Imaging Biomarkers Research Articles

MRI-Based Radiomics as a Promising Noninvasive Diagnostic Technique for Adenomyosis.

Background: MRI diagnostics are important for adenomyosis, especially in cases with inconclusive ultrasound. This study assessed the potential of MRI-based radiomics as a novel tool for differentiating between uteri with and without adenomyosis. Methods: This retrospective proof-of-principle single-center study included nine patients with and six patients without adenomyosis. All patients had preoperative T2w MR images and histological findings served as the reference standard. The uterus of each patient was segmented in 3D using dedicated software, and 884 radiomics features were extracted. After dimension reduction and feature selection, the diagnostic yield of individual and combined features implemented in the machine learning models were assessed by means of receiver operating characteristics analyses. Results: Eleven relevant radiomics features were identified. The diagnostic performance of individual features in differentiating adenomyosis from the control group was high, with areas under the curve (AUCs) ranging from 0.78 to 0.98. The performance of ML models incorporating several features was excellent, with AUC scores of 1 and an area under the precision-recall curve of 0.4. Conclusions: The set of radiomics features derived from routine T2w MRI enabled accurate differentiation of uteri with adenomyosis. Radiomics could enhance diagnosis and furthermore serve as an imaging biomarker to aid in personalizing therapies and monitoring treatment responses.

Exploratory study of using Magnetic resonance Prognostic Imaging markers for Radiotherapy In Cervix cancer (EMPIRIC): a prospective cohort study protocol

IntroductionRadical chemoradiotherapy represents the gold standard for locally advanced cervical cancer. However, despite significant progress in improving local tumour control, distant relapse continues to impact overall survival. The development of...

Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study

BackgroundDeep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort. MethodsAn automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012-2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α=0.05/1222). Findings17,646 adults (mean age, 56 years +/- 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95%CI], 0.59 [0.55-0.64]; P<0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P<0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P<0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P<0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P<0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P<0.0001). InterpretationPheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Glial Activity Load on PET Reveals Persistent "Smoldering" Inflammation in MS Despite Disease-Modifying Treatment: 18 F-PBR06 Study.

18 F-PBR06-PET targeting 18-kDa translocator protein can detect abnormal microglial activation (MA) in multiple sclerosis (MS). The objectives of this study are to develop individualized mapping of MA using 18 F-PBR06, to determine the effect of disease-modifying treatment (DMT) efficacy on reducing MA, and to determine its clinical, radiological, and serological correlates in MS patients. Thirty 18 F-PBR06-PET scans were performed in 22 MS patients (mean age, 46 ± 13 years; 16 females) and 8 healthy controls (HCs). Logarithmically transformed "glial activity load on PET" scores (calculated as the sum of voxel-by-voxel z -scores ≥4), "lnGALP," were compared between MS and HC and between MS subjects on high-efficacy DMTs (H-DMT, n = 13) and those on no or lower-efficacy treatment, and correlated with clinical measures, serum biomarkers, and cortical thickness. Cortical gray matter (CoGM) and white matter (WM) lnGALP scores were higher in MS versus HC (+33% and +48%, P < 0.001). In H-DMT group, CoGM and WM lnGALP scores were significantly lower than lower-efficacy treatment ( P < 0.01) but remained abnormally higher than in HC group ( P = 0.006). Within H-DMT patients, CoGM lnGALP scores correlated positively with physical disability, fatigue and serum glial fibrillary acid protein levels ( r = 0.65-0.79, all P 's < 0.05), and inversely with cortical thickness ( r = -0.66, P < 0.05). High-efficacy DMTs decrease, but do not normalize, CoGM and WM MA in MS patients. Such "residual" MA in CoGM is associated with clinical disability, serum biomarkers, and cortical degeneration. Individualized mapping of translocator protein PET using 18 F-PBR06 is clinically feasible and can potentially serve as an imaging biomarker for evaluating "smoldering" inflammation in MS patients.

Monomeric Far-red and Near-infrared Fluorescent Biliproteins of Ultrahigh Brightness.

