Node Surgery Research Articles

Association between Paravertebral Block and Pain Score at the Time of Hospital Discharge in Oncoplastic Breast Surgery: A Retrospective Cohort Study.

Using nonopioid analgesics may decrease the risk of patients chronically using opioids postoperatively. The authors evaluated the relationship between paravertebral block and pain score at the time of hospital discharge. The authors performed a retrospective cohort study of 89 women with American Society of Anesthesiologists Physical Status I to III undergoing oncoplastic breast surgery with 20 to 50 percent breast tissue removal and immediate contralateral reconstruction between August of 2015 and August of 2018. The primary outcome was pain score at hospital discharge with or without paravertebral block. The secondary outcome was postoperative length of stay. Data were analyzed using the Wilcoxon rank sum test, t test, Fisher's exact test, univariable and multivariable regression, Kaplan-Meier analyses, and Cox regression. Median pain score at hospital discharge was lower with paravertebral block [2 (interquartile range, 0 to 2) compared to 4 (interquartile range, 3 to 5); p < 0.001]. Multivariable regression revealed that pain score at the time of hospital discharge was inversely associated with paravertebral block after adjusting for age, body mass index, American Society of Anesthesiologists class, extent of lymph node surgery, and duration of surgery (p < 0.001). Pain score at hospital discharge was also associated with total opioid consumption during the first 24 hours after surgery (p = 0.001). Patients who received paravertebral blocks had median total 24-hour postoperative opioid consumption in morphine equivalents of 7 mg (interquartile range, 3 to 10 mg) compared with 13 mg (interquartile range, 7 to 18 mg) (p < 0.001), and median length of stay of 18 hours (interquartile range, 16 to 20 hours) compared with 22 hours (interquartile range, 21 to 27 hours) (p < 0.001). Paravertebral blocks are associated with decreased pain score at the time of hospital discharge. Therapeutic, III.

Intelligent Attack Detection Modelin lotusing Optimal Feature Selectionincorporated withOptimized DeepLearningArchitecture

Attacks and problem recognition within the Internet of Things (IoT) foundation is an increasing worry in the environment of IoT.Within the expanded utilization of IoT framework in each space, dangers and assaults in these foundations are additionally developing comparably. Some attacks like node tampering, malicious code injection, malicious node injection, DoS Attacks, Malicious scripts such assaults and inconsistencies which can cause an IoT framework failure. In this study deep learning architecture models were compared to attacks and anamolies on the IoT frameworks that could be predicted.Themainphases ofthe proposedanomalyorattackdetection model are Feature Extractionand Detection. Infeatureextractionprocess,the attributesinthe datasetisconsideredasfeatures,which arelike SystemID,SystemAddress,SystemType,SystemLocation,DestinationService Address,DestinationServiceType,DestinationLocation,Accessed NodeAddress,Accessed Node Type, Operation, Value, Timestamp, and Normality. Since thenumbersoffeatureare high,which increasesthe lengthofthefeature vector, optimalfeatureselectionprocesswillbedevelopedtoextractthe mostsignificantfeatures withuni.queinformation.Finally,theoptimizedDBN(Deep Belief Network)categorizesthedataintonormalas wellasattacks,anddetectseachcategoryofattacks.

Using patient and melanoma tissue information to predict the risk of cancer spreading to the lymph nodes (CP‐GEP tool)

British Journal of DermatologyVolume 184, Issue 5 p. e169-e169 Plain Language Summary Using patient and melanoma tissue information to predict the risk of cancer spreading to the lymph nodes (CP-GEP tool) First published: 04 May 2021 https://doi.org/10.1111/bjd.19904AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Linked Article: Mulder et al. Br J Dermatol 2021; 184:944–951. Melanoma is the most deadly type of skin cancer that develops in skin pigment cells. It affects about 132,000 people worldwide each year. The prognosis (predicting the outcome) in newly diagnosed patients strongly depends on the presence of spread from the skin into regional lymph nodes (glands) or even further into other parts of the body, such as the lungs and bones. Knowledge on whether the lymph nodes are affected by melanoma cells influences the type of cancer treatment given to patients. This is currently established by removal of the draining lymph node(s) of interest, a procedure that is known as a surgical sentinel lymph node biopsy. Cancer spread into the lymph node is present in roughly two out of 10 patients who undergo this procedure. Therefore, in the majority of patients this biopsy will not lead to a different treatment plan, but removing the lymph node(s) can lead to complications in one or two out of 10 patients. The CP-GEP tool was developed in a study of patients in the USA to calculate the risk of cancer spread into the lymph nodes. This study aimed to find out whether this tool could be of benefit in European patients as well. To calculate the risk (‘high’ or ‘low’), this tool combines information from the patient (age) and melanoma tissue (depth of the tumour and cancer genes). We compared the risk with the presence or absence of melanoma spread into the lymph nodes. This study shows that the CP-GEP tool can identify patients with melanoma who have a low risk of lymph node spread. In conclusion, the CP-GEP tool can be helpful to reduce the number of lymph node operations (and complications) in patients with melanoma. Volume184, Issue5May 2021Pages e169-e169 RelatedInformation

