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Scientific Reports Jul 2024Using 70 U/ml or 35 U/ml as CA125 routine abnormal threshold may result in omissions in the relapse detection of Ovarian cancer (OvCa). This study aimed to clarify the...
Using 70 U/ml or 35 U/ml as CA125 routine abnormal threshold may result in omissions in the relapse detection of Ovarian cancer (OvCa). This study aimed to clarify the association between a biochemical relapse (only the elevation of CA125) and an image-identified relapse to predict the relapsed lesions better. 162 patients who achieved complete clinical response were enrolled from women diagnosed with stage I-IV serous ovarian, tubal, and peritoneal cancers from January 2013 to June 2019 at our center. The CA125 level of 2 × nadir was defined as the indicator of image-identified relapse (P < 0.001). Compared to CA125 level exceeding 35 U/ml, the 2 × nadir of CA125 improve the sensitivity of image-identified relapse (84.9% vs 67.4%, P < 0.001); the 2 × nadir value can act as an earlier warning relapse signal with a longer median time to image-identified relapse (2.7 vs. 0 months, P < 0.001). Of the relapsed population, there was no difference of CA125 changing trend between the neoadjuvant chemotherapy (NACT) and primary debulking surgery (PDS) group after initial treatment. Compared with 35 U/ml, CA125 reaching 2 × nadir during the follow-up process might be a more sensitive and early relapse signal in patients with serous OvCa. This criterion may help guide patients to be recommended for imaging examination to detect potential relapse in time.
Topics: Humans; Female; CA-125 Antigen; Middle Aged; Ovarian Neoplasms; Neoplasm Recurrence, Local; Aged; Adult; Cystadenocarcinoma, Serous; Biomarkers, Tumor; Neoadjuvant Therapy; Retrospective Studies; Membrane Proteins
PubMed: 38951620
DOI: 10.1038/s41598-024-65760-4 -
Scientific Reports Jul 2024Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful...
Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans. This pipeline first detects and labels vertebral bodies across several commonly used sequences (e.g. T1w, T2w and STIR) and fields of view (e.g. lumbar, cervical, whole spine). Using these detections it then performs automated diagnosis for several spinal disorders, including intervertebral disc degenerative changes in T1w and T2w lumbar scans, and spinal metastases, cord compression and vertebral fractures. To achieve this, we propose a new method of vertebrae detection and labelling, using vector fields to group together detected vertebral landmarks and a language-modelling inspired beam search to determine the corresponding levels of the detections. We also employ a new transformer-based architecture to perform radiological grading which incorporates context from multiple vertebrae and sequences, as a real radiologist would. The performance of each stage of the pipeline is tested in isolation on several clinical datasets, each consisting of 66 to 421 scans. The outputs are compared to manual annotations of expert radiologists, demonstrating accurate vertebrae detection across a range of scan parameters. Similarly, the model's grading predictions for various types of disc degeneration and detection of spinal metastases closely match those of an expert radiologist. To aid future research, our code and trained models are made publicly available.
Topics: Humans; Magnetic Resonance Imaging; Spinal Diseases; Spine; Intervertebral Disc Degeneration; Image Processing, Computer-Assisted; Image Interpretation, Computer-Assisted
PubMed: 38951574
DOI: 10.1038/s41598-024-64580-w -
Scientific Reports Jul 2024A dielectric waveguide, inserted into blood vessels, supports its basic mode that is being scattered by a near-field intravascular inclusion. A rigorous integral...
A dielectric waveguide, inserted into blood vessels, supports its basic mode that is being scattered by a near-field intravascular inclusion. A rigorous integral equation formulation is performed and the electromagnetic response from that inhomogeneity is semi-analytically evaluated. The detectability of the formation, based on spatial distribution of the recorded signal, is estimated by considering various inclusion sizes, locations and textural contrasts. The proposed technique, with its variants and generalizations, provides a generic versatile toolbox to efficiently model biosensor layouts involved in healthcare monitoring and disease screening.