Far-red and near-infrared fluorescent proteins have regions of maximum transmission in most tissues and can be widely used as fluorescent biomarkers. We report that fluorescent phycobiliproteins originating from the phycobilisome core subunit ApcF2 can covalently bind biliverdin, named BDFPs. To further improve BDFPs, we conducted a series of studies. Firstly, we mutated K53Q and T144A of BDFPs to increase their effective brightness up to 190 % in vivo. Secondly, by homochromatic tandem fusion of high-brightness BDFPs to achieve monomerization, which increases the effective brightness by up to 180 % in vivo, and can effectively improve the labeling effect. By combining the above two approaches, the brightness of the tandem BDFPs was much improved compared with that of the previously reported fluorescent proteins in a similar spectral range. The tandem BDFPs were expressed stably while maintaining fluorescence in mammalian cells and Caenorhabditis elegans. They were also photostable and resistant to high temperature, low pH, and chemical denaturation. The tandem BDFPs advantages were proved in applications as biomarkers for imaging in super-resolution microscopy.

Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks.

Lung cancer, the second most common cancer, presents persistently dismal prognoses. Radiomics, a promising field, aims to provide novel imaging biomarkers to improve outcomes. However, clinical translation faces reproducibility challenges, despite efforts to address them with quality scoring tools. This study had two objectives: 1) identify radiomics biomarkers in post-radiotherapy stage III/IV nonsmall cell lung cancer (NSCLC) patients, 2) evaluate research quality using the CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score) frameworks, and formulate an amalgamated CLEAR-RQS tool to enhance scientific rigor. A systematic literature review (Jun-Aug 2023, MEDLINE/PubMed/SCOPUS) was conducted concerning stage III/IV NSCLC, radiotherapy, and radiomic features (RF). Extracted data included study design particulars, such as sample size, radiotherapy/CT technique, selected RFs, and endpoints. CLEAR and RQS were merged into a CLEAR-RQS checklist. Three readers appraised articles utilizing CLEAR, RQS, and CLEAR-RQS metrics. Out of 871 articles, 11 met the inclusion/exclusion criteria. The Median cohort size was 91 (range: 10-337) with 9 studies being single-center. No common RF were identified. The merged CLEAR-RQS checklist comprised 61 items. Most unreported items were within CLEAR's "methods" and "open-source," and within RQS's "phantom-calibration," "registry-enrolled prospective-trial-design," and "cost-effective-analysis" sections. No study scored above 50% on RQS. Median CLEAR scores were 55.74% (32.33/58 points), and for RQS, 17.59% (6.3/36 points). CLEAR-RQS article ranking fell between CLEAR and RQS and aligned with CLEAR. Radiomics research in post-radiotherapy stage III/IV NSCLC exhibits variability and frequently low-quality reporting. The formulated CLEAR-RQS checklist may facilitate education and holds promise for enhancing radiomics research quality. Current radiomics research in the field of stage III/IV postradiotherapy NSCLC is heterogenous, lacking reproducibility, with no identified imaging biomarker. Radiomics research quality assessment tools may enhance scientific rigor and thereby facilitate radiomics translation into clinical practice. There is heterogenous and low radiomics research quality in postradiotherapy stage III/IV nonsmall cell lung cancer. Barriers to reproducibility are small cohort size, nonvalidated studies, missing technical parameters, and lack of data, code, and model sharing. CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score), and the amalgamated CLEAR-RQS tool are useful frameworks for assessing radiomics research quality and may provide a valuable resource for educational purposes in the field of radiomics.

Unlocking ferroptosis in prostate cancer - the road to novel therapies and imaging markers.

Ferroptosis is a distinct form of regulated cell death that is predominantly driven by the build-up of intracellular iron and lipid peroxides. Ferroptosis suppression is widely accepted to contribute to the pathogenesis of several tumours including prostate cancer. Results from some studies reported that prostate cancer cells can be highly susceptible to ferroptosis inducers, providing potential for an interesting new avenue of therapeutic intervention for advanced prostate cancer. In this Perspective, we describe novel molecular underpinnings and metabolic drivers of ferroptosis, analyse the functions and mechanisms of ferroptosis in tumours, and highlight prostate cancer-specific susceptibilities to ferroptosis by connecting ferroptosis pathways to the distinctive metabolic reprogramming of prostate cancer cells. Leveraging these novel mechanistic insights could provide innovative therapeutic opportunities in which ferroptosis induction augments the efficacy of currently available prostate cancer treatment regimens, pending the elimination of major bottlenecks for the clinical translation of these treatment combinations, such as the development of clinical-grade inhibitors of the anti-ferroptotic enzymes as well as non-invasive biomarkers of ferroptosis. These biomarkers could be exploited for diagnostic imaging and treatment decision-making.

