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Abrego L, Zaikin A, Marino IP, Krivonosov MI, Jacobs I, Menon U, Gentry‐Maharaj A, Blyuss O. Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers. Cancer Med 2024; 13:e7163. [PMID: 38597129 PMCID: PMC11004913 DOI: 10.1002/cam4.7163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 03/16/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. METHODS Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. RESULTS We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125. CONCLUSIONS Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.
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Affiliation(s)
- Luis Abrego
- Department of Women's CancerEGA Institute for Women's Health, University College LondonLondonUK
- Department of MathematicsUniversity College LondonLondonUK
| | - Alexey Zaikin
- Department of Women's CancerEGA Institute for Women's Health, University College LondonLondonUK
- Department of MathematicsUniversity College LondonLondonUK
| | - Ines P. Marino
- Department of Biology and Geology, Physics and Inorganic ChemistryUniversidad Rey Juan CarlosMadridSpain
| | - Mikhail I. Krivonosov
- Research Center for Trusted Artificial IntelligenceIvannikov Institute for System Programming of the Russian Academy of SciencesMoscowRussia
- Institute of BiogerontologyLobachevsky State UniversityNizhny NovgorodRussia
| | - Ian Jacobs
- Department of Women's CancerEGA Institute for Women's Health, University College LondonLondonUK
| | - Usha Menon
- MRC Clinical Trials UnitUniversity College LondonLondonUK
| | - Aleksandra Gentry‐Maharaj
- Department of Women's CancerEGA Institute for Women's Health, University College LondonLondonUK
- MRC Clinical Trials UnitUniversity College LondonLondonUK
| | - Oleg Blyuss
- Department of Women's CancerEGA Institute for Women's Health, University College LondonLondonUK
- Wolfson Institute of Population HealthQueen Mary University of LondonLondonUK
- Department of Pediatrics and Pediatric Infectious Diseases, Institute of Child's HealthSechenov First Moscow State Medical University (Sechenov University)MoscowRussia
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Sushentsev N, Abrego L, Colarieti A, Sanmugalingam N, Stanzione A, Zawaideh JP, Caglic I, Zaikin A, Blyuss O, Barrett T. Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance. EUR UROL SUPPL 2023; 52:36-39. [PMID: 37182116 PMCID: PMC10172696 DOI: 10.1016/j.euros.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSADNA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSADA). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSADNA significantly outperformed both PSADA and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSADNA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml. Patient summary Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
- Corresponding author. Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK. Tel. +44 1223 336895.
| | - Luis Abrego
- Department of Women’s Cancer, Institute for Women’s Health, University College London, London, UK
| | - Anna Colarieti
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
- Unit of Radiology, IRCCS Policlinico San Donato, Milan, Italy
| | - Nimalan Sanmugalingam
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
| | - Arnaldo Stanzione
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Jeries Paolo Zawaideh
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
- Department of Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Iztok Caglic
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
| | - Alexey Zaikin
- Department of Women’s Cancer, Institute for Women’s Health, University College London, London, UK
- Department of Mathematics, University College London, London, UK
| | - Oleg Blyuss
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Tristan Barrett
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
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Sushentsev N, Rundo L, Abrego L, Li Z, Nazarenko T, Warren AY, Gnanapragasam VJ, Sala E, Zaikin A, Barrett T, Blyuss O. Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance. Eur Radiol 2023; 33:3792-3800. [PMID: 36749370 PMCID: PMC10182165 DOI: 10.1007/s00330-023-09438-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 01/03/2023] [Accepted: 01/09/2023] [Indexed: 02/08/2023]
Abstract
Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78-0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64-0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76-0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. KEY POINTS: •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
| | - Leonardo Rundo
- Department of Radiology, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA, Italy
| | - Luis Abrego
- Department of Women's Cancer, Institute for Women's Health, University College London, London, UK