Topics: Biosensing Techniques; Humans; Blood Vessels; Models, Theoretical
PubMed: 38951563
DOI: 10.1038/s41598-024-64633-0 -
Scientific Reports Jul 2024Digital positron emission tomography/computed tomography (PET/CT) has shown enhanced sensitivity and spatial resolution compared with analog PET/CT. The present study... (Comparative Study)
Comparative Study
Digital positron emission tomography/computed tomography (PET/CT) has shown enhanced sensitivity and spatial resolution compared with analog PET/CT. The present study compared the diagnostic performance of digital and analog PET/CT with [Ga]Ga-PSMA-11 in prostate cancer patients who experienced biochemical recurrence (BCR) after prostatectomy. Forty prostate cancer patients who experienced BCR, defined as serum prostate-specific antigen (PSA) concentrations exceeding 0.2 ng/mL after prostatectomy, were prospectively recruited. These patients were stratified into three groups based on their serum PSA levels. [Ga]Ga-PSMA-11 was injected into each patient, and images were acquired using both analog and digital PET/CT scanners. Analog and digital PET/CT showed comparable lesion detection rate (71.8% vs. 74.4%), sensitivity (85.0% vs. 90.0%), and positive predictive value (PPV, 100.0% vs. 100.0%). However, digital PET/CT detected more lesions (139 vs. 111) and had higher maximum standardized uptake values (SUVmax, 14.3 vs. 10.3) and higher kappa index (0.657 vs. 0.502) than analog PET/CT, regardless of serum PSA levels. On both analog and digital PET/CT, lesion detection rates and interrater agreement increased with increasing serum PSA levels. Compared with analog PET/CT, digital PET/CT detected more lesions with a higher SUVmax and better interrater agreement in prostate cancer patients who experienced BCR after prostatectomy.
Topics: Humans; Male; Prostatic Neoplasms; Prostatectomy; Positron Emission Tomography Computed Tomography; Aged; Prospective Studies; Gallium Radioisotopes; Middle Aged; Neoplasm Recurrence, Local; Gallium Isotopes; Prostate-Specific Antigen; Edetic Acid; Oligopeptides
PubMed: 38951530
DOI: 10.1038/s41598-024-65399-1 -
Scientific Reports Jul 2024Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some...
Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.
Topics: Neural Networks, Computer; Humans; Image Processing, Computer-Assisted; Algorithms; Diagnostic Imaging; Image Interpretation, Computer-Assisted
PubMed: 38951526
DOI: 10.1038/s41598-024-64982-w -
Nature Communications Jun 2024When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural...
When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations. Prior work has focused on the internal representations ("embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized "transformations" that integrate contextual information across words. Using functional MRI data acquired while participants listened to naturalistic stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers and context lengths in a low-dimensional cortical space.
Topics: Humans; Magnetic Resonance Imaging; Language; Brain; Male; Female; Adult; Neural Networks, Computer; Brain Mapping; Young Adult; Models, Neurological; Natural Language Processing
PubMed: 38951520
DOI: 10.1038/s41467-024-49173-5 -
Translational Psychiatry Jun 2024The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant...
Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome.
The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.
Topics: Humans; Connectome; Schizophrenia; Magnetic Resonance Imaging; Attention Deficit Disorder with Hyperactivity; Female; Male; Adult; Brain; Nerve Net; Bipolar Disorder; Young Adult; Middle Aged; Mental Disorders
PubMed: 38951513
DOI: 10.1038/s41398-024-02929-5 -
Communications Biology Jun 2024Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended...
Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.
Topics: Humans; Spectrum Analysis, Raman; Phenotype; Colonic Neoplasms; Cell Line, Tumor
PubMed: 38951178
DOI: 10.1038/s42003-024-06496-9 -
Scientific Reports Jul 2024Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and...
Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.
Topics: Animals; Diffusion Tensor Imaging; Rats; Brain; Signal-To-Noise Ratio; Phantoms, Imaging; Artifacts; Image Processing, Computer-Assisted; Anisotropy; Male
PubMed: 38951163
DOI: 10.1038/s41598-024-66076-z -
Communications Biology Jul 2024Brown and brown-like adipose tissues have attracted significant attention for their role in metabolism and therapeutic potential in diabetes and obesity. Despite...
Brown and brown-like adipose tissues have attracted significant attention for their role in metabolism and therapeutic potential in diabetes and obesity. Despite compelling evidence of an interplay between adipocytes and lymphocytes, the involvement of these tissues in immune responses remains largely unexplored. This study explicates a newfound connection between neuroinflammation and brown- and bone marrow adipose tissue. Leveraging the use of [F]F-AraG, a mitochondrial metabolic tracer capable of tracking activated lymphocytes and adipocytes simultaneously, we demonstrate, in models of glioblastoma and multiple sclerosis, the correlation between intracerebral immune infiltration and changes in brown- and bone marrow adipose tissue. Significantly, we show initial evidence that a neuroinflammation-adipose tissue link may also exist in humans. This study proposes the concept of an intricate immuno-neuro-adipose circuit, and highlights brown- and bone marrow adipose tissue as an intermediary in the communication between the immune and nervous systems. Understanding the interconnectedness within this circuitry may lead to advancements in the treatment and management of various conditions, including cancer, neurodegenerative diseases and metabolic disorders.
Topics: Animals; Humans; Adipose Tissue, Brown; Neuroinflammatory Diseases; Bone Marrow; Mice; Male; Glioblastoma; Mice, Inbred C57BL; Female; Multiple Sclerosis; Positron-Emission Tomography
PubMed: 38951146
DOI: 10.1038/s42003-024-06494-x