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Tesis sobre el tema "Radiomics analysis"

Autor: Grafiati

Publicado: 14 de diciembre de 2024

Última modificación: 31 de julio de 2025

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1

Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.

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Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine a

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2

Ortiz, Ramón Rafael. "Radiomics for diagnosis and assessing brain diseases: an approach based on texture analysis on magnetic resonance imaging." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/119118.

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[ES] En los últimos años, los investigadores han intentado explotar la información de las imágenes médicas a través de la evaluación de parámetros cuantitativos para ayudar a los clínicos con el diagnóstico de enfermedades. Esta práctica ha sido bautizada como radiomics. El análisis de texturas proporciona una gran variedad de parámetros que permiten cuantificar la heterogeneidad característica de diferentes tejidos, especialmente cuando se obtienen de imagen por resonancia magnética (IRM). Basándonos en esto, decidimos estudiar las posibilidades de los parámetros texturales extraídos de IRM p

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3

Iyer, Sukanya Raj. "Deformation heterogeneity radiomics to predict molecular sub-types and overall survival in pediatric Medulloblastoma." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1588601774292049.

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4

Wang, Dingqian. "Quantitative analysis with machine learning models for multi-parametric brain imaging data." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/22245.

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Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classif

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5

Boughdad, Sarah. "Contributions of radiomics in ¹⁸F-FDG PET/CT and in MRI in breast cancer." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS500.

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Le cancer du sein est une pathologie fréquente pour lequel les examens TEP/TDM au ¹⁸F-FDG et IRM mammaire sont fréquemment réalisés en routine. Il existe cependant une sous-utilisation des informations apportées par chacune de ces techniques d'imagerie. En pratique, l’interprétation de ces examens est principalement basée sur l’analyse visuelle et l'analyse « quantitative » se résume généralement au SUVmax seul en TEP/TDM et à l’étude du rehaussement du signal après injection de produit de contraste en IRM mammaire (DCE-MRI). L’arrivée de nouvelles machines hybrides TEP/ IRM, nous a amené à év

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6

Mahon, Rebecca N. "Advanced Imaging Analysis for Predicting Tumor Response and Improving Contour Delineation Uncertainty." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5516.

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ADVANCED IMAGING ANALYSIS FOR PREDICTING TUMOR RESPONSE AND IMPROVING CONTOUR DELINEATION UNCERTAINTY By Rebecca Nichole Mahon, MS A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2018 Major Director: Dr. Elisabeth Weiss, Professor, Department of Radiation Oncology Radiomics, an advanced form of imaging analysis, is a growing field of interest in medicine. Radiomics seeks to extract quantitative information from images through use of computer vision techniques to as

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7

Oliver, Jasmine Alexandria. "Increasing 18F-FDG PET/CT Capabilities in Radiotherapy for Lung and Esophageal Cancer via Image Feature Analysis." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6123.

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Positron Emission Tomography (PET) is an imaging modality that has become increasingly beneficial in Radiotherapy by improving treatment planning (1). PET reveals tumor volumes that are not well visualized on computed tomography CT or MRI, recognizes metastatic disease, and assesses radiotherapy treatment (1). It also reveals areas of the tumor that are more radiosensitive allowing for dose painting - a non-homogenous dose treatment across the tumor (1). However, PET is not without limitations. The quantitative unit of PET images, the Standardized Uptake Value (SUV), is affected by many factor

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8

Prasanna, Prateek. "NOVEL RADIOMICS FOR SPATIALLY INTERROGATING TUMOR HABITAT: APPLICATIONS IN PREDICTING TREATMENT RESPONSE AND SURVIVAL IN BRAIN TUMORS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case149624929700524.

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9

Chirra, Prathyush V. Chirra. "EMPIRICAL EVALUATION OFCROSS-SITE REPRODUCIBILITY ANDDISCRIMINABILITY OF RADIOMICFEATURES FOR CHARACTERIZINGTUMOR APPEARANCE ON PROSTATEMRI." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1528456281983062.

