Dataset Information

Evaluation of liver tumour response by imaging.

ABSTRACT: The goal of assessing tumour response on imaging is to identify patients who are likely to benefit - or not - from anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickly adopted by the oncology community. At the same time, many interventional oncology treatments were developed to target specific features of liver tumours that result in significant changes in tumours but have little effect on tumour size. New criteria focusing on the viable part of tumours were therefore designed to provide more appropriate feedback to guide patient management. Targeted therapy has resulted in a breakthrough that challenges conventional response criteria due to the non-linear relationship between response and tumour size, requiring the development of methods that emphasize the appearance of tumours. More recently, research into functional and quantitative imaging has created new opportunities in liver imaging. These results have suggested that certain parameters could serve as early predictors of response or could predict later tumour response at baseline. These approaches have now been extended by machine learning and deep learning. This clinical review focuses on the progress made in the evaluation of liver tumours on imaging, discussing the rationale for this approach, addressing challenges and controversies in the field, and suggesting possible future developments.

SUBMITTER: Gregory J

PROVIDER: S-EPMC7267412 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of liver tumour response by imaging.

Gregory Jules J Dioguardi Burgio Marco M Corrias Giuseppe G Vilgrain Valérie V Ronot Maxime M

JHEP reports : innovation in hepatology 20200428 3

The goal of assessing tumour response on imaging is to identify patients who are likely to benefit - or not - from anticancer treatment, especially in relation to survival. The World Health Organization was the first to develop assessment criteria. This early score, which assessed tumour burden by standardising lesion size measurements, laid the groundwork for many of the criteria that followed. This was then improved by the Response Evaluation Criteria in Solid Tumours (RECIST) which was quickl ...[more]

PMID: 32514496

Similar Datasets

Project description:ObjectiveTo investigate the performance of Liver Imaging Reporting and Data System (LI-RADS) v2017 treatment response algorithm for predicting hepatocellular carcinoma (HCC) viability after locoregional therapy (LRT) using the liver explant as reference.MethodsOne hundred fourteen patients with 206 HCCs who underwent liver transplantation (LT) after LRT for HCCs were included in this retrospective study. Two radiologists independently evaluated tumor viability using the LI-RADS and modified RECIST (mRECIST) with CT and MRI, respectively. The sensitivity and specificity of arterial phase hyperenhancement (APHE) and LR-TR viable criteria (any of three findings: APHE, washout, and enhancement pattern similar to pretreatment imaging) were compared using logistic regression. Receiver operating characteristics (ROC) analysis was used to compare the diagnostic performance between LI-RADS and mRECIST and between CT and MRI.ResultsThe sensitivity and specificity for diagnosing viable tumor were not significantly different between APHE alone and LR-TR viable criteria on CT (p = 0.054 and p = 0.317) and MRI (p = 0.093 and p = 0.603). On CT, the area under the ROC curve (AUC) of LI-RADS was significantly higher than that of mRECIST (0.733 vs. 0.657, p < 0.001). On MRI, there was no significant difference in AUCs between LI-RADS and mRECIST (0.802 vs. 0.791, p = 0.500). Intra-individual comparison of CT and MRI showed comparable AUCs using LI-RADS (0.783 vs. 0.795, p = 0.776).ConclusionsLI-RADS v2017 treatment response algorithm showed better diagnostic performance than mRECIST on CT. With LI-RADS, CT and MRI were comparable to diagnose tumor viability of HCC after LRT.Key points• Using Liver Imaging Reporting and Data System (LI-RADS) v2017 treatment response algorithm, the viability of hepatocellular carcinoma (HCC) after locoregional therapy (LRT) can be accurately diagnosed. • LI-RADS v2017 treatment response algorithm is superior to modified Response Evaluation Criteria in Solid Tumors for evaluating HCC viability using CT. • Either CT or MRI can be performed to assess tumor viability after LRT using LI-RADS v2017 treatment response algorithm.

Dataset Information

Evaluation of liver tumour response by imaging.

Publications

Evaluation of liver tumour response by imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets