Node Dimensions Research Articles

Tips and tricks for a proper radiological assessment of abdominal and pelvic lymph nodes.

The assessment of lymph node dimensions is a commonly used criterion in analyzing lymphatic involvement related to inflammatory or neoplastic diseases. However, it is important to understand that the interpretation of lymph nodes goes beyond simply considering their size. A pathologic lymph node can present with enlarged dimensions, a heterogeneous appearance, increased cortex thickness, irregular contours, or a lobulated shape. In this context, it is essential to consider not only the dimensions but also the morphology, attenuation, and enhancement of lymph nodes on imaging exams. This article aims to demonstrate how characteristics of lymph nodes, beyond their size, can provide crucial insights that assist in diagnostic reasoning, focusing on computed tomography. By emphasizing different enhancement patterns, attenuation, and the potential contents related to these patterns, the study seeks to show how these features can indicate possible differential diagnoses and guide more accurate clinical assessments.

Fixed-time synchronization of multilayered complex dynamic networks via quantized variable-gain saturated control

This paper studies fixed-time (FxT) quantitative synchronization of multilayered complex dynamic networks (CDNs). First, a new FxT stability theorem is established and two new estimations of the settling time of stability are acquired, which are more accurate than the existing ones. Then, by using the improved theorem, several sufficient conditions ensuring FxT synchronization of a multilayered CDN are derived via two classes of innovative quantized variable-gain saturated controllers, and some high-precision estimates of the synchronous settling time (SST) are attained. In general, the saturation function used to replace the signum function is nonlinear and even includes the odd-even requirement on the exponent, and no quantization is involved in the developed FxT controllers. However, in our design, the classical linear saturation function is employed and the input signals of the controllers are also quantized. Therefore, our proposed strategies are more practical and easier to implement. Besides, different from the traditional 2-norm/1-norm-based approaches, a novel nonsmooth Lyapunov function is constructed so that the resultant estimates of the SST are unrelated to either the network size or the node's dimension, which are less conservative and closer to the actual synchronized time. Lastly, some numerical examples are given to validate the theoretical results.

Pathological Investigation of Double–Stranded DNA Breaks and DNA Oxidation in Natural Infection with Mycobacterium avium subspecies paratuberculosis in Goats

Paratuberculosis, created by Mycobacterium avium subspecies paratuberculosis (MAP), manifests as a chronic affliction marked by persistent diarrhoea and granulomatous enteritis, pervasive in both domestic and global wild ruminants. In this investigation, DNA disruption in lesioned tissues of goat as natural infecte with MAP was pathologically assessed. Accordingly, goats manifesting symptoms suggestive to paratuberculosis, including pronounced emaciation and continual episodic diarrhoea, were subjected to an ELISA diagnostic procedure to ascertain the presence of MAP. This diagnostic approach confirmed the presence of the infectious agent in 20 patients. These patients were subsequently euthanized, and tissue samples from intestinal and regional lenf nods. It were subjected to Hematoxylin and Eosin (HE) staining for histopathological investigatıon, Ziehl Neelsen (ZN) staining to identify acid–fast mycobacteria, γ–H2AX to discern disruptions in double stranded DNA, and 8–Ohdg to detect DNA oxidation by immunohistochemical (IHC) method. Gross anatomical observation serous adipose atrophy, augmented dimensions of mesenterial lymphatic nodes, mucosal hypertrophy and non–retractable mucosal undulations. Histological assessment highlighted epithelial cellular degeneration, an abundance of epithelioid macrophages, lymphocytes, plasmocytes, infiltrating in mucosa. Acid–fast entities, discernible through ZN staining, appeared as luminescent red conglomerates in intestinal and mesenterial tissue. The immunohistochemical analyses evinced positive results for both γ–H2AX and 8–Ohdg across all sampled tissues. Intriguingly, this investigation presented the inaugural global evidence of γ–H2AX and 8–Ohdg expression in a natural MAP infection, demonstrating that this pathological agent precipitates DNA degradation and oxidation, thereby augmenting comprehension of the disease’s pathogenesis.

