{"id":18,"date":"2021-07-23T08:30:51","date_gmt":"2021-07-23T07:30:51","guid":{"rendered":"https:\/\/blog.uclm.es\/davidmolina\/?page_id=18"},"modified":"2022-10-17T16:08:49","modified_gmt":"2022-10-17T16:08:49","slug":"investigacion","status":"publish","type":"page","link":"https:\/\/blog.uclm.es\/davidmolina\/investigacion\/","title":{"rendered":"Investigaci\u00f3n\/Research"},"content":{"rendered":"<p><strong>2021<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/www.pnas.org\/content\/118\/6\/e2018110118\">Evolutionary dynamics at the tumor edge reveals metabolic imaging biomarkers.<\/a> <\/strong>Proceedings of the National Academy of Sciences (USA) 118(6) e2018110118 (2021).<\/li>\n<li><strong><a href=\"https:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1008266\">A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.<\/a> <\/strong> PLoS Computational Biology 17(2) e1008266 (2021).<\/li>\n<\/ul>\n<p><strong>2020<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/www.nature.com\/articles\/s41567-020-0978-6\">Universal scaling laws rule explosive growth in human cancers. <\/a><\/strong>Nature Physics 16, 1232-1237 (2020).<\/li>\n<\/ul>\n<p><strong>2019<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0377042718301602\">Approaching the Rank Aggregation Problem by Local Search-based Metaheuristics.<\/a> <\/strong>Journal of Computational and Applied Mathematics 354, 445-456 (2019).<\/li>\n<li><strong><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/30923085\/\">Morphologic features on MR imaging classify multifocal glioblastomas in different prognostic groups.<\/a> <\/strong>American Journal of Neuro-radiology 40 (4) 634-640 (2019).<\/li>\n<li><strong><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/30324390\/\">Morphological MRI-based features provide pretreatment and post-surgery survival prediction in glioblastoma.<\/a> <\/strong>European Radiology 29(4) 1968-1977 (2019).<\/li>\n<li><strong><a href=\"https:\/\/www.nature.com\/articles\/s41598-019-42326-3\">Prognostic models based on imaging findings in glioblastoma: Human versus Machine.<\/a> <\/strong> Scientific Reports 9:5982 (2019).<\/li>\n<\/ul>\n<p><strong>2018<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s12149-018-1253-0\">Intratumoral heterogeneity in 18F-FDG PET\/CT by textural analysis in breast cancer as predictive and prognostic subrogate. <\/a><\/strong>Annals of Nuclear Medicine 32(6), 379-388 (2018).<\/li>\n<li><strong><a href=\"https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.2018171051\">Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma.<\/a> <\/strong>Radiology 288(1), 218-225 (2018).<\/li>\n<li><strong><a href=\"https:\/\/asistdl.onlinelibrary.wiley.com\/doi\/10.1002\/asi.24040\">Consensus-Based Journal Rankings: A Complementary Tool for Bibliometric Evaluation.<\/a> <\/strong>Journal of the Association for Information Science and Technology 69(7), 936-948 (2018).<\/li>\n<li><strong><a href=\"https:\/\/www.elsevier.es\/en-revista-revista-espanola-medicina-nuclear-e-425-articulo-predictive-prognostic-potential-volume-based-metabolic-S2253808917302070\">Predictive and prognostic potential of volume-based metabolic variables obtained by basal 18F-FDG PET\/CT in breast cancer with neoadjuvant chemotherapy indication.<\/a> <\/strong>Revista Espa\u00f1ola de Medicina Nuclear e Imagen Molecular 37(2), 73-79 (2018).<\/li>\n<\/ul>\n<p><strong>2017<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253516300665\">Tackling the supervised label ranking problem by bagging weak learners.<\/a> <\/strong>Information Fusion 35, 38-50 (2017).<\/li>\n<li><strong><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/27329522\/\">Glioblastoma: Does the pre-treatment geometry matter? A postcontrast T1 MRI-based study.<\/a> <\/strong>European Radiology 27(3), 1096-1104 (2017).<\/li>\n<li><strong><a href=\"https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0178843\">Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization.<\/a> <\/strong>PLoS One 12(6):e0178843 (2017).<\/li>\n<li><strong><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11307-016-1034-x\">Metabolic tumor burden assessed by dual time point 18F-FDG PET\/CT in locally advanced breast cancer: Relation with tumor biology. <\/a><\/strong>Molecular Imaging and Biology 19(4), 636-644 (2017).