Change-point of multiple biomarkers in women with ovarian cancer

To date several algorithms for longitudinal analysis of ovarian cancer biomarkers have been proposed in the literature. An issue of specific interest is to determine whether the baseline level of a biomarker changes significantly at some time instant (change-point) prior to the clinical diagnosis of cancer. Such change-points in the serum biomarker Cancer Antigen 125 (CA125) time series data have been used in ovarian cancer screening, resulting in earlier detection with a sensitivity of 85% in the most recent trial, the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, number ISRCTN22488978; NCT00058032). Here we propose to apply a hierarchical Bayesian change-point model to jointly study the features of time series from multiple biomarkers. For this model we have analytically derived the conditional probability distribution of every unknown parameter, thus enabling the design of efficient Markov Chain Monte Carlo methods for their estimation. We have applied these methods to the estimation of change-points in time series data of multiple biomarkers, including CA125 and others, using data from a nested case-control study of women diagnosed with ovarian cancer in UKCTOCS. In this way we assess whether any of these additional biomarkers can play a role in change-point detection and, therefore, aid in the diagnosis of the disease in patients for whom the CA125 time series does not display a change-point. We have also investigated whether the change-points for different biomarkers occur at similar times for the same patient. The main conclusion of our study is that the combined analysis of a group of specific biomarkers may possibly improve the detection of change-points in time series data (compared to the analysis of CA125 alone) which, in turn, are relevant for the early diagnosis of ovarian cancer

Multi-marker longitudinal algorithms incorporating HE4 and CA125 in ovarian cancer screening of postmenopausal women

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2–5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871–0.952) and 90.5% (82.5–98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.Peer reviewe

Comparison of Longitudinal CA125 Algorithms as a First-Line Screen for Ovarian Cancer in the General Population

Purpose: In the United Kingdom Collaborative Trial of Ovarian Cancer Screening(UKCTOCS) women in the multimodal (MMS) arm had a serum CA125 test (first-line), with those at increased risk, having repeat CA125/ultrasound (second-line test). CA125 was interpreted using the 'Risk of Ovarian Cancer Algorithm'(ROCA). We report on performance of other serial algorithms and a single CA125 threshold as a first line screen in the UKCTOCS dataset. // Experimental Design: 50,083 post-menopausal women who attended 346,806 MMS screens were randomly split into training and validation sets, following stratification into cases (ovarian/tubal/peritoneal cancers) and controls. The two longitudinal algorithms, a new serial algorithm, method of mean trends (MMT) and the parametric empirical Bayes (PEB) were trained in the training set and tested in the blinded validation set and the performance characteristics, including that of a single CA125 threshold, were compared. // Results The area under receiver operator curve (AUC) was significantly higher (p=0.01) for MMT (0.921) compared to CA125 single threshold (0.884). At a specificity of 89.5%, sensitivities for MMT (86.5%;95%CI:78.4-91.9) and PEB (88.5%;95%CI:80.6-93.4) were similar to that reported for ROCA (sensitivity 87.1%; specificity 87.6%; AUC 0.915) and significantly higher than the single CA125 threshold (73.1%;95%CI:63.6-80.8). // Conclusions: These findings from the largest available serial CA125 data set in the general population provide definitive evidence that longitudinal algorithms are significantly superior to simple cut-offs for ovarian cancer screening. Use of these newer algorithms requires incorporation into a multimodal strategy. The results highlight the importance of incorporating serial change in biomarker levels in cancer screening/early detection strategies

A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer.

We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). We use statistical analysis techniques, such as the area under the Receiver Operating Characteristic (ROC) curve, for evaluating the performance of two techniques that aim at the classification of subjects as either healthy or suffering from the disease using time-series of multiple biomarkers as inputs. The first method relies on a Bayesian hierarchical model that establishes connections within a set of clinically interpretable parameters. The second technique is a purely discriminative method that employs a recurrent neural network (RNN) for the binary classification of the inputs. For the available dataset, the performance of the two detection schemes is similar (the area under ROC curve is 0.98 for the combination of three biomarkers) and the Bayesian approach has the advantage that its outputs (parameters estimates and their uncertainty) can be further analysed by a clinical expert.This research was funded by Cancer Research UK and the Eve Appeal Gynaecological Cancer Research Fund (grant ref. A12677) and was supported 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. We also acknowledge support by the grant of the Ministry of Education and Science of the Russian Federation Agreement No. 074-02-2018-330. I.P.M. and M.A.V. acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness (projects TEC2015-69868-C2-1-R and TEC2017-86921-C2-1-R)

A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer deaths worldwide, and its early detection is a critical determinant of whether curative treatment is achievable. Early stage HCC is typically asymptomatic. Thus, screening programmes are used for cancer detection in patients at risk of tumour development. Radiological screening methods are limited by imperfect data, cost and associated risks, and additionally are unable to detect lesions until they have grown to a certain size. Therefore, some screening programmes use additional blood/serum biomarkers to help identify individuals in whom to target diagnostic cancer investigations. The GALAD score, combining the levels of several blood biomarkers, age and sex, has been developed to identify patients with early HCC. Here we propose a Bayesian hierarchical model for an individual’s longitudinal GALAD scores whilst in HCC surveillance to identify potentially significant changes in the trend of the GALAD score, indicating the development of HCC, aiming to improve early detection compared to standard methods. An absorbent two-state continuous-time hidden Markov model is developed for the individual level longitudinal data where the states correspond to the presence/absence of HCC. The model is additionally informed by the information on the diagnosis by standard clinical practice, taking into account that HCC can be present before the actual diagnosis so that there may be false negatives within the diagnosis data. We fit the model to a Japanese cohort of patients undergoing HCC surveillance and show that the detection capability of this proposal is greater than using a fixed cut-point

Gynaecological Cancers Risk

The International Agency for Research on Cancer suggests that the burden of women’s cancers including breast, ovarian, and womb will rise by 50% over the next 20 years. It is essential for us to improve early diagnosis and prevention of these cancers in our health systems. The last decade has seen significant strides in our ability to understand and predict a woman’s risk of these cancers and offer personalized medicine approaches for risk management. There have been improvements in identifying individuals at increased risk, as well as implementing and evaluating strategies for screening and prevention. In this special collection, we bring together 16 articles from leading scientists and researchers. These capture some of the important advances observed in estimating cancer risk, providing genetic testing, offering risk management to those at increased risk, as well as screening and prevention of breast, ovarian, and womb cancers in women. This makes an important contribution to the rapidly advancing knowledge base across the area of personalized medicine and precision prevention of ovarian, endometrial, and breast cancers

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment