{"id":13380,"date":"2021-03-23T14:00:29","date_gmt":"2021-03-23T19:00:29","guid":{"rendered":"https:\/\/cornflowerblue-rail-980953.hostingersite.com\/?p=13380"},"modified":"2021-03-23T14:00:29","modified_gmt":"2021-03-23T19:00:29","slug":"whats-new-in-liver-imaging-french-march-2021","status":"publish","type":"post","link":"https:\/\/staging-hub.acoredu.com\/fr\/whats-new-in-liver-imaging-french-march-2021\/","title":{"rendered":"What’s new in Liver Imaging (French) – March 2021"},"content":{"rendered":"
Aspect en cible sur l\u2019imagerie pond\u00e9r\u00e9e en T2 et signes d\u2019atteinte vasculaire de la tumeur\u00a0: valeur diagnostique pour diff\u00e9rencier le CHC des autres carcinomes primaires du foie<\/strong><\/span><\/h5>\n

Cannella, R., Fraum, T.J., Ludwig, D.R.\u00a0et al.<\/p>\n

Eur Radiol\u00a0(2021).<\/p>\n

https:\/\/link.springer.com\/article\/10.1007\/s00330-021-07743-x<\/a><\/p>\n

Mots-cl\u00e9s :<\/strong> Carcinome h\u00e9patocellulaire, Cholangiocarcinome intrah\u00e9patique, Imagerie par r\u00e9sonance magn\u00e9tique<\/p>\n

Question clinique :<\/strong> L\u2019aspect en cible sur l\u2019imagerie pond\u00e9r\u00e9e en T2 et les signes d\u2019atteinte vasculaire de la tumeur peuvent-ils \u00eatre utilis\u00e9s comme caract\u00e9ristiques potentielles de LI-RADS pour diff\u00e9rencier le CHC des cancers non h\u00e9patocellulaires.<\/p>\n

Ce qui a \u00e9t\u00e9 fait\u00a0:<\/strong> L\u2019aspect en cible et les signes d\u2019invasion vasculaire tumorale ont \u00e9t\u00e9 compar\u00e9s dans les cholangiocarcinomes intrah\u00e9patiques et les cholangiocarcinomes h\u00e9patocellulaires combin\u00e9s<\/p>\n

Comment : <\/strong>\u00c9valuation r\u00e9trospective de 165 cholangiocarcinomes intrah\u00e9patiques, 74 cholangiocarcinomes h\u00e9patocellulaires combin\u00e9s et 136 CHC (contr\u00f4le). L\u2019aspect en cible et cinq caract\u00e9ristiques d\u2019invasion vasculaire tumorale (encastrement, r\u00e9tr\u00e9cissement, attachement, occlusion et oblit\u00e9ration) ont \u00e9t\u00e9 enregistr\u00e9s, et leurs sensibilit\u00e9s et sp\u00e9cificit\u00e9s ont \u00e9t\u00e9 calcul\u00e9es.<\/p>\n

Trouvailles et r\u00e9sultats :<\/strong> L\u2019aspect en cible sur l\u2019imagerie pond\u00e9r\u00e9e en T2 est hautement sp\u00e9cifique pour les cancers non h\u00e9patocellulaires (97,3% – 98,2%). L\u2019attachement et l\u2019occlusion sont les plus sp\u00e9cifiques, parmi les caract\u00e9ristiques de l\u2019invasion vasculaire de la tumeur, pour les cancers non h\u00e9patocellulaires (97,1% – 100% et 99,3%, respectivement). En revanche, l\u2019encastrement et l\u2019oblit\u00e9ration vasculaires ont les sensibilit\u00e9s les plus \u00e9lev\u00e9es (34,3% – 37,2% et 25,5% – 29,7% respectivement).<\/p>\n

Conclusion :<\/strong> L\u2019aspect en cible sur l\u2019imagerie pond\u00e9r\u00e9e en T2 ainsi que l\u2019attachement et l\u2019occlusion vasculaires ont d\u00e9montr\u00e9 une haute sp\u00e9cificit\u00e9 pour la diff\u00e9renciation des cancers non h\u00e9patocellulaires des CHC.<\/p>\n

Implications :<\/strong> L\u2019aspect en cible sur l\u2019imagerie pond\u00e9r\u00e9e en T2 pourrait \u00eatre une caract\u00e9ristique d\u2019imagerie de LI-RADS potentiellement int\u00e9ressante pour la cat\u00e9gorie LR-M.<\/p>\n

 <\/p>\n

Liver Imaging Reporting and Data System pour l\u2019IRM (LI-RADS) version 2018\u00a0: Impact et r\u00e9duction des caract\u00e9ristiques auxiliaires.<\/span> <\/strong><\/h5>\n

van der Pol CB, Dhindsa K, Shergill R, Zha N, Ferri M, Kagoma YK, Lee SY, Satkunasingham J, Wat J, Tsai S.<\/p>\n

