Imaging Biomarkers Research Articles

Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles

Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia’s underlying heterogeneity, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for clustering. In this study, we seek to overcome such limitations, using a data-driven unsupervised clustering algorithm named SillyPutty, in combination with hierarchical clustering on cognitive assessment scores to estimate subtypes within a real-world clinical dementia cohort. We use a longitudinal patient data set for our clustering analysis, instead of relying only on baseline visits, allowing us to explore the ongoing temporal relationship between subtypes and disease progression over time. Results showed that subtypes with very mild or mild dementia were more heterogenous in their cognitive profiles and risk of disease progression.

Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward

Abstract Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Toward this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumor visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumors that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep-learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions and prognostic evaluations for TNBC.

Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Features Can Predict Immune-Excluded Phenotype of Hepatocellular Carcinoma

Introduction: Immunotherapy is the first-line treatment for intermediate-advanced stage hepatocellular carcinoma (HCC), although its outcomes vary. This study aimed to identify imaging biomarkers of immunotherapy susceptibility linked to gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) and immune phenotypes, particularly immune-excluded phenotypes, with a tumor immune barrier. Methods: We performed immunohistochemical staining with a CD8+ antibody, and samples were classified into immune-inflamed, -intermediate, -excluded, and -ignored phenotypes. We assessed EOB-MRI findings obtained from 104 patients who underwent hepatectomy for HCC and evaluated the relationship between MRI findings and immune phenotype. Spatial transcriptome analysis of tumor tissues in each immune phenotype was performed to characterize the MRI findings. For validation, we analyzed the treatment effect on 60 nodules in another cohort of 27 patients who received combined immunotherapy using anti-programmed death-ligand 1 and anti-vascular endothelial growth factor (VEGF) antibodies. Results: HCCs with rim arterial phase hyperenhancement (APHE) (odds ratio [OR] 17.3 P=0.009), peritumoral enhancement on the arterial phase (OR 8.6, P<0.004), and intermediate intensity on the hepatobiliary phase (HBP) measured with a visual 3-point scale (OR 28.2, P=0.002) were associated with immune-excluded phenotype, where tumors tended to be larger and of the single nodular type with extranodular growth and confluent multinodular rather than the simple nodular type. Spatial transcriptome analysis revealed a spatial relationship among cytotoxic T lymphocytes, VEGF signals, and cancer-associated fibroblasts at the tumor-invasive margins in this phenotype. From the validation study, nodules with any one of these three imaging findings had a significantly prolonged time to-nodular progression (P=0.007, median not reached vs. 226 days). Conclusion: HCCs with rim APHE, peritumoral enhancement on arterial phase, and intermediate intensity on HBP with visual 3-point scale could be non-invasive biomarkers to predict the immune-excluded phenotype with tumor immune barrier. These HCCs were most likely to respond to the combined immunotherapy.

A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy

Abstract Objective To identify image biomarkers associated with overall life expectancy from low-dose computed tomography and integrate them as an index for assessing an individual’s health. Methods Two categories of CT image features, body composition tissues and cardiopulmonary vasculature characteristics, were quantified from LDCT scans in the Pittsburgh Lung Screening Study cohort(n = 3,635). Cox proportional-hazards models identified significant image features which were integrated with subject demographics to predict the subject’s overall hazard. Subjects were stratified using composite model predictions and feature-specific risk stratification thresholds. The model’s performance was validated extensively, including 5-fold cross-validation on PLuSS baseline, PLuSS follow-up examinations, and the National Lung Screening Trial (NLST). Results The composite model had significantly improved prognostic ability compared to the baseline model (p < 0.01) with AUCs of 0.774 (95% CI: 0.757-0.792) on PLuSS, 0.723 (95% CI: 0.703-0.744) on PLuSS follow-up, and 0.681 (95% CI: 0.651-0.710) on the NLST cohort. The identified high-risk stratum were several times more likely to die, with mortality rates of 79.34% on PLuSS, 76.47% on PLuSS follow-up, and 46.74% on NLST. Two cardiopulmonary structures (intrapulmonary artery vein ratio, intrapulmonary vein density) and two body composition tissues (SM density, bone density) identified high-risk patients. Conclusions Body composition and pulmonary vasculatures are predictive of an individual’s health risk; their integrations with subject demographics facilitate the assessment of an individual’s overall health status or susceptibility to disease. Advances in knowledge CT-computed body composition and vasculature biomarkers provide improved prognostic value. The integration of CT biomarkers and patient demographic information improves subject risk stratification.

