Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI)

Aguado-Sarrió, E.; Prats-Montalbán, José Manuel; Sanz-Requena, Roberto; García-Martí, Gracián; Martí‐Bonmatí, Luis; Ferrer, Alberto

doi:10.1016/j.chemolab.2017.04.003

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…A detailed formulation explanation can be found in Refs. [17,24]. In these models, the vascular and EES volumes can be represented as a system of partial differential equations where the contrast is exchanged between different subspaces.…”

Section: Clinical Models 221 Perfusion Pharmacokinetic Modelingmentioning

confidence: 99%

“…In this expression, R(t) only depends on the first (K trans , k ep , v e ) or second (Fp, PS, v e , v p ) generation perfusion biomarkers [8,24] at each time instant t. In order to obtain these biomarkers, the models need as input the reference arterial input function (C AIF (t)), which was calculated in this paper using a principal component analysis (PCA) model [25], by automatically selecting the pixels related to a pure arterial dynamic pattern [26]. Once the C AIF (t) was individually calculated for each patient, the perfusion sequence was analyzed pixel-by-pixel and the biomarkers were calculated using non-linear optimization algorithms.…”

Section: Clinical Models 221 Perfusion Pharmacokinetic Modelingmentioning

confidence: 99%

“…D T is a matrix containing in its rows each of the pure behaviors (pure spectrum associated to each physiological phenomena); C gathers in its rows the relative contribution of each behavior for each pixel of the image; and E is a residual matrix [14][15][16]24].…”

Section: Mcr-als Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Sequential multiblock partial least squares discriminant analysis for assessing prostate cancer aggressiveness with multiparametric magnetic resonance imaging

Aguado-Sarrió

Prats-Montalbán

Sanz-Requena

et al. 2022

Chemometrics and Intelligent Laboratory Systems

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Section: Clinical Models 221 Perfusion Pharmacokinetic Modelingmentioning

confidence: 99%

Section: Clinical Models 221 Perfusion Pharmacokinetic Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Sequential multiblock partial least squares discriminant analysis for assessing prostate cancer aggressiveness with multiparametric magnetic resonance imaging

Aguado-Sarrió

Prats-Montalbán

Sanz-Requena

et al. 2022

Chemometrics and Intelligent Laboratory Systems

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“…In the next chapters, PLS-based methods [83][84] are proposed in order to separate and classify ROIs selected by the specialists and associated to healthy tissue in the peripheral zone (HP) or dominant lesions (DL) with different grades of aggressiveness (Gleason,[13]). First, the capability of perfusion-based imaging biomarkers to detect and differentiate the cancerous tissue from the healthy tissue [115] will be analyzed. The procedure and results are explained in chapter 5.…”

Section: Mcr-diffusion Models Discussionmentioning

confidence: 99%

Application of multivariate image analysis to prostate cancer for improving the comprehension of the related physiological phenomena and the development and validation of new imaging biomarkers

