Issues to be considered to address the future liver remnant prior to major hepatectomy

Kishi, Yoji; Vauthey, Jean Nicolas

doi:10.1007/s00595-020-02088-2

Cited by 25 publications

(9 citation statements)

References 137 publications

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“…For the calculation of the FLR%, we used the ratio of the FLR to the total functional liver volume but not the ratio of the FLR to the standard liver volume (SLV) [SLV = (1,267.28 × body surface area) − 794.41]. However, there is controversy concerning which calculation method best reflects the total liver function ( 11 ). FLR is not the only predictor of PLF.…”

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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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%

“…V TotalLiver and V HepaticLesion represent the volume of the entire liver, including hepatic lesions and the volume of hepatic lesions within the liver that are planned to be removed, respectively ( 11 ).…”

Section: Methodsmentioning

confidence: 99%

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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“…As we have seen in this study in the 75H group, major resections leaving a remnant liver of adequate size and quality are not associated with the same changes of injury, sinusoidal collapse, apoptosis, and fibrosis as those seen with an insufficient liver remnant. This is the case for the majority of patients undergoing major hepatectomy in the clinical setting, where different strategies and algorithms have been devised and implemented to avoid insufficient liver remnant and moderate-to-severe (grade B/C) PHLF [7,28,29]. A large proportion of patients that actually do develop relevant PHLF die in the first postoperative weeks to months [30,31], while the greater part of the remainder subsequently succumbs to the oncological processes that were the indication for surgery in the first place [32][33][34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Somatostatin Therapy Improves Stellate Cell Activation and Early Fibrogenesis in a Preclinical Model of Extended Major Hepatectomy

Hessheimer

Vengohechea

Maza

et al. 2021

Cancers

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Liver resection treats primary and secondary liver tumors, though clinical applicability is limited by the remnant liver mass and quality. Herein, major hepatic resections were performed in pigs to define changes associated with sufficient and insufficient remnants and improve liver-specific outcomes with somatostatin therapy. Three experimental groups were performed: 75% hepatectomy (75H), 90% hepatectomy (90H), and 90% hepatectomy + somatostatin (90H + SST). Animals were followed for 24 h (N = 6) and 5 d (N = 6). After hepatectomy, portal pressure gradient was higher in 90H versus 75H and 90H + SST (8 (3–13) mmHg vs. 4 (2–6) mmHg and 4 (2–6) mmHg, respectively, p < 0.001). After 24 h, changes were observed in 90H associated with stellate cell activation and collapse of sinusoidal lumen. Collagen chain type 1 alpha 1 mRNA expression was higher, extracellular matrix width less, and percentage of collagen-staining areas greater at 24 h in 90H versus 75H and 90H + SST. After 5 d, remnant liver mass was higher in 75H and 90H + SST versus 90H, and Ki-67 immunostaining was higher in 90H + SST versus 75H and 90H. As well, more TUNEL-staining cells were observed in 90H versus 75H and 90H + SST at 5 d. Perioperative somatostatin modified portal pressure, injury, apoptosis, and stellate cell activation, stemming changes related to hepatic fibrogenesis seen in liver remnants not receiving treatment.

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“…Generally, for patients with normal liver function, the FLR must account for more than 25% of the total liver volume. For patients with liver cirrhosis, the FLR must account for more than 40% of the total liver volume 4 . Because of an inadequate FLR, less than 25% of patients are eligible for surgery at diagnosis.…”

Section: Local Treatmentmentioning

confidence: 99%

Precision medicine in the treatment of colorectal cancer with liver metastases

Xu,

Lv,

Zhu

et al. 2024

Cancer Biol Med

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Issues to be considered to address the future liver remnant prior to major hepatectomy

Cited by 25 publications

References 137 publications

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

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

Somatostatin Therapy Improves Stellate Cell Activation and Early Fibrogenesis in a Preclinical Model of Extended Major Hepatectomy

Precision medicine in the treatment of colorectal cancer with liver metastases

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