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Archivio Italiano Di Urologia,... Jun 2024To evaluate the accuracy of PSMA PET/CT in men with mpMRI PI-RADS score 5 negative biopsy histology.
INTRODUCTION
To evaluate the accuracy of PSMA PET/CT in men with mpMRI PI-RADS score 5 negative biopsy histology.
MATERIALS AND METHODS
From January 2011 to January 2023, 180 men with PI-RADS score 5 underwent systematic plus mpMRI/TRUS biopsy; 25/180 (13.9%) patients had absence of cancer and six months from biopsy were submitted to: digital rectal examination, PSA and PSA density exams, mpMRI and 68GaPSMA PET/CT evaluation (standardized uptake value "SUVmax" was reported).
RESULTS
In 24/25 (96%) patients PSA and PSA density significantly decreased, moreover, the PI-RADS score was downgraded resulting < 3; in addition, median SUVmax was 7.5. Only 1/25 (4%) man had an increased PSA value (from 10.5 to 31 ng/ml) with a confirmed PI-RADS score 5, SUVmax of 32 and repeated prostate biopsy demonstrating a Gleason score 9/ISUP Grade Group 5 PCa.
CONCLUSIONS
The strict follow up of men with PI-RADS score 5 and negative histology reduce the risk of missing csPCa especially if PSMA PET/CT evaluation is in agreement with downgrading of mpMRI (PI-RADS score < 3).
Topics: Humans; Male; Positron Emission Tomography Computed Tomography; Prostatic Neoplasms; Aged; Middle Aged; Biopsy; Prostate-Specific Antigen; Prostate; Retrospective Studies; Multiparametric Magnetic Resonance Imaging
PubMed: 38934527
DOI: 10.4081/aiua.2024.12358 -
Plant, Cell & Environment Jun 2024Aquatic ferns of the genus Azolla (Azolla) form highly productive symbioses with filamentous cyanobacteria fixing N in their leaf cavities, Nostoc azollae. Stressed...
Biosynthesis and differential spatial distribution of the 3-deoxyanthocyanidins apigenidin and luteolinidin at the interface of a plant-cyanobacteria symbiosis exposed to cold.
Aquatic ferns of the genus Azolla (Azolla) form highly productive symbioses with filamentous cyanobacteria fixing N in their leaf cavities, Nostoc azollae. Stressed symbioses characteristically turn red due to 3-deoxyanthocyanidin (DA) accumulation, rare in angiosperms and of unknown function. To understand DA accumulation upon cold acclimation and recovery, we integrated laser-desorption-ionization mass-spectrometry-imaging (LDI-MSI), a new Azolla filiculoides genome-assembly and annotation, and dual RNA-sequencing into phenotypic analyses of the symbioses. Azolla sp. Anzali recovered even when cold-induced DA-accumulation was inhibited by abscisic acid. Cyanobacterial filaments generally disappeared upon cold acclimation and Nostoc azollae transcript profiles were unlike those of resting stages formed in cold-resistant sporocarps, yet filaments re-appeared in leaf cavities of newly formed green fronds upon cold-recovery. The high transcript accumulation upon cold acclimation of AfDFR1 encoding a flavanone 4-reductase active in vitro suggested that the enzyme of the first step in the DA-pathway may regulate accumulation of DAs in different tissues. However, LDI-MSI highlighted the necessity to describe metabolite accumulation beyond class assignments as individual DA and caffeoylquinic acid metabolites accumulated differentially. For example, luteolinidin accumulated in epithelial cells, including those lining the leaf cavity, supporting a role for the former in the symbiotic interaction during cold acclimation.
PubMed: 38932650
DOI: 10.1111/pce.15010 -
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Jun 2024Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting...
Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of ischemic stroke. Accurate segmentation of the infarct is of great significance for selecting intervention treatment methods and evaluating the prognosis of patients. To address the issue of poor segmentation accuracy of existing methods for multiscale stroke lesions, a novel encoder-decoder architecture network based on depthwise separable convolution is proposed. Firstly, this network replaces the convolutional layer modules of the U-Net with redesigned depthwise separable convolution modules. Secondly, an modified Atrous spatial pyramid pooling (MASPP) is introduced to enlarge the receptive field and enhance the extraction of multiscale features. Thirdly, an attention gate (AG) structure is incorporated at the skip connections of the network to further enhance the segmentation accuracy of multiscale targets. Finally, Experimental evaluations are conducted using the ischemic stroke lesion segmentation 2022 challenge (ISLES2022) dataset. The proposed algorithm in this paper achieves Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity (SEN), and precision (PRE) scores of 0.816 5, 3.668 1, 0.889 2, and 0.894 6, respectively, outperforming other mainstream segmentation algorithms. The experimental results demonstrate that the method in this paper effectively improves the segmentation of infarct lesions, and is expected to provide a reliable support for clinical diagnosis and treatment.
Topics: Humans; Magnetic Resonance Imaging; Algorithms; Ischemic Stroke; Image Processing, Computer-Assisted; Multimodal Imaging; Neural Networks, Computer
PubMed: 38932540
DOI: 10.7507/1001-5515.202308001 -
[Pulmonary PET /CT image instance segmentation based on dense interactive feature fusion Mask RCNN].Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Jun 2024There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions,...
There are some problems in positron emission tomography/ computed tomography (PET/CT) lung images, such as little information of feature pixels in lesion regions, complex and diverse shapes, and blurred boundaries between lesions and surrounding tissues, which lead to inadequate extraction of tumor lesion features by the model. To solve the above problems, this paper proposes a dense interactive feature fusion Mask RCNN (DIF-Mask RCNN) model. Firstly, a feature extraction network with cross-scale backbone and auxiliary structures was designed to extract the features of lesions at different scales. Then, a dense interactive feature enhancement network was designed to enhance the lesion detail information in the deep feature map by interactively fusing the shallowest lesion features with neighboring features and current features in the form of dense connections. Finally, a dense interactive feature fusion feature pyramid network (FPN) network was constructed, and the shallow information was added to the deep features one by one in the bottom-up path with dense connections to further enhance the model's perception of weak features in the lesion region. The ablation and comparison experiments were conducted on the clinical PET/CT lung image dataset. The results showed that the APdet, APseg, APdet_s and APseg_s indexes of the proposed model were 67.16%, 68.12%, 34.97% and 37.68%, respectively. Compared with Mask RCNN (ResNet50), APdet and APseg indexes increased by 7.11% and 5.14%, respectively. DIF-Mask RCNN model can effectively detect and segment tumor lesions. It provides important reference value and evaluation basis for computer-aided diagnosis of lung cancer.
Topics: Humans; Lung Neoplasms; Positron Emission Tomography Computed Tomography; Lung; Algorithms; Image Processing, Computer-Assisted; Neural Networks, Computer
PubMed: 38932539
DOI: 10.7507/1001-5515.202309026 -
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Jun 2024Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is...
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.
Topics: Alzheimer Disease; Humans; Magnetic Resonance Imaging; Early Diagnosis; Neural Networks, Computer; Disease Progression; Algorithms
PubMed: 38932534
DOI: 10.7507/1001-5515.202310046 -
Sensors (Basel, Switzerland) Jun 2024This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands...
Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial-Spectral Fusion Features.
This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.
Topics: Glioma; Humans; Brain Neoplasms; Neoplasm Grading; Hyperspectral Imaging; Algorithms; Image Processing, Computer-Assisted
PubMed: 38931588
DOI: 10.3390/s24123803 -
Sensors (Basel, Switzerland) Jun 2024Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is...
Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.
PubMed: 38931562
DOI: 10.3390/s24123778 -
Sensors (Basel, Switzerland) Jun 2024Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on...
Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches-Audio Branch, Video Branch, and Text Branch-each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks-reading and interviewing-implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.
Topics: Humans; Depression; Video Recording; Emotions; Deep Learning; Facial Expression; Female; Male; Adult; Neural Networks, Computer
PubMed: 38931497
DOI: 10.3390/s24123714 -
Journal of Clinical Medicine Jun 2024: To investigate anatomical and functional changes of the macula caused by epiretinal membrane (ERM) peeling procedures in patients with or without posterior vitreous...
: To investigate anatomical and functional changes of the macula caused by epiretinal membrane (ERM) peeling procedures in patients with or without posterior vitreous detachment (PVD). : This is a multicentric prospective observational study on thirty-seven (37) patients affected by symptomatic ERM who underwent 25-gauge pars plana vitrectomy (PPV), induction of a PVD (as needed) and peeling of both the internal limiting membrane (ILM) and ERM. Optical coherence tomography-angiography (OCT-A) (, Nidek, Japan) and microperimetry (, Nidek, Japan) were performed; central retinal thickness (CRT), foveal avascular zone (FAZ) area and perimeter, vessel density and perfusion density, retinal sensitivity and fixation stability (as a total mean retinal sensitivity (MRS), and MRS in the ellipse area and bivariate contour ellipse area (BCEA)) were recorded at baseline and up to postoperative month 3. : Eyes were classified as having complete PVD (51.4%) or incomplete PVD (48.6%). At baseline, patients with incomplete PVD had worse best-corrected distance visual acuity (BCDVA), total MRS, MRS in the ellipse area and BCEA, and higher CRT than patients with complete PVD. At month 3, the differences in BCDVA between the two groups remained statistically significant, with patients with incomplete PVD having worse results (difference: 0.199 logMAR, < 0.001). The difference in the MRS in the ellipse area was statistically significant at month 3 (-3.378 Db, = 0.035), with greater improvement in patients with complete PVD. : Our study shows that patients with incomplete PVD have worse conditions at baseline than patients with complete PVD, and the differences in visual acuity and retinal sensitivity were maintained postoperatively.
PubMed: 38930094
DOI: 10.3390/jcm13123565 -
Children (Basel, Switzerland) May 2024The complete transposition of the great arteries (C-TGA) is a congenital cardiac anomaly characterized by the reversal of the main arteries. Early detection and precise... (Review)
Review
The complete transposition of the great arteries (C-TGA) is a congenital cardiac anomaly characterized by the reversal of the main arteries. Early detection and precise management are crucial for optimal outcomes. This review emphasizes the integral role of multimodal imaging, including fetal echocardiography, transthoracic echocardiography (TTE), cardiovascular magnetic resonance (CMR), and cardiac computed tomography (CCT) in the diagnosis, treatment planning, and long-term follow-up of C-TGA. Fetal echocardiography plays a pivotal role in prenatal detection, enabling early intervention strategies. Despite technological advances, the detection rate varies, highlighting the need for improved screening protocols. TTE remains the cornerstone for initial diagnosis, surgical preparation, and postoperative evaluation, providing essential information on cardiac anatomy, ventricular function, and the presence of associated defects. CMR and CCT offer additional value in C-TGA assessment. CMR, free from ionizing radiation, provides detailed anatomical and functional insights from fetal life into adulthood, becoming increasingly important in evaluating complex cardiac structures and post-surgical outcomes. CCT, with its high-resolution imaging, is indispensable in delineating coronary anatomy and vascular structures, particularly when CMR is contraindicated or inconclusive. This review advocates for a comprehensive imaging approach, integrating TTE, CMR, and CCT to enhance diagnostic accuracy, guide therapeutic interventions, and monitor postoperative conditions in C-TGA patients. Such a multimodal strategy is vital for advancing patient care and improving long-term prognoses in this complex congenital heart disease.
PubMed: 38929206
DOI: 10.3390/children11060626