Leveraging Variable Density Honeycomb Structures for Innovative Design in Mission-Critical Embedded Devices

The imperative for lightweighting technologies, paramount in mission-critical cyber-physical systems (CPSs) including aerospace, automotive and allied sectors, hinges upon optimizing energy efficiency and curbing product weight. Honeycomb structures, celebrated for their exceptional strength-to-weight ratio, have indisputably guided the pursuit of lightweight design. This paper expounds upon the versatility of honeycomb structures by scrutinizing their in-plane mechanical attributes. Leveraging finite element simulations and polynomial fitting, we enhance the prevailing equivalent elastic modulus model for uniform honeycomb structures, expanding its domain to encompass a broader spectrum of relative density values. Deliberations ensue concerning the model’s constraints and its inapplicability to nonuniform honeycomb structures. The investigation introduces nodes as pivotal influencers in the mechanical comportment of nonuniform honeycomb structures, delineating the nexus between the equivalent elastic modulus and node dimensions through a fusion of finite element simulations and mechanical experimentation. Furthermore, this research delves into the tenets and constructs of density-based variable density methodologies within the ambit of topology optimization, with an overarching goal of maximizing stiffness. We furnish a holistic design protocol for optimizing honeycomb structures, underscored by a pragmatic instantiation of the density-based variable density approach. Scrutinizing the geometric interplay between honeycomb structures and design spaces, we posit an innovative paradigm employing concentric circles to approximate cellular envelopes, streamlining numerical cartography and the conversion of optimization outputs into variable density honeycomb configurations. Evaluation of the in-plane mechanical attributes of variable density honeycomb structures reveals that TPU material augments the resilience of both uniform and variable density honeycomb structures, whereas topology optimization amplifies specific stiffness and resilience modulus in variable density honeycomb structures relative to their uniform counterparts. This study sheds light on the complexities of honeycomb structures, providing valuable insights for their optimization in lightweight applications.

Research on Collaborative Evaluation of Offshore Wind Power Value Chain in Guangxi-Based on BWM-FVIKOR Model

With the dynamic development of the offshore wind power industry and the global value chain, the traditional wind power industry is gradually turning to the offshore wind power value chain system that focuses on the value effect of the value chain, and it is of great significance to carry out value chain collaborative evaluation research. This paper puts forward the evaluation index system of Guangxi offshore wind power value chain from the comprehensive system dimension, key node dimension, subject contribution dimension and resource endowment dimension, builds the BWM-FVIKOR evaluation model, and proves the validity of the evaluation index and the feasibility of the evaluation model through example analysis, and this paper provides scientific and effective decision-making basis for the coordination of Guangxi offshore wind power value chain.

Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer's Disease and Cognitive Impairment.

Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed 'node-metric coupling' (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research.

Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method

The correlation between multiple patterns of micromagnetic signatures and the mechanical properties (yield strength (Rp) and tensile strength (Rm) of high-strength steel (referred to as DP590 steel in Chinese standards) was investigated in this study. Feedforward neural network (FF-NN) models with carefully selected magnetic features as input nodes were established for quantitative prediction of yield strength and tensile strength. The accuracy FF-NN models highly relied on the quality of calibration specimens and the way of selecting magnetic features. The variations of the measured target properties were used to evaluate the quality of the calibration specimens. The specimens with similar yield strength (or tensile strength) were merged to share the same target properties in the model training process. The results demonstrated that merging proper target properties (label) could improve the performance of the models in quantitative prediction of yield strength and tensile strength in DP590 steels. In addition, the performances of FF-NN models combined with the algorithms of ReliefF and ReliefF + clustering were evaluated. The comparison results proved that the FF-NN models employing input nodes selection strategy of ReliefF + clustering realized the advantages of smaller dimensions of input nodes, less training time consumption at the cost of slight accuracy reduction.

Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), reliable and rapid neighbour node discovery is considered as the crucial operation which frequently needs to be executed over the entire lifecycle. Several neighbour node discovery mechanisms are proposed for reducing the latency or extending the sensor nodes’ lifetime. But majority of the existing neighbour node discovery mechanisms failed in addressing the critical issues of real WSNs related to energy consumptions, constraints of latency, uncertainty of node behaviors, and communication collisions. In this paper, Hybrid Interval Type-2 Fuzzy Analytical Hierarchical Process (AHP) and Complex Proportional Assessment using Grey Theory (COPRAS-G)-based trusted neighbour node discovery scheme (FAHPCG) is proposed for better data dissemination process. In specific, Interval Type 2 Fuzzy AHP is applied for determining the weight of the evaluation criteria considered for neighbour node discovery, and then Grey COPRAS method is adopted for prioritizing the sensor nodes of the routing path established between the source and destination. It adopted the merits of fuzzy theory for handling the uncertainty and vagueness involved in the change in the behavior of sensor nodes during the process of neighbour discovery. It is proposed with the capability of exploring maximized number of factors that aids in exploring the possible dimensions of sensor nodes packet forwarding potential during the process of neighbour node discovery. The simulation results of the proposed FAHPCG scheme confirmed an improved neighbour node discovery rate of 23.18% and prolonged the sensor nodes lifetime to the maximum of 7.12 times better than the baseline approaches used for investigation.