Am J Roentgenol. (2021).<\/p>\n

https:\/\/www.ajronline.org\/doi\/10.2214\/AJR.20.23031<\/a><\/p>\n

Mots-cl\u00e9s :<\/strong> Carcinome h\u00e9patocellulaire, Imagerie par r\u00e9sonance magn\u00e9tique, Analyse des syst\u00e8mes<\/p>\n

Question clinique:<\/strong> Quel est l\u2019impact des caract\u00e9ristiques auxiliaires du LI-RADS v2018 sur l\u2019IRM? Peuvent-elles \u00eatre r\u00e9duites sans nuire \u00e0 la pr\u00e9cision de LI-RADS?<\/p>\n

Ce qui a \u00e9t\u00e9 fait\u00a0:<\/strong> La pr\u00e9cision diagnostique des cat\u00e9gories du LI-RADS a \u00e9t\u00e9 calcul\u00e9e avec et sans caract\u00e9ristiques auxiliaires. Des algorithmes d\u2019apprentissage automatique ont \u00e9t\u00e9 utilis\u00e9s pour identifier les caract\u00e9ristiques auxiliaires non contributives.<\/p>\n

Comment :<\/strong> \u00c9valuation r\u00e9trospective de 222 observations h\u00e9patiques pour la pr\u00e9sence ou l\u2019absence de caract\u00e9ristiques majeures et auxiliaires en IRM selon le LI-RADS v2018. Les cat\u00e9gories du LI-RADS et leur pr\u00e9cision diagnostique respective avec et sans caract\u00e9ristiques auxiliaires ont \u00e9t\u00e9 d\u00e9termin\u00e9es. Des algorithmes d\u2019apprentissage automatique ont \u00e9t\u00e9 utilis\u00e9s pour identifier les caract\u00e9ristiques auxiliaires non contributives, puis pour calculer les mesures de performance des cat\u00e9gories du LI-RADS en fonction des diff\u00e9rentes combinaisons de caract\u00e9ristiques auxiliaires.<\/p>\n

Trouvailles et r\u00e9sultats :<\/strong> Le pourcentage de CHC ne diff\u00e8re pas de mani\u00e8re significative entre lorsqu\u2019on utilise seulement les caract\u00e9ristiques majeures versus avec l\u2019ajout des caract\u00e9ristiques auxiliaires. \u00a0Cinq caract\u00e9ristiques auxiliaires ont \u00e9t\u00e9 identifi\u00e9es comme \u00e9tant non contributives : am\u00e9lioration de la couronne, nodules-dans-les-nodules, architecture en mosa\u00efque, produits sanguins en masse et graisse en masse.<\/p>\n

Conclusion\/Implications :<\/strong> Plusieurs caract\u00e9ristiques auxiliaires pourraient \u00eatre retir\u00e9es du LI-RADS v2018 pour l\u2019IRM sans compromettre sa pr\u00e9cision.<\/p>\n

 <\/p>\n

Apprentissage profond avec r\u00e9seau neuronal convolutif 3D pour la pr\u00e9diction non invasive de l\u2019invasion microvasculaire dans le carcinome h\u00e9patocellulaire<\/strong><\/span><\/h5>\n

Zhang Y, Lv X, Qiu J, Zhang B, Zhang L, Fang J, Li M, Chen L, Wang F, Liu S, Zhang S.<\/p>\n

J Magn Reson Imaging (2021).<\/p>\n

https:\/\/doi.org\/10.1002\/jmri.27538<\/a><\/p>\n

Mots-cl\u00e9s :<\/strong> Carcinome h\u00e9patocellulaire, Imagerie par r\u00e9sonance magn\u00e9tique, Apprentissage profond, Invasion microvasculaire.<\/p>\n

Question clinique :<\/strong> Pr\u00e9diction pr\u00e9op\u00e9ratoire de l\u2019invasion microvasculaire dans le CHC.<\/p>\n

Comment :<\/strong> Des \u00e9tudes de RM avec produit de contraste de 237 patients avec un CHC confirm\u00e9 en pathologie apr\u00e8s r\u00e9section chirurgicale ont \u00e9t\u00e9 utilis\u00e9es comme donn\u00e9es d\u2019entr\u00e9e pour quatre mod\u00e8les d\u2019apprentissage profond. Trois mod\u00e8les \u00e9taient bas\u00e9s sur une seule s\u00e9quence de RM, et un quatri\u00e8me mod\u00e8le de\u00a0\u00ab\u00a0fusion\u00a0\u00bb combinant les trois s\u00e9quences de RM. Ces mod\u00e8les ont \u00e9t\u00e9 utilis\u00e9s pour pr\u00e9dire la pr\u00e9sence ou l\u2019absence d\u2019invasion microvasculaire.<\/p>\n

