Imaging Biomarkers Research Articles

Addition of quantitative MRI to the routine clinical care of patients with multiple sclerosis-Results from the MAGNON project.

The revised Lublin classification offers a framework for categorizing multiple sclerosis (MS) according to the clinical course and imaging results. Diagnosis of secondary progressive MS (SPMS) is often delayed by a period of uncertainty. Several quantitative magnetic resonance imaging (qMRI) markers are associated with progressive disease states, but they are not usually available in clinical practice. The MAGNON project enrolled 629 patients (early relapsing-remitting MS (RRMS), n=51; RRMS with suspected SPMS, n=386; SPMS, n=192) at 55 centers in Germany. Routine magnetic resonance imaging (MRI) scans at baseline and after 12 months were analyzed using a centralized automatic processing pipeline to quantify lesions and normalized brain and thalamic volume. Clinical measures included relapse activity, disability, and MS phenotyping. Neurologists completed questionnaires before and after receiving the qMRI reports. According to the physicians' reports, qMRI results changed their assessment of the patient in 31.8% (baseline scan) and 27.6% (follow-up scan). For ∼50% of patients with RRMS with suspected SPMS, reports provided additional information that the patient was transitioning to SPMS. In >25% of all patients, this information influenced the physicians' assessment of the patient's current phenotype. However, actual changes of treatment were reported only in a minority of these patients. The MAGNON results suggest that standardized qMRI reports may be integrated into the routine clinical care of MS patients and support the application of the Lublin classification as well as treatment decisions. The highest impact was reported in patients with suspected SPMS, indicating a potential to reduce diagnostic uncertainty.

Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Radiogenomics of homologous recombination deficiency (HRD) in breast cancer: Radiomics-based image biomarker for HRD.

554 Background: Homologous recombination deficiency (HRD) in breast cancer is an actionable target, with treatment efficacy potentially linked to timely detection. Notably, BRCA1 and BRCA2 are key genes implicated in HRD, and their pathogenic germline mutations are crucial criteria for the use of poly (ADP-ribose) polymerase inhibitors (PARPi). Although germline BRCA1/2 mutations are detectable in only a small fraction (1-5%) of breast cancers, recent whole-genome sequencing (WGS) studies have revealed that up to 22% of breast cancers also exhibit HRD-like genomic characteristics, suggesting a broader potential for HRD-targeted therapy. This has spurred the development of genomic-feature-based HRD identification methods. However, the invasive and costly nature of these methods poses a challenge. This has prompted our investigation into non-invasive image biomarkers for HRD identification. Methods: Invasive breast cancer patients were recruited from March 2021 to August 2022. Eligibility criteria included availability of fresh-frozen tumor tissue for WGS and suitability for dynamic contrast-enhanced (DCE) MRI to facilitate radiomics analysis. The association between HRD and radiomic features as well as clinicopathologic factors was investigated through rigorous genomic and statistical analysis. Results: This study encompassed 145 patients aged 20 to 69 years, of whom 16.6% (24 out of 145) were genomically identified as HRD. All patients harboring pathogenic germline mutations in BRCA1 (n=3) or BRCA2 (n=2), along with concomitant somatic LOH, exhibited HRD. A comprehensive analysis of 214 radiomic features revealed that tumor sphericity, informational measure of correlation 1 (IMC1), size-zone non-uniformity normalized (SZNN), and small area emphasis (SAE) derived from subtracted early dynamic T1-weighted imaging were significantly correlated with HRD. Moreover, these radiomic features demonstrated substantial diagnostic value, as evidenced by their ROC-AUC and PR-AUC performance against baseline models: Sphericity (ROC: 0.62, p=0.055; PR: 0.22, p=0.053), SAE (ROC: 0.63, p=0.052; PR: 0.32, p=0.007), SZNN (ROC: 0.63, p=0.04; PR: 0.30, p=0.007), and IMC1 (ROC: 0.62, p=0.067; PR: 0.29, p=0.02). Additionally, our findings suggest that these radiomic features are more directly related to HRD than to the triple-negative phenotype, which is commonly linked to HRD. Conclusions: We found that four radiomic features from MRI have significant predictive value for identifying HRD in breast cancer. Given their fair predictability and immediate availability, these features are ideally suited for a screening approach, potentially identifying more patients eligible for targeted treatment. This finding represents a significant advancement in the fields of radiology and precision oncology, opening new avenues for patient-specific treatment strategies.

