{"id":67,"date":"2019-06-20T13:58:24","date_gmt":"2019-06-20T11:58:24","guid":{"rendered":"https:\/\/project.inria.fr\/rampart\/?page_id=67"},"modified":"2019-10-31T10:41:34","modified_gmt":"2019-10-31T09:41:34","slug":"publications","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/rampart\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"<p>&nbsp;<\/p>\n<ul>\n<li><label> Mylona E, Filias F, Ibrahim MD, Supiot S, Creange G, Magne N, Hatt M, Acosta O, De Crevoisier R: <\/label> <strong>Machine Learning and Oversampling techniques in prediction of urinary toxicity after radiotherapy for prostate cancer<\/strong> <label> ESTRO 39 <\/label> <label> : 2020 <\/label> <label> (submitted)<\/label><\/li>\n<li><label> MD. Ibrahim, E. Mylona, N. Boussion, O. Acosta, R. De Crevoisier, M. Hatt: <\/label> <strong>Predicting rectal bleeding in prostate cancer from dose volume histogram in a multicentric context<\/strong> <label> ESTRO 39 <\/label> <label> : 2020 <\/label> <label> (submitted)<\/label><\/li>\n<li><label> Mylona E, Acosta O, Lizee T, Lafond C, Crehange G, Magn\u00e9 N, Chiavassa S, Supiot S, Ospina JD, Campillo-Gimenez B, Castelli J, de Crevoisier R.: <\/label> <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0360301619301798\"> Voxel-Based Analysis for Identification of Urethrovesical Subregions Predicting Urinary Toxicity After Prostate Cancer Radiation Therapy <\/a> <label> Int J Radiat Oncol Biol Phys <\/label> <label> : 2019 <\/label><\/li>\n<li><label> Mylona E, Cicchetti A, Rancati T, Palorini F, Fiorino C, Supiot S, Magne N, Creange G, Valdagni R, Acosta O, de Crevoisier R.: <\/label> <strong>Local dose analysis to predict acute and late urinary toxicities after prostate cancer radiotherapy : assessment of cohort and method effects.<\/strong> <label> Radiother. Oncol. <\/label> <label> : 2019 <\/label> <label> (submitted)<\/label><\/li>\n<li><label> Mylona E, Lebreton C, Fontaine C, Crehange G, Magn\u00e9 N, Supiot S, de Crevoisier R, Acosta O.: <\/label> <strong>Comparison of machine learning algorithms and oversampling techniques for urinary toxicity prediction after prostate cancer radiotherapy.<\/strong> <label> IEEE proceedings <\/label> <label> : 2019 <\/label><\/li>\n<li><label> Mylona E, Acosta O, Lizee T, Lafond C, Crehange G, Magn\u00e9 N, Chiavassa S, Supiot S, Ospina JD, Campillo-Gimenez B, Castelli J, de Crevoisier R.: <\/label> <strong>Bladder and urethra subregions predicting urinary toxicity after prostate cancer radiotherapy.<\/strong> <label> ESTRO <\/label> <label> : 2019 <\/label><\/li>\n<li><label> Mylona E, Cicchetti A, Rancati T, Palorini F, Fiorino C, Supiot S, Magne N, Creange G, Valdagni R, Acosta O, de Crevoisier R.: <\/label> <a href=\"https:\/\/www.thegreenjournal.com\/article\/S0167-8140(19)31035-7\/fulltext\"> Predicting urinary toxicity via 2D and 3D dose map analyses in prostate cancer radiotherapy <\/a> <label> ESTRO <\/label> <label> : 2019 <\/label><\/li>\n<li><label> MD. Ibrahim, D. Visvikis, C. Cheze Le Rest, M. Hatt,: <\/label> <strong>Relying on deep convolutional neural networks on PET\/CT images for stage II and III non-small cell lung cancer outcome prediction<\/strong> <label> Annual congress of the EANM <\/label> <label> : 2019 <\/label><\/li>\n<li><label> Acosta O, Mylona E, Lafond C, Cr\u00e9hange G, Supiot S, Castelli J, de Crevoisier R.: <\/label> <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1278321819301623\"> Identification de sous-r\u00e9gions rectale et ur\u00e9trov\u00e9sicales hautement pr\u00e9dictives de toxicit\u00e9 en cas d\u2019irradiation prostatique. <\/a> <label> SFRO <\/label> <label> : 2019 <\/label><\/li>\n<li><label> Lizee T, Mylona E, Lafond C, Supiot S, Acosta O, de Crevoisier R.: <\/label> <a href=\"https:\/\/www.thegreenjournal.com\/article\/S0167-8140(18)31138-1\/fulltext\"> Analysis of the urethro-vesical region for urinary toxicity prediction after prostate radiotherapy. <\/a> <label> ESTRO <\/label> <label> : 2018 <\/label><\/li>\n<li><label> MD. Ibrahim, D. Visvikis, C. Cheze Le Rest, M. Hatt: <\/label> <strong>A 3D Deep Convolutional Neural Network for Lung Cancer Survival Prediction Using Transfer Learning<\/strong> <label> AAPM annual meeting <\/label> <label> : 2018 <\/label><\/li>\n<li><label> Acosta O, Mylona E, Le Dain M, Voisin C, Lizee T, Rigaud B, Lafond C, Gnep K, de Crevoisier R.: <\/label> <a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29031609\"> Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy <\/a> <label> Radiother. Oncol. <\/label> <label> : 2017 <\/label><\/li>\n<li id=\"aui_3_4_0_1_368\"><label> Liz\u00e9e T, Acosta O, Mylona E, Le Dain M, Lafond C, Riet FG, de Crevoisier.: <\/label> <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1278321817302822\"> D\u00e9veloppement d\u2019une m\u00e9thode de segmentation automatique de l\u2019ur\u00e8tre sur tomodensitom\u00e9trie de planification permettant d\u2019\u00e9valuer la dose ur\u00e9trale en cas de radioth\u00e9rapie prostatique. <\/a> <label> SFRO <\/label> <label> : 2017 <\/label><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>&nbsp; Mylona E, Filias F, Ibrahim MD, Supiot S, Creange G, Magne N, Hatt M, Acosta O, De Crevoisier R: Machine Learning and Oversampling techniques\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/rampart\/publications\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1611,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-67","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/pages\/67","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/users\/1611"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/comments?post=67"}],"version-history":[{"count":2,"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/pages\/67\/revisions"}],"predecessor-version":[{"id":103,"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/pages\/67\/revisions\/103"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/rampart\/wp-json\/wp\/v2\/media?parent=67"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}