Rapid Assessment of Liver Volumetry by a Novel Automated Segmentation Algorithm:

Multi-organ Radiomics-Based Prediction of Future Remnant Liver Hypertrophy Following Portal Vein Embolization

BACKGROUND

Portal vein embolization (PVE) is used to induce remnant liver hypertrophy prior to major hepatectomy. The purpose of this study was to evaluate the predictive value of baseline computed tomography (CT) data for future remnant liver (FRL) hypertrophy after PVE.

METHODS

In this retrospective study, all consecutive patients undergoing right-sided PVE with or without hepatic vein embolization between 2018 and 2021 were included. CT volumetry was performed before and after PVE to assess standardized FRL volume (sFRLV). Radiomic features were extracted from baseline CT after segmenting liver (without tumor), spleen and bone marrow. For selecting features that allow classification of response (hypertrophy ≥ 1.33), a stepwise dimension reduction was performed. Logistic regression models were fitted and selected features were tested for their predictive value. Decision curve analysis was performed on the test dataset.

RESULTS

A total of 53 patients with liver tumor were included in this study. sFRLV increased significantly after PVE, with a mean hypertrophy of FRL of 1.5 ± 0.3-fold. sFRLV hypertrophy ≥ 1.33 was reached in 35 (66%) patients. Three independent radiomic features, i.e. liver-, spleen- and bone marrow-associated, differentiated well between responders and non-responders. A logistic regression model revealed the highest accuracy (area under the curve 0.875) for the prediction of response, with sensitivity of 1.0 and specificity of 0.5. Decision curve analysis revealed a positive net benefit when applying the model.

CONCLUSIONS

This proof-of-concept study provides first evidence of a potential predictive value of baseline multi-organ radiomics CT data for FRL hypertrophy after PVE.

Lower Ratio of Liver Volume and Body Weight Is a Negative Predictor of Survival after Transjugular Intrahepatic Portosystemic Shunt

Transjugular intrahepatic portosystemic shunt (TIPS) is the most effective measure to treat complications of portal hypertension. However, liver function may deteriorate after TIPS. Predictors of liver function and outcome after TIPS are therefore important for management of TIPS patients. The study aimed to evaluate the impact of liver volume on transplant-free survival (TFS) after TIPS, as well as the evolution of liver volume and its relationship with liver function after TIPS. A retrospective analysis of all consecutive patients who underwent TIPS in a tertiary care university liver center between 2012 and 2017 (n = 216) was performed; n = 72 patients with complete prior and follow-up (FU) computed tomography (CT) imaging studies were included in the study. Volumetry of the liver was performed by a semi-automatic 9-lobe image segmentation algorithm at baseline and FU (FU 1: 90–180 d; FU 2: 180–365 d; FU 3: 365–545 d; FU 4: 545–730 d; FU 5: >730 d). Output variables were total liver volume (TLV, cm³), left liver volume (LLV, cm³), right liver volume (RLV, cm³) and TLV/body weight ratio. CT derived liver volumes were correlated with liver function tests, portosystemic pressure gradient (PPG) measurements and survival. To assess predictors of liver volume change over time we fitted linear mixed models. Kaplan–Meier analysis was performed and validated by matched pair analysis followed by Cox regression to determine independent prognostic factors for survival. The median TLV at baseline was 1507.5 cm³ (773.7–3686.0 cm³). Livers with higher baseline liver volumes and larger TLV/weight ratios retained their volume after an initial loss while smaller livers continuously lost volume after TIPS. At the first follow-up period (90–180 d post-TIPS) lower liver volumes and TLV/weight ratios were associated with higher bilirubin levels. Within the final multivariable model containing time (days since TIPS), baseline INR and baseline TLV, the average loss of liver volume was 0.74 mL per day after TIPS. Twelve-month overall transplant-free survival was 89% and median overall TFS was 33 months. The median TFS for a baseline TLV/body weight ratio > 20 was significantly higher compared with ≤20 (40.0 vs. 27.0 months, p = 0.010) while there were no differences regarding the indication for TIPS or etiology of liver disease in the matched pair analysis. Lower TLV/weight ratios before TIPS were associated with shorter TFS and should therefore be critically considered when selecting patients for TIPS. In addition, this study provides first evidence of an effect of TIPS on subsequent liver volume change and associated liver function.