Quantitative imaging biomarkers in the assessment of adrenal nodules

Quantitative imaging biomarkers in the assessment of adrenal nodules

Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma After Chemoradiotherapy using Planning CT-based Radiomics Model

Abstract Objectives Approximately 30% of non-metastatic anal squamous cell carcinoma (ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and currently available clinical variables are poor predictors of treatment response. We aimed to develop a model leveraging information extracted from radiation pretreatment planning CT to predict recurrence-free survival (RFS) in ASCC patients after CRT. Methods Radiomics features were extracted from planning CT images of 96 ASCC patients. Following pre-feature selection, the optimal feature set was selected via step-forward feature selection with a multivariate Cox proportional hazard model. The RFS prediction was generated from a radiomics-clinical combined model based on an optimal feature set with five repeats of nested five-fold cross validation. The risk stratification ability of the proposed model was evaluated with Kaplan-Meier analysis. Results Shape- and texture-based radiomics features significantly predicted RFS. Compared to a clinical-only model, radiomics-clinical combined model achieves better performance in the testing cohort with higher C-index (0.80 vs 0.73) and AUC (0.84 vs 0.78 for 1-year RFS, 0.84 vs 0.79 for 2-year RFS, and 0.85 vs 0.81 for 3-year RFS), leading to distinctive high- and low-risk of recurrence groups (p < 0.001). Conclusions A treatment planning CT based radiomics and clinical combined model had improved prognostic performance in predicting RFS for ASCC patients treated with CRT as compared to a model using clinical features only. Advances in knowledge The use of radiomics from planning CT is promising in assisting in personalized management in ASCC. The study outcomes support the role of planning CT-based radiomics as potential imaging biomarker.

Abstract 4136136: Left Ventricular Global Longitudinal Strain: An Imaging Marker Associated with Improved Survival in Paradoxical Low-Flow, Low-Gradient Severe Aortic Stenosis

Background: The optimal clinical management and timing of intervention are less well defined in paradoxical low-flow, low-gradient severe aortic stenosis (PLFLG AS). Left ventricular global longitudinal strain (LV-GLS) has been shown to predict outcomes in high flow severe AS, but there is lack of data in patients with PLFLG AS. Given the exaggerated LV hypertrophy and remodeling pattern in PLFLG AS, LV-GLS may be a mechanistic imaging marker for worse outcomes. Hypothesis: In patients with PLFLG AS, LV-GLS is associated with adverse clinical outcomes by detecting subclinical myocardial fibrosis resulting from myocardial remodeling due to LV pressure overload. Methods: We examined patients with PLFLG AS defined as AVA <1cm 2 , mean gradient <40mmHg and preserved left ventricular ejection fraction (LVEF) ≥50%, with low-flow state defined as transvalvular flow rate (Q) ≤210 ml/sec. Exclusion criteria included moderate or greater mitral or aortic regurgitation and presence of atrial fibrillation at the time of echocardiogram. LV strain analysis was performed using 2D-strain imaging software (TomTec, Chicago, IL). Clinical outcome data were obtained via the electronic health record. The primary outcome was defined as all-cause mortality. Patients were stratified by LV-GLS above and below the median value. Results: A total of 198 patients were included in the analysis, with a median LV-GLS of -12.3% [IQR: (-9.8,-16.3)]. Over a median follow-up time of 1.2 years, 130 deaths (65.6%) were identified. Patients with less negative LV-GLS (n=101) had worse survival than those with more negative LV-GLS (n=97; 22 vs 38%; p=0.02) (Fig 1). After multivariable adjustment for potential confounders (stroke volume index, aortic valve mean gradient, relative wall thickness, age, sex, heart failure, hypertension, coronary artery disease, and diabetes), less negative LV-GLS was independently associated with adverse outcomes (HR 1.56; 95% CI (1.03-2.38); p=0.04). Conclusions: In patients with PLFLG AS as defined by a transvalvular flow rate (Q) ≤210 ml/sec, reduced LV-GLS is associated with increased all-cause mortality. LV-GLS may serve as an imaging marker to help guide clinical decision making in PLFLG AS.

