Nomogram based on CT–derived extracellular volume for the prediction of post-hepatectomy liver failure in patients with resectable hepatocellular carcinoma

Peng, Yangling; Shen, Hesong; Tang, Hao; Huang, Yuanying; Xiao, Lan; Luo, Xianzhang; Zhang, Xiaoyue; Zhang, Jiuquan

doi:10.1007/s00330-022-08917-x

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…In The present study and our previous study [16] have both confirmed that CT-derived ECV of the background liver is strongly correlated with the postoperative pathological fibrosis stage, and CT-derived ECV is an independent predictor of PHLF. A higher ECV means more severe liver fibrosis and a higher risk for PHLF in patients with resectable HCC.…”

Section: Discussionsupporting

confidence: 89%

“…A total of 112 consecutive patients (between November 2019 and July 2022, at Chongqing General Hospital [CQGH]) who met the same inclusion criteria were enrolled and formed the external validation cohort. Some of these patients (121 patients in CQUCH and 81 patients in CQGH) were analyzed as a subcohort of another published study [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies [ 4 , 11 , 13 – 15 ] have shown that ECV can predict the degree of liver fibrosis. Our previous study [ 16 ] found that CT-derived ECV can predict PHLF in resectable HCC patients. However, in our previous study, the nomogram based on CT-derived ECV only considered the function of the future remnant liver and did not incorporate the volume of the future remnant liver.…”

Section: Introductionmentioning

confidence: 99%

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CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

Peng,

Tang,

Huang

et al. 2023

Insights Imaging

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Objectives Posthepatectomy liver failure (PHLF) is a severe complication of liver resection. We aimed to develop and validate a model based on extracellular volume (ECV) and liver volumetry derived from computed tomography (CT) for preoperative predicting PHLF in resectable hepatocellular carcinoma (HCC) patients. Methods A total of 393 resectable HCC patients from two hospitals were enrolled and underwent multiphasic contrast-enhanced CT before surgery. A total of 281 patients from our hospital were randomly divided into a training cohort (n = 181) and an internal validation cohort (n = 100), and 112 patients from another hospital formed the external validation cohort. CT-derived ECV was measured on nonenhanced and equilibrium phase images, and liver volumetry was measured on portal phase images. The model is composed of independent predictors of PHLF. The under the receiver operator characteristic curve (AUC) and calibration curve were used to reflect the predictive performance and calibration of the model. Comparison of AUCs used the DeLong test. Results CT-derived ECV, measured future liver remnant (mFLR) ratio, and serum albumin were independent predictors for PHLF in resectable HCC patients. The AUC of the model was significantly higher than that of the ALBI score in the training cohort, internal validation cohort, and external validation cohort (all p < 0.001). The calibration curve of the model showed good consistency in the training cohort and the internal and external validation cohorts. Conclusions The novel model contributes to the preoperative prediction of PHLF in resectable HCC patients. Critical relevance statement The novel model combined CT–derived extracellular volume, measured future liver remnant ratio, and serum albumin outperforms the albumin–bilirubin score for predicting posthepatectomy liver failure in patients with resectable hepatocellular carcinoma. Key points • CT-derived ECV correlated well with the fibrosis stage of the background liver. • CT-derived ECV and mFLR ratio were independent predictors for PHLF in HCC. • The AUC of the model was higher than the CT-derived ECV and mFLR ratio. • The model showed a superior predictive performance than that of the ALBI score. Graphical Abstract

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Section: Discussionsupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

Peng,

Tang,

Huang

et al. 2023

Insights Imaging

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“…Mathieu Prodeau et al ( 6 ), reported an ordinal PHLF prediction model for patients with cirrhosis based on 3 variables (i.e., platelet count, RTLV and ITT laparoscopy). Yangling Peng et al ( 25 ), showed a nomogram based on CT–derived extracellular volume for the prediction of PHLF in patients with HCC. Rong-yun Mai et al ( 24 ), developed and validated a multivariate deep learning model based on baseline characteristics, laboratory indicators and surgical situation for predicting the risk of severe PHLF in patients with HCC who underwent hemihepatectomy.…”

Section: Discussionmentioning

confidence: 99%

“…Contrast-enhanced computed tomography (CT) is a common examination for the assessment of liver disease, because the vascularity and contrast agent enhancement patterns of liver lesions provide useful information for evaluation. A previous study reported that a nomogram combining CT image, serum albumin (Alb) and serum total bilirubin (Tbil) showed a good performance for PHLF preoperative prediction in patients with resectable HCC ( 25 ).…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for prediction of post hepatectomy liver failure after hemihepatectomy using preoperative contrast-enhanced computed tomography: a retrospective study

Xing

et al. 2023

Front. Med.