Trouvailles et r\u00e9sultats :<\/strong> La pr\u00e9cision pr\u00e9dictive maximale a \u00e9t\u00e9 obtenue par le mod\u00e8le de fusion; 71%, avec une sensibilit\u00e9 de 55% et une sp\u00e9cificit\u00e9 de 81% sur l\u2019ensemble de donn\u00e9es de validation.<\/p>\n

Conclusion\/Implications :<\/strong> L\u2019apprentissage profonde pourrait \u00eatre utilis\u00e9 pour la pr\u00e9diction pr\u00e9op\u00e9ratoire de l\u2019invasion microvasculaire chez les patients atteints de CHC.<\/p>\n

 <\/p>\n

Analyse automatis\u00e9e des examens multiparam\u00e9trique d\u2019imagerie par r\u00e9sonance magn\u00e9tique et d\u2019\u00e9lastographie par r\u00e9sonance magn\u00e9tique pour la pr\u00e9diction de la st\u00e9atoh\u00e9patite non alcoolique<\/strong><\/span><\/h5>\n

Dzyubak B, Li J, Chen J, Mara KC, Therneau TM, Venkatesh SK, Ehman RL, Allen AM, Yin M.<\/p>\n

J Magn Reson Imaging (2021).<\/p>\n

https:\/\/doi.org\/10.1002\/jmri.27549<\/a><\/p>\n

Mots-cl\u00e9s :<\/strong> ERM, SHNA, FLDP, rigidit\u00e9 h\u00e9patique.<\/p>\n

Question clinique :<\/strong> Analyse automatis\u00e9e de la st\u00e9atose et de la rigidit\u00e9 h\u00e9patique ainsi que \u00a0la pr\u00e9diction de la SHNA sur la RM.<\/p>\n

Ce qui a \u00e9t\u00e9 fait\u00a0:<\/strong> Analyse manuelle de la fraction lipidique de la densit\u00e9 des protons (FLDP) et de la rigidit\u00e9 h\u00e9patique sur les \u00e9tudes par RM a \u00e9t\u00e9 compar\u00e9e \u00e0 une analyse bas\u00e9e enti\u00e8rement sur l\u2019apprentissage automatique. Un autre mod\u00e8le a \u00e9t\u00e9 d\u00e9velopp\u00e9 pour pr\u00e9dire la SHNA.<\/p>\n

Comment :<\/strong> \u00c9valuation prospective de 83 patients adultes avec de l\u2019ob\u00e9sit\u00e9 par \u00e9lastographie par RM (ERM) et par RM par d\u00e9placement chimique (IRM-DC). Tous les patients ont eu une biopsie du foie pour l\u2019\u00e9valuation de la SHNA. La FLDP et la rigidit\u00e9 h\u00e9patique ont \u00e9t\u00e9 calcul\u00e9es manuellement par deux radiologues. Un algorithme automatis\u00e9 a \u00e9t\u00e9 d\u00e9velopp\u00e9 pour la s\u00e9lection automatis\u00e9e de la r\u00e9gion d\u2019int\u00e9r\u00eat ainsi que le calcul de la FLDP et de la rigidit\u00e9 h\u00e9patique. Un autre mod\u00e8le a \u00e9t\u00e9 cr\u00e9\u00e9 pour pr\u00e9dire la SHNA diagnostiqu\u00e9e \u00e0 la pathologie bas\u00e9e sur la rigidit\u00e9 et la FLDP.<\/p>\n

Trouvailles et r\u00e9sultats :<\/strong> La concordance entre les mesures automatis\u00e9es et les radiologues lecteurs \u00e9tait \u00e9lev\u00e9e (R2<\/sup> = 0,87 pour la rigidit\u00e9 et R2<\/sup> = 0,99 pour la FLDP). L\u2019aire sous la courbe ROC pour le mod\u00e8le de pr\u00e9diction de la SHNA \u00e9tait de 0,87.<\/p>\n

Conclusion\/Implications :<\/strong> La SHNA pourrait \u00eatre pr\u00e9dite \u00e0 l\u2019aide d\u2019un algorithme d\u2019apprentissage automatique bas\u00e9 sur la rigidit\u00e9 h\u00e9patique et la FLDP sur l\u2019ERM et l\u2019IRM-DC, respectivement.<\/p>\n","protected":false},"excerpt":{"rendered":"Aspect en cible sur l\u2019imagerie pond\u00e9r\u00e9e en T2 et signes d\u2019atteinte vasculaire de la tumeur\u00a0: […]","protected":false},"author":3,"featured_media":2950,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[205],"tags":[],"class_list":["post-13380","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-non-classifiee"],"yoast_head":"\nWhat's new in Liver Imaging (French) - 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