Anion and Substituents Effect on Spectral‐Kinetic and Biological Characteristics of Spiropyran Salts

Spiropyran salts containing a cationic vinyl‐3H‐indolium moiety are characterized by NIR absorption and fluorescence of their merocyanine forms. This feature makes them promising fluorescent probes and markers for bioimaging. The article focuses on the synthesis and study of the spectral, kinetic and toxic characteristics of such compounds with respect to various substituents in different moieties and the type of anion. A detailed analysis of the acquired data made it possible to draw some important conclusions regarding the influence of structural factors, which will be very useful for the further rational design of such derivatives. In particular, it was shown that the counterion has minimal effect on the spectral and kinetic characteristics of the dyes but dramatically affects the toxicity of the compounds. Following selection of the most appropriate compounds, bioimaging experiments were carried out to visualize planktonic bacteria and bacterial biofilms of E. coli and A. calcoaceticus. The ability to visualize biofilms is critical for the diagnosis of chronic diseases. By the results of molecular docking a theoretical interaction pattern between spiropyran molecules and DNA was proposed.

Abstract 4141448: Evaluation of cardiac amyloidosis with cardiac MRI and association with endomyocardial biopsy findings

Introduction: Late gadolinium enhancement (LGE) observed on cardiac magnetic resonance imaging (CMR) has emerged as a potential indicator of myocardial involvement in cardiac amyloidosis (CA). However, discerning which LGE patterns in amyloidosis distinguish it from other cardiomyopathies remains unclear. This study aims to compare LGE characteristics in a predominantly African American cohort who underwent endomyocardial biopsies. Despite advancements in cardiac imaging techniques, the endomyocardial biopsy remains the gold standard for amyloidosis diagnosis. Insights garnered from this analysis could enhance diagnostic precision and facilitate earlier detection. Methods: In this retrospective study at MedStar Union Memorial Hospital in Baltimore, MD, patients who underwent both endomyocardial biopsy and CMR were included. The study compared individuals with biopsy-proven amyloidosis (both AL and ATTR) to those with negative biopsy results. LGE patterns were selected based on their frequent use in CMR interpretation. Statistical analysis employed Pearson chi-square and t-tests. Results: The cohort comprised 42 patients who underwent biopsy, with 13 patients (31%) with positive biopsy results. Among them, 24 were men (57%), and 81% identified as African American. Demographic, anthropomorphic, and objective clinical findings are detailed in Table 1. The average age of the cohort was 68 years, with those with biopsy-proven amyloidosis significantly older (75 vs. 61 years, p = 0.002). Although the biopsy-positive group exhibited a lower absolute NT-pro-BNP, the difference was not statistically significant. The most prevalent LGE patterns among the biopsy-positive group were diffuse (62%) and basal (31%). Notably, 69% of biopsy-positive patients exhibited an LGE pattern described as infiltrative. However, 38% of the biopsy-negative group also manifested infiltrative patterns on LGE, a difference not statistically significant compared to the biopsy-positive group. The positive predictive value of an infiltrative LGE pattern was low (45%). Conclusion: This study compares CMR LGE patterns in biopsy proven CA. Despite findings of an infiltrative LGE pattern on CMR, when using endomyocardial biopsy as the gold standard, the positive predictive value of LGE in differentiating CA patients was low. Further research and larger studies are warranted to explore additional imaging modalities or biomarkers that may complement or refine the diagnostic algorithm for CA.

