Multimodal Therapy Approaches for NUT Carcinoma by Dual Combination of Oncolytic Virus Talimogene Laherparepvec with Small Molecule Inhibitors.

NUT (nuclear-protein-in-testis) carcinoma (NC) is a highly aggressive tumor disease. Given that current treatment regimens offer a median survival of six months only, it is likely that this type of tumor requires an extended multimodal treatment approach to improve prognosis. In an earlier case report, we could show that an oncolytic herpes simplex virus (T-VEC) is functional in NC patients. To identify further combination partners for T-VEC, we have investigated the anti-tumoral effects of T-VEC and five different small molecule inhibitors (SMIs) alone and in combination in human NC cell lines. Dual combinations were found to result in higher rates of tumor cell reductions when compared to the respective monotherapy as demonstrated by viability assays and real-time tumor cell growth monitoring. Interestingly, we found that the combination of T-VEC with SMIs resulted in both stronger and earlier reductions in the expression of c-Myc, a main driver of NC cell proliferation, when compared to T-VEC monotherapy. These results indicate the great potential of combinatorial therapies using oncolytic viruses and SMIs to control the highly aggressive behavior of NC cancers and probably will pave the way for innovative multimodal clinical studies in the near future.

In Vitro Sensitivity of Neuroendocrine Neoplasms to an Armed Oncolytic Measles Vaccine Virus.

Neuroendocrine neoplasms represent a heterogenous group of rare tumors whose current therapeutic options show only limited efficacy. Oncolytic viruses exert their mode of action through (onco-)lysis of infected tumor cells and the induction of a systemic antitumoral immune response in a virus-induced inflammatory micromilieu. Here, we investigated the potential of our well-established second-generation suicide-gene armed oncolytic measles vaccine virus (MeV-SCD) in five human NEN cell lines. First, (i) expression of the MeV receptor CD46 and (ii) its correlation with primary infection rates were analyzed. Next, (iii) promising combination partners for MeV-SCD were tested by employing either the prodrug 5-fluorocytosine, which is converted into the chemotherapeutic compound 5-fluorouracil, or the mTOR-inhibitor everolimus. As a result, MeV-SCD was found to kill all NEN tumor cell lines efficiently in a dose-dependent manner. This oncolytic effect was further enhanced by exploiting the prodrug-converting system, which was found to be highly instrumental in overcoming the partial resistance found in a single NEN cell line. Furthermore, viral replication was unaffected by everolimus, which is a basic requirement for combined use in NEN patients. These data suggest that MeV-SCD has profound potential for patients with NEN, thus paving the way for early clinical trials.

A Clinical User Study Investigating the Benefits of Adaptive Volumetric Illumination Sampling.

Accurate and fast understanding of the patient's anatomy is crucial in surgical decision making and particularly important in visceral surgery. Sophisticated visualization techniques such as 3D Volume Rendering can aid the surgeon and potentially lead to a benefit for the patient. Recently, we proposed a novel volume rendering technique called Adaptive Volumetric Illumination Sampling (AVIS) that can generate realistic lighting in real-time, even for high resolution images and volumes but without introducing additional image noise. In order to evaluate this new technique, we conducted a randomized, three-period crossover study comparing AVIS to conventional Direct Volume Rendering (DVR) and Path Tracing (PT). CT datasets from 12 patients were evaluated by 10 visceral surgeons who were either senior physicians or experienced specialists. The time needed for answering clinically relevant questions as well as the correctness of the answers were analyzed for each visualization technique. In addition to that, the perceived workload during these tasks was assessed for each technique, respectively. The results of the study indicate that AVIS has an advantage in terms of both time efficiency and most aspects of the perceived workload, while the average correctness of the given answers was very similar for all three methods. In contrast to that, Path Tracing seems to show particularly high values for mental demand and frustration. We plan to repeat a similar study with a larger participant group to consolidate the results.

Suitability of DNN-based vessel segmentation for SIRT planning.

PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interventional planning of selective internal radiation therapy (SIRT), a treatment option for malignant tumors in the liver. In SIRT a catheter is placed through the aorta into the tumor-feeding hepatic arteries, injecting small beads filled with radiation emitting material for local radioembolization. In this study, we evaluate the suitability of a deep neural network (DNN) based vessel segmentation for SIRT planning. METHODS: We applied our DNN-based HA segmentation on 36 contrast-enhanced computed tomography (CT) scans from the arterial contrast agent phase and rated its segmentation quality as well as the overall image quality. Additionally, we applied a traditional machine learning algorithm for HA segmentation as comparison to our deep learning (DL) approach. Moreover, we assessed by expert ratings whether the produced HA segmentations can be used for SIRT planning. RESULTS: The DL approach outperformed the traditional machine learning algorithm. The DL segmentation can be used for SIRT planning in [Formula: see text] of the cases, while the reference segmentations, which were manually created by experienced radiographers, are sufficient in [Formula: see text]. Seven DL cases cannot be used for SIRT planning while the corresponding reference segmentations are sufficient. However, there are two DL segmentations usable for SIRT, where the reference segmentations for the same cases were rated as insufficient. CONCLUSIONS: HA segmentation is a difficult and time-consuming task. DL-based methods have the potential to support and accelerate the pre-interventional planning of SIRT therapy.

Establishing a New Platform to Investigate the Efficacy of Oncolytic Virotherapy in a Human Ex Vivo Peritoneal Carcinomatosis Model.

Oncolytic virotherapy constitutes a promising treatment option for many solid cancers, including peritoneal carcinomatosis (PC), which still represents a terminal stage of many types of tumors. To date, the in vitro efficacy of oncolytic viruses is mostly tested in 2D-cultured tumor cell lines due to the lack of realistic 3D in vitro tumor models. We have investigated the feasibility of virotherapy as a treatment option for PC in a human ex vivo peritoneum co-culture model. Human HT-29 cancer cells stably expressing marker genes GFP and firefly luciferase (GFP/luc) were cultured on human peritoneum and infected with two prototypic oncolytic viruses (GLV-0b347 and MeV-DsRed). Both viral constructs were able to infect HT-29 cells in patient-derived peritoneum with high tumor specificity. Over time, both GFP signal and luciferase activity decreased substantially, thereby indicating successful virus-induced oncolysis. Furthermore, immunohistochemistry stainings showed specific virotherapeutic infections of HT-29 cells and effective tumor cell lysis in infected co-cultures. Thus, the PC model established here provides a clinically relevant screening platform to evaluate the therapeutic efficacy of virotherapeutic compounds and also to investigate, in an autologous setting, the immunostimulatory potential of oncolytic viruses for PC in a unique human model system superior to standard 2D in vitro models.

Computer-assisted image-based risk analysis and planning in lung surgery - a review.

In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks.

Automatic liver tumor segmentation can facilitate the planning of liver interventions. For diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can yield a higher sensitivity than contrast-enhanced CT. However, most studies on automatic liver lesion segmentation have focused on CT. In this study, we present a deep learning-based approach for liver tumor segmentation in the late hepatocellular phase of DCE-MRI, using an anisotropic 3D U-Net architecture and a multi-model training strategy. The 3D architecture improves the segmentation performance compared to a previous study using a 2D U-Net (mean Dice 0.70 vs. 0.65). A further significant improvement is achieved by a multi-model training approach (0.74), which is close to the inter-rater agreement (0.78). A qualitative expert rating of the automatically generated contours confirms the benefit of the multi-model training strategy, with 66 % of contours rated as good or very good, compared to only 43 % when performing a single training. The lesion detection performance with a mean F1-score of 0.59 is inferior to human raters (0.76). Overall, this study shows that correctly detected liver lesions in late-phase DCE-MRI data can be automatically segmented with high accuracy, but the detection, in particular of smaller lesions, can still be improved.

Efficacy of Oncolytic Herpes Simplex Virus T-VEC Combined with BET Inhibitors as an Innovative Therapy Approach for NUT Carcinoma.

NUT carcinoma (NC) is an extremely aggressive tumor and current treatment regimens offer patients a median survival of six months only. This article reports on the first in vitro studies using immunovirotherapy as a promising therapy option for NC and its feasible combination with BET inhibitors (iBET). Using NC cell lines harboring the BRD4-NUT fusion protein, the cytotoxicity of oncolytic virus talimogene laherparepvec (T-VEC) and the iBET compounds BI894999 and GSK525762 were assessed in vitro in monotherapeutic and combinatorial approaches. Viral replication, marker gene expression, cell proliferation, and IFN-ß dependence of T-VEC efficiency were monitored. T-VEC efficiently infected and replicated in NC cell lines and showed strong cytotoxic effects. This implication could be enhanced by iBET treatment following viral infection. Viral replication was not impaired by iBET treatment. In addition, it was shown that pretreatment of NC cells with IFN-ß does impede the replication as well as the cytotoxicity of T-VEC. T-VEC was found to show great potential for patients suffering from NC. Of note, when applied in combination with iBETs, a reinforcing influence was observed, leading to an even stronger anti-tumor effect. These findings suggest combining virotherapy with diverse molecular therapeutics for the treatment of NC.

Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI.

BACKGROUND AND OBJECTIVE: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions. Despite the availability of multi-planar MR scans due to standardized protocols, the majority of segmentation approaches presented in the literature consider the axial scans only. In this work, we investigate whether a neural network processing anisotropic multi-planar images could work in the context of a semantic segmentation task, and if so, how this additional information would improve the segmentation quality. METHODS: We propose an anisotropic 3D multi-stream CNN architecture, which processes additional scan directions to produce a high-resolution isotropic prostate segmentation. We investigate two variants of our architecture, which work on two (dual-plane) and three (triple-plane) image orientations, respectively. The influence of additional information used by these models is evaluated by comparing them with a single-plane baseline processing only axial images. To realize a fair comparison, we employ a hyperparameter optimization strategy to select optimal configurations for the individual approaches. RESULTS: Training and evaluation on two datasets spanning multiple sites show statistical significant improvement over the plain axial segmentation (p<0.05 on the Dice similarity coefficient). The improvement can be observed especially at the base (0.898 single-plane vs. 0.906 triple-plane) and apex (0.888 single-plane vs. 0.901 dual-plane). CONCLUSION: This study indicates that models employing two or three scan directions are superior to plain axial segmentation. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. Thus, the proposed models have the potential to improve the outcome of prostate cancer diagnosis and therapies.

Case Report: Virtual and Interactive 3D Vascular Reconstruction Before Planned Pancreatic Head Resection and Complex Vascular Anatomy: A Bench-To-Bedside Transfer of New Visualization Techniques in Pancreatic Surgery.

Introduction: Bühler's anastomosis (or Bühler's arcade) is an embryonic relic and represents an arterio-arterial connection between the superior mesenteric artery and the celiac trunk. It can be found as a variety in 1-2% of patients. Case Presentation: We present a case of a patient with metatastatic squamous cell carcinoma of the lung. The patient was in stable disease for 4 years under palliative therapy (most recently second-line therapy with Nevolumab). In 2019, a locally advanced adenocarcinoma of the papilla vateri was diagnosed, additionally. The patient also underwent right hemicolectomy and patch plasty of the celiac trunk and superior mesenteric artery due to colonic ischemia and arteriosclerotic disease with 50-70% stenosis of the superior mesenteric artery several years ago. Due to a complex vascular prehistory, the standardized preoperative imaging was supplemented by two independent vascular reconstructions (a CT angiogram and a reconstruction based on the CT) for the planning of a pylorus-preserving pancreatic head resection and reconstruction according to Traverso-Longmire. In addition, a 3D print was produced. Both, the reconstruction based on the CT scan and the 3D print were created for off-label use as a part of a research project (VIVATOP: Versatile Immersive Virtual and Augmented Tangible OP). Discussion: In the standardized CT scan and in the clinical CT-angiography, there were no obvious surgically relevant anatomical variations. A Bühler anastomosis was detected in a digital, virtual and interactive 3D-reconstruction. In addition, in the 3D print of the abdominal site the anastomosis was seen as well. Intraoperatively, the presence of Bühler's anastomosis was confirmed. This information had a significant impact on the intraoperative approach. Retrospectively, the vessel variant could be surmised in the axial projection of the CT scan, if one knew what to look for. Conclusion: For the conduction of a safe surgical procedure, it is imperative that rare anatomical variations are known preoperatively. Increasing digitalization in surgical and perioperative preparation holds great potential for better planning and improved patient safety. Research and cooperation projects such as the VIVATOP project are instrumental for the development of new visualization techniques, which are able to enhance the understanding of complex anatomical relations.

Starvation-Induced Differential Virotherapy Using an Oncolytic Measles Vaccine Virus.