Implementation of Energy Efficient Fog based Health Monitoring and Emergency Admission Prediction System Using IoT

With rapid development in Information Communication Technology (ICT), Wearable Sensor Networks with Internet of Things (WSN-IoT) has produced several improvements in the smart world environment. One of the main research challenges in Wearable Sensor is energy, since all the sensor nodes operation depends on battery power consumption. Hence a new middleware has to be introduced between Wearable Sensor nodes and Cloud to reduce latency and Power Consumption problems. Overcrowding in hospital premise, detecting priority of hospital admission for patients, managing and monitoring health status of the patient constantly are daily problems in any health care system. Even though IoT based wearable sensors monitor health status of patients regularly and provide intent treatment in critical stage, but there is some block hole in that such as latency, energy issues and unawareness of medical execution plans and policies to preserve them from sudden attacks such as Heart attack. The proposed work is to implement energy efficient FoG based IoT network to monitor patients’ health conditions from chronic diseases and highlights utility of Deep Learning model for analyzing the health condition of patients and predicting Emergency readmission cases well in advance. This model is also compare with existing machine learning algorithms such as Gradient boosted, Decision tree, Random forest and Logistic regression to achieve more accuracy. This paper introduces preemptive interval scheduling algorithm with predictive analysis for constant monitoring of status for critical patients. By means of comparative analysis done in this work energy efficiency has been achieved prominently.

Fog-assisted Energy Efficient Cyber Physical System for Panic-based Evacuation during Disasters

Disasters around the world have adversely affected every aspect of life and panic-health of stranded persons is one such category. An effective and on-time evacuation from disaster-affected areas can avoid any panic-related health problems of the stranded persons. Although the nature of disasters differ in terms of how they occur, the evacuation of stranded persons faces approximately same set of issues related to the communication, time-sensitive computation and energy efficiency of the devices operated in the disaster-affected areas. In this paper, a cyber physical system (CPS) is proposed that takes into account various challenges of the disaster evacuation, so an efficient on-time and orderly evacuation of stranded panicked persons could be realized. The system employs fog-assisted mobile and UAV devices for time-sensitive computation services, data relaying and energy-aware computation. The system uses a fog-assisted two-factor energy-aware computation approach using data reduction, which enables the energy-efficient data reception and transmission (DRecTrans) operations at the fog nodes and compensates to extend the period for other functionalities. The data reduction at fog devices employs Novel Events Identification (NEI) and Principal Component Analysis (PCA) for detecting consecutive duplicate traffic and data summarization of high dimensional data, respectively. The proposed system operates in two spaces: physical and cyber. Physical space facilitates real-world data acquisition and information sharing with the concerned stakeholders (stranded persons, evacuation teams and medical professionals). The cyber space houses various data-analytics layers and comprises of two subspaces: fog and cloud. The fog space helps in providing real-time panic-health diagnostic and alert services and enables the optimized energy consumption of devices operate in disaster-affected areas, whereas the cloud space facilitates the monitoring and prediction of panic severity of the stranded persons, using a conditional probabilistic model and seasonal auto regression integrated moving average (SARIMA), respectively. Cloud space also facilitates the disaster mapping for converging the evacuation map to the actual situation of the disaster-affected area, and geographical population analysis (GPA) for the identification of the panic severity-based critical regions. The performance evaluation of the proposed CPS acknowledges its Logistic Regression-based panic-well being determination and real-time alert generation efficiency. The simulated implementation of NEI and PCA depicts the fog-assisted energy efficiency of the DRecTrans operations of the fog nodes. The performance evaluation of the proposed CPS also acknowledges the prediction efficiency of the SARIMA and disaster mapping accuracy through GPA. The proposed system also discusses a case study related to the pandemic disaster of coronavirus disease 2019 (COVID-19), where the system can help in panic-based selective testing of the persons, and preventing panic due to distressing period of COVID-19 outbreak.