Challenges in a Biological Definition of Alzheimer Disease.

It has been suggested that the diagnostic landscape of Alzheimer disease (AD) is undergoing a profound transformation, marked by a shift toward a biomarker-based approach, as proposed by the Revised Criteria for Diagnosis and Staging of Alzheimer's Disease. These criteria advocate for diagnosing AD solely on biomarkers, without requiring clinical symptoms. This article explores the drivers behind this transition, primarily influenced by the Food and Drug Administration's approval of amyloid-lowering treatments. We evaluate the proposed criteria, which allow for an AD diagnosis based on amyloid "A" or phosphorylated tau "T1" positivity through surrogate amyloid PET imaging, CSF, or plasma biomarkers, and consider the arguments for and against their use. The merits of the new criteria include a clearer definition of AD, which is currently used interchangeably to refer to both the presence of neuropathology and the clinical syndrome. We argue that a purely biological definition risks a category error and emphasize the need for longitudinal data to establish the lifetime risk of dementia in amyloid-positive and tau-positive individuals. We also caution against limiting the scope of biomarker-based AD diagnosis to amyloid and tau alone. In conclusion, we recommend that the criteria remain within the research domain for the present while advocating for the considered adoption of plasma biomarkers in clinical practice.

Abstract 4136971: Prognostic Value of Late Gadolinium Enhancement to Predict Non-Sustained Ventricular Tachycardia in Patients with Hypertrophic Cardiomyopathy: A Systematic Review and Meta-Analysis

Background: Non-sustained ventricular tachycardia (NSVT) has been shown to be independently associated with sudden cardiac death (SCD) among patients with hypertrophic cardiomyopathy (HCM). There is limited evidence regarding the efficacy of late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging to predict the incidence of NSVT. Aims: To address this gap in knowledge, our meta-analysis aims to comprehensively evaluate the prognostic value of LGE on cardiac magnetic resonance in predicting the incidence of NSVT in HCM. Methods: PubMed, Embase and Cochrane CENTRAL databses were queried from inception until May 2024 for all studies assessing the prognostic value of LGE on CMR in patients with HCM and reported the incidence of NSVT. Data were combined using a random-effects model meta-analysis to determine the pooled sensitivity, specificity and accuracy of LGE in predicting NSVT in patients with HCM. The association between the LGE extent on CMR and NSVT incidence was also assessed, and weighted mean differences (WMDs) were reported with 95% condidence intervals (CIs). Heterogeneity across the studies was evaluated using the Higgins I 2 statistic. Results: A total of twenty studies were included in our analysis. The pooled senstivity, specificity, and accuracy of LGE in predicting the incidence of NSVT among HCM patients was 91.33%, 37.45%, and 52.86%, respectively. Moreover, we saw a significantly greater extent of LGE (5.95%, CI: 3.08-8.81, P<0.0001) in patients who had NSVT, as compared to patients without NSVT ( Figure 1). Conclusion: Our meta-analysis underscores the significant prognostic value of LGE as a risk stratification tool on CMR imaging in predicting the incidence of NSVT in patients with HCM, by demonstrating a high sensitivity, and a notable association between the LGE extent and NSVT incidence. Nevertheless, the low to moderate Ispecificity and accuracy underscores the need for additional imaging biomarkers for risk stratification.

