Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer.

The Nottingham prognostic index plus (NPI+) is based on the assessment of biological class combined with established clinicopathologic prognostic variables providing improved patient outcome stratification for breast cancer superior to the traditional NPI. This study aimed to determine prognostic capability of the NPI+ in predicting risk of development of distant disease. A well-characterised series of 1073 primary early-stage BC cases treated in Nottingham and 251 cases from Budapest were immunohistochemically assessed for cytokeratin (Ck)5/6, Ck18, EGFR, oestrogen receptor (ER), progesterone receptor, HER2, HER3, HER4, Mucin 1 and p53 expression. NPI+ biological class and prognostic scores were assigned using individual algorithms for each biological class incorporating clinicopathologic parameters and investigated in terms of prediction of distant metastases-free survival (MFS). The NPI+ identified distinct prognostic groups (PG) within each molecular class which were predictive of MFS providing improved patient outcome stratification superior to the traditional NPI. NPI+ PGs, between series, were comparable in predicting patient outcome between series in luminal A, basal p53 altered and HER2+/ER+ (p > 0.01) tumours. The low-risk groups were similarly validated in luminal B, luminal N, basal p53 normal tumours (p > 0.01). Due to small patient numbers the remaining PGs could not be validated. NPI+ was additionally able to predict a higher risk of metastases at certain distant sites. This study may indicate the NPI+ as a useful tool in predicting the risk of metastases. The NPI+ provides accurate risk stratification allowing improved individualised clinical decision making for breast cancer.

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer.

BACKGROUND: Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. METHODS: In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes. RESULTS: Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI. CONCLUSION: This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making.

Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers.

BACKGROUND: Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alternative, immunohistochemical analysis applied to routinely processed tissue samples could be used to identify distinct biological classes of breast cancer. METHODS: In this study, 1073 archival breast tumours previously assessed for 25 key breast cancer biomarkers using immunohistochemistry and classified using clustering algorithms were further refined using naïve Bayes classification performance. Criteria for class membership were defined using the expression of a reduced panel of 10 proteins able to identify key molecular classes. We examined the association between these breast cancer classes with clinicopathological factors and patient outcome. RESULTS: We confirm patient classification similar to established genotypic biological classes of breast cancer in addition to novel sub-divisions of luminal and basal tumours. Correlations between classes and clinicopathological parameters were in line with expectations and showed highly significant association with patient outcome. Furthermore, our novel biological class stratification provides additional prognostic information to the Nottingham Prognostic Index. CONCLUSION: This study confirms that distinct molecular phenotypes of breast cancer can be identified using robust and routinely available techniques and both the luminal and basal breast cancer phenotypes are heterogeneous and contain distinct subgroups.

An intelligent multi-restart memetic algorithm for box constrained global optimisation.

In this paper, we propose a multi-restart memetic algorithm framework for box constrained global continuous optimisation. In this framework, an evolutionary algorithm (EA) and a local optimizer are employed as separated building blocks. The EA is used to explore the search space for very promising solutions (e.g., solutions in the attraction basin of the global optimum) through its exploration capability and previous EA search history, and local search is used to improve these promising solutions to local optima. An estimation of distribution algorithm (EDA) combined with a derivative free local optimizer, called NEWUOA (M. Powell, Developments of NEWUOA for minimization without derivatives. Journal of Numerical Analysis, 28:649-664, 2008), is developed based on this framework and empirically compared with several well-known EAs on a set of 40 commonly used test functions. The main components of the specific algorithm include: (1) an adaptive multivariate probability model, (2) a multiple sampling strategy, (3) decoupling of the hybridisation strategy, and (4) a restart mechanism. The adaptive multivariate probability model and multiple sampling strategy are designed to enhance the exploration capability. The restart mechanism attempts to make the search escape from local optima, resorting to previous search history. Comparison results show that the algorithm is comparable with the best known EAs, including the winner of the 2005 IEEE Congress on Evolutionary Computation (CEC2005), and significantly better than the others in terms of both the solution quality and computational cost.

Receiver operating characteristic analysis for intelligent medical systems--a new approach for finding confidence intervals.