| | - Zonglun Li
- Department of Mathematics, University College London, London, UK
| | - Tatiana Nazarenko
- Department of Women's Cancer, Institute for Women's Health, University College London, London, UK
- Department of Mathematics, University College London, London, UK
| | - Anne Y Warren
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Vincent J Gnanapragasam
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Cambridge Urology Translational Research and Clinical Trials Office, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK
| | - Evis Sala
- Department of Radiology, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Alexey Zaikin
- Department of Women's Cancer, Institute for Women's Health, University College London, London, UK
- Department of Mathematics, University College London, London, UK
| | - Tristan Barrett
- Department of Radiology, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Oleg Blyuss
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
- Center of Photonics, Lobachevsky University, Nizhny Novgorod, Russian Federation
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Abrego L, Gordleeva S, Kanakov O, Krivonosov M, Zaikin A. Estimating integrated information in bidirectional neuron-astrocyte communication. Phys Rev E 2021; 103:022410. [PMID: 33736090 DOI: 10.1103/physreve.103.022410] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 01/04/2021] [Indexed: 01/14/2023]
Abstract
There is growing evidence that suggests the importance of astrocytes as elements for neural information processing through the modulation of synaptic transmission. A key aspect of this problem is understanding the impact of astrocytes in the information carried by compound events in neurons across time. In this paper, we investigate how the astrocytes participate in the information integrated by individual neurons in an ensemble through the measurement of "integrated information." We propose a computational model that considers bidirectional communication between astrocytes and neurons through glutamate-induced calcium signaling. Our model highlights the role of astrocytes in information processing through dynamical coordination. Our findings suggest that the astrocytic feedback promotes synergetic influences in the neural communication, which is maximized when there is a balance between excess correlation and spontaneous spiking activity. The results were further linked with additional measures such as net synergy and mutual information. This result reinforces the idea that astrocytes have integrative properties in communication among neurons.
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Affiliation(s)
- Luis Abrego
- Department of Mathematics, University College London, London, United Kingdom
| | - Susanna Gordleeva
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia.,Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Oleg Kanakov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Mikhail Krivonosov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey Zaikin
- Department of Mathematics, University College London, London, United Kingdom.,Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Institute for Women's Health, University College London, London WC1E 6BT, United Kingdom.,Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
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Abrego L, Zaikin A. Integrated Information as a Measure of Cognitive Processes in Coupled Genetic Repressilators. Entropy (Basel) 2019; 21:e21040382. [PMID: 33267096 PMCID: PMC7514866 DOI: 10.3390/e21040382] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 04/01/2019] [Accepted: 04/02/2019] [Indexed: 11/16/2022]
Abstract
Intercellular communication and its coordination allow cells to exhibit multistability as a form of adaptation. This conveys information processing from intracellular signaling networks enabling self-organization between other cells, typically involving mechanisms associated with cognitive systems. How information is integrated in a functional manner and its relationship with the different cell fates is still unclear. In parallel, drawn originally from studies on neuroscience, integrated information proposes an approach to quantify the balance between integration and differentiation in the causal dynamics among the elements in any interacting system. In this work, such an approach is considered to study the dynamical complexity in a genetic network of repressilators coupled by quorum sensing. Several attractors under different conditions are identified and related to proposed measures of integrated information to have an insight into the collective interaction and functional differentiation in cells. This research particularly accounts for the open question about the coding and information transmission in genetic systems.