Sarrió¹

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A doctoral thesis submitted to the Department of applied statistics, Operations Research and QualityThis journey started six years ago after finishing the last exams of my Master degree.In that summer of 2013 I was thinking about my future and how to orientate the Master's thesis. Truly, I had no idea on what to do, but Alberto Ferrer, after the presentation of my work for his subject, suggested me to have a meeting with José Manuel Prats where they showed me the interesting new approach of "our" methods to medical imaging they were working on. I got involved so easy in this world by their hand, and after all this time, they have been the best supervisors I could ever imagined. I learnt how to investigate; they taught me about the importance of knowing the process in detail before trying anything, to argue everything, to propose new ideas, the importance of the testing/validation, to give a measure of the reliability of the results and more, a bunch things that will make this list extremely long. Thank you so much, Alberto and José Manuel. A few more words for JM, he has been always there for me, in the best and worst moments, he is an amazing professor and better person. To my second supervisor Roberto Sanz, it was always a pleasure to work with him. The relation between these two worlds (university and hospital) is never easy, but he managed to go with us together in the same direction during this journey. Roberto, thank you for your dedication and hard work of giving us always the best data set you can, listening to our ideas and contributing with your valuable advices. I would like to thank Luís Martí Bonmatí for introducing us into this world and saving some time from his busy schedule for us, always in a kind way. I would also like to thank all the people from the Hospital Quirón, without their work and their images, this thesis wouldn't have been possible.To my office mates. Dani, we know each other for a long time and we have travelled a lot together, it was always nice to have him on the other side of the table. To Raffa and Abel for helping me and answering to all my questions. To Borja and Antonio for having good times discussing and arguing together. To Alba and Joan, we didn't share so much time but they are magnificent and very talented people.To my "all time" friends in my little village, Kike, Mode, Jorge, Javi, Toni, Carles, etc. for the long nights of just talking. To my friends in the city, Vicci and Rivera, we lived together 4 years and I got so many precious memories that will stay with me forever.And last, but not least, to my family, they always supported me, gave me their love and tried to charm me up when I felt bad. But also to celebrate when we succeed. Unfortunately, my "uelos" aren't with us anymore, I dedicate this work to them and hope they would have been proud of me. Thank you.

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“…This medical image modality expresses each pixel's intensity as the concentration of an injected contrast agent, capturing the diffusion of the contrast in the tissue over a temporal sequence. The analysis of the contrast's washout dynamic can be used to develop effective techniques for cancer diagnosis [111]. The dataset has a pixels × f rames structure, with dimensions of N = 23193 pixes (151×432 originally) and K = 6 frames.…”

Section: Real Datasetsmentioning

confidence: 99%

Statistical Machine Learning in Biomedical Engineering

González Cebrián

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This thesis, developed under a research personnel formation grant from the Universitat Politècnica de València, aims to propose and apply methodologies of Statistical Machine Learning in Biomedical Engineering contexts. This concept seeks to combine machine learning models with the classic understanding and interpretability of statistical reasoning, resulting in technological solutions for biomedical problems that go beyond solely optimizing the predictive performance of models. To achieve this, two main objectives have been outlined, which also structure the document: proposing novel methodologies within the umbrella of Statistical Machine Learning and applying solutions to real biomedical problems while keeping this philosophy in mind. These objectives have materialized into methodological contributions for simulating outliers and imputing missing data in the presence of outliers and applied contributions to real cases for improving healthcare processes, enhancing disease diagnosis and prognosis, and standardizing measurement procedures in biotechnological environments. These contributions are articulated in chapters corresponding to the aforementioned two main parts. Finally, the conclusions and future lines of research conclude the document, reiterating the main messages of the contributions and the overall doctoral thesis and laying the groundwork for future lines of inquiry stemming from the work conducted throughout the doctorate. ix Gracias, finalmente, a mis más antiguas amistades: mi familia. Gracias por la paciencia que habéis tenido conmigo en mis días mudos de deprimente atropello melancólico y en mis huidas hacia delante por la puerta de atrás. Incluyo en este grupo a Cristina, ya que con tu trabajo has puesto el mundo a mis pies cuando sentía que lo tenía totalmente encima. Papá y mamá, gracias por enseñarme a vivir volando libre. Y a Ángela, gracias por tu fe en mí; siempre has sido la luz de todos mis pozos. Ya solo me queda decir que espero que el trabajo esté a vuestra altura. Os quiero.

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Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI)

Cited by 7 publications

References 29 publications

Sequential multiblock partial least squares discriminant analysis for assessing prostate cancer aggressiveness with multiparametric magnetic resonance imaging

Sequential multiblock partial least squares discriminant analysis for assessing prostate cancer aggressiveness with multiparametric magnetic resonance imaging

Application of multivariate image analysis to prostate cancer for improving the comprehension of the related physiological phenomena and the development and validation of new imaging biomarkers

Statistical Machine Learning in Biomedical Engineering

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