A three-classification model for identifying migraine with right-to-left shunt using lateralization of functional connectivity and brain network topology: a resting-state fMRI study

IntroductionRight-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.MethodsFirstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.ResultsOur experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.DiscussionThe results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

Abstract 4143225: Carotid Artery Ultrasound Grayscale and Incident Dementia: The Multi-Ethnic Study of Atherosclerosis (MESA)

Introduction: Vascular contributions to cognitive impairment and dementia are potentially modifiable. Early detection of reversible arterial injury may improve dementia risk stratification and provide an opportunity for treatment monitoring. We hypothesized that carotid ultrasound grayscale-median (GSM), a novel imaging biomarker of early arterial injury, would predict incident all-cause dementia in the Multi-Ethnic Study of Atherosclerosis (MESA). Methods: The MESA enrolled adults aged 45-84 years old who were free of atherosclerotic cardiovascular disease at baseline. Common carotid artery GSM (grayscale units) was measured at baseline. Incident all-cause dementia events were identified by hospital and death records. Adjusted Cox proportional hazards models with natural cubic splines allowing for non-linear effects investigated the associations of baseline GSM and incident all-cause dementia. Results: The 1,788 participants were a mean (standard deviation) 63.1 (10.3) years old (53% female). Over a median of 13.7 years, 157 all-cause dementia events occurred. Lower (worse) carotid GSM independently predicted incident all-cause dementia (Hazard Ratio [HR] 1st to 3rd tertile, 1.39 [95% Confidence Intervals, 1.08-1.80], p =0.04). In models adjusting additionally for carotid IMT there was no attenuation of the association of GSM and incident all-cause dementia (HR 1.63 [95% CI 1.13-2.35], p=0.033) (Figure 1). Conclusions: Lower GSM predicts incident all-cause dementia independent of carotid intima-media thickness, suggesting it may serve as an early marker of dementia risk.

Abstract 4122134: Relationships between aortic valve area planimetry on four-dimensional cardiac computed tomography and survival following transcatheter aortic valve replacement

Background: Aortic valve area planimetry (AVA CT ) is feasible on four-dimensional computed tomography (4D-CT) for grading aortic stenosis severity, with superior spatial but inferior temporal resolution to echocardiography, however its prognostic utility is not well-established. We evaluated the prognostic value of AVA CT in patients with significant aortic valve stenosis undergoing transcatheter aortic valve replacement (TAVR). Methods: We studied 1035 native aortic valve stenosis patients (age 79.0±9.0 years, 429 (41.5%) women) undergoing TAVR during 2019-2020. AVA CT and indexed to body surface area (AVAi CT ) were retrospectively measured on contrast-enhanced 4D-CT at peak systole, and standard statistical analyses performed to evaluate its associations with all-cause mortality during follow-up. Results: Mean AVA CT and AVAi CT were 1.02±0.21 cm 2 and 0.53±0.12 cm 2 /m 2 respectively, with 240 (23.2%) deaths during mean follow-up of 2.1±0.9 years. Areas under curves and optimal thresholds for identifying severe aortic stenosis (defined as AVA<1.0 cm 2 on echocardiography) were 0.78 and <1.10 cm 2 for AVA CT and 0.83 and <0.65 cm 2 /m 2 for AVAi CT . AVA CT and AVAi CT below these thresholds representing severe aortic stenosis were independently associated all-cause mortality during follow-up, with hazards ratios (95% confidence intervals) of 1.84 (1.34-2.53) and 2.38 (1.48-3.81) respectively in multivariable analyses. The Figure illustrates Kaplan-Meier survival curves for the cohort based on A) AVA CT and B) AVAi CT thresholds. Conclusion: Optimal thresholds of AVA CT (<1.10 cm 2 ) and AVAi CT (<0.65 cm 2 /m 2 ) were independently associated with worse survival in patients undergoing TAVR, indicating that AVA CT and AVAi CT as potentially useful additional imaging biomarkers in the diagnostic and prognostic evaluation of aortic stenosis patients.

