Value of gadoxetic acid-enhanced MRI for microvascular invasion of small hepatocellular carcinoma: a retrospective study

Zhou, Meng; Shan, Dan; Zhang, Chunhui; Nie, Jianhua; Wang, Guangyu; Zhang, Yanqiao; Zhou, Yang; Zheng, Tongsen

doi:10.1186/s12880-021-00572-w

Cited by 3 publications

(5 citation statements)

References 37 publications

(39 reference statements)

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“…However, this association was not indicated in our study, probably due to the selection bias. In addition, we found that the absence of halo signs and non-circular MR imaging features are the key predictor of MVI, which is consistent with previous studies ( 25 – 27 ). The results of observation on the pathological data indicated that among the current cases of MVI+ HCC, the most common is the single-nodular type and the multi-nodular type with additional nodular growth or fusion ( 28 ), which means the non-round tumor shape is the MR image feature of MVI+ HCC.…”

Section: Discussionsupporting

confidence: 92%

Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA

Zhang

Duan²,

Zhou³

et al. 2022

Front. Oncol.

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ObjectiveTo develop a radiomics nomogram for predicting microvascular invasion (MVI) before surgery in hepatocellular carcinoma (HCC) patients.Materials and MethodsThe data from a total of 189 HCC patients (training cohort: n = 141; validation cohort: n = 48) were collected, involving the clinical data and imaging characteristics. Radiomics features of all patients were extracted from hepatobiliary phase (HBP) in 15 min. Least absolute shrinkage selection operator (LASSO) regression and logistic regression were utilized to reduce data dimensions, feature selection, and to construct a radiomics signature. Clinicoradiological factors were identified according to the univariate and multivariate analyses, which were incorporated into the final predicted nomogram. A nomogram was developed to predict MVI of HCC by combining radiomics signatures and clinicoradiological factors. Radiomics nomograms were evaluated for their discrimination capability, calibration, and clinical usefulness.ResultsIn the clinicoradiological factors, gender, alpha-fetoprotein (AFP) level, tumor shape and halo sign served as the independent risk factors of MVI, with which the area under the curve (AUC) is 0.802. Radiomics signatures covering 14 features at HBP 15 min can effectively predict MVI in HCC, to construct radiomics signature model, with the AUC of 0.732. In the final nomogram model the clinicoradiological factors and radiomics signatures were integrated, outperforming the clinicoradiological model (AUC 0.884 vs. 0.802; p <0.001) and radiomics signatures model (AUC 0.884 vs. 0.732; p < 0.001) according to Delong test results. A robust calibration and discrimination were demonstrated in the nomogram model. The results of decision curve analysis (DCA) showed more significantly clinical efficiency of the nomogram model in comparison to the clinicoradiological model and the radiomic signature model.ConclusionsDepending on the clinicoradiological factors and radiological features on HBP 15 min images, nomograms can effectively predict MVI status in HCC patients.

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Section: Discussionsupporting

confidence: 92%

Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA

Zhang

Duan²,

Zhou³

et al. 2022

Front. Oncol.

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“…Due to the fact that the seven studies did not mention whether there was macrovascular invasion, we also marked it as an unclear risk of patient selection bias (8, 17-19, 24, 27, 28). Moreover, because the lesion size was limited in 4 studies (22,24,28,33), the patient selection bias was considered as "high." Six studies did not mention whether the radiologists were blinded to the pathology data (9,21,23,24,27,29) and were therefore marked as unclear risk of index bias domain.…”

Section: Study Characteristics and Quality Assessmentmentioning

confidence: 99%

“…Thirteen studies assessed the relationship between peritumoral enhancement on AP with Gd-EOB-DTPA-enhanced MRI and MVI (8,9,17,18,(20)(21)(22)(23)(24)(30)(31)(32)(33), including 2,071 HCC patients with 2,113 tumors. Of 2,113 tumors, 700 were pathologically diagnosed as MVI-positive (356 tumors with peritumoral enhancement on AP and 344 tumors without) and 1,413 as MVInegative (292 tumors with peritumoral enhancement on AP and 1,121 tumors without).…”

Section: Peritumoral Enhancement On Apmentioning

confidence: 99%

“…All 19 studies (8,9,(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) provided the relevant data of peritumoral hypointensity on HBP to predict MVI in HCC with disodium gadoxetate-enhanced MRI, including 2,699 HCC patients with 2,741 tumors. Of 2,741 tumors, 916 were pathologically diagnosed as MVI-positive (500 tumors with peritumoral hypointensity on HBP and 416 tumors without) and 1,825 as MVI-negative (274 tumors with peritumoral hypointensity on HBP and 1,551 tumors without).…”

Section: Peritumoral Hypointensity On Hbpmentioning

confidence: 99%

“…However, Ahn SJ et al and Ahn, S Y. et al found that peritumoral enhancement on AP and peritumoral hypointensity on HBP did not show a statistically significant association with MVI (P > 0.05) (17,18). In addition, the reported SEN and SPE of peritumoral hypointensity on HBP varied widely-0.38−0.81 and 0.56−0.97, respectively (8,9,(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). Yet, as the peritumoral microenvironment has received more attention in recent years, papers on the link between peritumoral imaging and MVI have been updated.…”

Section: Introductionmentioning

confidence: 98%

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Peritumoral Imaging Manifestations on Gd-EOB-DTPA-Enhanced MRI for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Zhu

Liu

et al. 2022

Front. Oncol.