Novel high-speed monolithic silicon detector for particle physics

This contribution presents simulation results, implementation, and first tests of a monolithic detector developed at KIT. It consists of a sensor diode tightly integrated with an analogue front-end based on SiGe (Silicon-Germanium) SG13G2 130 nm BiCMOS technology produced at the Leibniz Institute for High Performance Microelectronics (IHP). The pixel size is 100 μm × 100 μm, and the nwell charge collection node dimensions were reduced to 10 μm × 10 μm. We investigate the influence of this approach on sensor performance, spatial resolution via charge sharing and timing behaviour.

CTNRL

Network representation learning is one of the important works of analyzing network information. Its purpose is to learn a vector for each node in the network and map it into the vector space, and the resulting number of node dimensions is much smaller than the number of nodes in the network. Most of the current work only considers local features and ignores other features in the network, such as attribute features. Aiming at such problems, this paper proposes novel mechanisms of combining network topology, which models node text information and node clustering information on the basis of network structure and then constrains the learning process of network representation to obtain the optimal network node vector. The method is experimentally verified on three datasets: Citeseer (M10), DBLP (V4), and SDBLP. Experimental results show that the proposed method is better than the algorithm based on network topology and text feature. Good experimental results are obtained, which verifies the feasibility of the algorithm and achieves the expected experimental results.

Preoperative prediction of retroperitoneal lymph node involvement in clinical stage IB and IIA cervical cancer.

Lymph node metastasis (LNM) is the most important factor affecting survival in early-stage cervical cancer (CC). International Federation of Gynecology and Obstetrics revised the staging of CC in 2018 and reported LNM as a staging criterion. We investigated the preoperatively assessable risk factors associated with LNM in surgically treated stage IB1-IIA2 CC patients. This was a retrospective cohort study of women who underwent radical hysterectomy and pelvic lymphadenectomy with or without para-aortic lymphadenectomy for CC stage IB1-IIA2 from 2004 to 2019. All patients included in this study were examined with speculum inspection, parametrial assessment by rectovaginal palpation under general anesthesia, transvaginal ultrasonography, magnetic resonance imaging (MRI), and chest radiography. Clinical staging was done according to the preoperative findings. MRI was used to measure tumor and lymph node dimensions. Out of the 149 women included in the study, 29 (19.4%) had LNM. Univariate analysis revealed that larger tumor size (≥30 mm), lymphovascular space invasion (LVSI) detected with diagnostic biopsy, parametrial involvement, and deep stromal invasion status were significantly different between the group with LNM and the group without LNM. In multivariate analysis, specific preoperative risk factors such as MRI based tumor diameter ≥30 mm and LVSI (+) on the diagnostic biopsy were found to be independent risk factors for LNM in the multivariate analysis. The rate of LNM is high in patients with CC with a tumor size ≥30 mm and preoperative biopsy LVSI status even if they are clinically in early stages. Surgeons can take this into account while deciding between primary surgery and chemoradiotherapy in the treatment of CC.

Anatomic, computed tomographic, and ultrasonographic assessment of the lymph nodes in presumed healthy adult cats: the abdomen, pelvis, and hindlimb

