{"id":13869,"date":"2021-05-14T05:59:09","date_gmt":"2021-05-14T10:59:09","guid":{"rendered":"https:\/\/cornflowerblue-rail-980953.hostingersite.com\/?p=13869"},"modified":"2021-05-14T05:59:09","modified_gmt":"2021-05-14T10:59:09","slug":"whats-new-in-liver-imaging-portuguese-march-2021","status":"publish","type":"post","link":"https:\/\/staging-hub.acoredu.com\/pt-pt\/whats-new-in-liver-imaging-portuguese-march-2021\/","title":{"rendered":"What’s new in Liver Imaging (Portuguese) – March 2021"},"content":{"rendered":"

[embedyt] https:\/\/www.youtube.com\/watch?v=vf5cIqebA_Y[\/embedyt]<\/p>\n

\n

 <\/p>\n

Aspecto em alvo nas imagens ponderadas em T2 e sinais de envolvimento vascular tumoral: valor diagn\u00f3stico para diferenciar CHC de outros carcinomas prim\u00e1rios do f\u00edgado.<\/span> <\/strong><\/h5>\n

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

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

Dispon\u00edvel em: https:\/\/link.springer.com\/article\/10.1007\/s00330-021-07743-x<\/a><\/p>\n

Palavras-chave:<\/strong> Carcinoma hepatocelular, Colangiocarcinoma intra-hep\u00e1tico, Resson\u00e2ncia Magn\u00e9tica<\/p>\n

Hip\u00f3tese cl\u00ednica:<\/strong> O aspecto em alvo nas imagens ponderadas em T2 e sinais de envolvimento vascular tumoral podem ser usados como crit\u00e9rios potenciais do LI-RADS para diferenciar CHC de malignidades n\u00e3o-CHC.<\/p>\n

O que foi feito:<\/strong> O aspecto em alvo e sinais de invas\u00e3o vascular tumoral foram comparados em colangiocarcinomas intra-hep\u00e1ticos e combina\u00e7\u00e3o de hepato-colangiocarcinoma.<\/p>\n

Como foi feito:<\/strong> Avalia\u00e7\u00e3o retrospectiva de 165 colangiocarcinomas intrahep\u00e1ticos, 74 hepatocolangiocarcinomas e 136 CHC (grupo-controle). Aspecto em alvo e cinco crit\u00e9rios de invas\u00e3o vascular tumoral (envolvimento circunferencial, estreitamento, retra\u00e7\u00e3o, oclus\u00e3o e oblitera\u00e7\u00e3o) foram registradas, e suas sensibilidades e especificidades foram calculadas.<\/p>\n

Achados e resultados:<\/strong> O aspecto em alvo nas imagens ponderadas em T2 \u00e9 altamente espec\u00edfica para malignidades n\u00e3o-HCC (97,3%-98,2%). Retra\u00e7\u00e3o e oclus\u00e3o vascular s\u00e3o os mais espec\u00edficos, entre os crit\u00e9rios de invas\u00e3o vascular tumoral, para malignidades n\u00e3o-CHC (97,1%-100% e 99,3%, respectivamente). Em contrapartida, o envolvimento circunferencial e oblitera\u00e7\u00e3o vascular tiveram as sensibilidades mais altas (34,3% -37,2% e 25,5% -29,7%, respectivamente).<\/p>\n

Conclus\u00e3o:<\/strong> O aspecto em alvo nas imagens ponderadas em T2, retra\u00e7\u00e3o e oclus\u00e3o vascular demonstraram alta especificidade para diferencia\u00e7\u00e3o de neoplasias n\u00e3o-CHC de CHC.<\/p>\n

Implica\u00e7\u00f5es:<\/strong> O aspecto em alvo nas imagens ponderadas em T2 pode ser valioso como um potencial recurso de imagem LI-RADS para categoria LR-M.<\/p>\n

 <\/p>\n

Sistema de dados e laudos de imagem do f\u00edgado por resson\u00e2ncia magn\u00e9tica (LI-RADS) vers\u00e3o 2018: impacto e redu\u00e7\u00e3o dos crit\u00e9rios auxiliares<\/strong><\/span><\/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

Dispon\u00edvel em: https:\/\/www.ajronline.org\/doi\/10.2214\/AJR.20.23031<\/a><\/p>\n

Palavras-chave:<\/strong> Carcinoma hepatocelular, Resson\u00e2ncia Magn\u00e9tica e An\u00e1lise de sistema.<\/p>\n

Hip\u00f3tese cl\u00ednica:<\/strong> Qual \u00e9 o impacto dos crit\u00e9rios auxiliares do LI-RADS v2018 na resson\u00e2ncia magn\u00e9tica? E eles podem ser reduzidos sem prejudicar a acur\u00e1cia do LI-RADS?<\/p>\n

O que foi feito:<\/strong> A acur\u00e1cia diagn\u00f3stica das categorias LI-RADS foi calculada com e sem recursos auxiliares. Algoritmos de Machine learning foram usados \u200b\u200bpara identificar crit\u00e9rios auxiliares n\u00e3o contributivos.<\/p>\n

Como foi feito:<\/strong> Avalia\u00e7\u00e3o retrospectiva de 222 observa\u00e7\u00f5es hep\u00e1ticas para a presen\u00e7a ou aus\u00eancia de crit\u00e9rios principais e auxiliares na resson\u00e2ncia magn\u00e9tica de acordo com LI-RADS v2018. As categorias LI-RADS e sua respectiva acur\u00e1cia diagn\u00f3stica com e sem crit\u00e9rios auxiliares foram determinadas. Algoritmos de Machine learning foram usados \u200b\u200bpara identificar recursos auxiliares n\u00e3o contributivos e, em seguida, calcular as m\u00e9tricas de desempenho das categorias LI-RADS com base em diferentes combina\u00e7\u00f5es de crit\u00e9rios auxiliares.<\/p>\n

