Gut microbiota promote liver regeneration through hepatic membrane phospholipid biosynthesis

BACKGROUND & AIMS

Hepatocyte growth and proliferation depends on membrane phospholipid biosynthesis. Short-chain fatty acids (SCFAs) generated by bacterial fermentation, delivered through the gut-liver axis, significantly contribute to lipid biosynthesis. We therefore hypothesized that dysbiotic insults like antibiotic treatment not only affect gut microbiota, but also impair hepatic lipid synthesis and liver regeneration.

METHODS

Stable isotope labeling and 70% partial hepatectomy (PHx) was carried out in C57Bl/6J wild-type mice, in mice treated with broad-spectrum antibiotics, in germ-free mice and mice colonized with minimal microbiota. The microbiome was analyzed by 16S rRNA gene sequencing and microbial culture. Gut content, liver, blood and primary hepatocyte organoids were tested by mass spectrometry-based lipidomics, quantitative reverse-transcription PCR (qRT-PCR), immunoblot and immunohistochemistry for expression of proliferative and lipogenic markers. Matched biopsies from hyperplastic and hypoplastic liver tissue of patients subjected to surgical intervention to induce hyperplasia were analyzed by qRT-PCR for lipogenic enzymes.

RESULTS

Three days of antibiotic treatment induced persistent dysbiosis with significantly decreased beta-diversity and richness, but a massive increase of Proteobacteria, accompanied by decreased colonic SCFAs. After PHx, antibiotic-treated mice showed delayed liver regeneration, increased mortality, impaired hepatocyte proliferation and decreased hepatic phospholipid synthesis. Expression of the lipogenic enzyme SCD1 was upregulated after PHx but delayed by antibiotic treatment. Germ-free mice essentially recapitulated the phenotype of antibiotic treatment. Phospholipid biosynthesis, hepatocyte proliferation, liver regeneration and survival were rescued in gnotobiotic mice colonized with a minimal SCFA-producing microbial community. SCFAs induced the growth of murine hepatocyte organoids and hepatic SCD1 expression in mice. Further, SCD1 was required for proliferation of human hepatoma cells and was associated with liver regeneration in human patients.

CONCLUSION

Gut microbiota are pivotal for hepatic membrane phospholipid biosynthesis and liver regeneration.

IMPACT AND IMPLICATIONS

Gut microbiota affect hepatic lipid metabolism through the gut-liver axis, but the underlying mechanisms are poorly understood. Perturbations of the gut microbiome, e.g. by antibiotics, impair the production of bacterial metabolites, which normally serve as building blocks for membrane lipids in liver cells. As a consequence, liver regeneration and survival after liver surgery is severely impaired. Even though this study is preclinical, its results might allow physicians in the future to improve patient outcomes after liver surgery, by modulation of gut microbiota or their metabolites.

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

Development and Validation of a Deep Learning Algorithm to Differentiate Colon Carcinoma From Acute Diverticulitis in Computed Tomography Images

IMPORTANCE

Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care.

OBJECTIVE

To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study.

DESIGN, SETTING, AND PARTICIPANTS

In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support.

MAIN OUTCOMES AND MEASURES

To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons.

RESULTS

A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001).

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.

Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascularization

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis.

METHODS AND MATERIALS

Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR₁ area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery.

RESULTS

Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr -18%) and mouse (AUC60r -36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration.

CONCLUSION

In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens.

softALPPS - A novel, individual procedure for patients with advanced liver tumors

BACKGROUND

The first-line therapy for liver malignancies is a radical extended liver resection. This high-risk operation has a high incidence of post-hepatectomy liver failure (PHLF) due to a small future liver remnant (FLR). One of the procedures to increase the FLR is the associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) which is still associated with high morbidity and mortality. Here, we present a new, less invasive ALPPS variant that may be associated with lower morbidity.

METHODS

SoftALPPS is characterized by reduced trauma to the liver tissue and individual adaptation to the patient's health constitution. In softALPPS, portal vein embolization (PVE) is performed instead of portal vein ligation (PVL) after complete recovery of liver function. In addition, a non-absorbable foil was avoided in order to be able to extend the interval to step two or skip step two when required.

RESULTS

Four patients successfully underwent softALPPS. Two of these patients have been followed-up for over a year (one patient with Klatskin tumor, one patient with extensive HCC). Both patients show no evidence of recurrence after 12 months and are in good medical condition. The other two patients who recently had surgery are also doing well.