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

Fully automated whole-liver volume quantification on CT-image data: Comparison with manual volumetry using enhanced and unenhanced images as well as two different radiation dose levels and two reconstruction kernels

OBJECTIVES

To evaluate the accuracy of fully automated liver volume quantification vs. manual quantification using unenhanced as well as enhanced CT-image data as well as two different radiation dose levels and also two image reconstruction kernels.

MATERIAL AND METHODS

The local ethics board gave its approval for retrospective data analysis. Automated liver volume quantification in 300 consecutive livers in 164 male and 103 female oncologic patients (64±12y) performed at our institution (between January 2020 and May 2020) using two different dual-energy helicals: portal-venous phase enhanced, ref. tube current 300mAs (CARE Dose4D) for tube A (100 kV) and ref. 232mAs tube current for tube B (Sn140kV), slice collimation 0.6mm, reconstruction kernel I30f/1, recon. thickness of 0.6mm and 5mm, 80-100 mL iodine contrast agent 350 mg/mL, (flow 2mL/s) and unenhanced ref. tube current 100mAs (CARE Dose4D) for tube A (100 kV) and ref. 77mAs tube current for tube B (Sn140kV), slice collimation 0.6mm (kernel Q40f) were analyzed. The post-processing tool (syngo.CT Liver Analysis) is already FDA-approved. Two resident radiologists with no and 1-year CT-experience performed both the automated measurements independently from each other. Results were compared with those of manual liver volume quantification using the same software which was supervised by a senior radiologist with 30-year CT-experience (ground truth).

RESULTS

In total, a correlation of 98% was obtained for liver volumetry based on enhanced and unenhanced data sets compared to the manual liver quantification. Radiologist #1 and #2 achieved an inter-reader agreement of 99.8% for manual liver segmentation (p<0.0001). Automated liver volumetry resulted in an overestimation (>5% deviation) of 3.7% for unenhanced CT-image data and 4.0% for contrast-enhanced CT-images. Underestimation (<5%) of liver volume was 2.0% for unenhanced CT-image data and 1.3% for enhanced images after automated liver volumetry. Number and distribution of erroneous volume measurements using either thin or thick slice reconstructions was exactly the same, both for the enhanced as well for the unenhanced image data sets (p> 0.05).

CONCLUSION

Results of fully automated liver volume quantification are accurate and comparable with those of manual liver volume quantification and the technique seems to be confident even if unenhanced lower-dose CT image data is used.

Portal and hepatic vein embolization prior to major hepatectomy

PURPOSE

 To analyze safety and effectiveness of simultaneous portal and hepatic vein embolization (PHVE) or sequential hepatic vein embolization (HVE) compared to portal vein embolization (PVE) for future remnant liver (FRL) hypertrophy prior to major hepatic surgery.

METHODS

 Patients undergoing PVE, PHVE or HVE at our tertiary care center between 2018 and 2020 were retrospectively included. FRLV, standardized FRLV (sFRLV) and sFRLV growth rate per day were assessed via volumetry, as well as laboratory parameters.

RESULTS

 36 patients (f = 15, m = 21; median 64.5 y) were included, 16 patients received PHVE and 20 patients PVE, of which 4 received sequential HVE. Significant increase of FRLV was achieved with both PVE and PHVE compared to baseline (p < 0.0001). sFRLV growth rate did not significantly differ following PHVE (2.2 ± 1.2 %/d) or PVE (2.2 ± 1.7 %/d, p = 0.94). Left portal vein thrombosis (LPVT) was observed after PHVE in 6 patients and in 1 patient after PVE. Sequential HVE showed a considerably high growth rate of 1.42 ± 0.45 %/d after PVE.

CONCLUSION

 PHVE effectively induces FRL hypertrophy but yields comparable sFRLV to PVE. Sequential HVE further induces hypertrophy after insufficient growth due to PVE. Considering a potentially higher rate of LPVT after PHVE, PVE might be preferred in patients with moderate baseline sFRLV, with optional sequential HVE in non-sufficient responders.

Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach

Background

To evaluate whether machine learning algorithms allow the prediction of Child-Pugh classification on clinical multiphase computed tomography (CT).

Methods

A total of 259 patients who underwent diagnostic abdominal CT (unenhanced, contrast-enhanced arterial, and venous phases) were included in this retrospective study. Child-Pugh scores were determined based on laboratory and clinical parameters. Linear regression (LR), Random Forest (RF), and convolutional neural network (CNN) algorithms were used to predict the Child-Pugh class. Their performances were compared to the prediction of experienced radiologists (ERs). Spearman correlation coefficients and accuracy were assessed for all predictive models. Additionally, a binary classification in low disease severity (Child-Pugh class A) and advanced disease severity (Child-Pugh class ≥ B) was performed.

Results

Eleven imaging features exhibited a significant correlation when adjusted for multiple comparisons with Child-Pugh class. Significant correlations between predicted and measured Child-Pugh classes were observed (ρ_LA = 0.35, ρ_RF = 0.32, ρ_CNN = 0.51, ρ_ERs = 0.60; p < 0.001). Significantly better accuracies for the prediction of Child-Pugh classes versus no-information rate were found for CNN and ERs (p ≤ 0.034), not for LR and RF (p ≥ 0.384). For binary severity classification, the area under the curve at receiver operating characteristic analysis was significantly lower (p ≤ 0.042) for LR (0.71) and RF (0.69) than for CNN (0.80) and ERs (0.76), without significant differences between CNN and ERs (p = 0.144).

Conclusions

The performance of a CNN in assessing Child-Pugh class based on multiphase abdominal CT images is comparable to that of ERs.

Automatic atlas-based liver segmental anatomy identification for hepatic surgical planning

PURPOSE

For the liver to remain viable, the resection during hepatectomy procedure should proceed along the major vessels; hence, the resection planes of the anatomic segments are defined, which mark the peripheries of the self-contained segments inside the liver. Liver anatomic segments identification represents an essential step in the preoperative planning for liver surgical resection treatment.

METHOD

The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments.

RESULTS

The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy.

CONCLUSION

Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.

Does providing routine liver volume assessment add value when performing CT surveillance in cirrhotic patients?

BACKGROUND

The measurement of liver volume (LV) is considered to be an effective prognosticator for postoperative liver failure in patients undergoing hepatectomy. It is unclear whether LV can be used to predict mortality in cirrhotic patients.

METHODS

We enrolled 584 consecutive cirrhotic patients who underwent computerized topography (CT) of the abdomen for hepatocellular carcinoma surveillance and 50 age, gender, race, and BMI-matched controls without liver disease. Total LV (TLV), functional LV (FLV), and segmental liver volume (in cm³) were measured from CT imaging. Cirrhotic subjects were followed until death, liver transplantation, or study closure date of July 31, 2016. The survival data were assessed with log-rank statistics and independent predictors of survival were performed using Cox hazards model.

RESULTS

Cirrhotic subjects had significantly lower TLV, FLV, and segmental (all except for segments 1, 6, 7) volume when compared to controls. Subjects presenting with hepatic encephalopathy had significantly lower TLV and FLV than those without HE (p = 0.002). During the median follow-up of 1145 days, 112 (19%) subjects were transplanted and 131 (23%) died. TLV and FLV for those who survived were significantly higher than those who were transplanted or dead (TLV:1740 vs. 1529 vs. 1486, FLV 1691 vs. 1487 vs. 1444, p < 0.0001). In the Cox regression model, age, MELD score, TLV, or FLV were independent predictors of mortality.

CONCLUSION

Baseline liver volume is an independent predictor of mortality in subjects with cirrhosis. Therefore, it may be useful to provide these data while performing routine surveillance CT scan as an important added value. Further studies are needed to validate these findings and to better understand their clinical utility.

