curvature and global contour) on large multi-institutional cohorts can allow for robust risk assessment in Glioma. n=668 pre-operative T1w MRI scans for Glioma patients from 4 cohorts: University of California San Francisco “UCSF” (n=495), LUMIERE (n=86)), xCures (n=13), and Cleveland Clinic (CCF) (n=74) were analyzed. Subjects from UCSF, CCF, and xCures were used for training, whereas the LUMIERE cohort was used for hold-out testing. Following pre-processing, expert-vetted tumor segmentation into Edema (ED) and Enhancing tumor (ET) regions was performed. A total of n=1558 radiomic features were extracted from the tumor regions (779 per region), namely, 31 global contour, 20 curvature-based, 624 gradient co-occurrence statistics, and 104 Collage features. Cox regression model was employed to create a radiomic risk score (RRS) for every subject, which included radiomics features as well as on clinical and demographic variables (IDH, MGMT, sex, age), for survival analysis. RRS incorporated a total of 43 radiomic features, and the associated clinical and demographic variables (CDS), yielding significant differences between high and low risk groups in the training (RRS: p=0.043, HR = 2, CSD: p=0.0031, HR = 2.8) and testing sets (RRS: p=0.0096, HR = 1.8, CSD: p=0.0071, HR = 1.8). Combining RRS and CDS improved the OS prediction at group level (p=3.6e-6, 0.0011, HR=5.4, 2, for training and testing, respectively). Radiomics-based risk-stratification may be promising for reliable risk-stratification in glioma.

Glioma subtype prediction based on radiomics of tumor and peritumoral edema under automatic segmentation

Comprehensive and non-invasive preoperative molecular diagnosis is important for prognostic and therapy decision-making in adult-type diffuse gliomas. We employed a deep learning method for automatic segmentation of brain gliomas directly from conventional magnetic resonance imaging (MRI) scans of the tumor core and peritumoral edema regions based on available glioma MRI data provided in the BraTS2021. Three-dimensional volumes of interest were segmented from 424 cases of glioma imaging data retrospectively obtained from two medical centers using the segmentation method and radiomic features were extracted. We developed a subtype prediction model based on extracted radiomic features and analyzed significance and correlations between glioma morphological characteristics and pathological features using data from patients with adult-type diffuse glioma. The automated segmentation achieved mean Dice scores of 0.884 and 0.889 for the tumor core and whole tumor, respectively. The area under the receiver operating characteristic curve for the prediction of adult-type diffuse gliomas subtypes was 0.945. “Glioblastoma, IDH-wildtype”, “Astrocytoma, IDH-mutant”, and “Oligodendroglioma, IDH-mutant, 1p/19q-coded” showed AUCs of 0.96, 0.914, and 0.961, respectively, for subtype prediction. Glioma morphological characteristics, molecular and pathological levels, and clinical data showed significant differences and correlations. An automatic segmentation model for gliomas based on 3D U-Nets was developed, and the prediction model for gliomas built using the parameters obtained from the automatic segmentation model showed high overall performance.

Multi-parameter MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma

BackgroundPostoperative progressive cerebral edema and hemorrhage (PPCEH) are major complications after meningioma resection, yet their preoperative predictive studies are limited. The aim is to develop and validate a multiparametric MRI machine learning model to predict PPCEH after meningioma resection.MethodsThis retrospective study included 148 patients with meningioma. A stratified three-fold cross-validation was used to split the dataset into training and validation sets. Radiomics features from the tumor enhancement (TE) and peritumoral brain edema (PTBE) regions were extracted from T1WI, T2WI, and ADC maps. Support vector machine constructed different radiomics models, and logistic regression explored clinical risk factors. Prediction models, integrating clinical and radiomics features, were evaluated using the area under the curve (AUC), visualized in a nomogram.ResultsThe radiomics model based on TE and PTBE regions (training set mean AUC: 0.85 (95% CI: 0.78–0.93), validation set mean AUC: 0.77 (95%CI: 0.63–0.90)) outperformed the model with TE region solely (training set mean AUC: 0.83 (95% CI: 0.76–0.91), validation set mean AUC: 0.73 (95% CI: 0.58–0.87)). Furthermore, the combined model incorporating radiomics features, and clinical features of preoperative peritumoral edema and tumor boundary adhesion, had the best predictive performance, with AUC values of 0.87 (95% CI: 0.80–0.94) and 0.84 (95% CI: 0.72–0.95) for the training and validation set.ConclusionsWe developed a novel model based on clinical characteristics and multiparametric radiomics features derived from TE and PTBE regions, which can accurately and non-invasively predict PPCEH after meningioma resection. Additionally, our findings suggest the crucial role of PTBE radiomics features in understanding the potential mechanisms of PPCEH.

