Imaging Biomarkers Research Articles

Imaging biomarkers between primary and metastatic tumors revealed by total-body positron emission tomography/computed tomography-based radiomics: a case series

Imaging biomarkers between primary and metastatic tumors revealed by total-body positron emission tomography/computed tomography-based radiomics: a case series

Association of Loneliness with Functional Connectivity MRI, Amyloid-β PET, and Tau PET Neuroimaging Markers of Vulnerability for Alzheimer’s Disease

Background: Loneliness has been declared an “epidemic” associated with negative physical, mental, and cognitive health outcomes such as increased dementia risk. Less is known about the relationship between loneliness and advanced neuroimaging correlates of Alzheimer’s disease (AD). Objective: To assess whether loneliness was associated with advanced neuroimaging markers of AD using neuroimaging data from Framingham Heart Study (FHS) participants without dementia Methods: In this cross-sectional observational analysis, we used functional connectivity MRI (fcMRI), amyloid-β (Aβ) PET, and tau PET imaging data collected between 2016 and 2019 on eligible FHS cohort participants. Loneliness was defined as feeling lonely at least one day in the past week. The primary fcMRI marker was Default Mode Network intra-network connectivity. The primary PET imaging markers were Aβ deposition in precuneal and FLR (frontal, lateral parietal and lateral temporal, retrosplenial) regions, and tau deposition in the amygdala, entorhinal, and rhinal regions. Results: Of 381 participants (mean age 58 [SD 10]) who met inclusion criteria for fcMRI analysis, 5% were classified as lonely (17/381). No association was observed between loneliness status and network changes. Of 424 participants (mean age 58 [SD = 10]) meeting inclusion criteria for PET analyses, 5% (21/424) were lonely; no associations were observed between loneliness and either Aβ or tau deposition in primary regions of interest. Conclusions: In this cross-sectional study, there were no observable associations between loneliness and select fcMRI, Aβ PET, and tau PET neuroimaging markers of AD risk. These findings merit further investigation in prospective studies of community-based cohorts.

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.

Scottish Intercollegiate Guidelines Network (SIGN) Guidance on Dementia. The investigation of suspected dementia (SIGN 168) with focus on biomarkers.

This is an executive summary of the recent guidance produced by the SIGN dementia guideline group with regards to the investigation of suspected dementia. This is a sub-section of the broader SIGN 168 guideline released in November 2023. The guideline group included clinicians with expertise in Old Age Psychiatry, Neurology, Radiology and Nuclear Medicine supported by colleagues from the SIGN and Healthcare Improvement Scotland (HIS) teams. There was representation from carers and support organisations with experience of dementia, to ensure the recommendations were appropriate from the perspective of the people being assessed for possible dementia and their carers. As the 2018 National Institute for Health and Clinical Excellence (NICE) dementia review, included a review of the evidenced investigation of dementia, the SIGN guideline development group decided to focus on a review on the up to date evidence regarding the role of imaging and fluid biomarkers in the diagnosis of dementia. In order to give context to the consideration of more advanced diagnostic biomarker investigations, the guideline and this summary includes the NICE guidance on the use of standard investigations as well as more specialist investigations. The evidence review supports consideration of the use of structural imaging, nuclear medicine imaging and established Alzheimer's cerebrospinal fluid (CSF) biomarkers (amyloid and tau) in the diagnosis of dementia. Although routine use of amyloid PET imaging was not recommended, its potential use, under specialist direction, in patients with atypical or young onset presentations of suspected Alzheimer's dementia was included as a clinical good practice point.

Spatial relationship between left peri-atrial fat with underlying arrhythmogenic substrate in patients with atrial fibrillation - innocent bystander or culprit?