Development of a Patient-Specific 3D-Printed Liver Model for Preoperative Planning

INTRODUCTION

Liver surgery is widely used as a treatment modality for various liver pathologies. Despite significant improvement in clinical care, operative strategies, and technology over the past few decades, liver surgery is still risky, and optimal preoperative planning and anatomical assessment are necessary to minimize risks of serious complications. 3D printing technology is rapidly expanding, and whilst appliactions in medicine are growing, but its applications in liver surgery are still limited. This article describes the development of models of hepatic structures specific to a patient diagnosed with an operable hepatic malignancy.

METHODS

Anatomy data were segmented and extracted from computed tomography and magnetic resonance imaging of the liver of a single patient with a resectable liver tumor. The digital data of the extracted anatomical surfaces was then edited and smoothed, resulting in a set of digital 3D models of the hepatic vein, portal vein with tumor, biliary tree with gallbladder, and hepatic artery. These were then 3D printed.

RESULTS

The final models of the liver structures and tumor provided good anatomical detail and representation of the spatial relationships between the liver tumor and adjacent hepatic structures and could be easily manipulated and explored from different angles.

CONCLUSIONS

A graspable, patient-specific, 3D printed model of liver structures could provide an improved understanding of the complex liver anatomy and better navigation in difficult areas and allow surgeons to anticipate anatomical issues that might arise during the operation. Further research into adequate imaging, liver-specific volumetric software, and segmentation algorithms are worth considering to optimize this application.

Fast and accurate liver volumetry prior to hepatectomy

BACKGROUND

Volumetric assessment of the liver is essential in the prevention of postresectional liver failure after partial hepatectomy. Currently used methods are accurate but time-consuming. This study aimed to test a new automated method for preoperative volumetric liver assessment.

METHODS

Patients who underwent a contrast enhanced portovenous phase CT-scan prior to hepatectomy in 2012 were included. Total liver volume (TLV) and future remnant liver volume (FRLV) were measured using TeraRecon Aquarius iNtuition(®) (autosegmentation) and OsiriX(®) (manual segmentation) software by two observers for each software package. Remnant liver volume percentage (RLV%) was calculated. Time needed to determine TLV and FRLV was measured. Inter-observer variability was assessed using Bland-Altman plots.

RESULTS

Twenty-seven patients were included. There were no significant differences in measured volumes between OsiriX(®) and iNtuition(®). Moreover, there were significant correlations between the OsiriX(®) observers, the iNtuition(®) observers and between OsiriX(®) and iNtuition(®) post-processing systems (all R(2) > 0.97). The median time needed for complete liver volumetric analysis was 18.4 ± 4.9 min with OsiriX(®) and 5.8 ± 1.7 min using iNtuition(®) (p < 0.001).

CONCLUSION

Both OsiriX(®) and iNtuition(®) liver volumetry are accurate and easily applicable. However, volumetric assessment of the liver with iNtuition(®) auto-segmentation is three times faster compared to manual OsiriX(®) volumetry.

Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm

We developed a medical image segmentation and preoperative planning application which implements a semiautomatic and a hybrid semiautomatic liver segmentation algorithm. The aim of this study was to evaluate the feasibility of computer-assisted liver tumor surgery using these algorithms which are based on thresholding by pixel intensity value from initial seed points. A random sample of 12 patients undergoing elective high-risk hepatectomies at our institution was prospectively selected to undergo computer-assisted surgery using our algorithms (June 2013-July 2014). Quantitative and qualitative evaluation was performed. The average computer analysis time (segmentation, resection planning, volumetry, visualization) was 45 min/dataset. The runtime for the semiautomatic algorithm was <0.2 s/slice. Liver volumetric segmentation using the hybrid method was achieved in 12.9 s/dataset (SD ± 6.14). Mean similarity index was 96.2 % (SD ± 1.6). The future liver remnant volume calculated by the application showed a correlation of 0.99 to that calculated using manual boundary tracing. The 3D liver models and the virtual liver resections had an acceptable coincidence with the real intraoperative findings. The patient-specific 3D models produced using our semiautomatic and hybrid semiautomatic segmentation algorithms proved to be accurate for the preoperative planning in liver tumor surgery and effectively enhanced the intraoperative medical image guidance.