<\/li>\n<li><strong><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5691106\/\">Textural features and SUV-based variables assessed by dual time point 18F-FDG PET\/CT in locally advanced breast cancer.<\/a> <\/strong><br \/>\nAnnals of Nuclear Medicine 31(10) 726-735 (2017).<\/li>\n<li><strong><a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-75238-9_40\">Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction.<\/a> <\/strong>In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science, vol 10670. Springer. (2017).<\/li>\n<li><strong><a href=\"https:\/\/www.researchgate.net\/publication\/316517020_P0943_Novel_geometrical_imaging_biomarkers_predict_survival_and_allow_for_patient_selection_for_surgery_in_glioblastoma_patients\">Novel geometrical imaging biomarkers predict survival and allow for patient selection for surgery in glioblastoma patients. <\/a><\/strong>Neuro-Oncology 19(3):iii80-iii80 (2017).<\/li>\n<li><strong><a href=\"https:\/\/academic.oup.com\/neuro-oncology\/article\/19\/suppl_3\/iii44\/3743962\">Recommendations for computation of textural measures obtained from 3D brain tumor MRIs: A robustness analysis points out the need for standardization.<\/a> <\/strong>Neuro-Oncology 19 (3): iii44 (2017).<\/li>\n<li><strong><a href=\"https:\/\/academic.oup.com\/neuro-oncology\/article\/19\/suppl_3\/iii84\/3744121\">Towards individualized survival prediction in glioblastoma patients using machine learning methods.<\/a> <\/strong>Neuro-Oncology 19(3):iii84-iii84 (2017).<\/li>\n<\/ul>\n<p><strong>2016<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0161484\">Geometrical measures obtained from pretreatment postcontrast T1 weighted MRIs predict survival benefits from bevacizumab in glioblastoma patients.<\/a> <\/strong>PLoS One 11(8) e0161484 (2016).<\/li>\n<li><strong><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/27658261\/\">Influence of grey level and space discretization on brain tumor heterogeneity measures obtained from MRIs.<\/a> <\/strong>Computers in Biology and Medicine 78, 49-57 (2016).<\/li>\n<li><strong><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11307-016-1034-x\">Metabolic tumor burden assessed by dual time point 18F-FDG PET\/CT in breast cancer: Relation with tumor biology.<\/a> <\/strong>European Journal of Nuclear Medicine and Molecular Imaging 43, S74-S75 (2016).<\/li>\n<li><strong><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5124892\/\">Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.<\/a> <\/strong>British Journal on Radiology 89, 2016242 (2016).<\/li>\n<li><strong><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0096300316303769\">Using extensi\u00f3n sets to aggregate partial rankings in a flexible setting.<\/a> <\/strong>Applied Mathematics and Computation 290,208-223 (2016).<\/li>\n<li><strong><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S156849461500633X\">Using metaheuristic algorithms for the parameter estimation in generalized Mallows model.<\/a> <\/strong>Applied Soft Computing 38, 308-320 (2016).<\/li>\n<\/ul>\n<p><strong>2013<\/strong><\/p>\n<ul>\n<li><strong><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S009630031300831X\">Tackling the rank aggregation problem with evolutionary algorithms.<\/a> <\/strong>Applied Mathematics and Computation 222, 632-644 (2013).<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>2021 Evolutionary dynamics at the tumor edge reveals metabolic imaging biomarkers. Proceedings of the National Academy of Sciences (USA) 118(6) e2018110118 (2021). A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors. PLoS Computational Biology 17(2) e1008266 (2021). 2020 &hellip; <a href=\"https:\/\/blog.uclm.es\/davidmolina\/investigacion\/\">Sigue leyendo <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":155,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-18","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/pages\/18","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/users\/155"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/comments?post=18"}],"version-history":[{"count":2,"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/pages\/18\/revisions"}],"predecessor-version":[{"id":67,"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/pages\/18\/revisions\/67"}],"wp:attachment":[{"href":"https:\/\/blog.uclm.es\/davidmolina\/wp-json\/wp\/v2\/media?parent=18"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}