Abstract Introduction Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Consistent with prior evidence, we have previously shown that peri-atrial fat volume, but not fat attenuation (an imaging biomarker of inflammation), was associated with prevalent atrial fibrillation. Whether this association is secondary to "outside-to-inside" tissue cross talk with local adverse electrical remodelling of adjacent atrial cardiomyocytes is not known. Purpose In this novel proof of concept study, we aimed to identify a spatial relationship between left peri-atrial fat and potentially arrhythmogenic substrate (low voltage areas and slow conduction pathways) in the left atrial myocardium. Methods Contrast-enhanced cardiac computed tomography (CT) imaging and electroanatomic mapping, during coronary sinus pacing, were performed pre-ablation in atrial fibrillation patients (n=27). The left atrium and surrounding peri-atrial fat were segmented and quantified using seg3D2 and CEMRGapp. We performed registration of the electroanatomic map onto the CT-derived left atrial mesh using OpenEP and EP Workbench. Mean fat volume and attenuation around each electroanatomic point (within a 3mm radial region of interest) were correlated with local voltage and conduction velocity. Results A total of 24,839 electroanatomic points were registered onto the 3D CT-derived left atrial mesh, with a median of 935 points (range 673 to 1150) per patient. The average bipolar voltage and conduction velocity in the left atrium was 1.92±1.91 mV and 0.66±0.47 ms-1 respectively. In comparison to areas with little/no surrounding periatrial fat (Quartile 1, ≤0.93 mm3 fat per electroanatomic point), areas with the highest burden of fat (Quartile 4, ≥17.6 mm3) had significantly lower bipolar voltage (1.69±1.7 vs 2.09±2.0 mV, p&amp;lt;0.001) and conduction velocity (0.627±0.43 vs 0.700±0.47 ms-1, p&amp;lt;0.001). However, the overall correlation between periatrial fat volume with bipolar voltage (r=-0.06, p&amp;lt;0.001) and conduction velocity (r=-0.07, p&amp;lt;0.001) was weak. High, not low, fat attenuation (Quartile 4; &amp;gt;-46.5HU vs Quartile 1; ≤-75.5HU) was associated with increased bipolar voltage (2.23±2.1 vs 1.63±1.6 mV) and conduction velocity (0.703±0.42 vs 0.623±0.39 ms-1 respectively). Conclusion There is a weak correlation between periatrial fat and localized adverse electrical remodelling. The relationship between fat attenuation and electrophysiological properties was inverted. These findings confirm an association between peri-atrial fat burden but call question as to whether peri-atrial fat inflammation is implicated in atrial fibrillation pathogenesis.

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.

Quantitative Abdominal Arterial Calcification Correlates with Kidney Transplant Waitlist Mortality.

The scarcity of available organs for kidney transplantation has resulted in a substantial waiting time for patients with End Stage Kidney Disease (ESKD). This prolonged wait contributes to an increased risk of cardiovascular mortality. Calcification of large arteries is a high-risk factor in the development of cardiovascular diseases, and it is common among candidates for kidney transplant. The aim of this study is to correlate Abdominal Arterial Calcification (AAC) score value with mortality on the waitlist. We modified the coronary calcium score and used it to quantitate the AAC. We conducted a retrospective clinical study of all adult patients who were listed for kidney transplant, between 2005 and 2015, and had abdominal computed tomography scan. Patients were divided into two groups: those who died on the waiting list (DWL group) and those who survived on the waiting list (SWL group). Each 1000 increase in the AAC score value of the sum score of the abdominal aorta, bilateral common iliac, bilateral external iliac, and bilateral internal iliac was associated with increased risk of death (HR 1.034, 95%CI 1.013, 1.055) (p = 0.001). This association remained significant even after adjusting for various patient characteristics, including age, tobacco use, diabetes, coronary artery disease, and dialysis status. The study highlights the potential value of the AAC score as a noninvasive Imaging biomarker for kidney transplant waitlist patients. Incorporating the AAC scoring system into routine imaging reports could facilitate improved risk assessment and personalized care for kidney transplant candidates.

