Prediction and Prevention of Post-hepatectomy Liver Failure: Where Do We Stand?

Hanafy, Amr Shaaban

doi:10.14218/jcth.2021.00144

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…PLF is now recognized as having multifactorial causation, and the predictors include patient-dependent factors and surgery-dependent factors ( 13 ). These patient-dependent factors include preoperative bilirubin, international normalized ratio, and creatinine ( 14 ). In addition, the presence of malnutrition, diabetes mellitus, obesity, and liver damage due to chemotherapy is also associated with an increased risk of PLF ( 12 ).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the presence of malnutrition, diabetes mellitus, obesity, and liver damage due to chemotherapy is also associated with an increased risk of PLF ( 12 ). Surgery-dependent risk factors include intraoperative blood loss of more than 1,000–1,200 mL, the extent of surgery (i.e., minor hepatectomy, major hepatectomy, or extra major hepatectomy), an FLR <20%, and a prolonged operative time of more than 240 min ( 14 ). Among all these factors, the FLR (including the FLR volume and function) is considered one of most important predictors of PLF.…”

Section: Discussionmentioning

confidence: 99%

“…The FLR% cutoffs of 20%, 30%, and 40% in patients with healthy livers, hepatic steatosis, and cirrhosis, respectively, were accepted as the minimum FLR% values for major hepatectomy in this study ( 12 - 14 ).…”

Section: Methodsmentioning

confidence: 99%

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Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model

Xie¹,

Li²,

Lin³

et al. 2023

Quant Imaging Med Surg

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Background Recent reports have shown the potential for deep learning (DL) models to automatically segment of Couinaud liver segments and future liver remnant (FLR) for liver resections. However, these studies have mainly focused on the development of the models. Existing reports lack adequate validation of these models in diverse liver conditions and thorough evaluation using clinical cases. This study thus aimed to develop and perform a spatial external validation of a DL model for the automated segmentation of Couinaud liver segments and FLR using computed tomography (CT) in various liver conditions and to apply the model prior to major hepatectomy. Methods This retrospective study developed a 3-dimensional (3D) U-Net model for the automated segmentation of Couinaud liver segments and FLR on contrast-enhanced portovenous phase (PVP) CT scans. Images were obtained from 170 patients from January 2018 to March 2019. First, radiologists annotated the Couinaud segmentations. Then, a 3D U-Net model was trained in Peking University First Hospital (n=170) and tested in Peking University Shenzhen Hospital (n=178) in cases with various liver conditions (n=146) and in candidates for major hepatectomy (n=32). The segmentation accuracy was evaluated using the dice similarity coefficient (DSC). Quantitative volumetry to evaluate the resectability was compared between manual and automated segmentation. Results The DSC in the test data sets 1 and 2 for segments I to VIII was 0.93±0.01, 0.94±0.01, 0.93±0.01, 0.93±0.01, 0.94±0.00, 0.95±0.00, 0.95±0.00, and 0.95±0.00, respectively. The mean automated FLR and FLR% assessments were 493.51±284.77 mL and 38.53%±19.38%, respectively. The mean manual FLR and FLR% assessments were 500.92±284.38 mL and 38.35%±19.14%, respectively, in test data sets 1 and 2. For test data set 1, when automated segmentation of the FLR% was used, 106, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively; however, when manual segmentation of the FLR% was used, 107, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively. For test data set 2, all cases were categorized as candidates for major hepatectomy when automated and manual segmentation of the FLR% was used. No significant differences in FLR assessment (P=0.50; U=185,545), FLR% assessment (P=0.82; U=188,337), or the indications for major hepatectomy were noted between automated and manual segmentation (McNemar test statistic 0.00; P>0.99). Conclusions The DL model could be used to fully automate the segmentation of Couinaud liver segments and FLR with CT prior to major hepatectomy in an accurate and clinically practicable manner.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model

Xie¹,

Li²,

Lin³

et al. 2023

Quant Imaging Med Surg

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“… The prediction of resection based on the FLR% B-free status and FLR% B-filled status The optimal minimal FLR% varies for different liver pathologies, and FLR% values larger than 20%, 30%, and 40% in patients with healthy livers, hepatic steatosis, and cirrhosis, respectively, were classified as candidates for major hepatectomy in this study [ 21 – 23 ]. …”

Section: Methodsmentioning

confidence: 99%

Fully automated assessment of the future liver remnant in a blood-free setting via CT before major hepatectomy via deep learning

Xie,

Zhou,

Zhang

et al. 2024

Insights Imaging

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Objectives To develop and validate a deep learning (DL) model for automated segmentation of hepatic and portal veins, and apply the model in blood-free future liver remnant (FLR) assessments via CT before major hepatectomy. Methods 3-dimensional 3D U-Net models were developed for the automatic segmentation of hepatic veins and portal veins on contrast-enhanced CT images. A total of 170 patients treated from January 2018 to March 2019 were included. 3D U-Net models were trained and tested under various liver conditions. The Dice similarity coefficient (DSC) and volumetric similarity (VS) were used to evaluate the segmentation accuracy. The use of quantitative volumetry for evaluating resection was compared between blood-filled and blood-free settings and between manual and automated segmentation. Results The DSC values in the test dataset for hepatic veins and portal veins were 0.66 ± 0.08 (95% CI: (0.65, 0.68)) and 0.67 ± 0.07 (95% CI: (0.66, 0.69)), the VS values were 0.80 ± 0.10 (95% CI: (0.79, 0.84)) and 0.74 ± 0.08 (95% CI: (0.73, 0.76)), respectively No significant differences in FLR, FLR% assessments, or the percentage of major hepatectomy patients were noted between the blood-filled and blood-free settings (p = 0.67, 0.59 and 0.99 for manual methods, p = 0.66, 0.99 and 0.99 for automated methods, respectively) according to the use of manual and automated segmentation methods. Conclusion Fully automated segmentation of hepatic veins and portal veins and FLR assessment via blood-free CT before major hepatectomy are accurate and applicable in clinical cases involving the use of DL. Critical relevance statement Our fully automatic models could segment hepatic veins, portal veins, and future liver remnant in blood-free setting on CT images before major hepatectomy with reliable outcomes. Key Points Fully automatic segmentation of hepatic veins and portal veins was feasible in clinical practice. Fully automatic volumetry of future liver remnant (FLR)% in a blood-free setting was robust. No significant differences in FLR% assessments were noted between the blood-filled and blood-free settings. Graphical Abstract

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