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10

Basu, Satrajit. "Developing Predictive Models for Lung Tumor Analysis." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/3963.

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A CT-scan of lungs has become ubiquitous as a thoracic diagnostic tool. Thus, using CT-scan images in developing predictive models for tumor types and survival time of patients afflicted with Non-Small Cell Lung Cancer (NSCLC) would provide a novel approach to non-invasive tumor analysis. It can provide an alternative to histopathological techniques such as needle biopsy. Two major tumor analysis problems were addressed in course of this study, tumor type classification and survival time prediction. CT-scan images of 109 patients with NSCLC were used in this study. The first involved classifyi

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11

Spagnoli, Lorenzo. "COVID-19 prognosis estimation from CAT scan radiomics: comparison of different machine learning approaches for predicting patients survival and ICU Admission." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23926/.

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Since the start of 2020 Sars-COVID19 has given rise to a world-wide pandemic. In an attempt to slow down the spreading of this disease various prevention and diagnostic methods have been developed. In this thesis the attention has been put on Machine Learning to predict prognosis based on data originating from radiological images. Radiomics has been used to extract information from images segmented using a software from the hospital which provided both the clinical data and images. The usefulness of different families of variables has then been evaluated through their performance in the metho

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12

Captier, Nicolas. "Multimodal analysis of radiological, pathological, and transcriptomic data for the prediction of immunotherapy outcome in Non-Small Cell Lung Cancer patients." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS012.

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La survie globale des patients atteints de cancer du poumon non à petites cellules (CPNPC) métastatique a augmenté grâce à l’utilisation d’immunothérapies anti-PD1/PD-L1. Cependant, la durée de la réponse reste très variable d'un patient à l'autre, et seuls 20 à 30 % des patients sont encore en vie après deux ans. Par conséquent, de nouveaux biomarqueurs permettant de prédire la réponse au traitement et le pronostic des patients sont nécessaires pour guider la décision thérapeutique. Dans le cadre de mon doctorat, nous avons étudié des approches d'apprentissage automatique pour exploiter les d

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13

Kakino, Ryo. "Quantitative image analysis for prognostic prediction in lung SBRT." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263582.

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14

Antunes, Jacob T. Antunes. "Quantitative Treatment Response Characterization In Vivo: UseCases in Renal and Rectal Cancers." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1467987922.

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15

Perier, Cynthia. "Analyse quantitative des données de routine clinique pour le pronostic précoce en oncologie." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0219/document.

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L'évolution de la texture ou de la forme d'une tumeur à l'imagerie médicale reflète les modifications internes dues à la progression (naturelle ou sous traitement) d'une lésion tumorale. Dans ces travaux nous avons souhaité étudier l'apport des caractéristiques delta-radiomiques pour prédire l'évolution de la maladie. Nous cherchons à fournir un pipeline complet de la reconstruction des lésions à la prédiction, en utilisant seulement les données obtenues en routine clinique.Tout d'abord, nous avons étudié un sous ensemble de marqueurs radiomiques calculés sur IRM, en cherchant à établir quelle

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16

Ahrari, Shamimeh. "Implémentation de la radiomique en routine clinique : approche individuelle et analyse de la composante temporelle par des approches d’apprentissage automatique en TEP pour la neuro-oncologie." Electronic Thesis or Diss., Université de Lorraine, 2024. https://docnum.univ-lorraine.fr/public/DDOC_T_2024_0092_AHRARI.pdf.

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La caractérisation non-invasive des gliomes fait partie de la médecine personalisée, aidant ainsi les cliniciens à prendre des décisions optimales pour améliorer la survie des patients tout en préservant leur qualité de vie. Dans ce contexte, l’imagerie moléculaire Tomographie par Emission de Positons (TEP) avec des radiotraceurs marqués aux acides aminés tels que la 18F-FDOPA, est actuellement recommandée par les groupes d’experts internationaux comme un complément à l’imagerie par résonance magnétique conventionnelle. Les progrès dans l’analyse d’images sont désormais axés sur la quantificat

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17

Pattiam, Giriprakash Pavithran. "Systemic Identification of Radiomic Features Resilient to Batch Effects and Acquisition Variations for Diagnosis of Active Crohn's Disease on CT Enterography." Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1629542175523398.