BackgroundThe computed tomography (CT) and ultrasonography (US) features of lymph nodes of the abdomen, pelvis, and hindlimb in healthy cats are poorly described in the current literature. A prospective anatomic and reference interval study was therefore performed. The lymph nodes of six feline cadavers were identified, and dimensions were measured (length, width, and height). The lymph nodes from 30 healthy adult cats were identified and measured using CT (pre- and postcontrast) and US. The identification and dimensions of the separate lymph nodes were compared between imaging techniques and the anatomic study.ResultsThe identification of lymph nodes was most frequent in CT, and the dimensions were overall larger than those identified and measured in US and the anatomic study. The caudal epigastric and sacral lymph nodes were not identified in the anatomic study. The ischiatic, lumbar aortic, internal iliac, and caudal epigastric lymph nodes were not visualized in US. The height presented the main statistical differences among techniques. The lymph nodes were mainly homogeneous in pre- and postcontrast CT and US images. Some lymph nodes showed a hyperattenuating periphery with a hypoattenuating center (on pre- and postcontrast images) and a hypo-/isoechoic periphery with a hyperechoic center, representing the hilar fat. The lymph nodes were commonly elongated and rounded except for the jejunal lymph nodes, which had an irregular shape.ConclusionsThe assessment of most of the abdominal, pelvic, and hindlimb lymph nodes in the cat is feasible using CT and US, with CT performing best. Factors like the amount of adipose tissue and contrast administration subjectively improved the lymph node visualization and assessment. The measurements and features reported are proposed as reference values.

Improved GCN Framework for Human Motion Recognition

Human recognition models based on spatial-temporal graph convolutional neural networks have been gradually developed, and we present an improved spatial-temporal graph convolutional neural network to solve the problems of the high number of parameters and low accuracy of this type of model. The method mainly draws on the inception structure. First, the tensor rotation is added to the graph convolution layer to realize the conversion between graph node dimension and channel dimension and enhance the model’s ability to capture global information for small-scale tasks. Then the inception temporal convolution layer is added to build a multiscale temporal convolution filter to perceive temporal information under different time domains hierarchically from 4-time dimensions. It overcomes the shortcomings of temporal graph convolutional networks in the field of joint relevance of hidden layers and compensates for the information omission of small-scale graph tasks. It also limits the volume of parameters, decreases the arithmetic power, and speeds up the computation. In our experiments, we verify our model on the public dataset NTU RGB + D. Our method reduces the number of the model parameters by 50% and achieves an accuracy of 90% in the CS evaluation system and 94% in the CV evaluation system. The results show that our method not only has high recognition accuracy and good robustness in human behavior recognition applications but also has a small number of model parameters, which can effectively reduce the computational cost.

A novel semi local measure of identifying influential nodes in complex networks

How to identify influencers is very significance in mastering the nature of node, controlling spreading process in complex networks. In this research field, each method has its own advantages and limitations. For example, local metrics are relatively simple, global metrics can give better results, but the computational complexity is also relatively high. A semi-local approach on basis of node dimension is proposed to identify influencers. The node dimension can detect regions with different dimensional structures by scaling the local dimension on the scale. The saturation effect is discovered in the process of identifying influencers by the node dimension. When the maximum dimension radius is close to the mean shortest path length of networks, the method has better performance. Through the saturation effect, our approach can be a tradeoff between local and global metrics. In addition, we show the correlation between different measures and node dimension with different maximum dimension radii. We employ Susceptible-Infected-Recovered (SIR) model to verify the effectiveness of our designed approach. Simulation results indicate the superiority of our algorithm.

Network representation learning method embedding linear and nonlinear network structures

With the rapid development of neural networks, much attention has been focused on network embedding for complex network data, which aims to learn low-dimensional embedding of nodes in the network and how to effectively apply learned network representations to various graph-based analytical tasks. Two typical models exist namely the shallow random walk network representation method and deep learning models such as graph convolution networks (GCNs). The former one can be used to capture the linear structure of the network using depth-first search (DFS) and width-first search (BFS), whereas Hierarchical GCN (HGCN) is an unsupervised graph embedding that can be used to describe the global nonlinear structure of the network via aggregating node information. However, the two existing kinds of models cannot simultaneously capture the nonlinear and linear structure information of nodes. Thus, the nodal characteristics of nonlinear and linear structures are explored in this paper, and an unsupervised representation method based on HGCN that joins learning of shallow and deep models is proposed. Experiments on node classification and dimension reduction visualization are carried out on citation, language, and traffic networks. The results show that, compared with the existing shallow network representation model and deep network model, the proposed model achieves better performances in terms of micro-F1, macro-F1 and accuracy scores.

Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity

We develop a deep learning framework, spatio-temporal directed acyclic graph with attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional magnetic resonance imaging (fMRI). This ST-DAG-Att framework comprises of two neural networks, (1) spatio-temporal graph convolutional network (ST-graph-conv) to learn the spatial and temporal information of functional time series at multiple temporal and spatial graph scales, where the graph is represented by the brain functional network, the spatial convolution is over the space of this graph, and the temporal convolution is over the time dimension; (2) functional connectivity convolutional network (FC-conv) to learn functional connectivity features, where the functional connectivity is derived from embedded multi-scale fMRI time series and the convolutional operation is applied along both edge and node dimensions of the brain functional network. This framework also consists of an attention component, i.e., functional connectivity-based spatial attention (FC-SAtt), that generates a spatial attention map through learning the local dependency among high-level features of functional connectivity and emphasizing meaningful brain regions. Moreover, both the ST-graph-conv and FC-conv networks are designed as feed-forward models structured as directed acyclic graphs (DAGs). Our experiments employ two large-scale datasets, Adolescent Brain Cognitive Development (ABCD, n=7693) and Open Access Series of Imaging Study-3 (OASIS-3, n=1786). Our results show that the ST-DAG-Att model is generalizable from cognition prediction to age prediction. It is robust to independent samples obtained from different sites of the ABCD study. It outperforms the existing machine learning techniques, including support vector regression (SVR), elastic net's mixture with random forest, spatio-temporal graph convolution, and BrainNetCNN.

Radiomic Analysis of Tumour Heterogeneity Using MRI in Head and Neck Cancer Following Chemoradiotherapy: A Feasibility Study.

ObjectivesTo evaluate interval changes in heterogeneity on diffusion-weighted apparent diffusion coefficient (ADC) maps and T1-weighted post-gadolinium (T1w post gad) MRI in head and neck carcinoma (HNSCC), with and without chemo-radiotherapy (CRT) response.MethodsThis prospective observational cohort study included 24 participants (20 men, age 62.9 ± 8.8 years) with stage III and IV HNSCC. The primary tumour (n = 23) and largest lymph node (n = 22) dimensions, histogram parameters and grey-level co-occurrence matrix (GLCM) parameters were measured on ADC maps and T1w post gad sequences, performed pretreatment and 6 and 12 weeks post CRT. The 2-year treatment response at primary and nodal sites was recorded. The Wilcoxon signed-rank test was used to compare interval changes in parameters after stratifying for treatment response and failure (p < 0.001 statistical significance).Results23/23 primary tumours and 18/22 nodes responded to CRT at 2 years. Responding HNSCC demonstrated a significant interval change in ADC histogram parameters (kurtosis, coefficient of variation, entropy, energy for primary tumour; kurtosis for nodes) and T1w post gad GLCM (entropy and contrast in the primary tumour and nodes) by 6 weeks post CRT (p < 0.001). Lymph nodes with treatment failure did not demonstrate an interval alteration in heterogeneity parameters.ConclusionsADC maps and T1w post gad MRI demonstrate the evolution of heterogeneity parameters in successfully treated HNSCC by 6 weeks post CRT; however, this is not observed in lymph nodes failing treatment.Advances in KnowledgeEarly reduction in heterogeneity is demonstrated on MRI when HNSCC responds to CRT.

Optimization of Gate-All-Around Device to Achieve High Performance and Low Power with Low Substrate Leakage.

In this study on multi-nanosheet field-effect transistor (mNS-FET)—one of the gate-all-around FETs (GAAFET) in the 3 nm technology node dimension—3D TCAD (technology computer-aided design) was used to attain optimally reduced substrate leakage from options including a punch-through-stopper (PTS) doping scheme and a bottom oxide (BO) scheme for bottom isolation, with the performance improvement being shown in the circuit-level dynamic operation using the mNS-FET. The PTS doping concentration requires a high value of >5 × 1018 cm−3 to reduce gate induced drain leakage (GIDL), regardless of the presence or absence of the bottom isolation layer. When the bottom isolation is applied together with the PTS doping scheme, the capacitance reduction is larger than the on-state current reduction, as compared to when only the PTS doping concentration is applied. The effects of such transistor characteristics on the performance and capabilities of various circuit types—such as an inverter ring oscillator (RO), a full adder (FA) circuit, and a static random-access memory (SRAM)—were assessed. For the RO, applying BO along with the PTS doping allows the operating speed to be increased by 11.3% at the same power, or alternatively enables 26.4% less power consumption at the same speed. For the FA, power can be reduced by 6.45%, energy delay product (EDP) by 21.4%, and delay by 16.8% at the same standby power when BO and PTS are both applied. Finally, for the SRAM, read current (IREAD) increased by 18.7% and bit-line write margin (BWRM) increased by 12.5% at the same standby power. Through the circuit simulations, the Case 5 model (PTS doping concentration: 5.1 × 1018 cm−3, with BO) is the optimum condition for the best device and circuit performance. These observations confirm that PTS and bottom isolation applications in mNS-FETs can be utilized to enable the superior characteristics of such transistors to translate into high performance integrated circuits.