CONCLUSION

SoftALPPS offers the chance to curatively resect patients with high tumor burden of the liver even when the FLR is inadequate. This individual therapy method can give patients the possibility of complete tumor resection and can help to reduce perioperative morbidity.

SARS-CoV-2 serology increases diagnostic accuracy in CT-suspected, PCR-negative COVID-19 patients during pandemic

Background

In the absence of PCR detection of SARS-CoV-2 RNA, accurate diagnosis of COVID-19 is challenging. Low-dose computed tomography (CT) detects pulmonary infiltrates with high sensitivity, but findings may be non-specific. This study assesses the diagnostic value of SARS-CoV-2 serology for patients with distinct CT features but negative PCR.

Methods

IgM/IgG chemiluminescent immunoassay was performed for 107 patients with confirmed (group A: PCR + ; CT ±) and 46 patients with suspected (group B: repetitive PCR-; CT +) COVID-19, admitted to a German university hospital during the pandemic’s first wave. A standardized, in-house CT classification of radiological signs of a viral pneumonia was used to assess the probability of COVID-19.

Results

Seroconversion rates (SR) determined on day 5, 10, 15, 20 and 25 after symptom onset (SO) were 8%, 25%, 65%, 76% and 91% for group A, and 0%, 10%, 19%, 37% and 46% for group B, respectively; (p < 0.01). Compared to hospitalized patients with a non-complicated course (non-ICU patients), seroconversion tended to occur at lower frequency and delayed in patients on intensive care units. SR of patients with CT findings classified as high certainty for COVID-19 were 8%, 22%, 68%, 79% and 93% in group A, compared with 0%, 15%, 28%, 50% and 50% in group B (p < 0.01). SARS-CoV-2 serology established a definite diagnosis in 12/46 group B patients. In 88% (8/9) of patients with negative serology > 14 days after symptom onset (group B), clinico-radiological consensus reassessment revealed probable diagnoses other than COVID-19. Sensitivity of SARS-CoV-2 serology was superior to PCR > 17d after symptom onset.

Conclusions

Approximately one-third of patients with distinct COVID-19 CT findings are tested negative for SARS-CoV-2 RNA by PCR rendering correct diagnosis difficult. Implementation of SARS-CoV-2 serology testing alongside current CT/PCR-based diagnostic algorithms improves discrimination between COVID-19-related and non-related pulmonary infiltrates in PCR negative patients. However, sensitivity of SARS-CoV-2 serology strongly depends on the time of testing and becomes superior to PCR after the 2^nd week following symptom onset.

Prognostic Factors for Overall Survival in Advanced Intrahepatic Cholangiocarcinoma Treated with Yttrium-90 Radioembolization

Purpose: To evaluate factors associated with survival following transarterial ⁹⁰Y (yttrium) radioembolization (TARE) in patients with advanced intrahepatic cholangiocarcinoma (ICC). Methods: This retrospective multicenter study analyzed the outcome of three tertiary care cancer centers in patients with advanced ICC following resin microsphere TARE. Patients were included either after failed previous anticancer therapy, including relapse after surgical resection, or for having a minimum of 25% of total liver volume affected by ICC. Patients were stratified and response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) criteria at 3 months. Kaplan–Meier analysis was performed to analyze survival followed by cox regression to determine independent prognostic factors for survival. Results: 46 patients were included (19 male, 27 female), median age 62.5 years (range 29–88 years). A total of 65% of patients had undergone previous therapy, while 63% had a tumor volume > 25% of the entire liver volume. Median survival was 9.5 months (95% CI: 6.1–12.9 months). Due to loss in follow-up, n = 37 patients were included in the survival analysis. Cox regression revealed the extent of liver disease to one or both liver lobes being associated with survival, irrespective of tumor volume (p = 0.041). Patients with previous surgical resection of ICC had significantly decreased survival (3.9 vs. 12.8 months, p = 0.002). No case of radiation-induced liver disease was observed. Discussion: Survival after ⁹⁰Y TARE in patients with advanced ICC primarily depends on disease extent. Only limited prognostic factors are associated with a general poor overall survival.

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

Background

To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher’s exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used.

Results

The ML algorithm achieved 87% sensitivity (95% IC 67.3–92.7), 80% specificity (95% CI 74.0–86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001).

Conclusion

ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.

Electronic supplementary material

The online version of this article (10.1186/s41747-019-0119-0) contains supplementary material, which is available to authorized users.