A Modular Mobile Health App for Personalized Rehabilitation Throughout the Breast Cancer Care Continuum: Development Study.

BackgroundAlthough many mobile health (mHealth) apps have evolved as support tools for self-management of breast cancer, limited studies have developed a comprehensive app and described the algorithms for personalized rehabilitation throughout the breast cancer care continuum.ObjectiveThis study aimed to develop a comprehensive mobile app and to describe an algorithm that adjusts personalized content to facilitate self-management throughout the breast cancer care continuum.MethodsThe development process of the modular mHealth app included the following 4 steps: (1) organizing expert teams, (2) defining evidence-based fundamental content and modules, (3) classifying user information for algorithms to personalize the content, and (4) creating the app platform and connectivity to digital health care devices.ResultsWe developed a modular mHealth app service, which took 18 months, including a review of related literature and guidelines and the development of the app and connectivity to digital health care devices. A total of 11 functionalities were defined in the app with weekly analysis. The user information classification was formulated for personalized rehabilitation according to 5 key criteria: general user information, breast operation type, lymph node surgery type, chemotherapy and hormonal therapy use, and change in treatment after surgery. The main modules for personalized content included a self-monitoring screen, personalized health information, personalized exercise, and diet management.ConclusionsThe strength of this study was the development of a comprehensive mHealth app and algorithms to adjust content based on user medical information for personalized rehabilitation during the breast cancer care continuum.

A novel energy harvesting with middle-order weighted probability (EHMoWP) for performance improvement in wireless sensor network (WSN)

Wireless sensor network (WSN) has been a promising technology and widely involved in environmental monitoring. The limited energy supply associated with WSNs impacts the lifetime of the network and the energy constraints make it difficult for sensor nodes to harvest environmental energy. This drawback on the performance of WSN is improved through routing and clustering techniques for environmental energy harvesting and termed as harvesting wireless sensor network (EH-WSN). As a result of the unique characteristics of EH-WSN, the performance of typical clustering and routing protocols of WSN are ineffective in EH-WSN. A novel energy harvesting middle-order weighted probability (EHMoWP) is proposed in this paper for the static and dynamic operation of WSN nodes. The proposed EHMoWP approach is based on the integration of cluster head selection and routing, with the utilization of a distributed–centralized approach which cogitates the energy of nodes, harvested energy, clustering process, and neighbor nodes. Simulation outcomes showed that this proposed EHMoWP outperforms significantly the conventional techniques concerning residual energy and the number of alive and dead nodes. The proposed EHMoWP exhibits almost 10% higher residual energy with improved network lifetime which is measured in terms of alive node and dead node count. Based on the analysis of dead node and alive node estimation, it was observed that the number of alive nodes was 10% higher and the number of dead nodes was 10% fewer than in the conventional technique. This is because the proposed EHMoWP exhibits 2 dead nodes less and 2 alive nodes higher than the existing technique.

From systematic lymphadenectomy to sentinel lymph node mapping: a review on transitions and current practices in endometrial cancer staging.

Endometrial cancer care has undergone major changes in the past 30 years. In 1988, staging transitioned from clinical to surgical. Moreover, the surgical approach of choice is no longer open surgery, but minimally invasive surgery. An improvement in terms of nodal evaluation followed. Full systematic lymphadenectomy has been continuously replaced by sentinel lymph node mapping. Although sentinel lymph node mapping with a cervical injection of indocyanine green dye is rapidly gaining clinical acceptance, we lack consistent recommendations on a well-defined procedure that accurately and indolently assesses the lymph node status. Such recommendations are indispensable, as nodal status is the most important predictive factor of survival and is essential for tailoring adjuvant treatment to the risk of recurrence. This paper focuses on transitions in endometrial cancer care and highlights current data on sentinel lymph node mapping in endometrial cancer. We demonstrate that sentinel lymph node mapping is a safe and accurate strategy for nodal status evaluation with appropriate sensitivity, false-negative rate and negative predictive value in high- as well as low-risk settings. Furthermore, we elaborate on type and dose of tracer, site of injection, number of sentinel lymph nodes to be removed, sentinel lymph node mapping learning curve, operation mode and sentinel lymph node ultrastaging. In the future, guidelines with consistent recommendations on the above outlined features of sentinel lymph node mapping should be established to allow for a uniform and wide-spread application of the sentinel lymph node mapping procedure.

