Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

Hu, Hang-Tong; Shen, Shunli; Wang, Zhu; Shan, Quanyuan; Huang, Xiaowen; Zheng, Qiao; Xie, Xiaoyan; Lü, Ming-De; Wang, Wei

doi:10.1007/s00261-018-1646-5

Cited by 40 publications

(44 citation statements)

References 36 publications

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“…PT-E can reflect hemodynamic perfusion changes of HCC with MVI [11], which is useful for predicting MVI of HCC and the risk of ER. Our study found that PT-E was a significant indicator of ER, which was similar to previous reports [13, 14, 16, 30–32]. However, the prediction accuracy was low; in our study, the AUC of PT-E was only 0.61 (95% CI, 0.47 to 0.74).…”

Section: Discussionsupporting

confidence: 89%

CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation

et al. 2019

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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. Electronic supplementary material The online version of this article (10.1186/s40644-019-0197-5) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 89%

CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation

et al. 2019

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“…A recent meta-analysis focused on the association between peri-tumoral MRI features and MVI, which revealed a significant association between MVI and peri-tumoral enhancement and peritumoral hypointensity on hepatobiliary-phase MRIs. However, the diagnostic accuracy analysis of this previous study showed relatively high specificity (0.90-0.94), low sensitivity (0.29-0.40) in assessing MVI (31). In another study, three radiomic models were built by extracting radiomic features from both intra-tumoral and peri-tumoral regions of Gd-EOB-DTPA-enhanced MRI images, which yielded an AUC value of 0.83 in predicting MVI (23).…”

Section: Discussionmentioning

confidence: 70%

“…Previously, various research on preoperative identifying MVI by imaging modalities has been mainly focused on inside tumor features. In recent years, imaging features of peri-tumoral area have been proved to be more accurate (18), since peri-tumoral tissue is the first area to be invaded by MVI (31). A high level of placental growth factor (PlGF) and expression of vascular endothelial growth factor receptor (VEGFR-1) in peri-tumoral tissue has been associated with peri-tumoral MVI pathological angiogenesis and potential vascular invasion (39).…”

Section: Discussionmentioning

confidence: 99%

“…Pathologically, peritumoral areas is the first area of incidence of MVI. It acts as the main blood dissemination path to portal venous thrombosis, as well as metastases in both intrahepatic and extrahepatic areas (31). Therefore, comparing to the tumor area, imaging features involving peri-tumoral areas may reveal a more direct association with MVI (23).…”

Section: Introductionmentioning

confidence: 99%

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Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Liu

Xia

et al. 2020

Front. Oncol.

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Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross-and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

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“…[34] CECT [58] also demonstrated that a combination of two or more of the following; arterial peritumoral enhancement, irregular tumor margin, and peritumoral hypointensity on hepatobiliary phase, can be used as a preoperative imaging biomarker for predicting MVI, with specificity > 90%. Hu et al [59] also reported in a systemic review and meta-analysis that peritumoral enhancement and peritumoral hypointensity on hepatobiliary phase were highly specific (90%-94%) but low sensitive findings (29%-40%) for predicting MVI.…”

Section: Refmentioning

confidence: 97%

The prediction of microvascular invasion of hepatocellular carcinoma using multiple imaging modalities

Tamai¹

2018

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To implement an adequate treatment strategy for solitary hepatocellular carcinoma (HCC), the prediction of microvascular invasion (MVI) is crucial. Metastatic recurrences after curative treatments can result from occult metastasis derived from invisible MVI. For predicting MVI, poorly differentiated or non-singular nodular HCC with a high risk of MVI should be evaluated by common imaging modalities such as ultrasound, contrast enhanced computed tomography (CECT), or magnetic resonance imaging (MRI). Summarizing these predictabilities in previous reports, the accuracies for predicting MVI were 78% in contrast enhanced ultrasonography (CEUS), 76%-89% in CECT, and 62%-77% in MRI. Those for predicting poor differentiation were 69%-92% in CEUS, 52%-90% in CECT, and 71%-75% in MRI. Those for predicting non-singular nodular type were 92%-95% in CEUS, 81%-89% in MRI, and 91%-93% in the combination of MRI and CECT. Among common imaging modalities, MRI can provide tissue characterization of the HCC using signal intensity. Gadolinium-ethoxybenzyl diethylenetriamine penta-acetic acid-enhanced MRI including diffusion imaging is the most informative imaging modality to predict MVI. Combination of MRI with other imaging modalities or tumor markers may provide a more accurate predicting for MVI. HCC with a high risk of MVI should be treated as advanced HCC even after curative treatment.

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Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

Abstract: The two peritumoral imaging features are significantly associated with MVI. The two features highly suggest MVI only when present with a high false negative rate. Promotion of their diagnostic efficiency can be a worthwhile task for future research.

Cited by 40 publications

References 36 publications

CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation

CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

The prediction of microvascular invasion of hepatocellular carcinoma using multiple imaging modalities

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