Correlating survival outcomes in patients with advanced prostate cancer with novel hyperpolarized 13C MRI metabolic imaging biomarkers.

e15059 Background: Hyperpolarized 13C (HP 13C) MRI is a novel molecular imaging approach that detects aberrant aerobic metabolism, namely elevated rates of pyruvate-to-lactate conversion (kPL), in patients with advanced malignancies. Higher kPL has been shown to correlate with higher tumor grade among patients with localized prostate cancer (PC). This research sought to evaluate the correlation between kPL and clinical outcome measures in patients with advanced PC. Methods: Patients with metastatic or locally advanced PC (castration sensitive or resistant) were prospectively enrolled at our institution and underwent HP 13C MRI of one or more target lesions identified on prior conventional imaging. Voxelwise kPL values were computed in MATLAB using an inputless two site exchange model. kPL,median was defined as the median kPL value per-patient across all target lesions; kPL,kurtosis measured the tailedness of the kPL distribution per-patient. We retrospectively analyzed progression free survival (PFS) and overall survival (OS) in subgroups divided by metabolic activity below versus above cut-point (kPL,median <0.017 s-1 and ≥ 0.017s-1, respectively). Survival statistics were calculated using the Cox PH model. Results: Sixteen patients (6 with castration-sensitive and 10 with castration-resistant PC) were accrued. With a median follow-up of 22.0 months from the date of MRI, the lower-kPL subgroup had significantly longer median PFS (11.2 vs 0.5 months; p<0.01) and OS (NR vs 18.4 months; p<0.05) compared to the higher-kPL subgroup. By comparison, baseline serum PSA (PFS: p=0.19, OS: p=0.41) and LDH (PFS: p=0.60, OS: p=0.34) did not demonstrate a significant association with clinical outcomes. There was a strong negative correlation between kPL,kurtosis and OS (r = -0.75), and moderate negative correlations between kPL,median and OS (r = -0.45) as well as kPL,max with both PFS (r = -0.48) and OS (r = -0.54). There was no significant kPL,median difference between castration-sensitive and resistant subgroups (0.015±0.011 vs 0.016±0.006 s-1, p>0.8). Conclusions: HP 13C MRI-derived non-invasive metabolic biomarker kPL,median significantly correlated with clinical outcome measures in a retrospective analysis of patients with advanced PC, outperforming established serological prognostic markers PSA and LDH. Limitations include our small sample size, retrospective design, and heterogeneous patient population. Nevertheless, these encouraging preliminary results support further investigation of HP 13C MRI as a prognostic and/or predictive PC biomarker in multi-center prospective imaging trials. [Table: see text]

Decorrelation Time Mapping as an Analysis Tool for Nanobubble-Based Contrast Enhanced Ultrasound Imaging.

Nanobubbles (NBs; ~100-500 nm diameter) are preclinical ultrasound (US) contrast agents that expand applications of contrast enhanced US (CEUS). Due to their sub-micron size, high particle density, and deformable shell, NBs in pathological states of heightened vascular permeability (e.g. in tumors) extravasate, enabling applications not possible with microbubbles (~1000-10,000 nm diameter). A method that can separate intravascular versus extravascular NB signal is needed as an imaging biomarker for improved tumor detection. We present a demonstration of decorrelation time (DT) mapping for enhanced tumor NB-CEUS imaging. In vitro models validated the sensitivity of DT to agent motion. Prostate cancer mouse models validated in vivo imaging potential and sensitivity to cancerous tissue. Our findings show that DT is inversely related to NB motion, offering enhanced detail of NB dynamics in tumors, and highlighting the heterogeneity of the tumor environment. Average DT was high in tumor regions (~9 s) compared to surrounding normal tissue (~1 s) with higher sensitivity to tumor tissue compared to other mapping techniques. Molecular NB targeting to tumors further extended DT (11 s) over non-targeted NBs (6 s), demonstrating sensitivity to NB adherence. From DT mapping of in vivo NB dynamics we demonstrate the heterogeneity of tumor tissue while quantifying extravascular NB kinetics and delineating intra-tumoral vasculature. This new NB-CEUS-based biomarker can be powerful in molecular US imaging, with improved sensitivity and specificity to diseased tissue and potential for use as an estimator of vascular permeability and the enhanced permeability and retention (EPR) effect in tumors.