Abstract 4141497: Left ventricular diastolic function by echocardiography is related to extracellular volume and is an independent predictor of mortality in patients with aortic regurgitation

Background: Cardiac catheterization along with cardiac biopsy studies have shown the presence of abnormally increased left ventricular (LV) chamber stiffness in patients with severe aortic regurgitation (AR) and its significant correlation with interstitial fibrosis. LV diastolic dysfunction (DD) is associated with increased mortality in several patient populations. However, there is a paucity of data on the association of DD with mortality in patients with AR. Hypotheses: 1 - LV DD is related to extracellular volume (ECV), imaging marker of interstitial fibrosis, by cardiac magnetic resonance imaging (CMR) 2 - LV DD by echocardiography is associated with mortality in patients with AR Methods: We studied patients with moderate or greater AR who underwent echocardiographic and CMR imaging within 12 months. We excluded patients with concomitant valve disease, prior valve intervention, cardiomyopathy deemed unrelated to AR, congenital heart disease and terminal conditions. The primary outcome was all-cause death. CMR was used to obtain LV volumes, EF and mass, AR severity, scar extent by delayed hyperenhancement, and ECV. DD was assessed by comprehensive echocardiography. Results: The study included 323 patients. Mean age was 61.9 ± 16.0 years and 22% were female. Median follow-up was 3.8 years, during which 65 patients died and 172 had aortic valve replacement. Median LV EF was 56 (47-65) %, AR volume on CMR was 40 (30-61) mL, and AR fraction was 40 (33-47) %. ECV progressively increased with higher grades of LV DD (Figure 1). Table 1 shows the univariate associations with mortality in the study. On multivariable analysis based on the Lasso variable selection process, Euroscore, Charlson comorbidity index, DD grade, and RAP were associated with total mortality. Conclusion: ECV by CMR progressively increases with higher grades of DD by echocardiography, and DD is independently associated with mortality in patients with AR.

NIMG-06. AUTOMATED VOLUMETRY OF MENINGIOMAS IN CONTRAST-ENHANCED T1-WEIGHTED MRI USING DEEP LEARNING

Abstract BACKGROUND With the widespread use of MRI equipment and brain docks, there are more opportunities to perform follow-up observation for meningiomas. In recent years, attempts have been made to predict prognosis by using tumor volume as a highly objective imaging biomarker. On the other hand, manual lesion extraction by a reader with anatomical knowledge is indispensable for volume measurement, which requires a certain amount of labor and time. This is a major barrier to the generation of evidence and clinical application in this field. OBJECTIVE To establish the clinical significance of tumor volume as an imaging biomarker in meningiomas, and to develop a simple and highly reproducible application for automated volume measurement using artificial intelligence, and to investigate its accuracy and potential for clinical application. METHODS 178 patients with meningiomas who underwent MRI at Osaka University Hospital were included in the study. Contrast-enhanced T1-weighted images were used for manual lesion extraction by two neurosurgeons. These annotated image data were randomly divided into training (90%) and testing (10%). The deeplabV3 model was used to train the artificial intelligence, and leave-one-out cross-validation was used to train and validate the automated model. The test data were used for comparison, and the agreement rate (Dice index) on the images of the automatically extracted regions and the error rate of the calculated tumor volume (mean square error rate: RMSPE) were used as indices of measurement performance. RESULTS The average agreement rate between the automatically extracted area and the test data was 92.3±5.1%. The error rate of tumor volume was 9.5±1.2%. CONCLUSION This application using artificial intelligence was considered to have a certain validity in the accuracy of automatic lesion extraction. This method, which enables simple tumor volume measurement, is expected to create new imaging biomarkers that will contribute to the optimization and personalization of meningioma treatment.

