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Azzam F G Taktak - Top 30 Publications

Cluster analysis of multiplex ligation-dependent probe amplification data in choroidal melanoma.

To determine underlying correlations in multiplex ligation-dependent probe amplification (MLPA) data and their significance regarding survival following treatment of choroidal melanoma (CM).

Authors’ reply.

Single nucleotide polymorphism array analysis of uveal melanomas reveals that amplification of CNKSR3 is correlated with improved patient survival.

Metastatic death from uveal melanoma occurs almost exclusively with tumors showing monosomy of chromosome 3. However, approximately 5% of patients with a disomy 3 uveal melanoma develop metastases, and a further 5% of monosomy 3 uveal melanoma patients exhibit disease-free survival for >5 years. In the present study, whole-genome microarrays were used to interrogate four clinically well-defined subgroups of uveal melanoma: i) disomy 3 uveal melanoma with long-term survival; ii) metastasizing monosomy 3 uveal melanoma; iii) metastasizing disomy 3 uveal melanoma; and iv) monosomy 3 uveal melanoma with long-term survival. Cox regression and Kaplan-Meier survival analysis identified that amplification of the CNKSR3 gene (log-rank, P = 0.022) with an associated increase in its protein expression (log-rank, P = 0.011) correlated with longer patient survival. Although little is known about CNKSR3, the correlation of protein expression with increased survival suggests a biological function in uveal melanoma, possibly working to limit metastatic progression of monosomy 3 uveal melanoma cells.

Multiplex ligation-dependent probe amplification of conjunctival melanoma reveals common BRAF V600E gene mutation and gene copy number changes.

To determine the occurrence of BRAF V600E gene mutations and copy number changes of all autosome arms and genes known to be frequently altered in tumorigenesis in primary and metastatic conjunctival melanomas (CoMs).

Multiplex ligation-dependent probe amplification analysis of uveal melanoma with extraocular extension demonstrates heterogeneity of gross chromosomal abnormalities.

To determine whether biopsy of extraocular extension of uveal melanoma (UM) is representative of the intraocular tumor with respect to copy number of chromosomes 1p, 3, 6, and 8.

Estimating prognosis for survival after treatment of choroidal melanoma.

Choroidal melanoma is fatal in about 50% of patients. This is because of metastatic disease, which usually involves the liver. Kaplan-Meier survival curves based only on tumor size and extent do not give a true indication of prognosis. This is because the survival prognosis of choroidal melanoma correlates not only with clinical stage but also with histologic grade, genetic type, and competing causes of death. We have developed an online tool that predicts survival using all these data also taking normal life-expectancy into account. The estimated prognosis is accurate enough to be relevant to individual patients. Such personalized prognostication improves the well-being of patients having an excellent survival probability, not least because it spares them from unnecessary screening tests. Such screening can be targeted at high-risk patients, so that metastases are detected sooner, thereby enhancing any opportunities for treatment. Concerns about psychological harm have proved exaggerated. At least in Britain, patients want to know their prognosis, even if this is poor. The ability to select patients with a high risk of metastasis improves prospects for randomised studies evaluating systemic adjuvant therapy aimed at preventing or delaying metastatic disease. Furthermore, categorization of tissue samples according to survival prognosis enables laboratory studies to be undertaken without waiting many years for survival to be measured. As a result of advances in histologic and genetic studies, biopsy techniques and statistics, prognostication has become established as a routine procedure in our clinical practice, thereby enhancing the care of patients with uveal melanoma.

Genetic heterogeneity in uveal melanoma assessed by multiplex ligation-dependent probe amplification.

To determine intratumor genetic heterogeneity in uveal melanoma (UM) by multiplex ligation-dependent probe amplification (MLPA) in formalin-fixed, paraffin-embedded (FFPE) tumor tissues.

Whole-genome microarray detects deletions and loss of heterozygosity of chromosome 3 occurring exclusively in metastasizing uveal melanoma.

To detect deletions and loss of heterozygosity of chromosome 3 in a rare subset of fatal, disomy 3 uveal melanoma (UM), undetectable by fluorescence in situ hybridization (FISH).

Partial logistic artificial neural network for competing risks regularized with automatic relevance determination.

Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi (1995).

A web-based tool for the assessment of discrimination and calibration properties of prognostic models.

Prognostic models are developed to assist clinicians in making decisions regarding treatment and follow-up management. The accuracy of these models is often assessed either in terms of their discrimination performance or calibration but rarely both. In this paper, we describe the development of an online tool for discrimination using Harrell C index and calibration using a Hosmer-Lemeshow type analysis ( We show examples of using the tool on real data. We highlight situations where the model performed well in terms of either discrimination or calibration but not both depending on the sample size of the test set. We conclude that prognostic models should be assessed both in terms of discrimination and calibration and that calibration analysis should be carried out numerically and graphically.

Artificial neural networks estimating survival probability after treatment of choroidal melanoma.

To describe neural networks predicting survival from choroidal melanoma (i.e., any uveal melanoma involving choroid) and to demonstrate the value of entering age, sex, clinical stage, cytogenetic type, and histologic grade into the predictive model.

Automated post hoc removal of power-line and CRT frame pulse contamination from retinal and cortical evoked potentials (EPs).

Recordings of the ERG, PERG, VEP and their multi-focal variants are occasionally contaminated with harmonic noise arising from the mains supply and CRT monitors. These noise contributions can be modelled as distorted sinusoids and identified by means of non-linear multiple regression and removed: no a priori estimates of number or frequency of noise sources are required. This approach is termed noise cancellation and does not constitute any form of notch filter: the fidelity of the underlying waveform is preserved. Here the simple theory is illustrated in artificial datasets and then applied to clinical examples of PERG and VEP. The programming language used throughout is MatLab R13SP3 (Mathworks UK Ltd.).

On the use of multi-objective evolutionary algorithms for survival analysis.

This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.

The use of artificial neural networks in decision support in cancer: a systematic review.

Artificial neural networks have featured in a wide range of medical journals, often with promising results. This paper reports on a systematic review that was conducted to assess the benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. The number of clinical trials (CTs) and randomised controlled trials (RCTs) involving the use of ANNs in diagnosis and prognosis increased from 1 to 38 in the last decade. However, out of 396 studies involving the use of ANNs in cancer, only 27 were either CTs or RCTs. Out of these trials, 21 showed an increase in benefit to healthcare provision and 6 did not. None of these studies however showed a decrease in benefit. This paper reviews the clinical fields where neural network methods figure most prominently, the main algorithms featured, methodologies for model selection and the need for rigorous evaluation of results.

Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem.

This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to > 100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For < 10 years survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate.