Early Detection of Ovarian Cancer in Samples Pre-Diagnosis Using CA125 and MALDI-MS Peaks

Aim: A nested case-control discovery study was undertaken 10 test whether information within the serum peptidome can improve on the utility of CA125 for early ovarian cancer detection. Materials and Methods: High-throughput matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS) was used to profile 295 serum samples from women pre-dating their ovarian cancer diagnosis and from 585 matched control samples. Classification rules incorporating CA125 and MS peak intensities were tested for discriminating ability. Results: Two peaks were found which in combination with CA125 discriminated cases from controls up to 15 and 11 months before diagnosis, respectively, and earlier than using CA125 alone. One peak was identified as connective tissue-activating peptide III (CTAPIII), whilst the other was putatively identified as platelet factor 4 (PF4). ELISA data supported the down-regulation of PF4 in early cancer cases. Conclusion: Serum peptide information with CA125 improves lead time for early detection of ovarian cancer. The candidate markers are platelet-derived chemokines, suggesting a link between platelet function and tumour development

Conformal predictors in early diagnostics of ovarian and breast cancers

The paper describes an application of a recently developed machine learning technique called Mondrian predictors to risk assessment of ovarian and breast cancers. The analysis is based on mass spectrometry profiling of human serum samples that were collected in the United Kingdom Collaborative Trial of Ovarian Cancer Screening. The paper describes the technique and presents the results of classification (diagnosis) and the corresponding measures of confidence of the diagnostics. The main advantage of this approach is a proven validity of prediction. The paper also describes an approach to improve early diagnosis of ovarian and breast cancers since the data in the United Kingdom Collaborative Trial of Ovarian Cancer Screening were collected over a period of seven years and do allow to make observations of changes in human serum over that period of time. Significance of improvement is confirmed statistically (for up to 11 months for Ovarian Cancer and 9 months for Breast Cancer). In addition, the methodology allowed us to pinpoint the same mass spectrometry peaks as previously detected as carrying statistically significant information for discrimination between healthy and diseased patients. The results are discussed

Testing breast cancer serum biomarkers for early detection and prognosis in pre-diagnosis samples

This research was funded by the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre. UKCTOCS was core funded by the Medical Research Council, Cancer Research UK, and the Department of Health with additional support from the Eve Appeal, Special Trustees of Bart’s and the London, and Special Trustees of UCLH. OB and JFT also received support from the Eve Appeal Gynaecological Cancer Research Trust and Cancer Research UK PRC Programme Grant A12677

Signed-Error Conformal Regression

This paper suggests a modification of the Conformal Prediction framework for regression that will strengthen the associated guarantee of validity. We motivate the need for this modification and argue that our conformal regressors are more closely tied to the actual error distribution of the underlying model, thus allowing for more natural interpretations of the prediction intervals. In the experimentation, we provide an empirical comparison of our conformal regressors to traditional conformal regressors and show that the proposed modification results in more robust two-tailed predictions, and more efficient one-tailed predictions.Sponsorship:Swedish Foundation for Strategic Research through the project High-Performance Data Mining for Drug Effect Detection (IIS11-0053) and the Knowledge Foundation through the project Big Data Analytics by Online Ensemble Learning (20120192).</p

Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers

In the conformal prediction literature, it appears axiomatic that transductive conformal classifiers possess a higher predictive efficiency than inductive conformal classifiers, however, this depends on whether or not the nonconformity function tends to overfit misclassified test examples. With the conformal prediction framework’s increasing popularity, it thus becomes necessary to clarify the settings in which this claim holds true. In this paper, the efficiency of transductive conformal classifiers based on decision tree, random forest and support vector machine classification models is compared to the efficiency of corresponding inductive conformal classifiers. The results show that the efficiency of conformal classifiers based on standard decision trees or random forests is substantially improved when used in the inductive mode, while conformal classifiers based on support vector machines are more efficient in the transductive mode. In addition, an analysis is presented that discusses the effects of calibration set size on inductive conformal classifier efficiency.Sponsorship:This work was supported by the Swedish Foundationfor Strategic Research through the project High-Performance Data Mining forDrug Effect Detection (IIS11-0053) and the Knowledge Foundation through theproject Big Data Analytics by Online Ensemble Learning (20120192).</p