#2647 Assessing atherosclerosis using epicardial adipose tissue in hemodialysis patients: a meta-analysis

Abstract Background and Aims Hemodialysis (HD) patients had higher cardiovascular mortality due to accelerated atherosclerosis in such patients. Epicardial adipose tissue (EAT) thickness is a marker of atherosclerosis and independent predictor of coronary artery disease. The potential use of EAT as an imaging biomarker for cardiovascular disease risk stratification has attracted attention in the recent times. EAT's value in cases of HD hasn't been thoroughly analyzed, though. A meta-analysis of the literature assessing the mean differences in EAT value between HD patients and healthy controls was conducted. Method Pubmed, Google Scholar and Embase databases were searched from inception till 15 January 2024 for studies that measured EAT thickness of adult patients with HD and matched healthy controls. Outcomes included mean difference (MD) and 95% confidence interval (CI) of EAT thickness between the HD patients and the healthy controls. The results were combined using a fixed or random effect model as per heterogeneity among the included studies in Revman 5.4 software. The between-study heterogeneity of effect size was quantified using the Q statistic and I2. The results were represented in forest plots. Statistical significance was determined as P value of less than 0.05. Results Eight studies comprising 757 patients were included in this analysis. EAT thickness were significantly higher in HD patients as compared to healthy controls [MD: 1.88, Confidence Interval (CI): 0.74-3.02, I2 = 98%, P value = 0.001] (Fig. 1). Conclusion This meta-analysis concluded that HD patients are independently associated with higher EAT as compared to healthy controls. The utility of EAT as an imaging biomarker for predicting atherosclerosis in HD patients is promising. EAT represent readily available clinical markers useful in estimating risk of cardiovascular mortality in HD patients.

Dual-Energy CT muscle fat fraction as a new imaging biomarker of body composition and survival predictor in critically ill patients.

To analyze changes in the muscular fat fraction (FF) during immobilization at the intensive care unit (ICU) using dual-energy CT (DECT) and evaluate the predictive value of the DECT FF as a new imaging biomarker for morbidity and survival. Immobilized ICU patients (n = 81, 43.2% female, 60.3 ± 12.7 years) were included, who received two dual-source DECT scans (CT1, CT2) within a minimum interval of 10 days between 11/2019 and 09/2022. The DECT FF was quantified for the posterior paraspinal muscleby two radiologists using material decomposition. The skeletal muscle index (SMI), muscle radiodensity attenuation (MRA), subcutaneous-/ visceral adipose tissue area (SAT, VAT), and waist circumference (WC) were assessed. Reasons for ICU admission, clinical scoring systems, therapeutic regimes, and in-hospital mortality were noted. Linear mixed models, Cox regression, and intraclass correlation coefficients were employed. Between CT1 and CT2 (median 21 days), the DECT FF increased (from 20.9% ± 12.0 to 27.0% ± 12.0, p = 0.001). The SMI decreased (35.7 cm2/m2 ± 8.8 to 31.1 cm2/m2 ± 7.6, p < 0.001) as did the MRA (29HU ± 10 to 26HU ± 11, p = 0.009). WC, SAT, and VAT did not change. In-hospital mortality was 61.5%. In multivariable analyses, only the change in DECT FF was associated with in-hospital mortality (hazard ratio (HR) 9.20 [1.78-47.71], p = 0.008), renal replacement therapy (HR 48.67 [9.18-258.09], p < 0.001), and tracheotomy at ICU (HR 37.22 [5.66-245.02], p < 0.001). Inter-observer reproducibility of DECT FF measurements was excellent (CT1: 0.98 [0.97; 0.99], CT2: 0.99 [0.96-0.99]). The DECT FF appears to be suitable for detecting increasing myosteatosis. It seems to have predictive value as a new imaging biomarker for ICU patients. The dual-energy CT muscular fat fraction appears to be a robust imaging biomarker to detect and monitor myosteatosis. It has potential for prognosticating, risk stratifying, and thereby guiding therapeutic nutritional regimes and physiotherapy in critically ill patients. The dual-energy CT muscular fat fraction detects increasing myosteatosis caused by immobilization. Change in dual-energy CT muscular fat fraction was a predictor of in-hospital morbidity and mortality. Dual-energy CT muscular fat fraction had a predictive value superior to establishedCT body composition parameters.