Turn-Adopting Peptidomimetic as a Formyl Peptide Receptor-1 Antagonist.

The design, synthesis, and characterization of a new peptidomimetic acting as a formyl peptide receptor (FPR1) antagonist (N-19004) are herein reported. The molecule has been identified with docking studies of the highly potent FPR1 antagonist UPARANT on human receptor. N-19004 recapitulates all pharmacophoric groups necessary for recognition into a minimal structure, with a crucial role of the 2,6-diamino-thiophenyl scaffold mimicking the positions of Cα atoms of Arg residues in the turned Arg-Aib-Arg segment of UPARANT. N-19004 demonstrated to interfere with the biological properties of FPR1 both in vitro and in vivo. In a mouse model of choroidal neovascularization, N-19004 markedly reduced the size of laser-induced choroidal lesions, with reabsorption of the edema regions by a systemic administration route.

Cerebral infarcts, edema, hypoperfusion, and vasospasm in preeclampsia and eclampsia

Eclampsia, a serious pregnancy complication, is associated with cerebral edema and infarctions but the underlying pathophysiology remains largely unexplored. To assess the pathophysiology of eclampsia using specialized magnetic resonance imaging that measures diffusion, perfusion, and vasospasm. This was a cross-sectional study recruiting consecutive pregnant women between April 2018 to November 2021 at Tygerberg Hospital, Cape Town, South Africa. We recruited women with eclampsia, preeclampsia, and normotensive pregnancies who underwent magnetic resonance imaging after birth. Main outcome measures were cerebral infarcts, edema, and perfusion using intravoxel incoherent motion imaging and vasospasm using magnetic resonance imaging angiography. The imaging protocol was established before inclusion. Forty-nine women with eclampsia, 20 with preeclampsia and 10 normotensive women were included. Cerebral infarcts were identified in 34% of eclamptic, 5% of preeclamptic (risk difference (RD) 0.29; 95% confidence interval (CI) 0.06 to 0.52, p=0.012) and in no normotensive controls. Eclamptic women were more likely to have vasogenic cerebral edema compared to preeclamptic (80% vs 20%, RD 0.60; CI 0.34 to 0.85, p<.001) and normotensive women (RD 0.80; CI 0.47 to 1.00, p<.001). Diffusion was increased in eclampsia in the parietooccipital white matter (mean difference (MD) 0.02 x10-3 mm2/s, CI 0.00 to 0.05, p=0.045) and the caudate nucleus (MD 0.02 x10-3 mm2/s, CI 0.00 to 0.04, p=0.033) when compared to preeclamptic women. Diffusion was also increased in eclamptic women in the frontal (MD 0.07 x10-3 mm2/s, CI 0.02 to 0.12, p=0.012) and parietooccipital white matter (MD 0.05 x10-3 mm2/s, CI 0.02 to 0.07, p=0.03) and the caudate nucleus (MD 0.04 x10-3 mm2/s, CI 0.00 to 0.07, p=0.028) when compared to normotensive women. Perfusion was decreased in edematous regions. Hypoperfusion was present in the caudate nucleus in eclampsia (MD -0.17 x10-3 mm2/s, CI -0.27 to -0.06, p=0.003) when compared to preeclampsia. There were no signs of hyperperfusion. Vasospasm was present in 18% of eclamptic, 6% of preeclamptic and none of the controls. Eclampsia is associated with cerebral infarcts, vasogenic cerebral edema, vasospasm and decreased perfusion, all not usually evident on standard clinical imaging. This may explain why some have cerebral symptoms and signs despite having normal conventional imaging.

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Evaluation of Femoral Head Avascular Necrosis With Virtual Noncalcium Dual-Energy Computed Tomography.