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Affiliation(s)
- Luis Abrego
- Department of Mathematics, University College London, London WC1E 6BT, UK
- The Alan Turing Institute, London NW1 2DB, UK
| | - Alexey Zaikin
- Department of Mathematics, University College London, London WC1E 6BT, UK
- Institute for Women’s Health, University College London, London WC1E 6BT, UK
- Department of Applied Mathematics and Laboratory of Systems Biology of Aging, Lobachevsky State University of Nizhniy Novgorod, 603022 Nizhniy Novgorod, Russia
- Department of Pediatrics, Faculty of Pediatrics, Sechenov University, 119146 Moscow, Russia
- Correspondence:
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Seisenbaeva GA, Nedelec J, Daniel G, Tiseanu C, Parvulescu V, Pol VG, Abrego L, Kessler VG. Back Cover: Mesoporous Anatase TiO
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Nanorods as Thermally Robust Anode Materials for Li‐Ion Batteries: Detailed Insight into the Formation Mechanism (Chem. Eur. J. 51/2013). Chemistry 2013. [DOI: 10.1002/chem.201390205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Gulaim A. Seisenbaeva
- Department of Chemistry, BioCenter, Swedish University of Agricultural Sciences, Box 7015, 75007 Uppsala (Sweden)
| | - Jean‐Marie Nedelec
- CNRS UMR 6296, Institut de Chimie de Clermont‐Ferrand, Clermont Université, ENSCCF, BP 10448, F‐63177 Clermont‐Ferrand (France)
| | - Geoffrey Daniel
- Department of Forest Products/Wood Science, Swedish University of Agricultural Sciences, Box 7008, 75007 Uppsala (Sweden)
| | - Carmen Tiseanu
- National Institute for Laser, Plasma and Radiation Physics, P.O. Box MG‐36, RO 76900, Bucharest‐Magurele (Romania)
| | - Vasile Parvulescu
- Department of Chemistry, Univ. Bucuresti, B‐dul Regina Elisabeta, nr. 4‐12, Sector 3, 030018 Bucuresti (Romania)
| | - Vilas G. Pol
- Electrochemical Energy Storage Department, Chemical Sciences and Engineering Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439 (USA)
| | - Luis Abrego
- Electrochemical Energy Storage Department, Chemical Sciences and Engineering Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439 (USA)
| | - Vadim G. Kessler
- Department of Chemistry, BioCenter, Swedish University of Agricultural Sciences, Box 7015, 75007 Uppsala (Sweden)
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Seisenbaeva GA, Nedelec J, Daniel G, Tiseanu C, Parvulescu V, Pol VG, Abrego L, Kessler VG. Mesoporous Anatase TiO
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Nanorods as Thermally Robust Anode Materials for Li‐Ion Batteries: Detailed Insight into the Formation Mechanism. Chemistry 2013; 19:17439-44. [DOI: 10.1002/chem.201303283] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Indexed: 11/11/2022]
Affiliation(s)
- Gulaim A. Seisenbaeva
- Department of Chemistry, BioCenter, Swedish University of Agricultural Sciences, Box 7015, 75007 Uppsala (Sweden)
| | - Jean‐Marie Nedelec
- CNRS UMR 6296, Institut de Chimie de Clermont‐Ferrand, Clermont Université, ENSCCF, BP 10448, F‐63177 Clermont‐Ferrand (France)
| | - Geoffrey Daniel
- Department of Forest Products/Wood Science, Swedish University of Agricultural Sciences, Box 7008, 75007 Uppsala (Sweden)
| | - Carmen Tiseanu
- National Institute for Laser, Plasma and Radiation Physics, P.O. Box MG‐36, RO 76900, Bucharest‐Magurele (Romania)
| | - Vasile Parvulescu
- Department of Chemistry, Univ. Bucuresti, B‐dul Regina Elisabeta, nr. 4‐12, Sector 3, 030018 Bucuresti (Romania)
| | - Vilas G. Pol
- Electrochemical Energy Storage Department, Chemical Sciences and Engineering Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439 (USA)
| | - Luis Abrego
- Electrochemical Energy Storage Department, Chemical Sciences and Engineering Division, Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439 (USA)
| | - Vadim G. Kessler
- Department of Chemistry, BioCenter, Swedish University of Agricultural Sciences, Box 7015, 75007 Uppsala (Sweden)
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Rhodes PR, Welch GA, Abrego L. Stress corrosion cracking susceptibility of duplex stainless steels in sour gas environments. ACTA ACUST UNITED AC 1983. [DOI: 10.1007/bf02833502] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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