Biomarkers in neurodegenerative diseases: a broad overview

Degeneration and dysfunction of neurons in the brain are hallmarks of neurodegenerative diseases. Over the past decades, significant efforts have been devoted to the development and validation of biomarkers for neurodegenerative diseases. The range and diversity of biomarkers for central nervous system (CNS) diseases has continued to expand, encompassing biofluid-based sources such as blood or cerebrospinal fluid (CSF), nucleic acids, tissues, and imaging. While imaging and tissue biopsy-based markers are continually being identified and their applications expanding, they do have limitations compared with RNA and protein biomarkers. This review comprehensively summarizes various biomarkers, including microRNA (miRNA), long noncoding RNA (lncRNA), circulating miRNA (cimiRNA), and proteins, in the context of CNS disorders. In addition, the review emphasizes the existing limitations and challenges associated with the use of biomarkers in both clinical practice and research on neurodegenerative diseases. In conclusion, this review provides an insightful overview of the identified biomarkers for neurodegenerative diseases, underscoring the crucial role of biomarker research in combating these debilitating conditions. The article also highlights future challenges related to the implementation of novel biomarkers in clinical practice and trials, thereby contributing to the ongoing efforts to advance the understanding and management of neurodegenerative diseases.

Association of clinical outcome and imaging endpoints in extensive ischemic stroke—comparing measures of cerebral edema

Abstract Objectives Ischemic edema is associated with worse clinical outcomes, especially in large infarcts. Computed tomography (CT)–based densitometry allows direct quantification of absolute edema volume (EV), which challenges indirect biomarkers like midline shift (MLS). We compared EV and MLS as imaging biomarkers of ischemic edema and predictors of malignant infarction (MI) and very poor clinical outcome (VPCO) in early follow-up CT of patients with large infarcts. Materials and methods Patients with anterior circulation stroke, large vessel occlusion, and Alberta Stroke Program Early CT Score (ASPECTS) ≤ 5 were included. VPCO was defined as modified Rankin scale (mRS) ≥ 5 at discharge. MLS and EV were quantified at admission and in follow-up CT 24 h after admission. Correlation was analyzed between MLS, EV, and total infarct volume (TIV). Multivariable logistic regression and receiver operating characteristics curve analyses were performed to compare MLS and EV as predictors of MI and VPCO. Results Seventy patients (median TIV 110 mL) were analyzed. EV showed strong correlation to TIV (r = 0.91, p &lt; 0.001) and good diagnostic accuracy to classify MI (EV AUC 0.74 [95%CI 0.61–0.88] vs. MLS AUC 0.82 [95%CI 0.71–0.94]; p = 0.48) and VPCO (EV AUC 0.72 [95%CI 0.60–0.84] vs. MLS AUC 0.69 [95%CI 0.57–0.81]; p = 0.5) with no significant difference compared to MLS, which did not correlate with TIV &lt; 110 mL (r = 0.17, p = 0.33). Conclusion EV might serve as an imaging biomarker of ischemic edema in future studies, as it is applicable to infarcts of all volumes and predicts MI and VPCO in patients with large infarcts with the same accuracy as MLS. Clinical relevance statement Utilization of edema volume instead of midline shift as an edema parameter would allow differentiation of patients with large and small infarcts based on the extent of edema, with possible advantages in the prediction of treatment effects, complications, and outcome. Key Points • CT densitometry–based absolute edema volume challenges midline shift as current gold standard measure of ischemic edema. • Edema volume predicts malignant infarction and poor clinical outcome in patients with large infarcts with similar accuracy compared to MLS irrespective of the lesion extent. • Edema volume might serve as a reliable quantitative imaging biomarker of ischemic edema in acute stroke triage independent of lesion size.

Efficacy of pharmacologic interventions on magnetic resonance imaging biomarkers in patients with nonalcoholic fatty liver disease: systematic review and network meta‐analysis

Efficacy of pharmacologic interventions on magnetic resonance imaging biomarkers in patients with nonalcoholic fatty liver disease: systematic review and network meta‐analysis

Fractional amplitude of low-frequency fluctuation and voxel-mirrored homotopic connectivity in patients with persistent postural-perceptual dizziness: resting-state functional magnetic resonance imaging study.

