Parenclitic and Synolytic Networks Revisited

© 2021 Nazarenko, Whitwell, Blyuss and Zaikin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question—which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with a priori known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not a priori available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the “black-box” nature of other ML approaches.Peer reviewedFinal Published versio

Effect of noise in intelligent cellular decision making

Similar to intelligent multicellular neural networks controlling human brains, even single cells surprisingly are able to make intelligent decisions to classify several external stimuli or to associate them. This happens because of the fact that gene regulatory networks can perform as perceptrons, simple intelligent schemes known from studies on Artificial Intelligence. We study the role of genetic noise in intelligent decision making at the genetic level and show that noise can play a constructive role helping cells to make a proper decision. We show this using the example of a simple genetic classifier able to classify two external stimuli

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

© The Author(s) 2019. Published by Springer Nature on behalf of Cancer Research UK.BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.Peer reviewe

Improved early detection of ovarian cancer using longitudinal multimarker models

© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Background: Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer. Methods: This case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer. Results: The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time. Conclusions: We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.Peer reviewe

Breast Cancer Risk and Breast-Cancer-Specific Mortality following Risk-Reducing Salpingo-Oophorectomy in BRCA Carriers : A Systematic Review and Meta-Analysis

Funding This research was funded by Rosetrees Trust, grant number CF1\100001, and Barts Charity, grant number ECMG1C3R. The funders had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.Peer reviewedPublisher PD

Determining post-operative morbidity and mortality following gynecological oncology surgery : protocol for a multicenter, international, prospective cohort study (Global Gynaecological Oncology Surgical Outcomes Collaborative—GO SOAR)

Funding: The NHS Grampian Endowment Fund; Medtronic; Karl Storz.Peer reviewedPostprintsupplementary_dat

The Human Body as a Super Network : Digital Methods to Analyze the Propagation of Aging

© 2020 Whitwell, Bacalini, Blyuss, Chen, Garagnani, Gordleeva, Jalan, Ivanchenko, Kanakov, Kustikova, Mariño, Meyerov, Ullner, Franceschi and Zaikin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY - https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks—e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called “seven pillars of aging” combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.Peer reviewe

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.

Funder: National Institute of Health Research Cambridge Biomedical Research CentreFunder: Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and ManchesterFunder: Cambridge Experimental Cancer Medicine CentreFunder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370OBJECTIVES: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.The authors acknowledge support from National Institute of Health Research Cambridge Biomedical Research Centre, Cancer Research UK (Cambridge Imaging Centre grant number C197/A16465), the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, and the Cambridge Experimental Cancer Medicine Centre. T. Nazarenko is supported by a Medical Research Council grant (MR/R02524X/1). A. Suvorov is supported by the Ministry of Science and Higher Education of the Russian Federation within the programme developing World-Class Research Centres "Digital Biodesign and Personalized Healthcare" (075-15-2020-926)

Annual mammographic screening to reduce breast cancer mortality in women from age 40 years:long-term follow-up of the UK Age RCT

© Queen’s Printer and Controller of HMSO 2020. This work was produced by Duffy et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.BACKGROUND: There remains disagreement on the long-term effect of mammographic screening in women aged 40-49 years.OBJECTIVES: The long-term follow-up of a randomised controlled trial that offered annual mammography to women aged 40-49 years. The estimation of the effect of these mammograms on breast cancer and other-cause mortality, and the effect on incidence, with implications for overdiagnosis.DESIGN: An individually randomised controlled trial comparing offering annual mammography with offering usual care in those aged 40-48 years, and thus evaluating the effect of annual screening entirely taking place before the age of 50 years. There was follow-up for an average of 23 years for breast cancer incidence, breast cancer death and death from other causes. We analysed the mortality and incidence data by Poisson regression and estimated overdiagnosis formally using Markov process models.SETTING: Twenty-three screening units in England, Wales and Scotland within the NHS Breast Screening Programme.PARTICIPANTS: Women aged 39-41 years were recruited between 1990 and 1997. After exclusions, a total of 53,883 women were randomised to undergo screening (the intervention group) and 106,953 women were randomised to have usual care (the control group).INTERVENTIONS: The intervention group was invited to an annual breast screen with film mammography, two view at first screen and single view thereafter, up to and including the calendar year of their 48th birthday. The control group received no intervention. Both groups were invited to the National Programme from the age of 50 years, when screening is offered to all women in the UK.MAIN OUTCOME MEASURES: The main outcome measures were mortality from breast cancers diagnosed during the intervention phase of the trial (i.e. before the first National Programme screen at 50 years), mortality from all breast cancers diagnosed after randomisation, all-cause mortality, mortality from causes other than breast cancer, and the incidence of breast cancer.RESULTS: There was a statistically significant 25% reduction in mortality from breast cancers diagnosed during the intervention phase at 10 years' follow-up (relative rate 0.75, 95% confidence interval 0.58 to 0.97; p = 0.03). No reduction was observed thereafter (relative rate 0.98, 95% confidence interval 0.79 to 1.22). Overall, there was a statistically non-significant 12% reduction (relative rate 0.88, 95% confidence interval 0.74 to 1.03; p = 0.1). The absolute benefit remained approximately constant over time, at one death prevented per 1000 women screened. There was no effect of intervention on other-cause mortality (relative rate 1.02, 95% confidence interval 0.97 to 1.07; p = 0.4). The intervention group had a higher incidence of breast cancer than the control group during the intervention phase of the trial, but incidence equalised immediately on the first National Programme screen at the age of 50-52 years.LIMITATIONS: There was 31% average non-compliance with screening and three centres had to cease screening for resource and capacity reasons.CONCLUSIONS: Annual mammographic screening at the age of 40-49 years resulted in a relative reduction in mortality, which was attenuated after 10 years. It is likely that digital mammography with two views at all screens, as practised now, could improve this further. There was no evidence of overdiagnosis in addition to that which already results from the National Programme carried out at later ages.FUTURE WORK: There is a need for research on the effects of modern mammographic protocols and additional imaging in this age group.TRIAL REGISTRATION: Current Controlled Trials ISRCTN24647151.FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 55. See the NIHR Journals Library website for further project information. Other funding in the past has been received from the Medical Research Council, Cancer Research UK, the Department of Health and Social Care, the US National Cancer Institute and the American Cancer Society.Peer reviewe