How Do Quantitative Tissue Imaging Outcomes in Acute Ischemic Stroke Relate to Clinical Outcomes?

Infarct volume and other imaging markers are increasingly used as surrogate measures for clinical outcome in acute ischemic stroke research, but how improvements in these imaging surrogates translate into better clinical outcomes is currently unclear. We investigated how changes in infarct volume at 24 hours alter the probability of achieving good clinical outcome (modified Rankin Scale [mRS] 0-2). Data are from endovascular thrombectomy patients from the randomized controlled ESCAPE-NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischaemic Stroke) trial. Infarct volume at 24 hours was manually segmented on non-contrast computed tomography or diffusion-weighted magnetic resonance imaging. Probabilities of achieving good outcome based on infarct volume were obtained from a multivariable logistic regression model. The probability of good outcome was plotted against infarct volume using linear spline regression. A total of 1,099 patients were included in the analysis (median final infarct volume 24.9 mL [interquartile range: 6.6-92.2]). The relationship between total infarct volume and good outcome probability was nearly linear for infarct volumes between 0 mL and 250 mL. In this range, a 10% increase in the probability of achieving mRS 0-2 required a decrease in infarct volume of approximately 34.0 mL (95% confidence interval: -32.5 to -35.6). At infarct volumes above 250 mL, the probability of achieving mRS 0-2 probability was near zero. The relationships of tissue-specific infarct volumes and parenchymal hemorrhage volume generally showed similar patterns, although variability was high. There seems to be a near-linear association between total infarct volume and probability of achieving good outcome for infarcts up to 250 mL, whereas patients with infarct volumes greater than 250 mL are highly unlikely to have a favorable outcome.

Deep Learning–Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4)

PurposeWe introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). DesignRetrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images. ParticipantsA total of 3,456 adults aged between 51 and 102 years of whom OCT images were collected under the PINNACLE project. MethodsOur system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome MeasuresWe checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late stage wet and dry AMD against an established clinical grading system and a demographic baseline model. ResultsOverall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. ConclusionsUsing self-supervised deep learning we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.

Reduced oxygen extraction fraction in deep cerebral veins associated with cognitive impairment in multiple sclerosis.

Studying the relationship between cerebral oxygen utilization and cognitive impairment is essential to understanding neuronal functional changes in the disease progression of multiple sclerosis (MS). This study explores the potential of using venous susceptibility in internal cerebral veins (ICVs) as an imaging biomarker for cognitive impairment in relapsing-remitting MS (RRMS) patients. Quantitative susceptibility mapping derived from fully flow-compensated MRI phase data was employed to directly measure venous blood oxygen saturation levels (SvO2) in the ICVs. Results revealed a significant reduction in the susceptibility of ICVs (212.4 ± 30.8 ppb vs 239.4 ± 25.9 ppb) and a significant increase of SvO2 (74.5 ± 1.89% vs 72.4 ± 2.23%) in patients with RRMS compared with age- and sex-matched healthy controls. Both the susceptibility of ICVs (r = 0.508, p = 0.031) and the SvO2 (r = -0.498, p = 0.036) exhibited a moderate correlation with cognitive decline in these patients assessed by the Paced Auditory Serial Addition Test, while no significant correlation was observed with clinical disability measured by the Expanded Disability Status Scale. The findings suggest that venous susceptibility in ICVs has the potential to serve as a specific indicator of oxygen metabolism and cognitive function in RRMS. .

Prevalence of Cerebral Amyloid Angiopathy Pathology and Strictly Lobar Microbleeds in East-Asian Versus Western Populations: A Systematic Review and Meta-Analysis.