Intelligent systems are increasingly being deployed in medicine and healthcare, but there is a need for a robust and objective methodology for evaluating such systems. Potentially, receiver operating characteristic (ROC) analysis could form a basis for the objective evaluation of intelligent medical systems. However, it has several weaknesses when applied to the types of data used to evaluate intelligent medical systems. First, small data sets are often used, which are unsatisfactory with existing methods. Second, many existing ROC methods use parametric assumptions which may not always be valid for the test cases selected. Third, system evaluations are often more concerned with particular, clinically meaningful, points on the curve, rather than on global indexes such as the more commonly used area under the curve. A novel, robust and accurate method is proposed, derived from first principles, which calculates the probability density function (pdf) for each point on a ROC curve for any given sample size. Confidence intervals are produced as contours on the pdf. The theoretical work has been validated by Monte Carlo simulations. It has also been applied to two real-world examples of ROC analysis, taken from the literature (classification of mammograms and differential diagnosis of pancreatic diseases), to investigate the confidence surfaces produced for real cases, and to illustrate how analysis of system performance can be enhanced. We illustrate the impact of sample size on system performance from analysis of ROC pdf's and 95% confidence boundaries. This work establishes an important new method for generating pdf's, and provides an accurate and robust method of producing confidence intervals for ROC curves for the small sample sizes typical of intelligent medical systems. It is conjectured that, potentially, the method could be extended to determine risks associated with the deployment of intelligent medical systems in clinical practice.

The evaluation of an expert system for the analysis of umbilical cord blood.

An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of an infant immediately after delivery. This can provide information on the health of the new-born infant, guide requirements for neonatal care, but there are problems with the technique. Samples frequently contain errors in one or more of the important parameters, preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret error-free results. The development and implementation of an expert system to overcome these difficulties has previously been described. This expert system validates the raw data, provides an interpretation of the results for clinicians and archives all the results, including the quality control and calibration data, for permanent storage. Issues regarding the clinical evaluation of this system are now detailed further, along with some clinical results illustrating the potential of such a system.

The fuzzy medical group in the centre for computational intelligence.

In this paper, five ongoing or completed research projects in medicine using fuzzy sets and logic are summarized. They are, a lightweight fuzzy process for diagnosis using fuzzy symptoms, prediction of pulmonary embolisms from linguistic descriptions of perfusion and ventilation scans, application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification, the development of a fuzzy expert system for the analysis of umbilical cord blood, modeling nursing intuition using type 2 fuzzy sets. These projects use a variety of fuzzy methods including clustering, simple set aggregation and type 2 inferencing to achieve their aims. The ongoing research projects reflect an interest in using type 2 fuzzy sets for dealing with vagueness and linguistic knowledge which is commonly found in medical areas where perceptions rather than measurements are the norm.

The development and implementation of an expert system for the analysis of umbilical cord blood.

An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of the infant immediately after delivery. This can provide information on the health of the newborn infant, guide requirements for neonatal care, and is recommended practice of the Royal College of Obstetricians and Gynaecologists. However, there are problems with the technique. Samples frequently contain errors in one or more of the important parameters, preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret error-free results. In this paper the development and implementation of an expert system to overcome these difficulties is described. The expert system validates results, provides a textual interpretation and archives all results to database for audit, research and medico-legal purposes. The system has now been in routine clinical use for over 3 years in Plymouth, and has also been installed in several other hospitals in the UK. Results are presented in which the types and frequency of errors are established and the user acceptance of the system is determined.

Umbilical cord blood gas analysis at the time of delivery.

AIMS: it is now recommended that cord blood acid-base measurement is performed routinely at time of delivery in the UK as a measure of fetal response to labour. However, there remains some uncertainty about the value of this procedure. In this paper our experience of cord blood analysis is described and the literature is reviewed to: (1) provide an overview of the physiological basis of cord blood acid-base assessment; (2) describe the appropriate methodology and identify issues which have contributed to confusion and undermined the value of cord blood sampling; and (3) address the practical issues of cord blood sampling. CONCLUSIONS: cord blood acid-base measurement has a sound physiological basis. It provides objective information which is a useful adjunct to subjective methods of newborn assessment, enables babies at risk of neonatal morbidity to be identified, can be helpful in litigation cases and is a prerequisite for clinical audit. However, to be of benefit the information must be correct and correctly interpreted.

