The impact of lymphadenectomy and lymph node metastasis on the outcomes of radical cystectomy for bladder cancer

CONTEXT: The presence of lymph node metastases and the extent of lymphadenectomy have both been shown to influence the outcome of patients with muscle-invasive bladder cancer. OBJECTIVE: Current standards for detection of lymph node metastases, lymph-node mapping studies, histopathologic techniques, and risk factors in relation to lymph node involvement are discussed. The impact of lymph node metastases and the extent of lymphadenectomy on the outcome of patients treated with radical cystectomy are analyzed. EVIDENCE ACQUISITION: A systematic literature review of bladder cancer and lymph nodes was performed searching the electronic databases Pubmed/Medline, Cochrane, and Embase. Articles were selected based on title, abstract, study format, and content by a consensus of all participating authors. EVIDENCE SYNTHESIS: Lymph node status is highly consequential in bladder cancer patients because the presence of lymph node metastases is predictive of poor outcome. Knowledge of primary landing sites of lymph node metastases is important for optimum therapeutic management. Accurate pathologic work-ups of resected lymph node tissue are mandatory. Molecular markers could potentially guide therapeutic decisions in the future because they may enable the detection of micrometastatic disease. In current series, radical cystectomy with an extended lymphadenectomy seems to provide a clinically meaningful therapeutic benefit compared with a limited approach. However, the anatomic boundaries of lymph node dissection are still under debate. Therefore, large prospective multicenter trials are needed to validate the influence of extended lymph node dissection on disease-specific survival. CONCLUSIONS: An extended pelvic lymph node dissection (encompassing the external iliac vessels, the obturator fossa, the lateral and medial aspects of the internal iliac vessels, and at least the distal half of the common iliac vessels together with its bifurcation) can be curative in patients with metastasis or micrometastasis to a few nodes. Therefore, the procedure may be offered to all patients undergoing radical cystectomy for invasive bladder cancer.

Combined Ultrasmall Superparamagnetic Particles of Iron Oxide–Enhanced and Diffusion-weighted Magnetic Resonance Imaging Facilitates Detection of Metastases in Normal-sized Pelvic Lymph Nodes of Patients with Bladder and Prostate Cancer

Background Conventional cross-sectional imaging with computed tomography and magnetic resonance imaging (MRI) has limited accuracy for lymph node (LN) staging in bladder and prostate cancer patients. Objective To prospectively assess the diagnostic accuracy of combined ultrasmall superparamagnetic particles of iron oxide (USPIO) MRI and diffusion-weighted (DW) MRI in staging of normal-sized pelvic LNs in bladder and/or prostate cancer patients. Design, setting, and participants Examinations with 3-Tesla MRI 24–36 h after administration of USPIO using conventional MRI sequences combined with DW-MRI (USPIO-DW-MRI) were performed in 75 patients with clinically localised bladder and/or prostate cancer staged previously as N0 by conventional cross-sectional imaging. Combined USPIO-DW-MRI findings were analysed by three independent readers and correlated with histopathologic LN findings after extended pelvic LN dissection (PLND) and resection of primary tumours. Outcome measurements and statistical analysis Sensitivity and specificity for LN status of combined USPIO-DW-MRI versus histopathologic findings were evaluated per patient (primary end point) and per pelvic side (secondary end point). Time required for combined USPIO-DW-MRI reading was assessed. Results and limitations At histopathologic analysis, 2993 LNs (median: 39 LNs; range: 17–68 LNs per patient) with 54 LN metastases (1.8%) were found in 20 of 75 (27%) patients. Per-patient sensitivity and specificity for detection of LN metastases by the three readers ranged from 65% to 75% and 93% to 96%, respectively; sensitivity and specificity per pelvic side ranged from 58% to 67% and 94% to 97%, respectively. Median reading time for the combined USPIO-DW-MRI images was 9 min (range: 3–26 min). A potential limitation is the absence of a node-to-node correlation of combined USPIO-DW-MRI and histopathologic analysis. Conclusions Combined USPIO-DW-MRI improves detection of metastases in normal-sized pelvic LNs of bladder and/or prostate cancer patients in a short reading time.

Incidental detection of an occult oral malignancy with autofluorescence imaging: a case report.