Validity of the 2014 FIGO Stage IIIA1 Subclassification for Ovarian, Fallopian Tube, and Peritoneal Cancers.

The 2014 International Federation of Gynecology and Obstetrics (FIGO) classification subdivides patients with stage IIIA1 ovarian, fallopian tube, and peritoneal cancers by the greatest dimension of metastatic lymph node without supporting evidence. This study aimed to assess the validity of this subdivision. A retrospective single-institution cohort study was performed in patients with ovarian, fallopian tube, or peritoneal cancer from 2009 to 2020. We compared outcomes between patients diagnosed with IIIA1(i) (metastasis ≤10 mm in the greatest dimension) and IIIA1(ii) (metastasis >10 mm in the greatest dimension). Of the 895 patients, 46 (5.1%) were classified as stage IIIA1, 20 as IIIA1(i), and 26 as IIIA1(ii). In stage IIIA1(ii), there were significantly more cases of serous carcinoma (p<0.001), and the number of positive nodes and lymph node ratio were significantly higher than those in stage IIIA1(i) (p=0.001, p=0.002). Five-year progression-free survival was 68.7% in patients with stage IIIA1(i) cancer and 58.1% in those with stage IIIA1(ii) (p=0.58). Five-year overall survival was 83.1% in patients with stage IIIA1(i) cancer and 80.2% in those with stage IIIA1(ii) (p=0.44). Among other patient characteristics and pathologic findings, there were no prognostic factors for patients with stage IIIA1 cancer. In this retrospective cohort study, further classification of FIGO stage IIIA1 cancer was not significantly associated with patient outcomes.

Initial Institutional Experience with 18F-Fluciclovine PET-CT in Biochemical Recurrence of Prostate Cancer.

18F-fluciclovine (fluciclovine) is an amino acid analog approved by the Food and Drug Administration for use as a radiotracer in positron emission tomography (PET) in men with biochemical recurrence of suspected prostate cancer. The purpose of this study was to investigate the initial institutional experience with 18F-fluciclovine in the evaluation of prostate cancer with biochemical recurrence. This study was a retrospective review of 135 patients who underwent 18F-fluciclovine PET-computed tomography (PET-CT) at a single institution from August 2018 through January 2020. Prognostic information, including prostate-specific level antigen (PSA) at the time of diagnosis, initial risk, initial Gleason score, and initial stage, was reviewed as well as the PSA level at the time of the scan. The images were reviewed by two radiologists with fellowship training in nuclear medicine and additional training to interpret the fluciclovine studies. A minority of studies were reviewed by a third fellowship-trained radiologist under the guidance of the two nuclear medicine-trained radiologists. In cases with abnormal radiopharmaceutical uptake in lymph nodes, the short-axis dimension of the lymph node or largest lymph node with abnormal uptake was noted. If CT or bone scan was performed within 4 months of the 18F-fluciclovine PET-CT, findings on the alternate imaging were compared with the results of the 18F-fluciclovine PET-CT. Our institutional positivity rate was 75.6%, with 64 (67.4%) patients with metastatic disease and 71 (52.6%) patients with local recurrence detected by fluciclovine. As expected, the rate of positive examinations increased with increasing PSA values measured at the time of imaging (P < 0.001). Of the 54 patients with nodal disease, 35 had nonpathologically enlarged lymph nodes measuring <1 cm in maximum short-axis dimension. In more than half of the patients in this study, with conventional imaging, fluciclovine either discovered otherwise undetectable metastatic disease or suggested the presence of local recurrence. Our single-institution experience with 18F-fluciclovine PET-CT has the largest number of patients to date in the literature and demonstrates the ability of fluciclovine to help guide clinical management in the detection of early recurrent disease.