NIMG-12. EVALUATING THE SUPER T2-FLAIR MISMATCH SIGN IN OLIGODENDROGLIOMA, IDH-MUTANT AND 1P/19Q CODELETED

Abstract BACKGROUND We reported the super T2-FLAIR mismatch sign as a prognostic imaging biomarker for non-enhanced astrocytoma, IDH-mutant, CNS WHO grade 2 or 3. However, it remains unclear whether this novel imaging biomarker is applicable for oligodendroglioma, IDH-mutant and 1p/19q codeleted. METHODS We conducted a retrospective evaluation of non-enhanced oligodendroglioma, IDH-mutant and 1p/19q codeleted, including 14 cases from at our institute and 15 cases from TCGA/TCIA database. We defined “super T2-FLAIR mismatch sign” positivity as having a significantly strong low signal comparable to cerebrospinal fluid, rather than just a pale FLAIR low-signal tumor region as in regular T2-FLAIR mismatch. We evaluated the presence or absence of T2-FLAIR mismatch sign and super T2-FLAIR mismatch sign using preoperative MRI and statistically analyzed the progression-free survival (PFS) and overall survival (OS) using log-rank test. RESULTS The median age of this patient group was 47 (ranging 20-77) years and consisted with 16 males and 13 females. There were 27 cases of grade 2 and two cases of grade 3. The regular T2-FLAIR mismatch sign was observed in three cases (10%) (grade 2: 2 cases, grade 3: 1 case). While the super T2-FLAIR mismatch sign was observed in three cases (10%) (grade 2: 2 cases, grade 3: 1 case). One case had both regular and super T2-FLAIR mismatch signs. The mean follow-up period was 33.4 months, during which there were 3 cases of progression and 1 death. Both regular and super T2-FLAIR mismatch sign were not associated with PFS, OS. CONCLUSIONS The presence of the super T2-FLAIR mismatch sign was not associated with prognosis in oligodendroglioma, IDH-mutant and 1p/19q codeleted. Because the sample size was insufficient, further larger investigation through is needed to assess the utility of this sign.

NIMG-26. GLIOBLASTOMA TISSUE INVASION MAPPING WITH NOVEL HYPOXIA-TARGETED BLOOD OXYGENATION-LEVEL DEPENDENT MRI

Abstract BACKGROUND Glioblastoma is a devastating incurable malignant primary brain tumor exhibiting an erratic growth pattern and brain tissue invasion exceeding the typically depicted contrast-enhancing lesion on MRI. This behaviour is strongly associated with the presence of aberrant vasculature. Advanced clinical neuroimaging techniques, however, have not provided convincing imaging markers to determine glioblastoma tissue infiltration and monitor treatment effects. We investigated a new imaging technique based on blood oxygenation-level dependent (BOLD) MRI contrast with precise hypoxic respiratory targeting in order to better determine tumor tissue invasion not restricted to the typically depicted focal glioblastoma lesion on contrast-enhanced MRI. METHODS A computer-controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during echo-planar-imaging acquisition in patients with brain tumors.8,9 Data were preprocessed using SPM and in-house written MATLAB scripts. The induced BOLD signal changes were used to calculate %BOLD signal change during hypoxic stimulus with respect to the baseline, contrast to noise (CNR) ratio and goodness-of-fit (Rsquared) for each voxel in the whole-brain and automatically segmented region of interest (contrast-enhancement, necrosis, edema) and overlayed using color-maps onto anatomical high resolution T1. RESULTS Hypoxic targeting during gas modulation could be performed with high reproducibility, caused no discomfort in patients with brain tumors and was able to induce strong signal change during hypoxic stimulus with high contrast-noise ratio and Rsquared. In particular, when compared with other tumors, BOLD hypoxia glioblastoma tissue invasion maps show very distinct hypoxic signal responses in some cases extending beyond the focal -contrast-enhancing- glioblastoma lesion. CONCLUSION The ability to map at voxel level BOLD hypoxia signal patterns may determine a novel therapeutic imaging marker based on structural vascular alterations in the tumour tissue infiltration zone before these become appreciable with standard neuroimaging. This may support a refinement and tailoring of tumor resection and improve follow-up after resection and during treatment i.e. chemoradiotherapy.

