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ABSTRACT: 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.
American Journal Of Pathology 01/2013; · 4.89 Impact Factor
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ABSTRACT: 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.
Progress in Retinal and Eye Research 05/2011; 30(5):285-95. · 9.45 Impact Factor
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ABSTRACT: To determine intratumor genetic heterogeneity in uveal melanoma (UM) by multiplex ligation-dependent probe amplification (MLPA) in formalin-fixed, paraffin-embedded (FFPE) tumor tissues.
DNA was extracted from whole tumor sections and from two to nine different areas microdissected from 32 FFPE UMs. Thirty-one loci on chromosomes 1, 3, 6, and 8 were tested with MLPA for copy number changes. The tumor was considered heterogeneous at a locus if (1) the difference in dosage quotients (DQs) of any two areas was 0.2 or more, and (2) the DQs of the areas belonged to different ranges.
Comparison of MLPA data obtained from microdissected areas of the UMs showed heterogeneity in 1 to 26 examined loci in 24 (75%) tumors, with only 25% of the tumors being homogeneous. Intratumor heterogeneity of 3p12.2, 6p21.2, and 8q11.23 was most common, occurring in >30% of the UMs. Gains of chromosome 3 were observed in four UMs, with three of these tumors showing the highest degree of heterogeneity. Copy number variation was associated with differences in tumor cell type, but not with differences in tumor pigmentation or reactive inflammation. UMs with genetic heterogeneity across multiple sample sites showed equivocal MLPA results when the whole tumor section was examined. These results suggest that different clones dilute MLPA results.
Heterogeneity of chromosomal abnormalities of chromosomes 1, 3, 6, and 8 is present in most UMs. This heterogeneity causes equivocal MLPA results. One random tumor sample may not be representative of the whole tumor and, therefore, may be insufficient for prognostic testing.
Investigative ophthalmology & visual science 10/2010; 51(10):4898-905. · 3.43 Impact Factor
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ABSTRACT: 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).
IEEE Transactions on Neural Networks 08/2009; 20(9):1403-16. · 2.95 Impact Factor
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ABSTRACT: 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 (http://clinengnhs.liv.ac.uk/AADP/AADP_Welcome.htm). 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.
Computers in Biology and Medicine 08/2008; 38(7):785-91. · 1.09 Impact Factor
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ABSTRACT: 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.
Nonrandomized case series.
Patients resident in mainland Britain treated by the first author for choroidal melanoma between 1984 and 2006.
A conditional hazard estimating neural network (CHENN) was trained according to the Bayesian formalism with a training set of 1780 patients and evaluated with a test set of another 874 patients. Conditional hazard estimating neural network-generated survival curves were compared with those obtained with Kaplan-Meier analyses. A second model was created with information on chromosome 3 loss, using training and test sets of 211 and 140 patients, respectively.
Comparison of CHENN survival curves with Kaplan-Meier analyses. Representative results showing all-cause survival and inferred melanoma-specific mortality, according to age, sex, clinical stage, cytogenetic type, and histologic grade.
The predictive model plotted a survival curve with 95% credibility intervals for patients with melanoma according to relevant risk factors: age, sex, largest basal tumor diameter, ciliary body involvement, extraocular extension, tumor cell type, closed loops, mitotic rate, and chromosome 3 loss (i.e., monosomy 3). A survival curve for the age-matched general population of the same sex allowed estimation of the melanoma-related mortality. All-cause survival curves generated by the CHENN matched those produced with Kaplan-Meier analysis (Kolmogorov-Smirnov, P<0.05). In older patients, however, the estimated melanoma-related mortality was lower with the CHENN, which accounted for competing risks, unlike Kaplan-Meier analysis. Largest basal tumor diameter was most predictive of mortality in tumors showing histologic and cytogenetic features of high-grade malignancy. Ciliary body involvement and extraocular extension lost significance when cytogenetic and histologic data were included in the model. Patients with a monosomy 3 melanoma of a particular size were predicted to have shorter survival if their tumor showed epithelioid cells and closed loops.
Estimation of survival prognosis in patients with choroidal melanoma requires multivariate assessment of age, sex, clinical tumor stage, cytogenetic melanoma type, and histologic grade of malignancy.
Ophthalmology 03/2008; 115(9):1598-607. · 5.45 Impact Factor
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ABSTRACT: 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.
Biosystems 02/2007; 87(1):31-48. · 1.78 Impact Factor
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ABSTRACT: 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.
Neural Networks 06/2006; 19(4):408-15. · 2.18 Impact Factor
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ABSTRACT: 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.).
Documenta Ophthalmologica 06/2006; 112(3):169-75. · 2.11 Impact Factor
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ABSTRACT: 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.
Physics in Medicine and Biology 01/2004; 49(1):87-98. · 2.83 Impact Factor
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ABSTRACT: 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.
Biosystems.
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[show abstract]
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ABSTRACT: 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.
Neural Networks.