Persistent postural-perception dizziness (PPPD) is a chronic subjective form of dizziness characterized by the exacerbation of dizziness with active or passive movement, complex visual stimuli, and upright posture. Therefore, we aimed to analyze the resting-state functional magnetic resonance imaging (fMRI) in patients with PPPD using fractional amplitude of low-frequency fluctuation (fALFF) and voxel-mirrored homotopic connectivity (VMHC) and evaluate the correlation between abnormal regions in the brain and clinical features to investigate the pathogenesis of PPPD. Thirty patients with PPPD (19 females and 11 males) and 30 healthy controls (HC) (18 females and 12 males) were closely matched for age and sex. The fALFF and VMHC methods were used to investigate differences in fMRI (BOLD sequences) between the PPPD and HC groups and to explore the associations between areas of functional abnormality and clinical characteristics (Dizziness, Anxiety, Depression, and Duration). Compared to the HC group, patients with PPPD displayed different functional change patterns, with increased fALFF in the right precuneus and decreased VMHC in the bilateral precuneus. Additionally, patients with PPPD had a positive correlation between precuneus fALFF values and dizziness handicap inventory (DHI) scores, and a negative correlation between VMHC values and the disease duration. Precuneus dysfunction was observed in patients with PPPD. The fALFF values correlated with the degree of dizziness in PPPD, and changes in VMHC values were associated with the duration of dizziness, suggesting that fMRI changes in the precuneus of patients could be used as a potential imaging marker for PPPD.

Abnormal Cardiac Magnetic Resonance-Derived Ascending Aortic Area Strain Demonstrates Altered Ventriculo-Vascular Function in Marfan Syndrome.

There remains a need for improved imaging markers for risk stratification and treatment guidance in Marfan syndrome (MFS). After aortic root replacement (ARR), vascular remodeling and progressive aneurysm formation can occur due to alterations in up- and downstream wall biomechanics and hemodynamics. We aim to compare the ventriculo-vascular properties of patients with MFS with controls, and investigate the correlation between ascending aortic area strain and descending aortic area strain (DAAS) with other clinical variables. Nineteen patients with MFS (47% males), including 6 with ARR were studied. In 26 studies, aortic area strain was measured using cross-sectional cardiac magnetic resonance images at the ascending and proximal descending aortic levels. Left atrial, left ventricular longitudinal, and left ventricle circumferential strain (left atrial longitudinal strain, left ventricular longitudinal strain, and left ventricular circumferential strain, respectively) were measured using cardiac magnetic resonance-feature tracking. Compared with healthy controls, patients with MFS had significantly impaired left ventricular longitudinal strain and left ventricular circumferential strain (-15.8 ± 4.7 vs -19.7 ± 4.8, P = 0.005, and -17.7 ± 4.0 vs -27.0 ± 4.1, P < 0.001). Left atrial longitudinal strain was comparable between patients with MFS and controls. AAAS was significantly reduced (19.0 [11.9, 23.7] vs 46.1 ± 11.3, P < 0.001), whereas DAAS was not significantly decreased. AAAS and DAAS were negatively correlated with age, whereas no significant associations were identified with left ventricle function indices. No significant differences were observed between the ventriculo-vascular properties of patients with MFS who underwent ARR and those who did not. Patients with MFS demonstrated impaired ventricular and vascular function compared with healthy controls. Further investigations are warranted to determine clinical utility of aortic stiffness indices for predicting primary and repeat aortic events.

Development and validation of an MRI-Based nomogram to predict the effectiveness of immunotherapy for brain metastasis in patients with non-small cell lung cancer.

The variability and unpredictability of immune checkpoint inhibitors (ICIs) in treating brain metastases (BMs) in patients with advanced non-small cell lung cancer (NSCLC) is the main concern. We assessed the utility of novel imaging biomarkers (radiomics) for discerning patients with NSCLC and BMs who would derive advantages from ICIs treatment. Data clinical outcomes and pretreatment magnetic resonance images (MRI) were collected on patients with NSCLC with BMs treated with ICIs between June 2019 and June 2022 and divided into training and test sets. Metastatic brain lesions were contoured using ITK-SNAP software, and 3748 radiomic features capturing both intra- and peritumoral texture patterns were extracted. A clinical radiomic nomogram (CRN) was built to evaluate intracranial progression-free survival, progression-free survival, and overall survival. The prognostic value of the CRN was assessed by Kaplan-Meier survival analysis and log-rank tests. In the study, a total of 174 patients were included, and 122 and 52 were allocated to the training and validation sets correspondingly. The intratumoral radiomic signature, peritumoral radiomic signature, clinical signature, and CRN predicted intracranial objective response rate. Kaplan-Meier analyses showed a significantly longer intracranial progression-free survival in the low-CRN group than in the high-CRN group (p < 0.001). The CRN was also significantly associated with progression-free survival (p < 0.001) but not overall survival. Radiomics biomarkers from pretreatment MRI images were predictive of intracranial response. Pretreatment radiomics may allow the early prediction of benefits.