Starvation sensitizes tumor cells to chemotherapy while protecting normal cells at the same time, a phenomenon defined as differential stress resistance. In this study, we analyzed if starvation would also increase the oncolytic potential of an oncolytic measles vaccine virus (MeV-GFP) while protecting normal cells against off-target lysis. Human colorectal carcinoma (CRC) cell lines as well as human normal colon cell lines were subjected to various starvation regimes and infected with MeV-GFP. The applied fasting regimes were either short-term (24 h pre-infection) or long-term (24 h pre- plus 96 h post-infection). Cell-killing features of (i) virotherapy, (ii) starvation, as well as (iii) the combination of both were analyzed by cell viability assays and virus growth curves. Remarkably, while long-term low-serum, standard glucose starvation potentiated the efficacy of MeV-mediated cell killing in CRC cells, it was found to be decreased in normal colon cells. Interestingly, viral replication of MeV-GFP in CRC cells was decreased in long-term-starved cells and increased after short-term low-glucose, low-serum starvation. In conclusion, starvation-based virotherapy has the potential to differentially enhance MeV-mediated oncolysis in the context of CRC cancer patients while protecting normal colon cells from unwanted off-target effects.

Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections.

PURPOSE: To compare manual corrections of liver masks produced by a fully automatic segmentation method based on convolutional neural networks (CNN) with manual routine segmentations in MR images in terms of inter-observer variability and interaction time. METHODS: For testing, patient's precise reference segmentations that fulfill the quality requirements for liver surgery were manually created. One radiologist and two radiology residents were asked to provide manual routine segmentations. We used our automatic segmentation method Liver-Net to produce liver masks for the test cases and asked a radiologist assistant and one further resident to correct the automatic results. All observers were asked to measure their interaction time. Both manual routine and corrected segmentations were compared with the reference annotations. RESULTS: The manual routine segmentations achieved a mean Dice index of 0.95 and a mean relative error (RVE) of 4.7%. The quality of liver masks produced by the Liver-Net was on average 0.95 Dice and 4.5% RVE. Liver masks resulting from manual corrections of automatically generated segmentations compared to routine results led to a significantly lower inter-observer variability (mean per case absolute RVE difference across observers 0.69%) when compared to manual routine ones (2.75%). The mean interaction time was 2 min for manual corrections and 10 min for manual routine segmentations. CONCLUSIONS: The quality of automatic liver segmentations is on par with those from manual routines. Using automatic liver masks in the clinical workflow could lead to a reduction of segmentation time and a more consistent liver volume estimation across different observers.

Multimodal image registration for liver radioembolization planning and patient assessment.

PURPOSE: Multimodal imaging plays a key role in patient assessment and treatment planning in liver radioembolization. It will reach its full potential for convenient use in combination with deformable image registration methods. A registration framework is proposed for multimodal liver image registration of multi-phase CT, contrast-enhanced late-phase T1, T2, and DWI MRI sequences. METHODS: A chain of four pair-wise image registrations based on a variational registration framework using normalized gradient fields as distance measure and curvature regularization is introduced. A total of 103 cases of 35 patients was evaluated based on anatomical landmarks and deformation characteristics. RESULTS: Good anatomical correspondence and physical plausibility of the deformation fields were attained. The global mean landmark errors vary from 3.20 to 5.36 mm, strongly influenced by low resolved images in z-direction. Moderate volume changes are indicated by mean minimum and maximum Jacobian determinants of 0.44 up to 1.88. No deformation foldings were detected. The mean average divergence of the deformation fields range from 0.08 to 0.16 and the mean harmonic energies vary from 0.08 to 0.58. CONCLUSION: The proposed registration solutions enable the combined use of information from multimodal imaging and provide an excellent basis for patient assessment and primary planning for liver radioembolization.

Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing.

Automatic liver tumor segmentation would have a big impact on liver therapy planning procedures and follow-up assessment, thanks to standardization and incorporation of full volumetric information. In this work, we develop a fully automatic method for liver tumor segmentation in CT images based on a 2D fully convolutional neural network with an object-based postprocessing step. We describe our experiments on the LiTS challenge training data set and evaluate segmentation and detection performance. Our proposed design cascading two models working on voxel- and object-level allowed for a significant reduction of false positive findings by 85% when compared with the raw neural network output. In comparison with the human performance, our approach achieves a similar segmentation quality for detected tumors (mean Dice 0.69 vs. 0.72), but is inferior in the detection performance (recall 63% vs. 92%). Finally, we describe how we participated in the LiTS challenge and achieved state-of-the-art performance.

Focused scores enable reliable discrimination of small differences in steatosis.