Hyperspectral retinal imaging biomarkers of ocular and systemic diseases.

Hyperspectral imaging is a frontier in the field of medical imaging technology. It enables the simultaneous collection of spectroscopic and spatial data. Structural and physiological information encoded in these data can be used to identify and localise typically elusive biomarkers. Studies of retinal hyperspectral imaging have provided novel insights into disease pathophysiology and new ways of non-invasive diagnosis and monitoring of retinal and systemic diseases. This review provides a concise overview of recent advances in retinal hyperspectral imaging.

Myocardial Recovery and Relapse in Heart Failure With Improved Ejection Fraction

Abstract Purpose of review The purpose of this review is to discuss myocardial recovery in heart failure with reduced ejection fraction (HFrEF) and to summarize the contemporary insights regarding heart failure with improved ejection fraction (HFimpEF). Recent findings Improvement in left ventricular ejection fraction (LVEF ≥ 40%) with improved prognosis can be achieved in one out of three (10–40%) patients with HFrEF treated with guideline-directed medical therapy. Clinical predictors include non-ischemic etiology of HFrEF, less abnormal blood or imaging biomarkers, and lack of specific pathogenic genetic variants. However, a subset of patients may ultimately relapse, suggesting that many patients are merely in remission rather than having fully recovered. Summary Patients with HFimpEF have improved prognosis but nonetheless remain at risk of relapse and long-term adverse events. Future studies will hopefully chart the natural history of HFimpEF and identify clinical predictors such as blood or novel imaging biomarkers that distinguish subgroups of patients based on differential trajectory and prognosis.

Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma.

To investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC). Seventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors. Seventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = -0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = -0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02). Tumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment. Tissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy. Apparent diffusion coefficient reflects cellularity of hepatocellular carcinoma. Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes. Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

Performance of T2 mapping in the staging of Graves' ophthalmopathy based on different region of interest selection methods.

Discriminating the stage of Graves' ophthalmopathy (GO) is crucial for clinical decision. Application of conventional T2-weighted imaging in the staging is still limited. To evaluate the performance of T2 mapping based on two different regions of interest (ROIs) for staging GO. In total, 56 GO patients were retrospectively enrolled and divided into two groups according to the clinical activity score (CAS). T2 relaxation time (T2RT) of extraocular muscle (EOM) on T2 mapping based on two different ROIs (T2RTROI-1: ROIs were drawn separately in the four EOMs; T2RTROI-2: ROI was drawn in the most inflamed EOM) was measured and compared between active and inactive groups. Both T2RTROI-1 and T2RTROI-2 values in the active GO were significantly higher than those of inactive GO (P <0.001). T2RTROI-1 and T2RTROI-2 values were positively correlated with CAS (rs=0.73, 0.69; P <0.001). When the T2RTROI-1 value of 83.3 ms and T2RTROI-2 value of 106.3 ms were used as cutoff values for staging GO, respectively, the best results were obtained with areas under the curve (AUCs) of 0.822 and 0.827. There was no significant difference for AUCs between T2RTROI-1 and T2RTROI-2 (P = 0.751). Excellent and good inter-observer agreements were achieved in quantitative measurements for T2RTROI-1 and T2RTROI-2 values, respectively, with intraclass correlation coefficients of 0.954 and 0.882. The T2RT values derived from two different ROIs were useful for assessment of disease activity. Taking reproducibility and diagnostic performance into consideration, T2RTROI-1 would be an ideal image biomarker for staging GO compared to T2RTROI-2.