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Perspective] Delta Radiomics — Potential Role in Head Neck Cancer

INTRODUCTION: Delta radiomics is a tool used to assess the response of oncologic patients undergoing immunotherapy. It extracts high-dimensional quantitative features from medical images, providing information about cancer's phenotype, genotype, and tumoral microenvironment. This analysis could help avoid invasive procedures and help choose the most suitable therapeutic in multiple therapeutic options. METHOD: Radiomics has gained interest as an imaging biomarker for predicting response to various immunotherapies. Delta radiomics assesses feature variations from one time point to another based on subsequent images, offering higher value for treatment-outcome prediction or patient stratification into risk categories. CONCLUSION: It has potential benefits for clinical endpoints in oncology, such as differential diagnosis, prognosis, treatment response prediction, and evaluation of side effects. Further research with prospective and multicentre studies is needed for clinical validation of delta radiomics approaches. STATEMENT OF CLINICAL SIGNIFICANCE: In head and neck oncology, delta radiomics can be used to enhance the precision of diagnosis, assess tumor response, forecast normal tissue toxicity, predict clinical outcome, and pinpoint characteristics for treatment modification. Patients' quality of life may be enhanced by it. It can support post-treatment surveillance. Additionally, it can support the delivery of individualized care based on a patient's reaction to medication and radiation.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Chitosan lecithin nanocomposite based electrochemical biosensor for glycine detection in biological matrices

Increased glycine concentrations are associated with altered metabolism of cancer cells and is reflected in the bodily fluids of the brain cancer patients. Various studies have been conducted in past to detect glycine as an imaging biomarker via NMR Spectroscopy tools. However, the use is limited because of the low concentration and different in vivo detection due to overlapping of peaks with myo-inositol in same spectral position. Alongside, little is known about the electrochemical potential of Glycine as a biomarker for brain cancer. The prime impetus of this study was to check the feasibility of glycine as non-invasive biomarker for brain cancer. A divergent approach to detect glycine “non-enzymatically” via unique chitosan lecithin nanocomposite has been utilised during this study. The electrochemical inactivity at provided potential that prevented glycine to get oxidized or reduced without mediator was compensated utilising the chitosan-lecithin nanocomposite. Thus, a redox mediator (Prussian blue) was used for high sensitivity and indirect detection of glycine. The chitosan nanoparticles-lecithin nanocomposite is used as a matrix. The electrochemical analysis of the onco-metabolomic biomarker (glycine) utilizing cyclic voltammetry in glycine spiked multi-Purpose artificial urine was performed to check distribution of glycine over physiological range of glycine. A wide linear range of response varying over the physiological range from 7 to 240 μM with a LOD 8.5 μM was obtained, showing potential of detection in biological samples. We have further evaluated our results via simulating the interaction of mediator and matrix with Glycine by HOMO-LUMO band fluctuations.

Contemporary Imaging Assessment of Strictures and Fibrosis in Crohn Disease, With Focus on Quantitative Biomarkers: From the AJR Special Series on Imaging of Fibrosis.

Patients with Crohn disease commonly have bowel strictures develop, which exhibit varying degrees of inflammation and fibrosis. Differentiation of the distinct inflammatory and fibrotic components of strictures is key for the optimization of therapeutic management and for the development of antifibrotic drugs. Cross-sectional imaging techniques, including ultrasound, CT, and MRI, allow evaluation of the full thickness of the bowel wall as well as extramural complications and associated mesenteric abnormalities. Although promising data have been reported for a range of novel imaging biomarkers for detection of fibrosis and quantification of the degree of fibrosis, these biomarkers lack sufficient validation and standardization for clinical use. Additional methods, including PET with emerging radiotracers, artificial intelligence, and radiomics, are also under investigation for stricture characterization. In this review, we highlight the clinical relevance of identifying fibrosis in Crohn disease, review the histopathologic aspects of strictures in Crohn disease, summarize the morphologic imaging findings of strictures, and explore contemporary developments in the use of cross-sectional imaging techniques for detecting and characterizing intestinal strictures, with attention given to emerging quantitative biomarkers.

Association of Pelvic Organ Prolapse and Changes in Bone Imaging Biomarkers in Postmenopausal Women with Low Bone Mineral Density

Association of Pelvic Organ Prolapse and Changes in Bone Imaging Biomarkers in Postmenopausal Women with Low Bone Mineral Density