2023
DOI: 10.1007/s00330-023-09419-0
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Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

Abstract: Objectives The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. Methods In this bicentric study, 285 patients with HC… Show more

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Cited by 16 publications
(6 citation statements)
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“…The removal perimeter required for precision hepatic resection is also more accurate than in conventional surgery. Intraoperatively, only the liver branch including the tumor and the portal vein is removed, and R0 resection can be maximized [13][14][15][16]. The analysis of this study indicates that it is feasible to choose precision liver resection for primary hepatocellular carcinoma cases, because it can greatly improve the recovery of liver function after surgery.…”
Section: Comparison Of Inflammation Indicators Between the Two Groupsmentioning
confidence: 90%
“…The removal perimeter required for precision hepatic resection is also more accurate than in conventional surgery. Intraoperatively, only the liver branch including the tumor and the portal vein is removed, and R0 resection can be maximized [13][14][15][16]. The analysis of this study indicates that it is feasible to choose precision liver resection for primary hepatocellular carcinoma cases, because it can greatly improve the recovery of liver function after surgery.…”
Section: Comparison Of Inflammation Indicators Between the Two Groupsmentioning
confidence: 90%
“…Extracting deep learning features using VGGNet-19 from contrast-enhanced MRI images, the deep learning nomogram, incorporating multiphase deep learning signatures, performed well on both the training (AUC: 0.949) and validation sets (AUC: 0.909). Independent predictors for early recurrence included microvascular invasion, tumor number, and the deep learning signature (105). Lv et al introduced an AI -powered approach for predicting the 3-year recurrence of HCC using contrast-enhanced CT radiomic profiles.…”
Section: Prognosticationmentioning
confidence: 99%
“…69 Moreover, several studies have used deep learning approaches to encode conventional MRI for stratifying patients at high risk of early HCC recurrence after resection. 52,76 In a 172-patient study, Chen et al investigated the predictive ability of four different machine learning classifiers and deep learning with clinical features had the best performance with an AUC of 0.97 in predicting treatment effect for HCC patients who underwent TACE. 77 In another study, He et al applied a deep learning model combining multidimensional information, such as whole slide images for assessing recurrence risk under liver transplantation, achieving an AUC of 0.87.…”
Section: Mri-based Deep Learning For Hccmentioning
confidence: 99%
“…For example, a model based on the ResNet18, the model includes residual connections for the direct flow of information from one layer to another, bypassing intermediate layers, was developed in a multi‐center and prospective validation study showing the ability in stratifying postoperative survival 69 . Moreover, several studies have used deep learning approaches to encode conventional MRI for stratifying patients at high risk of early HCC recurrence after resection 52,76 . In a 172‐patient study, Chen et al investigated the predictive ability of four different machine learning classifiers and deep learning with clinical features had the best performance with an AUC of 0.97 in predicting treatment effect for HCC patients who underwent TACE 77 .…”
Section: Mri‐based Deep Learning For Hccmentioning
confidence: 99%