Novel inflammatory and insulin resistance indices provide a clue in cerebral amyloid angiopathy

This study investigated the correlation of newly identified inflammatory and insulin resistance indices with cerebral amyloid angiopathy (CAA), and explored their potential to differentiate CAA from hypertensive arteriopathy (HA). We retrospectively analyzed 514 consecutive patients with cerebral small vessel disease (CSVD)-related haemorrhage, comparing the differences in novel inflammatory and insulin resistance indices between patients with CAA and HA. Univariate regression, LASSO and multivariate regression were used to screen variables and construct a classification diagnosis nomogram. Additionally, these biomarkers were explored in patients with mixed haemorrhagic CSVD. Inflammatory indices were higher in CAA patients, whereas insulin resistance indices were higher in HA patients. Further analysis identified neutrophil-to-lymphocyte ratio (NLR, OR 1.17, 95% CI 1.07–1.30, P < 0.001), and triglyceride–glucose index (TyG, OR = 0.56, 95% CI 0.36–0.83, P = 0.005) as independent factors for CAA. Therefore, we constructed a CAA prediction nomogram without haemorrhagic imaging markers. The nomogram yielded an area under the curve (AUC) of 0.811 (95% CI 0.764–0.865) in the training set and 0.830 (95% CI 0.718–0.887) in the test set, indicating an ability to identify high-risk CAA patients. These results show that CSVD patients can be phenotyped using novel inflammatory and insulin resistance indices, potentially allowing identification of high-risk CAA patients without haemorrhagic imaging markers.

Mediation of White Matter Alterations in the Association Between Ventricular Dilation and Cognitive Decline in Hydrocephalus Patients: An MRI Study.

Cognitive impairment is commonly observed in hydrocephalus patients. Ventricular enlargement compresses brain parenchyma, especially the white matter (WM). To investigate whether the relationship between ventricular dilation and cognitive decline in hydrocephalus patients is mediated by WM alterations. Retrospective. 51 communicating hydrocephalus patients (median age, 54 years), 50 obstructive hydrocephalus patients (median age, 49 years), and 53 control subjects (median age, 50 years). Diffusion tensors imaging, 3D T1 BRAVO, 3D FIESTA, CUBE T2, and FLAIR sequences at 3T. DTI parameters (skeletonized fractional anisotropy (FA), skeletonized mean diffusivity (MD), and peak width of skeletonized mean diffusivity p(PSMD)) were extracted using FSL software. Global, periventricular, and deep white matter hyperintensity (WMH) volumes, degree of ventricular enlargement (Evans index), and other conventional imaging markers (number of lacunes and perivascular spaces, intracranial and brain volume) were extracted using united imaging intelligence. Cognitive tests included Montreal cognitive assessment (MoCA), clock drawing test (CDT), and vocabulary fluency test (VFT). Multivariable linear regression analysis, mediation analyses, and dominance analysis. P-value <0.05 was considered significant. The degree of ventricular dilation, DTI parameters, and cognitive function scores were interrelated. The skeletonized FA values (β = -0.0917, 95% confidence interval (CI): -0.205, -0.024) and normalized global WMH volume (β = -0.0635, 95% CI: -0.13, -0.0005) together mediated 37.2% of the association between Evans index and MoCA. A comparable causal pathway was found for periventricular WMHs but not for deep WMHs. Dominance analysis indicated skeletonized FA values had a greater impact on cognition than WMH volume. The skeletonized FA values also mediated the association between Evans index and CDT (β = -0.0897, 95% CI: -0.165, -0.026) and VFT (β = -0.1589, 95% CI: -0.27, -0.083). WM alterations were causal mediators between ventricular dilation and cognitive decline in hydrocephalus patients. 3. Stage 3.

Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly.