BACKGROUND: Autofluorescence imaging is used widely for diagnostic evaluation of various epithelial malignancies. Cancerous lesions display loss of autofluorescence due to malignant changes in epithelium and subepithelial stroma. Carcinoma of unknown primary site presents with lymph node or distant metastasis, for which the site of primary tumour is not detectable. We describe here the use of autofluorescence imaging for detecting a clinically innocuous appearing occult malignancy of the palate which upon pathological examination was consistent with a metastatic squamous cell carcinoma. CASE DESCRIPTION: A submucosal nodule was noted on the right posterior hard palate of a 59-year-old white female during clinical examination. Examination of this lesion using a multispectral oral cancer screening device revealed loss of autofluorescence at 405 nm illumination. An excisional biopsy of this nodule, confirmed the presence of a metastatic squamous cell carcinoma. Four years ago, this patient was diagnosed with metastatic squamous cell carcinoma of the right mid-jugular lymph node of unknown primary. She was treated with external beam irradiation and remained disease free until current presentation. CONCLUSION: This case illustrates the important role played by autofluorescence tissue imaging in diagnosing a metastatic palatal tumour that appeared clinically innocuous and otherwise would not have been biopsied.

Metastases in normal-sized pelvic lymph nodes: detection with diffusion-weighted MR imaging

PURPOSE To prospectively assess the diagnostic performance of diffusion-weighted (DW) magnetic resonance (MR) imaging in the detection of pelvic lymph node metastases in patients with prostate and/or bladder cancer staged as N0 with preoperative cross-sectional imaging. MATERIALS AND METHODS This study was approved by an independent ethics committee. Written informed consent was obtained from all patients. Patients with no enlarged lymph nodes on preoperative cross-sectional images who were scheduled for radical resection of the primary tumor and extended pelvic lymph node dissection were enrolled. All patients were examined with a 3-T MR unit, and examinations included conventional and DW MR imaging of the entire pelvis. Image analysis was performed by three independent readers blinded to any clinical information. Metastases were diagnosed on the basis of high signal intensity on high b value DW MR images and morphologic features (shape, border). Histopathologic examination served as the standard of reference. Sensitivity and specificity were calculated, and bias-corrected 95% confidence intervals (CIs) were obtained with the bootstrap method. The Fleiss and Cohen κ and median test were applied for statistical analyses. RESULTS A total of 4846 lymph nodes were resected in 120 patients. Eighty-eight lymph node metastases were found in 33 of 120 patients (27.5%). Short-axis diameter of these metastases was less than or equal to 3 mm in 68, more than 3 mm to 5 mm in 13, more than 5 mm to 8 mm in five; and more than 8 mm in two. On a per-patient level, the three readers correctly detected metastases in 26 (79%; 95% CI: 64%, 91%), 21 (64%; 95% CI: 45%, 79%), and 25 (76%; 95% CI: 60%, 90%) of the 33 patients with metastases, with respective specificities of 85% (95% CI: 78%, 92%), 79% (95% CI: 70%, 88%), and 84% (95% CI: 76%, 92%). Analyzed according to hemipelvis, lymph node metastases were detected with histopathologic examination in 44 of 240 pelvic sides (18%); the three readers correctly detected these on DW MR images in 26 (59%; 95% CI: 45%, 73%), 19 (43%; 95% CI: 27%, 57%), and 28 (64%; 95% CI: 47%, 78%) of the 44 cases. CONCLUSION DW MR imaging enables noninvasive detection of small lymph node metastases in normal-sized nodes in a substantial percentage of patients with prostate and bladder cancer diagnosed as N0 with conventional cross-sectional imaging techniques.

Implications of intraglandular lymph node metastases in primary carcinomas of the parotid gland

OBJECTIVES/HYPOTHESIS Assess the diagnostic and prognostic relevance of intraglandular lymph node (IGLN) metastases in primary parotid gland carcinomas (PGCs). STUDY DESIGN Retrospective study at a tertiary referral university hospital. METHODS We reviewed the records of 95 patients with primary PGCs, treated at least surgically, between 1997 and 2010. We assessed the clinicopathological associations of IGLN metastases, their prognostic significance, and predictive value in the diagnosis of occult neck lymph node metastases RESULTS Twenty-four (25.26%) patients had IGLN metastases. This feature was significantly more prevalent in patients with advanced pT status (P = .01), pN status (P < .01), and overall stage (P < .001); high-risk carcinomas (P = .01); as well as in patients with treatment failures (P < .01). IGLN involvement was significantly associated with decreased univariate disease-free survival (P < .001). Positive and negative predictive values and accuracy for IGLN involvement in the detection of occult neck lymph node metastases were 63.64%, 90.48%, and 84.91%, respectively. The diagnostic values were generally higher in patients with low-risk subtype of PGCs. CONCLUSIONS IGLN involvement provides prognostic information and is associated with advanced tumoral stage and higher risk of recurrence. This feature could be used as a potential readout to determine whether a neck dissection in clinically negative neck lymph nodes is needed or not. LEVEL OF EVIDENCE 4.

