000149217 001__ 149217 000149217 005__ 20250127135740.0 000149217 037__ $$aTAZ-TFM-2024-735 000149217 041__ $$aeng 000149217 1001_ $$aHernández Alemán, Hugo 000149217 24200 $$aComparative visual analysis for multi-modal liver data 000149217 24500 $$aComparative visual analysis for multi-modal liver data 000149217 260__ $$aZaragoza$$bUniversidad de Zaragoza$$c2024 000149217 500__ $$aRealicé el programa Erasmus+ en la Universidad Técnica de Viena este curso 2023-24, realizando únicamente este TFM. Los créditos ya están convalidados, y la calificación ya está puesta también en mi Expediente de Unizar. Simplemente se realiza este depósito porque se requería por parte de la Oficina de Internacionales. 000149217 506__ $$aby-nc-sa$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc-sa/3.0/ 000149217 520__ $$aRadiomics refers to the field of study that involves the extraction and analysis of a large number of quantitative features from medical imaging data, allowing for a more comprehensive and quantitative understanding of diseases. Radiomics is particularly useful in oncology for cancer diagnosis, treatment planning, and monitoring. On the other hand, Visual Analytics (VA) is an interdisciplinary field that combines interactive visualizations with analytical techniques to explore and understand complex datasets. The main goals are to facilitate the exploration of data, to enable users to gain insights (patterns, outliers, meaningful information...etc), and to make informed decisions. By bringing together the benefits of both radiomics and VA, researchers and clinicians can get important information improving the understanding and management of diseases. In this work, we present the development of a VA framework for radiomic data analysis, providing researchers and clinicians with an intuitive platform to extract meaningful insights from the medical images of liver cancer patients. This cohort comprises a total of 19 individuals sourced from the University of San Diego, some of whom have Hepatocellular Carcinoma (HCC) or Metastasis (MET). Furthermore, these patients have undergone radiotherapy, and their data also includes information on whether they have experienced any adverse effects (toxicity or non-toxicity). Imaging studies in the context of radiotherapy often focus on various aspects related to the treatment process, so in our dataset, we are able to study the breathing cycle, treatment average, and dose distribution. Our framework uses all these radiomic features to characterize tumors, identify patterns or correlations, and make cohort stratification. As a trial to unveil new information, Couinaud segmentation of the liver is performed, useful in radiology for planning and helping in localizing specific radiomic features of liver segments. We evaluate the stratification of patients using certain metrics such as the Silhouette Score, Inertia, or the Davies-Bouldin index. Additionally, by utilizing radiomic data and dosage information, we try to find differences between the patient groups we have (hepatocarcinoma/metastatic or toxicity/non-toxicity) through statistical tests or graphical representations, and also the prediction of toxicity. Finally, knowing the precise position of the tumor thanks to Couinaud's segments and dosage measurements, we identify areas of the liver that may be over or under-irradiated.<br /> 000149217 521__ $$aMáster Universitario en Ingeniería Biomédica 000149217 540__ $$aDerechos regulados por licencia Creative Commons 000149217 691__ $$a0 000149217 692__ $$a 000149217 700__ $$aRaidou, Renata Georgia (Supervisora en la Universidad Técnica de Viena) $$edir. 000149217 700__ $$aTellería Oriols, Carlos$$edir. 000149217 7102_ $$aUniversidad de Zaragoza$$bInformática e Ingeniería de Sistemas$$cLenguajes y Sistemas Informáticos 000149217 8560_ $$f875703@unizar.es 000149217 8564_ $$s11928209$$uhttps://zaguan.unizar.es/record/149217/files/TAZ-TFM-2024-735.pdf$$yMemoria (eng) 000149217 909CO $$ooai:zaguan.unizar.es:149217$$pdriver$$ptrabajos-fin-master 000149217 950__ $$a 000149217 951__ $$adeposita:2025-01-27 000149217 980__ $$aTAZ$$bTFM$$cEINA 000149217 999__ $$a20240624121831.CREATION_DATE