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18

Khalid, Fahad. "Magnetic Resonance Imaging and Genomic Mutation in Diffuse Intrinsic Pontine Glioma : Machine Learning Approaches for a Comprehensive Analysis." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST006.

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Le diagnostic du gliome infiltrant du tronc cérébral (GITC) chez les enfants est l'un des plus éprouvants en oncologie pédiatrique. Malgré de nombreux essais cliniques explorant divers traitements, le pronostic reste sombre, la plupart des patients succombant entre 9 et 11 mois après le diagnostic. Les mutations génétiques clé associées au GITC incluent H3K27M, ACVR1 et TP53. Chaque mutation a des caractéristiques distinctes, poussant les médecins à suggérer des thérapies personnalisées, soulignant l'importance d'une détection précise des mutations pour guider le traitement. Situées dans la ré

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19

Magnin, Benoît. "Développement et validation de techniques d'analyse d'imagerie radiologique de tumeurs solides." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://theses.bu.uca.fr/nondiff/2024UCFA0180_MAGNIN.pdf.

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Notre objectif était de valider de nouvelles techniques de reconstruction d'images, de développer et valider des techniques d'analyse d'images par radiomique en imagerie oncologique, et enfin d'étudier l'influence de ces nouvelles techniques sur la stabilité de la radiomique.Notre première étude portait sur l'impact des reconstructions d'images par Deep Learning (DLIR) sur la détection des métastases hépatiques en scanner. 121 scanners de patients avec métastases hépatiques ont été reconstruits par reconstruction itérative (50%-ASiR-V) et trois niveaux de DLIR. Pour chaque reconstruction, deux

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20

Biondi, Michelangelo. "A general method for radiomic features selection - A SPECT simulation study." Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1086938.

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Introduction There are several radiological techniques, and, in this study, we focused the Single Photon Emission Computed Tomography (SPECT) imaging. It is possible to reconstruct the unknown tracer distribution inside the body by applying tomographic reconstruction algorithms such as Filtered Back Projection (FBP) and Ordered Subset Expectation Maximisation (OSEM) to the acquired data. Nowadays, thanks to technological innovations, a new branch of research has rapidly evolved: the radiomics. In practice, radiomics tries to assess whether the “textural features” of images in regions related t

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21

Shafiq, ul Hassan Muhammad. "Characterization of Computed Tomography Radiomic Features using Texture Phantoms." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7642.

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Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these v

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22

Leger, Stefan. "Radiomics risk modelling using machine learning algorithms for personalised radiation oncology." 2018. https://tud.qucosa.de/id/qucosa%3A34254.

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One major objective in radiation oncology is the personalisation of cancer treatment. The implementation of this concept requires the identification of biomarkers, which precisely predict therapy outcome. Besides molecular characterisation of tumours, a new approach known as radiomics aims to characterise tumours using imaging data. In the context of the presented thesis, radiomics was established at OncoRay to improve the performance of imaging-based risk models. Two software-based frameworks were developed for image feature computation and risk model construction. A novel data-driven approac

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23

Altini, Nicola. "Computational imaging for precision medicine: the emergence of radiomics, pathomics and deep learning." Doctoral thesis, 2022. https://hdl.handle.net/11589/245880.

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The purpose of this Ph.D. thesis is to illustrate the research works carried out during the conceptualization, design, implementation, and evaluation of novel Clinical Decision Support Systems (CDSSs) based on Radiomics, Pathomics and Deep Learning (DL) techniques. CDSSs can be effective systems for implementing Precision Medicine into clinical practice since they permit the objective and repeatable evaluation of patients. Precision Medicine can enable the improvement of the healthcare system by employing a personal healthcare process for the health status of an individual patient, which evol

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24

"Texture Analysis Platform for Imaging Biomarker Research." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.46331.

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abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error

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