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ObjectivePost-hepatectomy liver failure (PHLF) remains clinical challenges after major hepatectomy. The aim of this study was to establish and validate a deep learning model to predict PHLF after hemihepatectomy using preoperative contrast-enhancedcomputed tomography with three phases (Non-contrast, arterial phase and venous phase).Methods265 patients undergoing hemihepatectomy in Sir Run Run Shaw Hospital were enrolled in this study. The primary endpoint was PHLF, according to the International Study Group of Liver Surgery’s definition. In this study, to evaluate the proposed method, 5-fold cross-validation technique was used. The dataset was split into 5 folds of equal size, and each fold was used as a test set once, while the other folds were temporarily combined to form a training set. Performance metrics on the test set were then calculated and stored. At the end of the 5-fold cross-validation run, the accuracy, precision, sensitivity and specificity for predicting PHLF with the deep learning model and the area under receiver operating characteristic curve (AUC) were calculated.ResultsOf the 265 patients, 170 patients with left liver resection and 95 patients with right liver resection. The diagnosis had 6 types: hepatocellular carcinoma, intrahepatic cholangiocarcinoma, liver metastases, benign tumor, hepatolithiasis, and other liver diseases. Laparoscopic liver resection was performed in 187 patients. The accuracy of prediction was 84.15%. The AUC was 0.7927. In 170 left hemihepatectomy cases, the accuracy was 89.41% (152/170), and the AUC was 82.72%. The accuracy was 77.47% (141/182) with liver mass, 78.33% (47/60) with liver cirrhosis and 80.46% (70/87) with viral hepatitis.ConclusionThe deep learning model showed excellent performance in prediction of PHLF and could be useful for identifying high-risk patients to modify the treatment planning.

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Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma

Xue,

Zhu,

Fang

et al. 2024

J of Ultrasound Medicine

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PurposePosthepatectomy liver failure (PHLF) is a major cause of postoperative mortality in hepatocellular carcinoma (HCC) patients. The study aimed to develop a method based on the two‐dimensional shear wave elastography and clinical data to evaluate the risk of PHLF in HCC patients with chronic hepatitis B.MethodsThis multicenter study proposed a deep learning model (PHLF‐Net) incorporating dual‐modal ultrasound features and clinical indicators to predict the PHLF risk. The datasets were divided into a training cohort, an internal validation cohort, an internal independent testing cohort, and three external independent testing cohorts. Based on ResNet50 pretrained on ImageNet, PHLF‐Net used a progressive training strategy with images of varying granularity and incorporated conventional B‐mode and elastography images and clinical indicators related to liver reserve function.ResultsIn total, 532 HCC patients who underwent hepatectomy at five hospitals were enrolled. PHLF occurred in 147 patients (27.6%, 147/532). The PHLF‐Net combining dual‐modal ultrasound and clinical indicators demonstrated high effectiveness for predicting PHLF, with AUCs of 0.957 and 0.923 in the internal validation and testing sets, and AUCs of 0.950, 0.860, and 1.000 in the other three independent external testing sets. The performance of PHLF‐Net outperformed models of single‐ and dual‐modal US.ConclusionsPreoperative ultrasound imaging combining clinical indicators can effectively predict the PHLF probability in patients with HCC. In the internal and external validation sets, PHLF‐Net demonstrated its usefulness in predicting PHLF.

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Nomogram based on CT–derived extracellular volume for the prediction of post-hepatectomy liver failure in patients with resectable hepatocellular carcinoma

Cited by 9 publications

References 32 publications

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

CT-derived extracellular volume and liver volumetry can predict posthepatectomy liver failure in hepatocellular carcinoma

A deep learning model for prediction of post hepatectomy liver failure after hemihepatectomy using preoperative contrast-enhanced computed tomography: a retrospective study

Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma

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