Our aim was to investigate the effectiveness of the dual-energy computed tomography (DECT) virtual noncalcium (VNCa) technique in avascular necrosis (AVN) for detecting bone marrow edema (BME) and staging. This prospective study included adult patients diagnosed with unilateral or bilateral femoral head AVN between January 2023 and December 2023, who had magnetic resonance imaging (MRI) and DECT. Two participants were excluded from the study due to undergoing surgical procedures during the period between the scans. Two reviewers, blinded to MRI images and clinical data, visually examined color-coded VNCa pictures to assess BME using a binary classification (0 = normal bone marrow, 1 = BME). Same 2 reviewers also used color-coded and nonmapped images to stage AVN in accordance to the "Association for Research on Osseous Circulation" (ARCO) staging system. Interobserver agreements for the visual evaluation and staging were calculated with κ coefficient. Following a visual assessment of BME and the staging of AVN, same 2 reviewers conducted CT density measurements on regions of BME regions utilizing DECT noncalcium images. An independent third investigator (reference standard) utilized MRI, x-ray, and clinical data to confirm the definitive diagnosis and staging of AVN. A P value less than 0.05 was considered statistically significant. Fifty patients (28 men, 22 women, mean age: 44.2 ± 13.1 years, range: 25-75 years) were included in the final analysis. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the VNCa technique in detecting BME were 96.0%, 94.4%, 97.9%, 89.4%, and 95.6%, respectively, for reviewer 1; and 96.0%, 88.9%, 96.0%, 88.9%, and 94.1%, respectively, for reviewer 2. Interobserver agreement was almost perfect ( κ = 0.84). Both reviewer 1 and reviewer 2 accurately classified 92.7% of the AVNs. The density measurements showed a statistically significant difference ( P = 0.001) between the edema regions and the normal marrow regions. No statistically significant difference was observed in the density measurements of edema regions at different stages ( P = 0.13). DECT VNCa technique exhibits excellent performance in detecting BME in hip AVN cases, as well as accurately determining the stage of AVN.

Deep learning models for rapid discrimination of high-grade gliomas from solitary brain metastases using multi-plane T1-weighted contrast-enhanced (T1CE) images.

High-grade gliomas (HGG) and solitary brain metastases (SBM) are two common types of brain tumors in middle-aged and elderly patients. HGG and SBM display a high degree of similarity on magnetic resonance imaging (MRI) images. Consequently, differential diagnosis using preoperative MRI remains challenging. This study developed deep learning models that used pre-operative T1-weighted contrast-enhanced (T1CE) MRI images to differentiate between HGG and SBM before surgery. By comparing various convolutional neural network models using T1CE image data from The First Medical Center of the Chinese PLA General Hospital and The Second People's Hospital of Yibin (Data collection for this study spanned from January 2016 to December 2023), it was confirmed that the GoogLeNet model exhibited the highest discriminative performance. Additionally, we evaluated the individual impact of the tumoral core and peritumoral edema regions on the network's predictive performance. Finally, we adopted a slice-based voting method to assess the accuracy of the validation dataset and evaluated patient prediction performance on an additional test dataset. The GoogLeNet model, in a five-fold cross-validation using multi-plane T1CE slices (axial, coronal, and sagittal) from 180 patients, achieved an average patient accuracy of 92.78%, a sensitivity of 95.56%, and a specificity of 90.00%. Moreover, on an external test set of 29 patients, the model achieved an accuracy of 89.66%, a sensitivity of 90.91%, and a specificity of 83.33%, with an area under the curve of 0.939 [95% confidence interval (CI): 0.842-1.000]. GoogLeNet performed better than previous methods at differentiating HGG from SBM, even for core and peritumoral edema in both. HGG and SBM could be fast screened using this end-to-end approach, improving workflow for both tumor treatments.