Factors associated with melanoma-related limb lymphoedema

Background Melanoma-related limb lymphoedema is a well-known late effect following sentinel node biopsy (SNB), and lymph node dissection (LND) in patients treated of melanoma. However, data on associated risk factors are sparse. This study aimed to investigate factors associated with melanoma-related limb lymphoedema. Methods The present cross-sectional single-center clinical study included patients between 18 and 75 years with American Joint Committee on Cancer Stages I–III melanoma treated with wide local excision (WLE) and unilateral axillary or inguinal SNB and/or completion LND (CLND) or therapeutic LND (TLND). The diagnosis of secondary unilateral limb lymphoedema was based on the history, symptoms, and physical examination and staged according to the International Society of Lymphology (ISL). Data on factors associated with lymphoedema were analysed with binary logistic regression models. Results In total, 642 patients were eligible, of which 435 (68%) patients participated in the study. Among these 431 patients, 109 (25%) had lymphoedema of which 48 (44%), and 61 (56%) were classified with ISL Stages I and II–III, respectively. Multivariate analyses identified primary tumour on the limb (odds ratio [OR], 2.28; 95% confidence interval [CI], 1.17–4.56; p value .017), inguinal surgery (OR, 6.91; 95% CI, 3.49–14.11; p value <.0001), LND (OR, 6.45; 95% CI, 3.18–13.57; p value <.0001), and persistent pain at the site of lymph node surgery as factors associated with lymphoedema (OR, 3.52; 95% CI, 1.54–8.19; p value .003). Multivariable analysis of ISL Stage II–III lymphoedema further identified limb cellulitis to be associated with lymphoedema (OR 5.74; 95% CI, 2.11–15.99; p value .0006). Conclusions Melanoma-related limb lymphoedema is associated with inguinal surgery, LND, primary tumour on the limb, persistent pain at the site of lymph node surgery, and cellulitis of the limb. This study highlights the importance of increasing awareness, improving prevention, and treatment of melanoma-related limb lymphoedema.

The saga of lymph node surgery in malignant melanoma: a history repeating itself in gynecologic oncology.

In view of the high morbidity to complete lymphadenectomy, the sentinel node procedure is currently being investigated in different cancers with the aim of reducing morbidity and of achieving oncological outcomes similar to complete lymphadenectomy. In this review, a parallel will be drawn between the evolutions of the place of lymph node surgery in the management of melanoma and gynecologic cancers. Currently, in the case of cutaneous melanoma, the indications of lymph node surgery are becoming increasingly rare. In contrast with the scarce available evidence in gynecologic cancer, this trend is supported by multicenter randomized studies with a large number of patients for cutaneous melanoma. The second difference is the demonstrated therapeutic benefit of immunotherapies and targeted therapies in cutaneous melanoma, which are less significant in gynecological cancers for which the mainstay of treatment is surgery. According to this review, it seems that in gynecologic oncology, we could in the next decades follow the same pathway currently taken in the management of lymph nodes in malignant melanoma.

DGA domain detection and botnet prevention using Q-learning for POMDP

An effective method for preventing the operation of computer network nodes for organizing a botnet is proposed. A botnet is a collection of devices connected via the Internet for the purpose of organizing DDoS attacks, stealing data, sending spam and other malicious actions. The described method implies the detection of generated domain names in DNS queries using a neural network with parallel organization of convolutional and bidirectional recurrent layers. The effectiveness of the method is based on the assumption that generated domain names are used to create a botnet for merging. Experiments confirm that the proposed neural network is superior to the accuracy of existing counterparts on the UMUDGA dataset. The estimation of the quality of recognition of generated domain names using ROC analysis is calculated for a trained neural network. The article also formulates a model for controlling detectors using a partially observable Markov decisionmaking process to block infected nodes of a computer network. The search for the optimal policy for the formulated model by means of Q-learning of value agents is proposed. A comparative analysis of the average, minimum and maximum value of actions taken by agents in the process of interacting with the environment is carried out.

Pathologic nodal staging for clinically node negative soft tissue sarcoma of the extremities.

