Liver segments: an anatomical rationale for explaining inconsistencies with Couinaud's eight-segment concept

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis

PURPOSE

To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis.

MATERIALS AND METHODS

Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve.

RESULTS

ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone.

CONCLUSIONS

The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.

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.

Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis

Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.

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.

The error analysis of Lobular and segmental division of right liver by volume measurement

The aim of this study is to explore the inconsistencies between right liver volume as measured by imaging and the actual anatomical appearance of the right lobe. Five healthy donated livers were studied. The liver slices were obtained with hepatic segments multicolor-infused through the portal vein. In the slices, the lobes were divided by two methods: radiological landmarks and real anatomical boundaries. The areas of the right anterior lobe (RAL) and right posterior lobe (RPL) on each slice were measured using Photoshop CS5 and AutoCAD, and the volumes of the two lobes were calculated. There was no statistically significant difference between the volumes of the RAL or RPL as measured by the radiological landmarks (RL) and anatomical boundaries (AB) methods. However, the curves of the square error value of the RAL and RPL measured using CT showed that the three lowest points were at the cranial, intermediate, and caudal levels. The U- or V-shaped curves of the square error rate of the RAL and RPL revealed that the lowest value is at the intermediate level and the highest at the cranial and caudal levels. On CT images, less accurate landmarks were used to divide the RAL and RPL at the cranial and caudal layers. The measured volumes of hepatic segments VIII and VI would be less than their true values, and the measured volumes of hepatic segments VII and V would be greater than their true values, according to radiological landmarks. Clin. Anat. 30:585-590, 2017. © 2017 Wiley Periodicals, Inc.

Computer-assisted surgery planning in children with complex liver tumors identifies variability of the classical Couinaud classification

BACKGROUND

In complex malignant pediatric liver tumors there is an ongoing discussion regarding surgical strategy; for example, primary organ transplantation versus extended resection in hepatoblastoma involving 3 or 4 sectors of the liver. We evaluated the possible role of computer-assisted surgery planning in children with complex hepatic tumors.

METHODS

Between May 2004 and March 2016, 24 Children with complex liver tumors underwent standard multislice helical CT scan or MRI scan at our institution. Imaging data were processed using the software assistant LiverAnalyzer (Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany). Results were provided as Portable Document Format (PDF) with embedded interactive 3-dimensional surface mesh models.

RESULTS

Median age of patients was 33months. Diagnoses were hepatoblastoma (n=14), sarcoma (n=3), benign parenchyma alteration (n=2), as well as hepatocellular carcinoma, rhabdoid tumor, focal nodular hyperplasia, hemangioendothelioma, or multiple hepatic metastases of a pancreas carcinoma (each n=1). Volumetry of liver segments identified remarkable variations and substantial aberrances from the Couinaud classification. Computer-assisted surgery planning was used to determine surgical strategies in 20/24 children; this was especially relevant in tumors affecting 3 or 4 liver sectors. Primary liver transplantation could be avoided in 12 of 14 hepaoblastoma patients who theoretically were candidates for this approach.

CONCLUSIONS

Computer-assisted surgery planning substantially contributed to the decision for surgical strategies in children with complex hepatic tumors. This tool possibly allows determination of specific surgical procedures such as extended surgical resection instead of primary transplantation in certain conditions.

Anatomy of the liver: an outline with three levels of complexity--a further step towards tailored territorial liver resections

The vascular anatomy of the liver can be described at three different levels of complexity according to the use that the description has to serve. The first--conventional--level corresponds to the traditional 8-segments scheme of Couinaud and serves as a common language between clinicians from different specialties to describe the location of focal hepatic lesions. The second--surgical--level, to be applied to anatomical liver resections and transplantations, takes into account the real branching of the major portal pedicles and of the hepatic veins. Radiological and surgical techniques exist nowadays to make full use of this anatomy, but this requires accepting that the Couinaud scheme is a simplification, and looking at the vascular architecture with an unprejudiced eye. The third--academic--level of complexity concerns the anatomist, and the need to offer a systematization that resolves the apparent contradictions between anatomical literature, radiological imaging, and surgical practice. Based on the real number of second-order portal branches that, although variable averages 20, we submit a system called the "1-2-20 concept", and suggest that it fits best the number of actual--as opposed to idealized--anatomical liver segments.

Concepts for Liver Segment Classification: Neither Old Ones nor New Ones, but a Comprehensive One

Concepts dealing with the subdivision of the human liver into independent vascular and biliary territories are applied routinely in radiological, surgical, and gastroenterological practice. Despite Couinaud's widely used eight-segments scheme, opinions on the issue differ considerably between authors. The aim of this article is to illustrate the scientific basis for understanding and harmonizing inconsistencies between seemingly contradictory observations. Possible clinical implications are addressed.

Advances in research of segmental anatomy of the liver

Since the concept of liver segmental anatomy was first put forward by Francis Glisson in 1654, Hjortsjo segmentation, Healey arteriobiliary segmentation, Couinaud portal and hepatic vein segmentation have been proposed. Although the nomenclature of hepatic anatomy and resections was introduced in the international conference of HPB held in Brisbane, Australia in 2000, the development of liver anatomical techniques (such as iconography and virtual digital technique) as well as the constantly updating knowledge about segmentation of the liver lobe and understanding of the anatomy have raised some new issues. Retrospective analysis and understanding of the features of various segmentation methods and the rules of clinical practice will help us find the most suitable idea of "precise liver resection" in nowadays.

Interventional planning of liver resections: an overview

Liver cancer is the third most common type of cancer. Among available treatment options, a surgical resection offers the best prognosis for long-term survival. It is important that such a surgical procedure is carefully prepared. Modern computer technology offers convenient ways to simulate different resection scenarios and help to determine the best treatment for a given case. This paper provides a non-exhaustive overview of existing computer-based systems for interventional planning of liver resections. They are reviewed according to their medical use case, e.g. if they support typical or atypical resections.