BACKGROUND: Automated image analysis enables quantitative measurement of steatosis in histological images. However, spatial heterogeneity of steatosis can make quantitative steatosis scores unreliable. To improve the reliability, we have developed novel scores that are "focused" on steatotic tissue areas. METHODS: Focused scores use concepts of tile-based hotspot analysis in order to compute statistics about steatotic tissue areas in an objective way. We evaluated focused scores on three data sets of images of rodent liver sections exhibiting different amounts of dietary-induced steatosis. The same evaluation was conducted with the standard steatosis score computed by most image analysis methods. RESULTS: The standard score reliably discriminated large differences in steatosis (intraclass correlation coefficient ICC = 0.86), but failed to discriminate small (ICC = 0.54) and very small (ICC = 0.14) differences. With an appropriate tile size, mean-based focused scores reliably discriminated large (ICC = 0.92), small (ICC = 0.86) and very small (ICC = 0.83) differences. Focused scores based on high percentiles showed promise in further improving the discrimination of very small differences (ICC = 0.93). CONCLUSIONS: Focused scores enable reliable discrimination of small differences in steatosis in histological images. They are conceptually simple and straightforward to use in research studies.

Landmark-based evaluation of a deformable motion correction for DCE-MRI of the liver.

PURPOSE: Annotation of meaningful landmark ground truth on DCE-MRI is difficult and laborious. Motion correction methods applied to DCE-MRI of the liver are thus mostly evaluated using qualitative or indirect measures. We propose a novel landmark annotation scheme that facilitates the generation of landmark ground truth on larger clinical datasets. METHODS: In our annotation scheme, landmarks are equally distributed over all time points of all available dataset cases and annotated by multiple observers on a per-pair basis. The scheme is used to annotate 26 DCE-MRI of the liver. A subset of the ground truth is used to optimize parameters of a deformable motion correction. Several variants of the motion correction are evaluated on the remaining cases with respect to distances of corresponding landmarks after registration, deformation field properties, and qualitative measures. RESULTS: A landmark ground truth on 26 cases could be generated in under 12 h per observer with a mean inter-observer distance below the mean voxel diagonal. Furthermore, the landmarks are spatially well distributed within the liver. Parameter optimization significantly improves the performance of the motion correction, and landmark distance after registration is 2 mm. Qualitative evaluation of the motion correction reflects the quantitative results. CONCLUSIONS: The annotation scheme makes a landmark-based evaluation of motion corrections for hepatic DCE-MRI practically feasible for larger clinical datasets. The comparably large number of cases enables both optimization and evaluation of motion correction methods.

Younger Age is an Independent Factor for Graft Weight Overestimation: Analysis of the Clinical Impact on Recipient Outcomes in 340 Japanese Living Liver Donors.

BACKGROUND: Accurate preoperative estimation of graft weight is essential for improving outcomes in living donor liver transplantation. METHODS: This retrospective study sought to identify factors associated with graft weight overestimation. From April 2006 to August 2015, 340 living donors were assigned to no-overestimate (n = 284) or overestimate (n = 56) groups. We defined graft weight overestimation as a discrepancy ≥15% between estimated graft volume and actual graft weight. Donor data were compared, and associated factors for graft weight overestimation were analyzed. Recipient outcomes were compared between the groups according to identified factors. RESULTS: Donors were significantly younger in the overestimate group than in the no-overestimate group (35.0 vs. 46.0 years; p < 0.001). Multivariate analysis identified donor age <45 years as an independent risk factor for graft weight overestimation (odds ratio 2.068; 95% confidence interval 1.114-3.839; p = 0.021). Among recipients with donors <45 years (n = 168), incidence of small-for-size dysfunction (SFSD) was significantly higher in the overestimate group than in the no-overestimate group (7/37 patients vs. 7/131 patients; p = 0.016); no significant difference was observed among recipients with donors ≥45 years (n = 172). First-year mortality was lower in SFSD recipients with donors <45 years (14.3 vs. 60.9%, p = 0.007). Among recipients with younger donors, graft survival was not significantly different between overestimate and no-overestimate groups. CONCLUSIONS: Younger donor age was an independent risk factor for graft weight overestimation leading to SFSD in recipients, but did not impair graft survival.

Predictive SIRT dosimetry based on a territorial model.