Synovial, clear cell, angiosarcoma, rhabdomyosarcoma, and epithelioid (SCARE) soft tissue sarcoma are at risk for nodal involvement, although the nodal positivity rates and impact on prognostication in clinically node negative patients are not well described. Patients with extremity SCARE sarcoma without clinical nodal involvement undergoing surgical resection in the National Cancer Database (2004-2017) were included. Logistic regression was used to evaluate the likelihood of nodal surgery and nodal positivity. Kaplan-Meier method and Cox regression were used to assess associations of nodal status to overall survival. We included 4158 patients, and 669 patients (16%) underwent regional lymph node surgery (RLNS). On multivariable logistic analysis, patients with epithelioid (odds ratio [OR]: 3.77; p < .001) and clear cell (OR: 6.38; p < .001) were most likely to undergo RLNS. Forty-five patients (7%) had positive nodes. Clear cell sarcoma (14%) and angiosarcoma (13%) had the highest rates of nodal positivity. Patients with positive nodes had reduced 5-year overall survival, and the stratification was largest in clear cell and angiosarcoma. Discordance exists between selection for pathologic nodal evaluation and factors associated with nodal positivity. Clinically node negative patients with clear cell and angiosarcoma should be considered for pathologic nodal evaluation.

Smart Microgrid Energy Market: Evaluating Distributed Ledger Technologies for Remote and Constrained Microgrid Deployments

The increasing strain on ageing generation infrastructure has seen more frequent instances of scheduled and unscheduled blackouts, rising reliability on fossil fuel based energy alternatives and a slow down in efforts towards achieving universal access to electrical energy in South Africa. To try and relieve the burden on the National Grid and still progress electrification activities, the smart microgrid model and secure energy trade paradigm is considered—enabled by the Industrial IoT (IIoT) and distributed ledger technologies (DLTs). Given the high availability requirements of microgrid operations, the limited resources available on IIoT devices and the high processing and energy requirements of DLT operations, this work aims to determine the effect of native DLT algorithms when implemented on IIoT edge devices to assess the suitability of DLTs as a mechanism to establish a secure, energy trading market for the Internet of Energy. Metrics such as the node transaction time, operating temperature, power consumption, processor and memory usage are considered towards determining possible interference on the edge node operation. In addition, the cost and time required for mining operations associated with the DLT-enabled node are determined in an effort to predict the cost to end users—in terms of fees payable and mobile data costs—as well as predicting the microgrid’s growth and potential blockchain network slowdown.

A novel framework for policy based on-chain governance of blockchain networks

Although blockchains are systems that are intended to survive on their own, they still may need human intervention like other Parallel and Distributed Computing (PDC) architectures. The reasons for this intervention are twofold: management of blockchain nodes and governance of blockchain. The second focuses on sustaining blockchain unlike the first one concerning with operation of individual nodes. Proposed blockchain governance models so far include cumbersome processes in which deciding and deployment of changes rely on the involvement of multiple actors. They have been criticized from various aspects such as reducing the decentralized nature of blockchain. In this paper, we propose a novel on-chain governance model exploiting both policy-based management and decentralized identity technologies. Since it allows controlling the behavior of the system via higher level expressions that are easy to understand, the policy based management concept will fit in well with blockchain ecosystems having stakeholders from different knowledge levels. This model is not only more effective regarding existing off-chain governance models but also is simpler and more user friendly than other on-chain governance models. Besides, the emerging decentralized identity concept has the potential for solving many problems related to decision-making process of blockchain governance. By employing this concept, our approach fortifies decentralization, security and fairness while providing well-directed governance. The smart contract based modular design of our framework opens the door for adoption of its components by other governance models as well.