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to (i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired APOE ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and (ii) to determine how different cerebral pathologies (i.e., Aβ deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample. Sixty APOE ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60 to 75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aβ-PET ([11C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [11C]PiB standardized uptake value ratio was calculated for regions typical for Aβ accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex). Serum NfL concentration was increased in APOE ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in APOE ε4 homozygotes compared with non-carriers (6.71ml (0.86) vs. 7.2ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aβ pathology. Plasma t-tau concentration did not associate with any of the measured pathologies. Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.

Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients.

Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). Plasma (Aβ42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. Cognitive decline was significantly predicted in MCI by baseline plasma NfL (β=-0.55), GFAP (β=-0.36), hippocampal volume (β = 0.44), centiloid (β=-0.38), and tau-SUVR (β=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.

The development process of ‘fit-for-purpose’ imaging biomarkers to characterize the tumor microenvironment

Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, reliable biomarkers for the assessment of immune features in patients with cancer are of significant importance. Biomarkers (BM) that enable a comprehensive and repeatable assessment of the tumor microenvironment (TME), the lymphoid system, and the dynamics induced by drug treatment can fill this gap. Medical imaging, notably positron emission tomography (PET) and magnetic resonance imaging (MRI), providing whole-body imaging BMs, might deliver such BMs. However, those imaging BMs must be well characterized as being ‘fit for purpose’ for the intended use. This review provides an overview of the key steps involved in the development of ‘fit-for-purpose’ imaging BMs applicable in drug development, with a specific focus on pharmacodynamic biomarkers for assessing the TME and its modulation by immunotherapy. The importance of the qualification of imaging BMs according to their context of use (COU) as defined by the Food and Drug Administration (FDA) and National Institutes of Health Biomarkers, EndpointS, and other Tools (BEST) glossary is highlighted. We elaborate on how an imaging BM qualification for a specific COU can be achieved.

Point based weakly semi-supervised biomarker detection with cross-scale and label assignment in retinal OCT images

Background and ObjectiveOptical coherence tomography (OCT) is currently one of the most advanced retinal imaging methods. Retinal biomarkers in OCT images are of clinical significance and can assist ophthalmologists in diagnosing lesions. Compared with fundus images, OCT can provide higher resolution segmentation. However, image annotation at the bounding box level needs to be performed by ophthalmologists carefully and is difficult to obtain. In addition, the large variation in shape of different retinal markers and the inconspicuous appearance of biomarkers make it difficult for existing deep learning-based methods to effectively detect them. To overcome the above challenges, we propose a novel network for the detection of retinal biomarkers in OCT images. MethodsWe first address the issue of labeling cost using a novel weakly semi-supervised object detection method with point annotations which can reduce bounding box-level annotation efforts. To extend the method to the detection of biomarkers in OCT images, we propose multiple consistent regularizations for point-to-box regression network to deal with the shortage of supervision, which aims to learn more accurate regression mappings. Furthermore, in the subsequent fully supervised detection, we propose a cross-scale feature enhancement module to alleviate the detection problems caused by the large-scale variation of biomarkers. We also propose a dynamic label assignment strategy to distinguish samples of different importance more flexibly, thereby reducing detection errors due to the indistinguishable appearance of the biomarkers. ResultsWhen using our detection network, our regressor also achieves an AP value of 20.83 s when utilizing a 5 % fully labeled dataset partition, surpassing the performance of other comparative methods at 5 % and 10 %. Even coming close to the 20.87 % result achieved by Point DETR under 20 % full labeling conditions. When using Group R-CNN as the point-to-box regressor, our detector achieves 27.21 % AP in the 50 % fully labeled dataset experiment. 7.42 % AP improvement is achieved compared to our detection network baseline Faster R-CNN. ConclusionsThe experimental findings not only demonstrate the effectiveness of our approach with minimal bounding box annotations but also highlight the enhanced biomarker detection performance of the proposed module. We have included a detailed algorithmic flow in the supplementary material.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.