Possible differences in the prevalence of cerebral amyloid angiopathy (CAA) in East-Asian compared to Western populations have received little attention, and results so far have been ambiguous. Our aim is to compare the prevalence of CAA neuropathology and magnetic resonance imaging markers of CAA in East-Asian and Western cohorts reflecting the general population, cognitively normal elderly, patients with Alzheimer's disease (AD), and patients with (lobar) intracerebral hemorrhage (ICH). We performed a systematic literature search in PubMed and Embase for original research papers on the prevalence of CAA and imaging markers of CAA published up until February 17th 2022. Records were screened by two independent reviewers. Pooled estimates were determined using random-effects models. We compared studies from Japan, China, Taiwan, South Korea (East-Asian cohorts) to studies from Europe or North America (Western cohorts) by meta-regression models. We identified 12,257 unique records, and we included 143 studies on Western study populations and 53 studies on East-Asian study populations. Prevalence of CAA neuropathology did not differ between East-Asian and Western cohorts in any of the investigated patient domains. The prevalence of strictly lobar microbleeds was lower in East-Asian cohorts of population-based individuals (5.6% vs. 11.4%, P=0.020), cognitively normal elderly (2.6% vs. 11.4%, P=0.001), and patients with ICH (10.2% vs. 24.6%, P<0.0001). However, age was in general lower in the East-Asian cohorts. The prevalence of CAA neuropathology in the general population, cognitively normal elderly, patients with AD, and patients with (lobar) ICH is similar in East-Asian and Western countries. In East-Asian cohorts reflecting the general population, cognitively normal elderly, and patients with ICH, strictly lobar microbleeds were less prevalent, likely due to their younger age. Consideration of potential presence of CAA is warranted in decisions regarding antithrombotic treatment and potential new anti-amyloid-β immunotherapy as treatment for AD in East-Asian and Western countries alike.

Analysis of optical coherence tomography biomarker probability detection in central serous chorioretinopathy by using an artificial intelligence-based biomarker detector

AimTo adopt a novel artificial intelligence (AI) optical coherence tomography (OCT)-based program to identify the presence of biomarkers associated with central serous chorioretinopathy (CSC) and whether these can differentiate between acute and chronic central serous chorioretinopathy (aCSC and cCSC).MethodsMulticenter, observational study with a retrospective design enrolling treatment-naïve patients with aCSC and cCSC. The diagnosis of aCSC and cCSC was established with multimodal imaging and for the current study subsequent follow-up visits were also considered. Baseline OCTs were analyzed by an AI-based platform (Discovery® OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). This software allows to detect several different biomarkers in each single OCT scan, including subretinal fluid (SRF), intraretinal fluid (IRF), hyperreflective foci (HF) and flat irregular pigment epithelium detachment (FIPED). The presence of SRF was considered as a necessary inclusion criterion for performing biomarker analysis and OCT slabs without SRF presence were excluded from the analysis.ResultsOverall, 160 eyes of 144 patients with CSC were enrolled, out of which 100 (62.5%) eyes were diagnosed with cCSC and 60 eyes (34.5%) with aCSC. In the OCT slabs showing presence of SRF the presence of biomarkers was found to be clinically relevant (> 50%) for HF and FIPED in aCSC and cCSC. HF had an average percentage of 81% (± 20) in the cCSC group and 81% (± 15) in the aCSC group (p = 0.4295) and FIPED had a mean percentage of 88% (± 18) in cCSC vs. 89% (± 15) in the aCSC (p = 0.3197).ConclusionWe demonstrate that HF and FIPED are OCT biomarkers positively associated with CSC when present at baseline. While both HF and FIPED biomarkers could aid in CSC diagnosis, they could not distinguish between aCSC and cCSC at the first visit. AI-assisted biomarker detection shows promise for reducing invasive imaging needs, but further validation through longitudinal studies is needed.

