Suitability of PSA-detected localised prostate cancers for focal therapy: experience from the ProtecT study.

BACKGROUND: Contemporary screening for prostate cancer frequently identifies small volume, low-grade lesions. Some clinicians have advocated focal prostatic ablation as an alternative to more aggressive interventions to manage these lesions. To identify which patients might benefit from focal ablative techniques, we analysed the surgical specimens of a large sample of population-detected men undergoing radical prostatectomy as part of a randomised clinical trial. METHODS: Surgical specimens from 525 men who underwent prostatectomy within the ProtecT study were analysed to determine tumour volume, location and grade. These findings were compared with information available in the biopsy specimen to examine whether focal therapy could be provided appropriately. RESULTS: Solitary cancers were found in prostatectomy specimens from 19% (100 out of 525) of men. In addition, 73 out of 425 (17%) men had multiple cancers with a solitary significant tumour focus. Thus, 173 out of 525 (33%) men had tumours potentially suitable for focal therapy. The majority of these were small, well-differentiated lesions that appeared to be pathologically insignificant (38-66%). Criteria used to select patients for focal prostatic ablation underestimated the cancer's significance in 26% (34 out of 130) of men and resulted in overtreatment in more than half. Only 18% (24 out of 130) of men presumed eligible for focal therapy, actually had significant solitary lesions. CONCLUSION: Focal therapy appears inappropriate for the majority of men presenting with prostate-specific antigen-detected localised prostate cancer. Unifocal prostate cancers suitable for focal ablation are difficult to identify pre-operatively using biopsy alone. Most lesions meeting criteria for focal ablation were either more aggressive than expected or posed little threat of progression.

Ensembled artificial neural networks to predict the fitness score for body composition analysis.

OBJECTIVES: To predict the nutrition and health status of staff and students in Yuan Ze University and select the influential variables from the total body composition variables, which should have similar predictive ability with the whole factors. DESIGN: Spontaneous and voluntary physical examination. SETTING: Sanitary and Health Care Section of Yuan Ze University in Taiwan. PARTICIPANTS: 1227 staff and students. MEASUREMENTS: With the help of Inbody720TM, 139 body composition variables were measured and 60 variables were retained after data pre-processing. An ensembled artificial neural networks (EANN) prediction model was established and seven different methods for assessing variables importance were applied. Besides, classical linear and logistic regression models were developed for comparison with EANN prediction results. RESULTS: The prediction performance of EANN model was satisfactory (RMSE (train) = 0.2686, RMSE (validation) = 0.2648, RMSE (test) = 0.3492). Since both the actual and simulation fitness score were at the range of 0 to 100, according to rounding off rule, the simulated value was almost the same with actual value. Besides, 12 important variables were obtained by seven methods for quantifying variable importance in EANN, which had similar predictive capability with 60 variables (RMSE (train) = 0.3263, RMSE (validation) = 0.322, RMSE (test) = 0.3226). The linear and logistic regression models results were both evidently worse than EANN results. CONCLUSION: The results confirm that EANN is appropriate to approximate such a complicated, non-invasive and highly non-linear problem as body composition analysis. It can be helpful for nutritionists to manage and improve the nutrition and health condition of staff and students, by adjusting the 12 most important variables.

Artificial intelligence and bladder cancer arrays.

Non-muscle invasive bladder cancer is a heterogenous disease whose management is dependent upon the risk of progression to muscle invasion. Although the recurrence rate is high, the majority of tumors are indolent and can be managed by endoscopic means alone. The prognosis of muscle invasion is poor and radical treatment is required if cure is to be obtained. Progression risk in non-invasive tumors is hard to determine at tumor diagnosis using current clinicopathological means. To improve the accuracy of progression prediction various biomarkers have been evaluated. To discover novel biomarkers several authors have used gene expression microarrays. Various statistical methods have been described to interpret array data, but to date no biomarkers have entered clinical practice. Here, we describe a new method of microarray analysis using neurofuzzy modeling (NFM), a form of artificial intelligence, and integrate it with artificial neural networks (ANN) to investigate non-muscle invasive bladder cancer array data (n=66 tumors). We develop a predictive panel of 11 genes, from 2800 expressed genes, that can significantly identify tumor progression (average Logrank p = 0.0288) in the analyzed cancers. In comparison, this panel appears superior to those genes chosen using traditional analyses (average Logrank p = 0.3455) and tumor grade (Logrank, p = 0.2475) in this non-muscle invasive cohort. We then analyze panel members in a new non-muscle invasive bladder cancer cohort (n=199) using immunohistochemistry with six commercially available antibodies. The combination of 6 genes (LIG3, TNFRSF6, KRT18, ICAM1, DSG2 and BRCA2) significantly stratifies tumor progression (Logrank p = 0.0096) in the new cohort. We discuss the benefits of the transparent NFM approach with respect to other reported methods.

Comparative study of intelligent models for the prediction of bladder cancer progression.

New techniques for the prediction of tumour behaviour are needed since statistical analysis has low accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide suitable methods. We have compared the predictive accuracies of neuro-fuzzy modelling (NFM), artificial neural networks (ANN) and traditional statistical methods for the prediction of bladder cancer. Experimental molecular biomarkers, including p53 expression and gene methylation, and conventional clinicopathological data were studied in a cohort of 117 patients with bladder cancer. For all 3 methods, models were produced to predict the presence and timing of tumour progression. Both methods of AI predicted progression with an accuracy ranging from 88-100%, which was superior to logistic regression, and NFM appeared to be better than ANN at predicting the timing of progression.

Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control.

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

Survey on the use of smart and adaptive engineering systems in medicine.

In this paper, the current published knowledge about smart and adaptive engineering systems in medicine is reviewed. The achievements of frontier research in this particular field within medical engineering are described. A multi-disciplinary approach to the applications of adaptive systems is observed from the literature surveyed. The three modalities of diagnosis, imaging and therapy are considered to be an appropriate classification method for the analysis of smart systems being applied to specified medical sub-disciplines. It is expected that future research in biomedicine should identify subject areas where more advanced intelligent systems could be applied than is currently evident. The literature provides evidence of hybridisation of different types of adaptive and smart systems with applications in different areas of medical specifications.

A survey of fuzzy logic monitoring and control utilisation in medicine.

Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical models in terms of linguistic rules. Since the 1980s new techniques have appeared from which fuzzy logic has been applied extensively in medical systems. The justification for such intelligent systems driven solutions is that biological systems are so complex that the development of computerised systems within such environments is not always a straightforward exercise. In practice, a precise model may not exist for biological systems or it may be too difficult to model. In most cases fuzzy logic is considered to be an ideal tool as human minds work from approximate data, extract meaningful information and produce crisp solutions. This paper surveys the utilisation of fuzzy logic control and monitoring in medical sciences with an analysis of its possible future penetration.

Intelligent control of a cryogenic cooling plant based on blackboard system architecture.

Intelligent system techniques have been rapidly assimilating into process control engineering, with many applications reported in the last decade. Intelligent control is bringing a new perspective as well as new challenges to process control. In this paper, a software architecture for a Blackboard for Integrated Intelligent Control Systems (BIICS) is described. The system is designed to simultaneously support multiple heterogeneous intelligent methodologies, such as neural networks. expert systems, fuzzy logic, neural networks and genetic algorithms. It will be shown how such methodologies can be readily assimilated into the software architecture. The BIICS system represents a multi-purpose platform for design and simulation of intelligent control paradigms for different kinds of processes. Currently the system utilizes intelligent control techniques (neuro-fuzzy and genetic optimization) for controlling a cryogenic plant used for superconductor testing at temperatures below 100 K.