Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis

被引:7
|
作者
Bodard, Sylvain [1 ,2 ,3 ]
Liu, Yan [4 ,5 ]
Guinebert, Sylvain [1 ,2 ]
Kherabi, Yousra [2 ]
Asselah, Tarik [2 ,6 ]
机构
[1] Hop Univ Necker Enfants Malad, AP HP Ctr, Serv Radiol Adulte, F-75015 Paris, France
[2] Univ Paris Cite, Fac Med, F-75007 Paris, France
[3] Sorbonne Univ, CNRS, INSERM, UMR 7371, F-75006 Paris, France
[4] Kings Coll London, Fac Life Sci & Med, London WC2R 2LS, England
[5] Median Technol, 1800 Route Cretes, F-06560 Valbonne, France
[6] Hop Beaujon, AP HP Nord, Serv dHepatol, INSERM,UMR1149, F-92110 Clichy, France
关键词
hepatocellular carcinoma; imaging phenomics; radiomics; risk stratification and prognostication; PREOPERATIVE PREDICTION; CANCER STATISTICS; EARLY RECURRENCE; NOMOGRAM; SIGNATURES; SURVIVAL; CT; MODEL;
D O I
10.3390/cancers15030743
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary The poor prognosis of advanced hepatocellular carcinoma (HCC) warrants a personalized approach. Our objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE and showed that imaging phenomics is an effective solution to predict prognosis or treatment response in patients with HCC. Background: Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. Methods: We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form. Results: Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76-0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72-0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76-0.84). Conclusions: Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.
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页数:15
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