Neural network analysis of clinicopathological factors in urological disease: a critical evaluation of available techniques

Finne, Patrik; Finne, Ralf; Stenman, Ulf‐Hǎkan

doi:10.1046/j.1464-4096.2001.02461.x

Cited by 14 publications

(13 citation statements)

References 37 publications

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“…Molecular approaches characterized by high specificity have variable sensitivity, perhaps because of the presence of low tumour DNA quantities in urine or the high level of contamination with normal DNA. Consequently, our secondary objective was to define a panel of markers that might be more reliable and accurate than existing methods of molecular diagnosis [22,30]. Accuracy can often be improved by testing more markers and genes, but this is not a realistic, cost‐effective option for a routine test.…”

Section: Discussionmentioning

confidence: 99%

A comparison of the performance of microsatellite and methylation urine analysis for predicting the recurrence of urothelial cell carcinoma, and definition of a set of markers by Bayesian network analysis

et al. 2008

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OBJECTIVE To compare the potential of two diagnostic methods for detecting recurrence of urothelial cell carcinoma (UCC) of the bladder, by (i) detecting alterations in microsatellite DNA markers and loss of heterozygosity (LOH), and (ii) detecting aberrant gene hypermethylation, as UCC has a high recurrence rate in the urinary tract and the disease can invade muscle if new tumours are overlooked. PATIENTS AND METHODS Over 1 year, urine samples were retrieved from 40 patients already diagnosed with bladder UCC (30 pTa, two pTis, eight pT1). Samples were collected 6 months after bladder tumour resection, during the follow‐up schedule. We used samples to analyse nine microsatellite markers and the methylation status of 11 gene promoters. Receiver operating characteristic curves were generated and Bayesian statistics used to create an interaction network between recurrence and the biomarkers. RESULTS During the study, 15 of the 40 patients (38%) had a tumour recurrence and 14 were identified by cystoscopy (reference method). Overall, microsatellite markers (area under curve, AUC 0.819, 95% confidence interval, CI, 0.677–0.961) had better performance characteristics than promoter hypermethylation (AUC 0.448, 0.259–0.637) for detecting recurrence. A marker panel of IFNA, MBP, ACTBP2, D9S162 and of RASSF1A, and WIF1 generated a higher diagnostic accuracy of 86% (AUC 0.92, 0.772–0.981). CONCLUSION Microsatellite markers have better performance characteristics than promoter hypermethylation for detecting UCC recurrence. These data support the further development of a combination of only six markers from both methods in urinary DNA. Once validated, it could be used routinely during the follow‐up for the early detection and surveillance of UCC from the lower and upper urinary tract.

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Section: Discussionmentioning

confidence: 99%

A comparison of the performance of microsatellite and methylation urine analysis for predicting the recurrence of urothelial cell carcinoma, and definition of a set of markers by Bayesian network analysis

et al. 2008

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“…Likely, we are now bound to discover biomarkers that are less sensitive or specific but that could be used in panels, in combination with powerful bioinformatic tools (such as artificial neural networks, logistic regression, etc. ), to devise diagnostic algorithms with improved sensitivity and specificity (19,20). These efforts are currently ongoing.…”

Section: Cancer Biomarkersmentioning

confidence: 99%

Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: Opportunities and potential limitations