Sudden Cardiac Death Risk Stratification in Heart Failure With Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) poses a significant clinical challenge, with sudden cardiac death (SCD) emerging as one of the leading causes of mortality. Despite advancements in cardiovascular medicine, predicting and preventing SCD in HFpEF remains complex due to multifactorial pathophysiological mechanisms and patient heterogeneity. Unlike heart failure with reduced ejection fraction, where impaired contractility and ventricular remodeling predominate, HFpEF pathophysiology involves heavy burden of comorbidities such as hypertension, obesity, and diabetes. Diverse mechanisms, including diastolic dysfunction, microvascular abnormalities, and inflammation, also contribute to distinct disease and SCD risk profiles. Various parameters such as clinical factors and electrocardiogram features have been proposed in SCD risk assessment. Advanced imaging modalities and biomarkers offer promise in risk prediction, yet comprehensive risk stratification models specific to HFpEF ar0e lacking. This review offers recent evidence on SCD risk factors and discusses current therapeutic strategies aimed at reducing SCD risk in HFpEF.

Left atrial appendage closure for patients with atrial fibrillation at high intracranial haemorrhagic risk

Background and objectivesAlthough left atrial appendage closure (LAAC) is performed in patients with non-valvular atrial fibrillation (NVAF) at increased risk of intracranial haemorrhage (ICH), outcome data are scarce. We assessed...

Type II Acute Macular Neuroretinopathy Secondary to Malaria

To the best of our knowledge, we present the first case of type II acute macular neuroretinopathy (AMN) exhibiting in a patient suffering from malarial retinopathy concomitant with cerebral malaria acquired after travelling to West Africa without taking the necessary antimalarial prophylaxis. The patient complained of bilateral blurring of vision after being removed off sedation whilst at the intensive care unit. Subsequent examination revealed bilateral retinal haemorrhages, cotton-wool spots, and foveal pigmentary changes in keeping malarial retinopathy. Macular optical coherence tomography (OCT) revealed patchy hyperreflective changes at the level of the outer plexiform and outer nuclear layers (ONL) in keeping with the areas of deep capillary plexus flow void noted on OCT-angiography (OCT-A). This case report sheds more light on the extent of neurosensory retinal ischaemia in malarial retinopathy and showcases a new imaging biomarker which may be utilized in assessing and quantifying the functional deficit created by this disease.

Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.

Serum NfL and GFAP as biomarkers of progressive neurodegeneration in TBI.

We examined spatial patterns of brain atrophy after mild, moderate, and severe traumatic brain injury (TBI), the relationship between progression of brain atrophy with initial traumatic axonal injury (TAI), cognitive outcome, and with serum biomarkers of brain injury. A total of 143 patients with TBI and 43 controls were studied cross-sectionally and longitudinally up to 5 years with multiple assessments, which included brain magnetic resonance imaging, cognitive testing, and serum biomarkers. TBI patients showed progressive volume loss regardless of injury severity over several years, andTAI was independently associated with accelerated brain atrophy. Cognitive performance improved over time. Higher baseline serum neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) were associated with greater rate of brain atrophy over 5 years. Spatial patterns of atrophy differ by injury severity and TAI is associated with the progression of brain atrophy. Serum NfL and GFAP show promise as non-invasive prognostic biomarkers of progressive neurodegeneration in TBI. In this longitudinal study of patient with mild, moderate, and severe traumatic brain injury (TBI) who were assessed with paired magnetic resonance imaging (MRI), blood biomarkers, and cognitive assessments, we found that brain atrophy after TBI is progressive and continues for many years even after a mild head trauma without signs of brain injury on conventional MRI. We found that spatial pattern of brain atrophy differs between mild, moderate, and severe TBI, where in patients with mild TBI , atrophy is mainly seen in the gray matter, while in those with moderate to severe brain injury atrophy is predominantly seen in the subcortical gray matter and whiter matter. Cognitive performance improves over time after a TBI. Serum measures of neurofilament light or glial fibrillary acidic protein are associated with progression of brain atrophy after TBI.

