Predicting microvascular invasion in hepatocellular carcinoma: A dual‐institution study on gadoxetate disodium‐enhanced <scp>MRI</scp>

Jiang, Hanyu; Wei, Jingwei; Fu, Fangfang; Wei, Hong; Qin, Yun; Duan, Ting; Chen, Weixia; Xie, Kunlin; Lee, Jeong Min; Bashir, Mustafa R.; Wang, Meiyun; Song, Bin; Tian, Jie

doi:10.1111/liv.15231

Cited by 42 publications

(38 citation statements)

References 37 publications

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“…Peritumoral hypointensity on HBP might be a result of impaired functions of peritumoral hepatocyte organic anion-transporting polypeptide transporters due to perfusion alterations in the hepatocytes around the HCC ( 28 ). It may be caused by impaired hepatocyte function ( 29 ) or Kupffer cell damage in neoplastic arterial portal shunts, as portal vein branches are blocked by cancer embolus ( 30 ). HBP hypointensity is a prominent imaging feature of HCC based on Gd-BOPTA-enhanced MRI, indicating a lack of functional hepatocytes in the tumor ( 31 ).…”

Section: Discussionmentioning

confidence: 99%

A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm)

Zhang

Huo²,

Zhang³

et al. 2022

Front. Oncol.

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PurposeThe present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options.Methods164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility.ResultsElevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits.ConclusionIn conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options.

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Section: Discussionmentioning

confidence: 99%

A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm)

Zhang

Huo²,

Zhang³

et al. 2022

Front. Oncol.

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“…Peritumoral hypointensity in hepatobiliary phase (HBP), one non‐LI‐RADS feature currently, is defined as the presence of a wedge‐shaped or flame‐like hypointense area adjacent to the tumour border on HBP images 14 . It has been identified as a predictor of MVI and postsurgical recurrence of HCC 14,29,38,49,58 . The possible mechanism underlying the association of peritumoral hypointensity in HBP with MVI is that hemodynamic alterations because of tumour obstruction of microscopic portal venules result in the decreased expression of organic anion‐transporting polypeptide (OATP) transporters, known to import gadoxetic acid into hepatocytes, leading to the reduced uptake of gadoxetic acid in the hepatocytes around the tumour 14 .…”

Section: Prognostic Imaging Features At Ct/mrimentioning

confidence: 99%

“…56 These may help explain why HCCs with nonsmooth tumour margin tend to be more aggressive (e.g., MVI and progenitor subtype) and have a higher risk of recurrence than the others. 14,27,29,30,39,49,57…”

Section: Nonsmooth Tumour Marginmentioning

confidence: 99%

“…14 It has been identified as a predictor of MVI and postsurgical recurrence of HCC. 14,29,38,49,58 The possible mechanism underlying the association of peritumoral hypointensity in HBP with MVI is that hemodynamic alterations because of tumour obstruction of microscopic portal venules result in the decreased expression of organic anion-transporting polypeptide (OATP) transporters, known to import gadoxetic acid into hepatocytes, leading to the reduced uptake of gadoxetic acid in the hepatocytes around the tumour. 14 Moreover, MVI is an important risk factor for the recurrence of HCC after surgical treatment.…”

Section: Peritumoral Hypointensity In Hepatobiliary Phasementioning

confidence: 99%

“…24 Whilst LI-RADS has been widely accepted as an effective diagnostic system, emerging data provide new insights into its potential prognostic utilities in HCC. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] Since prognostic imaging features in these studies were assessed according to standardized criteria, it will be easy to generalize published results once established. In this scenario, understanding the prognostic implications of LI-RADS may help optimize the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes.…”

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Prognostic implications of CT/MRI LI‐RADS in hepatocellular carcinoma: State of the art and future directions

Wei

Yang

Chen

et al. 2022

Liver International

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Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. 1,2 Over the decades, management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics (e.g., size, number and vascular invasion) and clinical variables. [3][4][5][6][7][8][9] Nevertheless, these algorithms are unlikely to recapitulate the full landscape of tumour aggressiveness and allow accurate prognosis stratification.

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Deep learning‐based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole‐slide histopathology images

Zhang,

Yu,

Liang

et al. 2024

Cancer Medicine

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BackgroundMicrovascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time‐consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep‐learning model that could significantly improve the efficiency and accuracy of MVI diagnosis.Materials and MethodsWe collected H&E‐stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep‐learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve.ResultsWe successfully developed a MVI artificial intelligence diagnostic model (MVI‐AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI‐AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI.ConclusionsWe developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

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Predicting microvascular invasion in hepatocellular carcinoma: A dual‐institution study on gadoxetate disodium‐enhanced MRI

Cited by 42 publications

References 37 publications

A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm)

A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm)

Prognostic implications of CT/MRI LI‐RADS in hepatocellular carcinoma: State of the art and future directions

Deep learning‐based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole‐slide histopathology images

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