Diamandis¹

2004

Molecular & Cellular Proteomics

188

249

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Serum proteomic profiling, by using surfaced-enhanced laser desorption/ionization-time-of-flight mass spectrometry, is one of the most promising new approaches for cancer diagnostics. Exceptional sensitivities and specificities have been reported for some cancer types such as prostate, ovarian, breast, and bladder cancers. These sensitivities/specificities are far superior to those obtained by using classical cancer biomarkers. In this review, I concentrate more on questions that cast doubt on the results reported and propose experiments to investigate these questions in detail, before the technique is used at the clinic. It is clear that the method needs to be externally and thoroughly validated before clinical implementation is warranted. Molecular & Cellular Proteomics 3:367-378, 2004.Our current efforts to combat cancer are not very successful. Despite the recent spectacular advances in molecular medicine, genomics, proteomics, and translational research, mortality rates for the most prevalent cancers have not been significantly reduced. Some of the best available options to combat cancer include primary prevention, earlier diagnosis, and improved therapeutic interventions. We are now witnessing the development of new drugs against cancer that are based on rational instead of empirical designs. There is hope that some of these drugs will prove to be more effective at the clinic than older generations of medicines. In terms of primary prevention, we do not as yet have at hand any robust strategies, because the mechanisms of cancer initiation and progression are still largely unknown.One of the best strategies to combat cancer now is by early diagnosis and administration of effective treatment (1). Another approach includes close monitoring of the cancer patient after initial treatment (usually surgery) to detect early relapse and then prescribe additional therapy. A third valuable approach would be the stratification of patients into subgroups that respond better to different types of treatment (individualized therapy). Medical imaging and serum or tissue biomarkers are valuable tools for monitoring these patients in order to optimize clinical outcomes.In this review, I will concentrate on mass spectrometry as a diagnostic and cancer biomarker discovery tool. Much has been published on this technology, and excellent reviews have already been prepared (2-12). My presentation will be biased toward underlining potential limitations that have not been adequately addressed in the already existing extensive literature. MASS SPECTROMETRYMass spectrometry has been used as a diagnostic tool in clinical laboratories for many decades. This technology has been coupled with gas chromatography (GC/MS) 1 and has been used with success for the identification and quantification of relatively small molecules (with molecular mass Ͻ1,000 Da). Such molecules could be highly informative in newborn screening programs (13), toxicological and forensic applications (14), for delineating various types of inborn errors of metabolism (15...

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“…1) [15]. About 90% of the ANNs presented in clinical medicine are MLPs [5]; Fig. 2 is an MLP model with two input variables, three hidden neurones and one output neurone.…”

Section: Patients Subjects and Methodsmentioning

confidence: 99%

Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method

Chiang

Chen³

et al. 2003

BJU International

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OBJECTIVETo use information from genetic polymorphisms and from patients (drinking/ exercise habits) to identify their association with stone disease, the main analytical and predictive tools being discriminant analysis (DA) and artificial neural networks (ANNs). PATIENTS, SUBJECTS AND METHODSUrinary stone disease is common in Taiwan; the formation of calcium oxalate stone is reportedly associated with genetic polymorphisms but there are many of these. Genotyping requires many individuals and markers because of the complexity of gene-gene and gene-environmental factor interactions. With the development of artificial intelligence, data-mining tools like ANNs can be used to derive more from patient data in predicting disease. Thus we compared 151 patients with calcium oxalate stones and 105 healthy controls for the presence of four genetic polymorphisms; cytochrome p450c17, E-cadherin, urokinase and vascular endothelial growth factor (VEGF). Information about environmental factors, e.g. water, milk and coffee consumption, and outdoor activities, was also collected. Stepwise DA and ANNs were used as classification methods to obtain an effective discriminant model. RESULTSWith only the genetic variables, DA successfully classified 64% of the participants, but when all related factors (gene and environmental factors) were considered simultaneously, stepwise DA was successful in classifying 74%. The results for DA were best when six variables (sex, VEGF, stone number, coffee, milk, outdoor activities), found by iterative selection, were used. The ANN successfully classified 89% of participants and was better than DA when considering all factors in the model. A sensitivity analysis of the input parameters for ANN was conducted after the ANN program was trained; the most important inputs affecting stone disease were genetic (VEGF), while the second and third were water and milk consumption. CONCLUSIONSWhile data-mining tools such as DA and ANN both provide accurate results for assessing genetic markers of calcium stone disease, the ANN provides a better prediction than the DA, especially when considering all (genetic and environmental) related factors simultaneously. This model provides a new way to study stone disease in combination with genetic polymorphisms and environmental factors.

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Neural network analysis of clinicopathological factors in urological disease: a critical evaluation of available techniques

Cited by 14 publications

References 37 publications

A comparison of the performance of microsatellite and methylation urine analysis for predicting the recurrence of urothelial cell carcinoma, and definition of a set of markers by Bayesian network analysis

A comparison of the performance of microsatellite and methylation urine analysis for predicting the recurrence of urothelial cell carcinoma, and definition of a set of markers by Bayesian network analysis

Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: Opportunities and potential limitations

Prediction of stone disease by discriminant analysis and artificial neural networks in genetic polymorphisms: a new method

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