A prospective observational role of magnetic resonance imaging and magnetic resonance spectroscopy in evaluation of choline levels in gall bladder carcinoma in tertiary care hospital in central India

Background Gall bladder carcinoma (GBC) is a challenging malignancy characterized by late-stage diagnosis and poor prognosis. Early detection and accurate staging are crucial for improving patient outcomes. Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) offer non-invasive imaging modalities that may aid in the evaluation of GBC. Choline, a metabolite detected by MRS, has been implicated in tumor growth and may serve as a potential biomarker for GBC. This study aims to investigate the role of MRI and MRS in evaluating choline levels as biomarkers for GBC. Method A prospective observational study will be conducted involving patients with suspected or confirmed GBC referred to the Department of Radiology, AVBRH, Sawangi. Patients meeting inclusion criteria will undergo MRI scans and MRS examinations to assess tumor characteristics and choline levels. Data collected will include MRI images, MRS spectra, histopathological results, and clinical outcomes. Statistical analysis will be performed to examine correlations between MRI findings, choline levels, and histopathological characteristics. Expected Outcome It is anticipated that MRI and MRS will demonstrate utility in evaluating choline levels as imaging biomarkers for GBC. Correlations between choline levels, tumor characteristics, and clinical outcomes are expected to provide valuable insights into the role of choline in GBC pathogenesis and prognosis. If successful, MRI and MRS could serve as non-invasive tools for early detection, staging, and treatment response monitoring in GBC, ultimately leading to improved patient outcomes and management strategies.

The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings. To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort. A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where is weighted volume of frontal and temporal lobes and of parietal and occipital lobes. and are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD. A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001). The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.

Clinical predictive factors and imaging biomarkers of treatment response to half dose PDT in patients with chronic central serous chorioretinopathy

PurposeTo determine the clinical and imaging biomarkers of the response to half-dose photodynamic therapy (HD-PDT) in patients with central serous chorioretinopathy (CSC) MethodsClinical records and baseline ophthalmic images of 67 chronic CSC patients who underwent HD-PDT were assessed. In addition to demographic data, optical coherence tomography (OCT), fluorescein angiography (FA) and fundus autofluorescence (FAF) images were analyzed for specific biomarkers. The patients were categorized to early responder and late responder based on the time needed for complete resolution of subretinal fluid after PDT (less than 1 month vs. more than 1 month). The baseline clinical and imaging biomarkers were compared between the two groups. ResultsSeventy-three eyes of 67 patients were included in the study. The mean response time to PDT was 1.63 ± 1.48 months with 82.2% (60/73) of eyes categorized as early responder. The mean response time to PDT in delayed-response group was 4.15±1.51 months. In multivariate analysis, delayed response to PDT was associated with lacking history of systemic corticosteroid consumption, lacking history of pretreatment with eplerenone or acetazolamide before PDT and presence of hyperreflective foci in baseline OCT images (all p values < 0.05). There was no association between final visual outcome and late response to PDT. ConclusionThe presence of inflammatory biomarkers such as hyperreflective foci in baseline OCT images might be indicative of resistance to PDT. Moreover, the effect of pretreatment with mineralocorticoid antagonist on the response to PDT in chronic CSC should be explored in future prospective studies.

Injectable Thermogelling Nanostructured Ink as Simultaneous Optical and Magnetic Resonance Imaging Contrast Agent for Image-Guided Surgery.

The development of efficient and biocompatible contrast agents is particularly urgent for modern clinical surgery. Nanostructured materials raised great interest as contrast agents for different imaging techniques, for which essential features are high contrasts, and in the case of precise clinical surgery, minimization of the signal spatial dispersion when embedded in biological tissues. This study deals with the development of a multimodal contrast agent based on an injectable hydrogel nanocomposite containing a lanthanide-activated layered double hydroxide coupled to a biocompatible dye (indocyanine green), emitting in the first biological window. This novel nanostructured thermogelling hydrogel behaves as an efficient tissue marker for optical and magnetic resonance imaging because the particular formulation strongly limits its spatial diffusion in biological tissue by exploiting a simple injection. The synergistic combination of these properties permits to employ the hydrogel ink simultaneously for both optical and magnetic resonance imaging, easy monitoring of the biological target, and, at the same time, increasing the spatial resolution during a clinical surgery. The biocompatibility and excellent performance as contrast agents are very promising for possible use in image-guided surgery, which is currently one of the most challenging topics in clinical research.