TY - JOUR
T1 - Histopathological growth patterns of liver metastasis
T2 - updated consensus guidelines for pattern scoring, perspectives and recent mechanistic insights
AU - Latacz, Emily
AU - Höppener, Diederik
AU - Bohlok, Ali
AU - Leduc, Sophia
AU - Tabariès, Sébastien
AU - Fernández Moro, Carlos
AU - Lugassy, Claire
AU - Nyström, Hanna
AU - Bozóky, Béla
AU - Floris, Giuseppe
AU - Geyer, Natalie
AU - Brodt, Pnina
AU - Llado, Laura
AU - Van Mileghem, Laura
AU - De Schepper, Maxim
AU - Majeed, Ali W.
AU - Lazaris, Anthoula
AU - Dirix, Piet
AU - Zhang, Qianni
AU - Petrillo, Stéphanie K.
AU - Vankerckhove, Sophie
AU - Joye, Ines
AU - Meyer, Yannick
AU - Gregorieff, Alexander
AU - Roig, Nuria Ruiz
AU - Vidal-Vanaclocha, Fernando
AU - Denis, Larsimont
AU - Oliveira, Rui Caetano
AU - Metrakos, Peter
AU - Grünhagen, Dirk J.
AU - Nagtegaal, Iris D.
AU - Mollevi, David G.
AU - Jarnagin, William R.
AU - D’Angelica, Michael I.
AU - Reynolds, Andrew R.
AU - Doukas, Michail
AU - Desmedt, Christine
AU - Dirix, Luc
AU - Donckier, Vincent
AU - Siegel, Peter M.
AU - Barnhill, Raymond
AU - Gerling, Marco
AU - Verhoef, Cornelis
AU - Vermeulen, Peter B.
N1 - © 2022. The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022/10/5
Y1 - 2022/10/5
N2 - The first consensus guidelines for scoring the histopathological growth patterns (HGPs) of liver metastases were established in 2017. Since then, numerous studies have applied these guidelines, have further substantiated the potential clinical value of the HGPs in patients with liver metastases from various tumour types and are starting to shed light on the biology of the distinct HGPs. In the present guidelines, we give an overview of these studies, discuss novel strategies for predicting the HGPs of liver metastases, such as deep-learning algorithms for whole-slide histopathology images and medical imaging, and highlight liver metastasis animal models that exhibit features of the different HGPs. Based on a pooled analysis of large cohorts of patients with liver-metastatic colorectal cancer, we propose a new cut-off to categorise patients according to the HGPs. An up-to-date standard method for HGP assessment within liver metastases is also presented with the aim of incorporating HGPs into the decision-making processes surrounding the treatment of patients with liver-metastatic cancer. Finally, we propose hypotheses on the cellular and molecular mechanisms that drive the biology of the different HGPs, opening some exciting preclinical and clinical research perspectives.
AB - The first consensus guidelines for scoring the histopathological growth patterns (HGPs) of liver metastases were established in 2017. Since then, numerous studies have applied these guidelines, have further substantiated the potential clinical value of the HGPs in patients with liver metastases from various tumour types and are starting to shed light on the biology of the distinct HGPs. In the present guidelines, we give an overview of these studies, discuss novel strategies for predicting the HGPs of liver metastases, such as deep-learning algorithms for whole-slide histopathology images and medical imaging, and highlight liver metastasis animal models that exhibit features of the different HGPs. Based on a pooled analysis of large cohorts of patients with liver-metastatic colorectal cancer, we propose a new cut-off to categorise patients according to the HGPs. An up-to-date standard method for HGP assessment within liver metastases is also presented with the aim of incorporating HGPs into the decision-making processes surrounding the treatment of patients with liver-metastatic cancer. Finally, we propose hypotheses on the cellular and molecular mechanisms that drive the biology of the different HGPs, opening some exciting preclinical and clinical research perspectives.
UR - http://www.scopus.com/inward/record.url?scp=85131305358&partnerID=8YFLogxK
U2 - 10.1038/s41416-022-01859-7
DO - 10.1038/s41416-022-01859-7
M3 - Review article
C2 - 35650276
AN - SCOPUS:85131305358
SN - 0007-0920
VL - 127
SP - 988
EP - 1013
JO - British Journal of Cancer
JF - British Journal of Cancer
IS - 6
ER -