A Comparison of Radiocolloid and Indocyanine Green Fluorescence Imaging, Sentinel Lymph Node Mapping in Patients with Cervical Cancer Undergoing Laparoscopic Surgery.

BACKGROUND AND PURPOSE (99)TC combined with blue-dye mapping is considered the best sentinel lymph node (SLN) mapping technique in cervical cancer. Indocyanine green (ICG) with near infrared fluorescence imaging has been introduced as a new methodology for SLN mapping. The aim of this study was to compare these two techniques in the laparoscopic treatment of cervical cancer. METHODS Medical records of patients undergoing laparoscopic SLN mapping for cervical cancer with either (99)Tc and patent blue dye (Group 1) or ICG (Group 2) from April 2008 until August 2012 were reviewed. Sensitivity, specificity, and overall and bilateral detection rates were calculated and compared. RESULTS Fifty-eight patients were included in the study-36 patients in Group 1 and 22 patients in Group 2. Median tumor diameter was 25 and 29 mm, and mean SLN count was 2.1 and 3.7, for Groups 1 and 2, respectively. Mean non-SLN (NSLN) count was 39 for both groups. SLNs were ninefold more likely to be affected by metastatic disease compared with NSLNs (p < 0.005). Sensitivity and specificity were both 100 %. Overall detection rates were 83 and 95.5 % (p = nonsignificant), and bilateral detection rates were 61 and 95.5 % (p < 0.005), for Groups 1 and 2, respectively. In 75 % of cases, SLNs were located along the external or internal iliac nodal basins. CONCLUSIONS ICG SLN mapping in cervical cancer provides high overall and bilateral detection rates that compare favorably with the current standard of care.

Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems

Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing the intrusive activity from the legal one using string metric algorithms. The major results of the implemented simulation experiments are presented and discussed as well.

I/Q imbalance in two-way AF relaying: Performance analysis and detection mode switch

This paper studies the impact of in-phase and quadrature-phase imbalance (IQI) in two-way amplify-and-forward (AF) relaying systems. In particular, the effective signal-to-interference-plus-noise ratio (SINR) is derived for each source node, considering four different linear detection schemes, namely, uncompensated (Uncomp) scheme, maximal-ratio-combining (MRC), zero-forcing (ZF) and minimum mean-square error (MMSE) based schemes. For each proposed scheme, the outage probability (OP) is investigated over independent, non-identically distributed Nakagami-m fading channels, and exact closed-form expressions are derived for the first three schemes. Based on the closed-form OP expressions, an adaptive detection mode switching scheme is designed for minimizing the OP of both sources. An important observation is that, regardless of the channel conditions and transmit powers, the ZF-based scheme should always be selected if the target SINR is larger than 3 (4.77dB), while the MRC-based scheme should be avoided if the target SINR is larger than 0.38 (-4.20dB).

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Strategic Alert Throttling for Intrusion Detection Systems

Network intrusion detection systems are themselves becoming targets of attackers. Alert flood attacks may be used to conceal malicious activity by hiding it among a deluge of false alerts sent by the attacker. Although these types of attacks are very hard to stop completely, our aim is to present techniques that improve alert throughput and capacity to such an extent that the resources required to successfully mount the attack become prohibitive. The key idea presented is to combine a token bucket filter with a realtime correlation algorithm. The proposed algorithm throttles alert output from the IDS when an attack is detected. The attack graph used in the correlation algorithm is used to make sure that alerts crucial to forming strategies are not discarded by throttling.

Integrating Innate and Adaptive Immunity for Intrusion Detection

Network Intrusion Detection Systems (NIDS) monitor a net- work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

An Immune Network Intrusion Detection System Utilising Correlation Context

Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDSs rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to the IDS problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

Strategic Alert Throttling for Intrusion Detection Systems

Network intrusion detection systems are themselves becoming targets of attackers. Alert flood attacks may be used to conceal malicious activity by hiding it among a deluge of false alerts sent by the attacker. Although these types of attacks are very hard to stop completely, our aim is to present techniques that improve alert throughput and capacity to such an extent that the resources required to successfully mount the attack become prohibitive. The key idea presented is to combine a token bucket filter with a realtime correlation algorithm. The proposed algorithm throttles alert output from the IDS when an attack is detected. The attack graph used in the correlation algorithm is used to make sure that alerts crucial to forming strategies are not discarded by throttling.

DCA for bot detection

Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.

An immune inspired Network Intrusion Detection System utilising correlation

Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDSs rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to the IDS problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.