Umbilical cord blood gas analysis at delivery: a time for quality data.

OBJECTIVES: To address the practical problems of routine umbilical cord blood sampling, to determine the ranges for pH, PCO2 and base deficit and to examine the relationships of these parameters between cord vessels. DESIGN: An observational study of umbilical cord artery and vein blood gas results. SETTING: A large district general hospital in the UK. SUBJECTS: One thousand nine hundred and forty-two cord results from 2013 consecutive pregnancies of 34 weeks or more gestation, monitored by fetal scalp electrode during labour. RESULTS: Only 1448 (74.6%) of the 1942 supposedly paired samples had validated pH and PCO2 data both from an artery and the vein; 54 (2.8%) had only one blood sample available, 90 (4.6%) had an error in the pH or PCO2 of one vessel and in 350 (18%) pairs the differences between vessels indicated that they were not sampled from artery and vein as intended. Only 60% of the cases with an arterial pH less than 7.05 had evidence of a metabolic acidosis (base deficit in the extracellular fluid 10 mmol/l or more). Of all the cases, 2.5% had a venous-arterial pH difference greater than 0.22 units. CONCLUSIONS: Both artery and vein cord samples must be taken and the results screened to ensure separate vessels have been sampled. Interpretation of the results requires the examination of PCO2 and base deficit of the extracellular fluid from each vessel as well as the pH. Confusion about the value of cord gas measurements may be due to the use of erroneous data and inadequate definitions of acidosis which do not differentiate between respiratory and metabolic components.

A multicentre comparative study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram.

OBJECTIVES: To investigate 1. whether an intelligent computer system could obtain a performance in labour management comparable with experts when using cardiotocograms (CTGs), patient information, and fetal blood sampling and 2. whether experts could be consistent and agree in their management of labour. SUBJECTS: An intelligent computer system and 17 clinicians experienced in fetal monitoring from 16 centres in the UK. DESIGN: Fifty cases with complete intrapartum CTGs and clinical data were reviewed by each expert and the system independently on two occasions, at least one month apart. Each CTG was scored in 15 min segments according to a protocol and estimates of the cervical dilatation and fetal scalp blood pH were given when requested. MAIN OUTCOME MEASURES: Consistency and agreement in the recorded scores, agreement and timing of cases recommended for caesarean sections, fetal blood sampling rates, intervention in cases with poor outcome and intervention in cases with good clinical outcome. RESULTS: The system: 1. Agreed with experts well and significantly better than chance (67.33%, kappa = 0.31, P << 0.001). 2. Was highly consistent (99.16%, kappa = 0.98, P << 0.001) when used by two operators independently. 3. Recommended no unnecessary intervention in cases with normal delivery and good condition (cord artery pH > 7.15, vein pH > 7.20, 5 min Apgar > or = 9 and no resuscitation). This was better than all but two of the experts. 4. Recommended delivery by caesarean section in 11 cases; at least 15 of the 17 experts in each review also recommended caesarean section delivery in these cases. The majority did so within 15 min of the system and two-thirds did so within 30 min. 5. Identified as many of the birth asphyxiated cases (cord arterial pH < 7.05 and BDecf > or = 12, and Apgar score at 5 min < or = 7 with neonatal morbidity) as the majority of experts and one more than was acted upon clinically. The experts were found to be consistent and to agree. There was good agreement in the cases and the timing of caesarean section recommendations. The majority of experts did not recommend operative intervention in cases which had a normal delivery and good outcome, but did recommend operative interventions in 10 of 12 cases delivered with cord arterial pH < 7.05. However, in one of the cases delivered with birth asphyxia, 14 of the 17 experts and the system failed to recommend intervention. CONCLUSIONS: The system's performance was found to be indistinguishable from the experts' in the 50 cases examined, but it was more consistent. This demonstrates the potential for an intelligent computer system to improve the interpretation of the CTG and decrease intervention. Furthermore, the good performance of most experts in this study demonstrates the potential effectiveness of the CTG and raises important questions regarding why the CTG has fallen short of expectations in current practice.