Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC

Wang, Xinxin; Zhang, Ziqian; Zhou, Xueyan; Zhang, Yuning; Zhou, Jiamin; Tang, Shuli; Liu, Yang; Zhou, Yang

doi:10.1016/j.ejrad.2020.109361

Cited by 8 publications

(12 citation statements)

References 31 publications

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“…In our study, among the LDA models based on various sequences, MDF AP and MDF PVP were significant independent factors for the prediction of MVI, and showed satisfactory predictive efficacy with an AUC greater than 0.80. Histogram parameters have been used in quantitative analysis of MVI in clinical studies[ 19 , 20 ]. Li et al [ 19 ] performed histogram analysis of intravoxel incoherent motion and the best parameter provided a sensitivity of 81% and specificity of 85%[ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…It was based on whole tumor volume, but only 41 patients were enrolled. Wang et al [ 20 ] used computational quantitative measures based on the maximum cross-sectional area to predict MVI of small HCC, but only in HBP images[ 20 ]; the AUC, sensitivity, and specificity were 0.91, 0.87, and 0.80, respectively. In our study, the radiomic analysis-based MDF outperformed each individual histogram parameter in predicting the presence of MVI.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, there is not yet radiomics study based on linear discriminant analysis (LDA) algorithm to predict MVI. Additionally, even without spatial information, histogram analysis alone can indicate a gray-level distribution and is used for MVI prediction[ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

Li¹,

Zhu²,

Gao³

et al. 2022

WJG

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BACKGROUND The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning. AIM To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC. METHODS A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software. RESULTS The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI ( P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy. CONCLUSION Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

Li¹,

Zhu²,

Gao³

et al. 2022

WJG

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“…Imaging features include the number, size, tumor morphology, envelope integrity, abnormal peritumor enhancement in the arterial phase, and peritumor low signal in the hepatobiliary phase. [2][3][4] Laboratory indices include hepatitis B virus replication, alkaline phosphatase (ALP) level, alphafetoprotein (AFP) level, Ki-67-positive cell index, abnormal prothrombin (PIVKA-II), and steroid hormones. [5][6][7][8][9][10] However, few investigations have addressed whether abdominal fat content is associated with MVI.…”

Section: Introductionmentioning

confidence: 99%

Preoperative Assessment of Abdominal Adipose Tissue to Predict Microvascular Invasion in Small Hepatocellular Carcinoma

Lü

Xie

et al. 2021

J Clin Transl Hepatol

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Background and Aims: Microvascular invasion (MVI) affects recurrence after treatment of small hepatocellular carcinoma (sHCC) of ≤3 cm in size. The present study aimed to investigate whether abdominal subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT) are associated with MVI in patients with sHCC. Methods: A total of 124 patients with pathologicallyconfirmed sHCC diagnosed on surgical resection at the First Hospital Affiliated to Army Military University were recruited and divided into two groups according to MVI classification criteria (i.e., MVI-positive or MVI-negative). The SAT, VAT, and IMAT areas at the lumbar 3 vertebral level were imaged with abdominal computed tomography and measured using ImageJ software. Their association with MVI in sHCC was analyzed. Results: Of the 124 patients with sHCC, 67 were MVI-positive and 57 were MVI-negative. Univariate analysis revealed a significant difference in the abdominal VAT and SAT between the MVI-positive and MVI-negative groups (p<0.05), with an area under the receiver operating characteristic curve of 0.76 and 0.65, respectively. Conclusions: The results of this study suggest that the areas of abdominal SAT and VAT are of significant clinical value because they can effectively predict the MVI status in patients with sHCC.

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“…Recent studies have demonstrated that MVI is the main adverse prognostic factor influencing small HCCs, and it is recommended for guidance of individualized treatment of small HCCs 21,22 . However, the incidence of MVI in small HCC (≤3 cm) is low, and there have been few studies that focus on MVI in small HCC 23 . Pathologically, the area around the tumor is the primary site of MVI 24 .…”

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confidence: 99%

Preoperative Evaluation of Gd‐EOB‐DTPA‐Enhanced MRI Radiomics‐Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two‐Center Study

Tian

Hua

Peng

et al. 2022

Magnetic Resonance Imaging

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Background Preoperative evaluation of microvascular invasion (MVI) in small solitary hepatocellular carcinoma (HCC; maximum lesion diameter ≤ 3 cm) is important for treatment decisions. Purpose To apply gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced MRI to develop and validate a nomogram for preoperative evaluation of MVI in small solitary HCC and to compare the effectiveness of radiomics evaluation models based on different volumes of interest (VOIs). Study Type Retrospective. Population A total of 196 patients include 62 MVI‐positive and 134 MVI‐negative patients were enrolled (training cohort, n = 105; testing cohort, n = 45; external validation cohort, n = 46). Field Strength/Sequence 3.0 T, fat suppressed fast‐spin‐echo T2‐weighted and Gd‐EOB‐DTPA‐enhanced T1‐weighted magnetization‐prepared rapid gradient‐echo sequences. Assessment Radiomics features were extracted on T2‐weighted, arterial phase (AP), and hepatobiliary phase (HBP) images from different VOIs (VOIintratumor and VOIintratumor+peritumor) and filtered by the least absolute shrinkage selection operator (LASSO) regression. From VOIintratumor and VOIintratumor+peritumor, eight radiomics models were constructed based on three MRI sequences (T2‐weighted, AP, and HBP) and fused sequences (combined of three sequences). Nomograms were constructed of a clinical‐radiological (CR) model and a clinical‐radiological‐radiomics (CRR) model. Statistical Tests One‐way analysis of variance, independent t‐test, Chi‐square test or Fisher's exact test, Wilcoxon rank‐sum test, LASSO, logistic regression analysis, area under the curve (AUC), nomograms, decision curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and DeLong test. Results Among eight radiomics models, the fused sequences‐based VOIintratumor+peritumor radiomics model showed the best performance. The CRR model containing the best performance radiomics model and CR model with the AUC values were 0.934, 0.889, and 0.875, respectively. NRI and IDI analyses showed that the CRR model improved evaluation efficacy over the CR model for all three cohorts (all P‐value <0.05). Data Conclusion The CRR model nomogram could preoperatively evaluate MVI in small solitary HCC. The radiomics model based on VOIintratumor+peritumor might achieve better evaluation results. Evidence Level 4 Technical Efficacy Stage 2

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Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC

Cited by 8 publications

References 31 publications

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

Preoperative Assessment of Abdominal Adipose Tissue to Predict Microvascular Invasion in Small Hepatocellular Carcinoma

Preoperative Evaluation of Gd‐EOB‐DTPA‐Enhanced MRI Radiomics‐Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two‐Center Study

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