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1篇 您的检索式：作者名="Terrence Chi Hong Hui"

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		题名	作者	年代	出处	被引量
	1	Efficacy of texture analysis of pre-operative magnetic resonance imaging in predicting microvascular invasion in hepatocellular carcinoma显示文摘BACKGROUND Presence of microvascular invasion(MVI)indicates poorer prognosis postcurative resection of hepatocellular carcinoma(HCC),with an increased chance of tumour recurrence.By present standards,MVI can only be diagnosed postoperatively on histopathology.Texture analysis potentially allows identification of patients who are considered‘high risk’through analysis of pre-operative magnetic resonance imaging(MRI)studies.This will allow for better patient selection,improved individualised therapy(such as extended surgical margins or adjuvant therapy)and pre-operative prognostication.AIM This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC.METHODS Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed.Exclusion criteria:No preoperative MRI,significant movement artefacts,loss-to-follow-up,ruptured HCCs,previous hepatectomy and adjuvant therapy.Fifty patients were divided into MVI(n=15)and non-MVI(n=35)groups based on tumour histology.Selected images of the tumour on post-contrast-enhanced T1-weighted MRI were analysed.Both qualitative(performed by radiologists)and quantitative data(performed by software)were obtained.Radiomics texture parameters were extracted based on the largest cross-sectional area of each tumor and analysed using MaZda software.Five separate methods were performed.Methods 1,2 and 3 exclusively made use of features derived from arterial,portovenous and equilibrium phases respectively.Methods 4 and 5 made use of the comparatively significant features to attain optimal performance.RESULTS Method 5 achieved the highest accuracy of 87.8%with sensitivity of 73%and specificity of 94%.CONCLUSION Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8%and can potentially impact clinical management.	Jordan Zheng Ting Sim Terrence Chi Hong Hui Tong Kuan Chuah Hsien Min Low Cher Heng Tan Vishal GShelat	2022	World Journal of Clinical Oncology2022，13，11：	3

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