BACKGROUND: In the planning of selective internal radiation therapy (SIRT) for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on arterial liver territories is proposed. This model is dedicated to characterize the variability of dose within the whole liver. For an arbitrary partition, the generalized absorbed dose is derived from the classical partition model. This enables to consider normal liver partitions for each arterial perfusion supply area and one partition for each tumor for activity and dose calculation. The proposed method excludes a margin of 11 mm emitting range around tumor volumes from normal liver to investigate the impact on activity calculation. Activity and dose calculation was performed for five patients using the body-surface-area (BSA) method, the classical and territorial partition model. RESULTS: The territorial model reaches smaller normal liver doses and significant higher tumor doses compared to the classical partition model. The exclusion of a small region around tumors has a significant impact on mean liver dose. Determined tumor activities for the proposed method are higher in all patients when limited by normal liver dose. Activity calculation based on BSA achieves in all cases the lowest amount. CONCLUSIONS: The territorial model provides a more local and patient-individual dose distribution in normal liver taking into account arterial supply areas. This proposed arterial liver territory-based partition model may be used for SPECT-independent activity calculation and dose prediction under the condition of an artery-based simulation for particle distribution.

Visualization of Vascular and Parenchymal Regeneration after 70% Partial Hepatectomy in Normal Mice.

A modified silicone injection procedure was used for visualization of the hepatic vascular tree. This procedure consisted of in-vivo injection of the silicone compound, via a 26 G catheter, into the portal or hepatic vein. After silicone injection, organs were explanted and prepared for ex-vivo micro-CT (µCT) scanning. The silicone injection procedure is technically challenging. Achieving a successful outcome requires extensive microsurgical experience from the surgeon. One of the challenges of this procedure involves determining the adequate perfusion rate for the silicone compound. The perfusion rate for the silicone compound needs to be defined based on the hemodynamic of the vascular system of interest. Inappropriate perfusion rate can lead to an incomplete perfusion, artificial dilation and rupturing of vascular trees. The 3D reconstruction of the vascular system was based on CT scans and was achieved using preclinical software such as HepaVision. The quality of the reconstructed vascular tree was directly related to the quality of silicone perfusion. Subsequently computed vascular parameters indicative of vascular growth, such as total vascular volume, were calculated based on the vascular reconstructions. Contrasting the vascular tree with silicone allowed for subsequent histological work-up of the specimen after µCT scanning. The specimen can be subjected to serial sectioning, histological analysis and whole slide scanning, and thereafter to 3D reconstruction of the vascular trees based on histological images. This is the prerequisite for the detection of molecular events and their distribution with respect to the vascular tree. This modified silicone injection procedure can also be used to visualize and reconstruct the vascular systems of other organs. This technique has the potential to be extensively applied to studies concerning vascular anatomy and growth in various animal and disease models.

Quantification of Hepatic Vascular and Parenchymal Regeneration in Mice.

BACKGROUND: Liver regeneration consists of cellular proliferation leading to parenchymal and vascular growth. This study complements previous studies on cellular proliferation and weight recovery by (1) quantitatively describing parenchymal and vascular regeneration, and (2) determining their relationship. Both together are needed to (3) characterize the underlying growth pattern. METHODS: Specimens were created by injecting a polymerizing contrast agent in either portal or hepatic vein in normal or regenerating livers after 70% partial hepatectomy. 3D image data were obtained through micro-CT scanning. Parenchymal growth was assessed by determining weight and volume of the regenerating liver. Vascular growth was described by manually determined circumscribed parameters (maximal vessel length and radius of right inferior portal/hepatic vein), automatically determined cumulative parameters (total edge length and total vascular volume), and parameters describing vascular density (total edge length/volume, vascular volume fraction). The growth pattern was explored by comparing the relative increase of these parameters to the increase expected in case of isotropic expansion. RESULTS: Liver volume recovery paralleled weight recovery and reached 90% of the original liver volume within 7 days. Comparing radius-related vascular parameters immediately after surgical resection and after virtual resection in-silico revealed a slight increase, possibly reflecting the effect of resection-induced portal hyperperfusion. Comparing length-related parameters between post-operative day 7 and after virtual resection showed similar vascular growth in both vascular systems investigated. In contrast, radius-related parameters increased slightly more in the portal vein. Despite the seemingly homogeneous 3D growth, the observed vascular parameters were not compatible with the hypothesis of isotropic expansion of liver parenchyma and vascular structures. CONCLUSION: We present an approach for the quantitative analysis of the vascular systems of regenerating mouse livers. We applied this technique for assessing the hepatic growth pattern. Prospectively, this approach can be used to investigate hepatic vascular regeneration under different conditions.