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PurposeThe aim was to investigate the association between microvascular invasion (MVI) and the peritumoral imaging features of gadolinium ethoxybenzyl DTPA-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) in hepatocellular carcinoma (HCC).MethodsUp until Feb 24, 2022, the PubMed, Embase, and Cochrane Library databases were carefully searched for relevant material. The software packages utilized for this meta-analysis were Review Manager 5.4.1, Meta-DiSc 1.4, and Stata16.0. Summary results are presented as sensitivity (SEN), specificity (SPE), diagnostic odds ratios (DORs), area under the receiver operating characteristic curve (AUC), and 95% confidence interval (CI). The sources of heterogeneity were investigated using subgroup analysis.ResultsAn aggregate of nineteen articles were remembered for this meta-analysis: peritumoral enhancement on the arterial phase (AP) was described in 13 of these studies and peritumoral hypointensity on the hepatobiliary phase (HBP) in all 19 studies. The SEN, SPE, DOR, and AUC of the 13 investigations on peritumoral enhancement on AP were 0.59 (95% CI, 0.41−0.58), 0.80 (95% CI, 0.75−0.85), 4 (95% CI, 3−6), and 0.73 (95% CI, 0.69−0.77), respectively. The SEN, SPE, DOR, and AUC of 19 studies on peritumoral hypointensity on HBP were 0.55 (95% CI, 0.45−0.64), 0.87 (95% CI, 0.81−0.91), 8 (95% CI, 5−12), and 0.80 (95% CI, 0.76−0.83), respectively. The subgroup analysis of two imaging features identified ten and seven potential factors for heterogeneity, respectively.ConclusionThe results of peritumoral enhancement on the AP and peritumoral hypointensity on HBP showed high SPE but low SEN. This indicates that the peritumoral imaging features on Gd-EOB-DTPA-enhanced MRI can be used as a noninvasive, excluded diagnosis for predicting hepatic MVI in HCC preoperatively. Moreover, the results of this analysis should be updated when additional data become available. Additionally, in the future, how to improve its SEN will be a new research direction.

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Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomics features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study

Zuo

Qiu

et al. 2022

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OBJECTIVES: To establish and evaluate a machine learning radiomics model based on grayscale and Sonazoid contrast enhanced ultrasound images for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: 100 cases of histopathological confirmed HCC lesions were prospectively included. Regions of interest were segmented on both grayscale and Kupffer phase of Sonazoid contrast enhanced (CEUS) images. Radiomic features were extracted from tumor region and region containing 5 mm of peritumoral liver tissues. Maximum relevance minimum redundancy (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) were used for feature selection and Support Vector Machine (SVM) classifier was trained for radiomic signature calculation. Radiomic signatures were incorporated with clinical variables using univariate-multivariate logistic regression for the final prediction of MVI. Receiver operating characteristic curves, calibration curves and decision curve analysis were used to evaluate model’s predictive performance of MVI. RESULTS: Age were the only clinical variable significantly associated with MVI. Radiomic signature derived from Kupffer phase images of peritumoral liver tissues (kupfferPT) displayed a significantly better performance with an area under the receiver operating characteristic curve (AUROC) of 0.800 (95% confidence interval: 0.667, 0.834), the final prediction model using Age and kupfferPT achieved an AUROC of 0.804 (95% CI: 0.723, 0.878), accuracy of 75.0%, sensitivity of 87.5% and specificity of 69.1%. CONCLUSIONS: Radiomic model based on Kupffer phase ultrasound images of tissue adjacent to HCC lesions showed an observable better predictive value compared to grayscale images and has potential value to facilitate preoperative identification of HCC patients at higher risk of MVI.

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Value of gadoxetic acid-enhanced MRI for microvascular invasion of small hepatocellular carcinoma: a retrospective study

Cited by 3 publications

References 37 publications

Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA

Radiomics nomogram for prediction of microvascular invasion in hepatocellular carcinoma based on MR imaging with Gd-EOB-DTPA

Peritumoral Imaging Manifestations on Gd-EOB-DTPA-Enhanced MRI for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomics features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study

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