IMG-13. PREDICTION OF PEDIATRIC MEDULLOBLASTOMA SUBGROUPS USING CLINICO-RADIOMIC ANALYSIS

Abstract BACKGROUND Pediatric medulloblastoma is an aggressive brain tumor and tailored treatment has the potential to lead to better patient outcomes. The WHO 2021 classification of medulloblastoma involves an integrated diagnosis that incorporates genetically defined characteristics, including molecular subgroups (WNT-activated, SHH-activated TP53 wildtype, SHH-activated TP53-mutant, and non-WNT/non-SHH). Clinically acquired radiology (MRI) imaging characteristics could provide a non-invasive, pre-treatment biomarker for such subgroups faster than standard methylation-based turn-around times, and for use in countries where methylation is not available. METHODS Herein, we utilize a multi-institutional dataset of multi-parametric, clinical MRIs of pediatric medulloblastoma patients from the Children’s Brain Tumor Network (median age = 8.9 years) to assess the predictive value of patient-level radiomic and clinical factors. 89 subjects with treatment-naïve T1w/T1w contrast-enhanced/T2w/FLAIR images and molecular subgroups (derived from methylation profiling or RNA sequencing) were included (SHH=18, WNT=10, non-SHH/WNT=61). Radiomic features were extracted from a radiologist-defined volumetric (3D) segmentation for each subject separately (including solid tumor, cystic, and peritumoral edema regions). Clinical variables included sex, age at diagnosis, and metastatic disease. 20 top performing features were selected based on an ANOVA between features/classes and subsequently used to train and evaluate three separate classification models based on radiomic, clinical, or clinico-radiomic features (Linear SVM; leave-one-subject-out cross-validation). RESULTS The combined clinico-radiomic model had the top performance (AUC=0.83) followed by radiomic (0.78) and clinical (0.5) for predicting SHH/WNT vs. non-SHH/WNT groups. Predictive radiomic features included intensity-based statistics (T1w, T2w, T1w-CE), texture (T2w), and morphological shape characteristics. CONCLUSIONS Our findings show potential for early prediction of molecular subgroups using baseline imaging that could lend to faster decision-making in patient treatment planning. Future work aims to evaluate the inclusion of histological characteristics for a full integrated diagnostic approach as well as assessment of prognostic value of the combined multi-omic features.

Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.

To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. Asupport vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). Radiomics analysis of quantitative maps from synthetic MRI provides anew quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.

A Clinical Fusion Model Based on Radiomics Features and Deep Learning for Predicting CDKN2A/B Homozygous Deletion Status in IDH-mutant Diffuse Astrocytoma

Purpose: To construct a fusion model for predicting CDKN2A/B homozygous deletion status in patients with isocitrate dehydrogenase (IDH)-mutant diffuse astrocytoma by combining the radiomics features and deep learning (DL). Methods: A total of 200 IDH-mutant astrocytoma (103 CDKN2A/B homozygous deletion (HD) and 97 CDKN2A/B non-homozygous deletion (NHD)) patients were retrospectively enrolled in the training cohort (n = 140) and the external test cohort (n = 60) for the prediction of CDKN2A/B homozygous deletion status in patients with IDH-mutant astrocytoma. DL model was constructed by SE-Net model, radiomics features of different regions (edema, tumor and overall lesion) were extracted using Pyradiomics, and radiomics model was built by selecting 4 features in the edema region and 7 features in the tumor region by the least absolute shrinkage and selection operator (LASSO). Finally, a fusion model was jointly constructed by the DL model, radiomics model, and clinical features. The predictive performance of the 3 models was evaluated using calibration curves and decision curves, and compared with the fusion model. Results: Based on the results of the different models, we finally selected a fusion model consisting of DL model, radiomics model, and clinical features. The fusion model showed the best performance with an area under the curve (AUC) of 0.958 in the training cohort and 0.914 in the test cohort. Conclusions: The clinical fusion model based on radiomics features and DL features showed good performance in predicting CDKN2A/B homozygous deletion status in patients with IDH-mutant diffuse astrocytoma. Key Points: 1) Used DL and radiomics to non-invasively predict the CDKN2A/B homozygous deletion status. 2) The model can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma patients. 3) Our result improved classification accuracy and demonstrated better performance in the fusion model.

Two-Stage Training Framework Using Multicontrast MRI Radiomics for IDH Mutation Status Prediction in Glioma.

Purpose To develop a radiomics framework for preoperative MRI-based prediction of isocitrate dehydrogenase (IDH) mutation status, a crucial glioma prognostic indicator. Materials and Methods Radiomics features (shape, first-order statistics, and texture) were extracted from the whole tumor or the combination of nonenhancing, necrosis, and edema regions. Segmentation masks were obtained via the federated tumor segmentation tool or the original data source. Boruta, a wrapper-based feature selection algorithm, identified relevant features. Addressing the imbalance between mutated and wild-type cases, multiple prediction models were trained on balanced data subsets using random forest or XGBoost and assembled to build the final classifier. The framework was evaluated using retrospective MRI scans from three public datasets (The Cancer Imaging Archive [TCIA, 227 patients], the University of California San Francisco Preoperative Diffuse Glioma MRI dataset [UCSF, 495 patients], and the Erasmus Glioma Database [EGD, 456 patients]) and internal datasets collected from the University of Texas Southwestern Medical Center (UTSW, 356 patients), New York University (NYU, 136 patients), and University of Wisconsin-Madison (UWM, 174 patients). TCIA and UTSW served as separate training sets, while the remaining data constituted the test set (1617 or 1488 testing cases, respectively). Results The best performing models trained on the TCIA dataset achieved area under the receiver operating characteristic curve (AUC) values of 0.89 for UTSW, 0.86 for NYU, 0.93 for UWM, 0.94 for UCSF, and 0.88 for EGD test sets. The best performing models trained on the UTSW dataset achieved slightly higher AUCs: 0.92 for TCIA, 0.88 for NYU, 0.96 for UWM, 0.93 for UCSF, and 0.90 for EGD. Conclusion This MRI radiomics-based framework shows promise for accurate preoperative prediction of IDH mutation status in patients with glioma. Keywords: Glioma, Isocitrate Dehydrogenase Mutation, IDH Mutation, Radiomics, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Moassefi and Erickson in this issue.

Intratumoral and Peritumoral Edema Radiomics Based on Fat-Suppressed T2- Weighted Imaging for Preoperative Prediction of Triple-Negative Breast Cancer.

Our aim was to explore the feasibility of using radiomics data derived from intratumoral and peritumoral edema on fat-suppressed T2-weighted imaging (T2 FS) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (non-TNBC). This retrospective study enrolled 174 breast cancer patients. According to the MRI examination time, patients before 2021 were divided into training (n = 119) or internal test (n = 30) cohorts at a ratio of 8:2. Patients from 2022 were included in the external test cohort (n = 25). Four regions of interest for each lesion were defined: intratumoral regions, peritumoral edema regions, regions with a combination of intratumoral and peritumoral edema, and regions with a combination of intratumoral and 5-mm peritumoral. Four radiomic signatures were built using the least absolute shrinkage and selection operator (LASSO) method after selecting features. Furthermore, a radio mic-radiological model was constructed using a combination of intratumoral and peritumoral edema regions along with clinical-radiologic features. Area under the receiver operating characteristic curve (AUC) calculations, decision curve analysis, and calibration curve analysis were performed to assess the performance of each model. The radiomic-radiological model showed the highest AUC values of 0.906 (0.788-1.000) and 82.5 (0.622-0.947) in both the internal and external test sets, respectively. The radiology-radiomic model exhibited excellent predictive performance, as evidenced by the calibration curves and decision curve analysis. The ensemble model based on T2 FS-based radiomic features of intratumoral and peritumoral edema, along with radiological factors, performed better in distinguishing TNBC from non-TNBC than a single model. We explored the possibility of developing explainable models to support the clinical decision-making process.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.

Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade. A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice. The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort. This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Sex-Specific Differences in Patients with IDH1-Wild-Type Grade 4 Glioma in the ReSPOND Consortium.

Understanding sex-based differences in patients with glioblastoma is necessary for accurate personalized treatment planning to improve patient outcomes. Our purpose was to investigate sex-specific differences in molecular, clinical, and radiologic tumor parameters, as well as survival outcomes in patients with glioblastoma, isocitrate dehydrogenase-1 wild-type (IDH1-WT), grade 4. Retrospective data of 1832 patients with glioblastoma, IDH1-WT with comprehensive information on tumor parameters was acquired from the Radiomics Signatures for Precision Oncology in Glioblastoma consortium. Data imputation was performed for missing values. Sex-based differences in tumor parameters, such as age, molecular parameters, preoperative Karnofsky performance score (KPS), tumor volumes, epicenter, and laterality were assessed through nonparametric tests. Spatial atlases were generated by using preoperative MRI maps to visualize tumor characteristics. Survival time analysis was performed through log-rank tests and Cox proportional hazard analyses. Glioblastoma was diagnosed at a median age of 64 years in women compared with 61.9 years in men (false discovery rate [FDR] = 0.003). Men had a higher KPS (above 80) as compared with women (60.4% women versus 69.7% men, FDR = 0.044). Women had lower tumor volumes in enhancing (16.7 cm3 versus 20.6 cm3 in men, FDR = 0.001), necrotic core (6.18 cm3 versus 7.76 cm3 in men, FDR = 0.001), and edema regions (46.9 cm3 versus 59.2 cm3 in men, FDR = 0.0001). The right temporal region was the most common tumor epicenter in the overall population. Right as well as left temporal lobes were more frequently involved in men. There were no sex-specific differences in survival outcomes and mortality ratios. Higher age, unmethylated O6-methylguanine-DNA-methyltransferase promoter and undergoing subtotal resection increased the mortality risk in both men and women. Our study demonstrates significant sex-based differences in clinical and radiologic tumor parameters of patients with glioblastoma. Sex is not an independent prognostic factor for survival outcomes and the tumor parameters influencing patient outcomes are identical for men and women.