F-18 Fluorodeoxyglucose Positron Emission Tomography with Computed Tomography Has High Diagnostic Value for Pelvic and Distant Staging in Patients with High-risk Penile Carcinoma

BackgroundFor penile cancer patients with pelvic metastases, multimodal treatment is advised, but pelvic lymph node metastases are often found upon surgical resection only. Early selection for multimodal treatment requires reliable noninvasive staging. ObjectiveTo evaluate the diagnostic value of 18F-fluorodeoxyglucose positron emission tomography with computed tomography (FDG-PET/CT) for staging pelvic lymph nodes and distant metastases in high-risk penile cancer patients. Design, setting, and participantsFDG-PET/CT scans performed in patients with clinically overt inguinal lymph node metastases and/or high-risk primary tumors (bulky T3 or T4) were retrospectively analyzed. Outcome measurements and statistical analysisAll scans were reviewed by two independent nuclear medicine physicians staging the pelvic nodes and distant metastases. FDG-PET/CT findings were compared with histology after node dissection if available, or with positive imaging or follow-up of at least 1 yr. Results and limitationsBetween 2006 and 2016, 61 patients met the inclusion criteria. For staging of pelvic nodes, sensitivity was 85% (specificity 75%, negative predictive value [NPV] 90%, and positive predictive value [PPV] 65%). For the detection of distant metastases, FDG-PET/CT had a PPV of 93%. Results are limited by the retrospective design and the lack of direct comparison with CT scanning alone. ConclusionsFDG-PET/CT has high sensitivity and a high NPV for staging of pelvic lymph nodes in high-risk penile cancer. It also has a high PPV for the detection of distant metastases, which were found in 23% of patients. Therefore, FDG-PET/CT enables early selection for multimodal treatment of patients with pelvic metastases and may help avoid futile treatment of patients with distant metastases. Patient summaryWe studied whether positron emission tomography with computed tomography (PET/CT) scans in patients with advanced penile cancer can detect metastases before lymph node surgery is done. PET/CT scans can detect or rule out pelvic lymph node metastases, and can detect distant metastases. This helps in making timely treatment decisions (before surgery).

Nomogram for predicting the overall survival of the patients with oesophageal signet ring cell carcinoma.

BackgroundThe purpose of this study was to explore the prognostic factors of oesophageal signet ring cell (SRC) carcinoma and to construct a nomogram for predicting the outcome of SRC carcinoma of oesophagus.MethodsA total of 968 cases of oesophageal SRC carcinoma were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. Cases were divided into training cohort and validation cohort. Univariate and multivariable Cox analyses was performed to select the predictors of overall survival (OS for the nomogram. The performance of nomogram was validated with Harrell’s concordance index (C-index), calibration curves and decision curve analysis (DCA).ResultsThe 1- and 5-year OS in the training cohort were 0.446 and 0.146, respectively, and the 1- and 5-year OS in the validation cohort were 0.459 and 0.138. The independent prognostic factors for establishing the nomogram were marital status, invasion of the surrounding tissue, lymph node metastasis, distant metastasis, surgery and chemotherapy. The Harrell’s c-index value of the training cohort and validation cohort were 0.723 and 0.708. In the calibration curves, the predicted survival probability and the actual survival probability have a considerable consistency. DCA indicated the favourable potential clinical utility of the nomogram.ConclusionsA nomogram to predict the OS of patients with oesophageal SRC carcinoma was established. The validation of the nomogram fully demonstrates its great performance.

Selection-based Low-cost Check Node Operation for Extended Min-Sum Algorithm

Although non-binary low-density parity-check (NB-LDPC) codes have better error-correction capability than that of binary LDPC codes, their decoding complexity is significantly higher. Therefore, it is crucial to reduce the decoding complexity of NB-LDPC while maintaining their error-correction capability to adopt them for various applications. The extended min-sum (EMS) algorithm is widely used for decoding NB-LDPC codes, and it reduces the complexity of check node (CN) operations via message truncation. Herein, we propose a low-cost CN processing method to reduce the complexity of CN operations, which take most of the decoding time. Unlike existing studies on low complexity CN operations, the proposed method employs quick selection algorithm, thereby reducing the hardware complexity and CN operation time. The experimental results show that the proposed selection-based CN operation is more than three times faster and achieves better error-correction performance than the conventional EMS algorithm.

Diagnostic performance of preoperative [18F]FDG-PET/CT for lymph node staging in vulvar cancer: a large single-centre study