Achados e resultados:<\/strong> A porcentagem de CHCs n\u00e3o diferiu significativamente com o uso dos crit\u00e9rios principais isoladamente e com a aplica\u00e7\u00e3o dos crit\u00e9rios auxiliares. Cinco crit\u00e9rios auxiliares foram identificados como n\u00e3o contribuintes: realce em coroa, n\u00f3dulo-em-n\u00f3dulo, arquitetura em mosaico, gordura na les\u00e3o ou focos hem\u00e1ticos na les\u00e3o.<\/p>\n

Conclus\u00e3o:<\/strong> V\u00e1rios crit\u00e9rios auxiliares podem ser removidos do LI-RADS v2018 para resson\u00e2ncia magn\u00e9tica sem comprometer sua acur\u00e1cia.<\/p>\n

 <\/p>\n

Deep Learning com rede neural convolucional 3D para previs\u00e3o n\u00e3o invasiva de invas\u00e3o microvascular no carcinoma hepatocelular.<\/span> <\/strong><\/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

Dispon\u00edvel em: https:\/\/doi.org\/10.1002\/jmri.27538<\/a><\/p>\n

Palavras-chave:<\/strong> Carcinoma hepatocelular, Resson\u00e2ncia Magn\u00e9tica, Deep Learning, Invas\u00e3o microvascular<\/p>\n

Hip\u00f3tese cl\u00ednica:<\/strong> Previs\u00e3o pr\u00e9-operat\u00f3ria de invas\u00e3o microvascular em CHC.<\/p>\n

Como foi feito:<\/strong> Estudos de RM com contraste de 237 pacientes com CHC confirmado patologicamente ap\u00f3s a ressec\u00e7\u00e3o cir\u00fargica foram usados \u200b\u200bcomo entrada para quatro modelos de deep Learning. Tr\u00eas modelos foram baseados em uma \u00fanica sequ\u00eancia de RM e um quarto modelo de \u201cfus\u00e3o\u201d combinando as tr\u00eas sequ\u00eancias de RM. Esses modelos foram usados \u200b\u200bpara prever a presen\u00e7a ou aus\u00eancia de invas\u00e3o microvascular.<\/p>\n

Achados e resultados:<\/strong> a maior acur\u00e1cia preditiva foi alcan\u00e7ada pelo modelo de fus\u00e3o; 71%, com sensibilidade de 55% e especificidade de 81% no conjunto de dados de valida\u00e7\u00e3o.<\/p>\n

Conclus\u00e3o:<\/strong> Deep Learning pode ser usado para previs\u00e3o pr\u00e9-operat\u00f3ria de invas\u00e3o microvascular em pacientes com CHC.<\/p>\n

 <\/p>\n

An\u00e1lise automatizada de RM multiparam\u00e9trica \/ elastografia por RM para previs\u00e3o de esteatohepatite n\u00e3o alco\u00f3lica.<\/span> <\/strong><\/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

Dispon\u00edvel em: https:\/\/doi.org\/10.1002\/jmri.27549<\/a><\/p>\n

Palavras-chave:<\/strong> Elastografia por RM, DHGNA, PDFF, rigidez hep\u00e1tica.<\/p>\n

Hip\u00f3tese cl\u00ednica:<\/strong> An\u00e1lise automatizada de esteatose e rigidez hep\u00e1tica e previs\u00e3o de DHGNA por RM.<\/p>\n

O que foi feito:<\/strong> A an\u00e1lise manual da fra\u00e7\u00e3o de gordura de densidade de pr\u00f3tons (PDFF) e rigidez do f\u00edgado em estudos de RM foi comparada com a an\u00e1lise baseada em machine learning totalmente automatizada. Outro modelo foi desenvolvido para prever DHGNA.<\/p>\n

Como foi feito:<\/strong> Avalia\u00e7\u00e3o prospectiva de 83 pacientes adultos com obesidade com RM com elastografia (MRE) e RM com deslocamento qu\u00edmico (CS-MRI). Todos os pacientes foram submetidos a bi\u00f3psia hep\u00e1tica para avalia\u00e7\u00e3o de DHGNA. PDFF e rigidez hep\u00e1tica foram calculados manualmente por dois radiologistas. Um algoritmo automatizado foi desenvolvido para sele\u00e7\u00e3o automatizada de regi\u00e3o de interesse e c\u00e1lculo de PDFF e rigidez do f\u00edgado. Outro modelo foi criado para prever DHGNA com diagn\u00f3stico de patologia baseado na rigidez e PDFF.<\/p>\n

Achados e resultados:<\/strong> A concord\u00e2ncia entre as medidas automatizadas e os leitores foi alta (R2 = 0,87 para rigidez e R2 = 0,99 para PDFF). A \u00e1rea sob a curva ROC para o modelo de previs\u00e3o para DHGNA foi de 0,87.<\/p>\n

Conclus\u00e3o:<\/strong> DHGNA pode ser prevista usando um algoritmo de machine learning automatizado com base na rigidez do f\u00edgado e PDFF em MRE e CS-MRI, respectivamente.<\/p>\n","protected":false},"excerpt":{"rendered":"[embedyt] https:\/\/www.youtube.com\/watch?v=vf5cIqebA_Y[\/embedyt]   Aspecto em alvo nas imagens ponderadas em T2 e sinais de envolvimento […]","protected":false},"author":3,"featured_media":2953,"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":[207],"tags":[],"class_list":["post-13869","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sem-categoria"],"yoast_head":"\nWhat's new in Liver Imaging (Portuguese) - 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