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Does Enhancement or Perfusion on Preprocedure CT Predict Outcomes After Embolization of Hepatocellular Carcinoma?

ABSTRACT: RATIONALE AND OBJECTIVE:The objective of this study was to evaluate whether quantitative enhancement or perfusion measurements on preprocedure triphasic computed tomography (CT) can be used to predict response or overall survival after embolization of hepatocellular carcinoma. MATERIALS AND METHODS:The institutional review board approved this retrospective review of 63 patients with hepatocellular carcinoma treated with particle embolization between March 2009 and December 2014. Quantitative enhancement and perfusion measurements were performed on the target tumor and the background liver on the triphasic CT performed before treatment. Microvascular invasion (MVI) and degree of differentiation were determined from a core biopsy specimen. Quantitative enhancement and perfusion values were then correlated with pathology (two-tailed t test), response to embolization on modified Response Evaluation Criteria In Solid Tumors (two-tailed t test), and overall survival after embolization (Cox proportional hazards model). RESULTS:Arterial enhancement did not predict immediate response or overall survival after embolization. The degree of differentiation or presence of MVI also did not predict immediate response or overall survival after embolization. However, high hepatic artery coefficient or low portal vein coefficient, both in the tumor (P?=?.011 and P?=?.004) and in the background liver (P?=?.015 and P?=?.009), were associated with worse survival. Hepatic artery coefficient, both in the tumor (P?=?.025) and in the background liver (P?=?.013), were independent predictors of survival in a multivariate model including the Child-Pugh score and the BCLC stage. CONCLUSIONS:Tumor and liver perfusion parameters estimated from preprocedure triphasic CT were predictive of survival after embolization. Arterial-phase enhancement and histology (degree of differentiation or MVI) did not predict immediate response or overall survival after particle embolization.

SUBMITTER: Borgheresi A

PROVIDER: S-EPMC6160360 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Does Enhancement or Perfusion on Preprocedure CT Predict Outcomes After Embolization of Hepatocellular Carcinoma?

Borgheresi Alessandra A Gonzalez-Aguirre Adrian A Brown Karen T KT Getrajdman George I GI Erinjeri Joseph P JP Covey Anne A Yarmohammadi Hooman H Ziv Etay E Sofocleous Constantinos T CT Boas Franz Edward FE

Academic radiology 20180327 12

<h4>Rationale and objective</h4>The objective of this study was to evaluate whether quantitative enhancement or perfusion measurements on preprocedure triphasic computed tomography (CT) can be used to predict response or overall survival after embolization of hepatocellular carcinoma.<h4>Materials and methods</h4>The institutional review board approved this retrospective review of 63 patients with hepatocellular carcinoma treated with particle embolization between March 2009 and December 2014. Q ...[more]

PMID: 29602726

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Project description:OBJECTIVE:To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. MATERIALS AND METHODS:In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). RESULTS:By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E. CONCLUSION:The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.

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Does Enhancement or Perfusion on Preprocedure CT Predict Outcomes After Embolization of Hepatocellular Carcinoma?

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Does Enhancement or Perfusion on Preprocedure CT Predict Outcomes After Embolization of Hepatocellular Carcinoma?

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