[99mTc]Tc-HYNIC-ALUG SPECT/CT in the initial staging of 227 consecutive patients with newly diagnosed prostate cancer: a 5-year monocentric retrospective study.

Purpose: The purpose of this study was to assess the effectiveness of [99mTc]Tc-HYNIC-ALUG SPECT/CT in the initial staging of patients with newly diagnosed PCa. Methods: A retrospective analysis was conducted on 227 consecutive patients who underwent [99mTc]Tc-HYNIC-ALUG SPECT/CT imaging for the primary staging of newly diagnosed PCa. The presence and location of PSMA-positive lesions were determined, and the maximum standardized uptake values (SUVmax) of the primary prostate tumor were also measured. The metastatic findings and SUVmax were stratified according to International Society of Urological Pathology (ISUP) grade, prostate-specific antigen (PSA) levels, and D'Amico classification. Furthermore, the [99mTc]Tc-HYNIC-ALUG SPECT/CT findings were compared to the histopathological findings in patients who had undergone radical prostatectomy with pelvic lymph node dissection (PLND). Results: Of the 227 patients, 92.1% (209/227) had positive [99mTc]Tc-HYNIC-ALUG SPECT/CT findings. Advanced disease was detected in 38.8% (88/227) of the patients and was positively correlated with increasing ISUP grade and PSA levels. Lymph node metastases (both pelvic and extrapelvic), bone metastases, and visceral metastases were detected in 30.0% (68/227), 25.6% (58/227), and 3.1% (7/227) of the patients, respectively. For the 129 patients who underwent radical prostatectomy with PLND, the sensitivity of [99mTc]Tc-HYNIC-ALUG SPECT/CT in the evaluation of PCa was 90.7% (117/129). The sensitivity, specificity, accuracy, and positive and negative predictive values for detecting pelvic lymph node metastases on [99mTc]Tc-HYNIC-ALUG SPECT/CT were 23.5% (12/51), 93.6% (73/78), 65.9% (85/129), 70.6% (12/17), and 65.2% (73/112), respectively. Among the 209 patients with PSMA-avid primary prostate disease, the SUVmax of the primary prostate tumor was significantly associated with ISUP grade (p<0.0001), PSA levels (p<0.0001), D'Amico classification (p<0.0001), and advanced disease (p<0.0001). Receiver operating characteristic (ROC) analysis revealed that a PSA level >19.8 ng/ml and SUVmax of the primary prostate tumor >7.4 had a sensitivity of 71.6% and 71.6% and specificity of 76.9% and 82.6%, respectively, for detecting metastatic disease. Conclusions: [99mTc]Tc-HYNIC-ALUG SPECT/CT emerges as a valuable imaging tool for the initial staging of newly diagnosed PCa. The presence of advanced disease and the SUVmax of the primary prostate tumor were positively correlated with ISUP grade and PSA levels.

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.

Distribution-Guided Network Thresholding for Functional Connectivity Analysis in fMRI-Based Brain Disorder Identification.

Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.g., strong associations) between brain regions in subject groups. In this work, we propose a distribution-guided network thresholding learning (DNTL) method for FC network analysis in brain disorder identification with rs-fMRI. Specifically, for each connection of a pair of brain regions, we propose to determine its specific threshold based on the distribution of connection strength (i.e., temporal correlation) between subject groups (e.g., patients and normal controls). The proposed DNTL can adaptively yield an FC-specific threshold for each connection in an FC network, thus preserving diversity of temporal correlation among different brain regions. Experiment results on 365 subjects from two datasets (i.e., ADNI and ADHD-200) suggest that the DNT method outperforms state-of-the-art methods in brain disorder identification with rs-fMRI data.

Convergent Evolution in Breadth of Two VH6-1-Encoded Influenza Antibody Clonotypes from a Single Donor.

Understanding how broadly neutralizing antibodies (bnAbs) to influenza hemagglutinin (HA) naturally develop in humans is critical to the design of universal influenza vaccines. Several classes of bnAbs directed to the conserved HA stem were found in multiple individuals, including one encoded by heavy-chain variable domain VH6-1. We describe two genetically similar VH6-1 bnAb clonotypes from the same individual that exhibit different developmental paths toward broad neutralization activity. One clonotype evolved from a germline precursor recognizing influenza group 1 subtypes to gain breadth to group 2 subtypes. The other clonotype recognized group 2 subtypes and developed binding to group 1 subtypes through somatic hypermutation. Crystal structures reveal that the specificity differences are primarily mediated by complementarity-determining region H3 (CDR H3). Thus, while VH6-1 provides a framework for development of HA stem-directed bnAbs, sequence differences in CDR H3 junctional regions during VDJ recombination can alter reactivity and evolutionary pathways toward increased breadth.

A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

Probability method for Cerenkov luminescence tomography based on conformance error minimization.

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust. In addition, the reconstructed result may not match reality since the difference between the highest and lowest uptakes of the resulting radiotracers may be considerably large, therefore the biological significance is lost. In this paper, based on the minimization of a conformance error, a probability method is proposed that consists of qualitative and quantitative modules. The proposed method first pinpoints the organ that contains the light source. Next, we developed a 0-1 linear optimization subject to a space constraint to model the CLT inverse problem, which was transformed into a forward problem by employing a region growing method to solve the optimization. After running through all of the elements used to grow the sources, a source sequence was obtained. Finally, the probability of each discrete node being the light source inside the organ was reconstructed. One numerical study and two in vivo experiments were conducted to verify the performance of the proposed algorithm, and comparisons were carried out using the hp-finite element method (hp-FEM). The results suggested that our proposed probability method was more robust and reasonable than hp-FEM.