Prolonged venous transit is associated with worse neurological recovery in successfully reperfused large vessel strokes

AbstractObjectiveVenous outflow (VO) impairment predicts unfavorable outcomes in patients with acute ischemic stroke caused by large vessel occlusion (AIS‐LVO). Prolonged venous transit (PVT), a visual qualitative VO marker on CT perfusion (CTP) time to maximum (Tmax) maps, has been associated with unfavorable 90‐day functional outcomes despite successful reperfusion. This study investigates the association between PVT and percent change on the National Institutes of Health Stroke Scale (NIHSS) among AIS‐LVO patients who have undergone successful reperfusion.MethodsWe performed a retrospective analysis of prospectively collected data from consecutive adult AIS‐LVO patients with successful reperfusion (modified Thrombolysis in Cerebral Infarction 2b/2c/3). PVT+ was defined as Tmax ≥10 s in the superior sagittal sinus, torcula, or both. The primary outcome was continuous NIHSS percent change and dichotomous NIHSS percent change ≥70%. Regression analyses were performed to assess the effect of PVT on NIHSS percent change.ResultsIn 119 patients of median (IQR) age 71 (63–81) years, the admission and discharge NIHSS scores were significantly higher in PVT+ patients compared to PVT− patients (17 [14–23.5] vs. 13 [9.5–19], p = 0.011, and 7.5 [4–12] vs. 3 [1–7], p < 0.001, respectively). After adjusting for age, sex, hypertension, diabetes, atrial fibrillation, administration of intravenous thrombolysis (IVT), Alberta Stroke Program Early CT Scores (ASPECTS), mTICI 2c and/or 3, Tmax >6 s volume, and hemorrhagic transformation, PVT+ was significantly associated with lower NIHSS percent change (B = −0.163, 95%CI −0.326 to −0.001, p = 0.049) and was less likely to achieve higher than 70% NIHSS improvement (OR = 0.331, 95% CI 0.127–0.863, p = 0.024).InterpretationPVT+ was significantly associated with reduced neurological improvement despite successful reperfusion in AIS‐LVO patients, highlighting the critical role of VO impairment in short‐term functional outcomes. These findings further validate PVT as a valuable adjunct imaging biomarker derived from CTP for assessing VO profiles in AIS‐LVO.

Myocardial Strain Assessment for Early Duchenne Muscular Dystrophy Diagnosis in Pediatric Patients Using Cardiac MRI

Assessing myocardial strain remains challenging, particularly in the pediatric population, due to the smaller heart sizes, higher heart rates, and variability in strain parameters compared to adult populations. This study aimed to investigate the utility of myocardial strain measurements using cardiac magnetic resonance-feature tracking (CMR-FT) for early diagnosis of Duchenne muscular dystrophy (DMD) in pediatric patients. Twenty-eight DMD patients and 20 healthy controls were involved in this study. Global circumferential, longitudinal, and radial strain (GCS, GLS, and GRS) were measured for the left ventricle (LV) using CMR-FT. Segmental strain values only of the inferolateral and anterolateral LV segments in DMD patients without late gadolinium enhancement (LGE) and DMD patients with LGE were compared to the healthy controls. Strain measurements using CMR-FT in DMD patients were considerably lower than those of healthy controls, with all p-values lower than 0.001. DMD patients without LGE showed decreased inferolateral and anterolateral segmental values only relative to healthy controls. The same behavior was maintained for the LV geometry. Multivariable linear regression demonstrated that the end-systole (ES) wall thicknesses and thickening were associated with decreased GCS and GLS. CMR-FT is crucial in detecting cardiac abnormalities in patients with DMD. It represents an innovative imaging biomarker that can detect initial myocardial alterations in DMD cardiomyopathy without relying on gadolinium.