PurposeThis retrospective study aimed to assess the diagnostic performance of preoperative [18F]FDG-PET/CT in predicting the groin and pelvic lymph node (LN) status in a large single-centre series of vulvar cancer patients.MethodsBetween January 2013 and October 2018, among all consecutive women with proven vulvar cancer submitted to [18F]FDG-PET/CT, 160 patients were included. LNs were analysed by two qualitative methods assessing PET information (defined as visual assessment) and a combination of PET and low-dose CT information (defined as overall assessment), respectively, as well as semi-quantitative analysis (LN-SUVmax). Sensitivity, specificity, accuracy, positive and negative predictive values (PPV and NPV) in predicting the groin and pelvic LN status were calculated in the overall study population; a subset analysis of groin parameters in clinically/ultrasonography negative patients was also performed. Histopathology was the reference standard.ResultsAll patients underwent vulvar and inguinofemoral LN surgery, and 35 pelvic LN surgery. Overall, 338 LN sites (296 groins and 42 pelvic sites) were histologically examined with 30.4% prevalence of metastatic groins and 28.6% for metastatic pelvic sites. In the overall study population, sensitivity (95% confidence interval, CI), specificity (95% CI), accuracy (95% CI), PPV (95% CI) and NPV (95% CI) at the groin level were 85.6% (78.3–92.8), 65.5% (59.0–72.0), 71.6% (66.5–76.8), 52.0% (44.0–60.1) and 91.2% (86.7–95.8) for visual assessment; 78.9% (70.5–87.3), 78.2% (72.5–83.8), 78.4% (73.7–83.1), 61.2% (52.3–70.1) and 89.4% (85.0–93.9) for overall assessment; and 73.3% (64.2–82.5), 85.0% (80.1–89.8), 81.4% (77.0–85.8), 68.0% (58.8–77.3) and 87.9% (83.4–92.5) for semi-quantitative analysis (SUVmax cut-off value 1.89 achieved by ROC analysis). Similar results were observed in the pelvis-based analysis.ConclusionIn this large single-centre series of vulvar cancer patients, [18F]FDG-PET/CT showed good values of sensitivity and NPV in discriminating metastatic from non-metastatic LNs. In routine clinical practice, qualitative analysis is a reliable interpretative criterion making unnecessary commonly used semi-quantitative methods such as SUVmax.

Evaluating the Impact of Immediate Lymphatic Reconstruction for the Surgical Prevention of Lymphedema.

Breast cancer-related lymphedema affects one in five patients. Its risk is increased by axillary lymph node dissection and regional lymph node radiotherapy. The purpose of this study was to evaluate the impact of immediate lymphatic reconstruction or the lymphatic microsurgical preventative healing approach on postoperative lymphedema incidence. The authors performed a retrospective review of all patients referred for immediate lymphatic reconstruction at the authors' institution from September of 2016 through February of 2019. Patients with preoperative measurements and a minimum of 6 months' follow-up data were identified. Medical records were reviewed for demographics, cancer treatment data, intraoperative management, and lymphedema incidence. A total of 97 women with unilateral node-positive breast cancer underwent axillary nodal surgery and attempt at immediate lymphatic reconstruction over the study period. Thirty-two patients underwent successful immediate lymphatic reconstruction with a mean patient age of 54 years and body mass index of 28 ± 6 kg/m2. The median number of lymph nodes removed was 14 and the median follow-up time was 11.4 months (range, 6.2 to 26.9 months). Eighty-eight percent of patients underwent adjuvant radiotherapy of which 93 percent received regional lymph node radiotherapy. Mean L-Dex change was 2.9 units and mean change in volumetry by circumferential measurements and perometry was -1.7 percent and 1.3 percent, respectively. At the end of the study period, we found an overall 3.1 percent rate of lymphedema. Using multiple measurement modalities and strict follow-up guidelines, the authors' findings support that immediate lymphatic reconstruction at the time of axillary surgery is a promising, safe approach for lymphedema prevention in a high-risk patient population. Therapeutic, IV.

On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications

In healthcare applications, the remote monitoring of moving patients depends on wearable nodes that should be mobile. Thus, wearable nodes should be power mains-disconnected most of the time to enable natural wandering of patients in the area. Thus, easy-to-use models are utilized in a seamless way. From this perspective, it becomes necessary to develop a generation of wearable nodes that are energy self-sustainable with minimal dependency on fixed power sources and also more safe in light of world health organization recommendations. In this paper, a solar energy harvesting technique is proposed to provide a mains power supply for an independent continuous operation of a patient monitoring node in sunny environments. A case study is built experimentally whereas the proposed designed node is architected as a combined node that enables parallel measurements of heart rate, blood oxygen saturation (SpO2), and body temperature. The experimental results show that the wearable node can survive more than 28 h without battery recharging from the mains. While the charging time of the battery from the solar energy harvesting is approximately 2 h.