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

PURPOSE

Development of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.

METHODS

The retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm was trained on 70% of the patients (N = 28) and tested on 30% (N = 17) to predict KRT81+ vs. KRT81- tumor subtypes. A gradient-boosted survival regression model was fit to the disease-free and overall survival data. Chemotherapy response and survival were assessed stratified by subtype and radiomic signature. Radiomic feature importance was ranked.

RESULTS

The mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. The mean±STDEV concordance indices between the disease-free and overall survival predicted by the model based on the radiomic parameters and actual patient survival were 0.76±0.05 and 0.71±0.06, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81- patients (7.0 vs. 22.6 months, HR 4.03, log-rank-test P = <0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 2.33, P = 0.037) compared to KRT81- patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 2.41, P = 0.027). Entropy was ranked as the most important radiomic feature.

CONCLUSIONS

The machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for disease-free and overall patient survival and response to chemotherapy.

Characterization of Cardiac Dynamics in an Acute Myocardial Infarction Model by Four-Dimensional Optoacoustic and Magnetic Resonance Imaging

Extraction of murine cardiac functional parameters on a beat-by-beat basis is limited with the existing imaging modalities due to insufficient three-dimensional temporal resolution. Faster volumetric imaging methods enabling in vivo characterization of functional parameters are poised to advance cardiovascular research and provide a better understanding of the mechanisms underlying cardiac diseases. We present a new approach based on analyzing contrast-enhanced optoacoustic (OA) images acquired at high volumetric frame rate without using cardiac gating or other approaches for motion correction. We apply an acute murine myocardial infarction model optimized for acquisition of artifact-free optoacoustic imaging data to study cardiovascular hemodynamics. Infarcted hearts (n = 21) could be clearly differentiated from healthy controls (n = 9) based on a significantly higher pulmonary transit time (PTT) (2.25 [2.00-2.41] s versus 1.34 [1.25-1.67] s, p = 0.0235), while no statistically significant difference was observed in the heart rate (318 [252-361] bpm versus 264 [252-320] bpm, p = 0.3129). Nevertheless, nonlinear heartbeat dynamics was stronger in the healthy hearts, as evidenced by the third harmonic component in the heartbeat spectra. MRI data acquired from the same mice further revealed that the PTT increases with the size of infarction and similarly increases with reduced ejection fraction. Moreover, an inverse relationship between infarct PTT and time post-surgery was found, which suggests the occurrence of cardiac healing. In combination with the proven ability of optoacoustics to track targeted probes within the injured myocardium, our method can depict cardiac anatomy, function, and molecular signatures, with both high spatial and temporal resolution. Volumetric four-dimensional optoacoustic characterization of cardiac dynamics with supreme temporal resolution can capture cardiovascular dynamics on a beat-by-beat basis in mouse models of myocardial ischemia.

Targeting Elastase for Molecular Imaging of Early Atherosclerotic Lesions

OBJECTIVE

Neutrophils accumulate in early atherosclerotic lesions and promote lesion growth. In this study, we evaluated an elastase-specific near-infrared imaging agent for molecular imaging using hybrid fluorescence molecular tomography/x-ray computed tomography.

APPROACH AND RESULTS

Murine neutrophils were isolated from bone marrow and incubated with the neutrophil-targeted near-infrared imaging agent Neutrophil Elastase 680 FAST for proof of principle experiments, verifying that the elastase-targeted fluorescent agent is specifically cleaved and activated by neutrophil content after lysis or cell stimulation. For in vivo experiments, low-density lipoprotein receptor-deficient mice were placed on a Western-type diet and imaged after 4, 8, and 12 weeks by fluorescence molecular tomography/x-ray computed tomography. Although this agent remains silent on injection, it produces fluorescent signal after cleavage by neutrophil elastase. After hybrid fluorescence molecular tomography/x-ray computed tomography imaging, mice were euthanized for whole-body cryosectioning and histological analyses. The in vivo fluorescent signal in the area of the aortic arch was highest after 4 weeks of high-fat diet feeding and decreased at 8 and 12 weeks. Ex vivo whole-body cryoslicing confirmed the fluorescent signal to locate to the aortic arch and to originate from the atherosclerotic arterial wall. Histological analysis demonstrated the presence of neutrophils in atherosclerotic lesions.

CONCLUSIONS

This study provides evidence that elastase-targeted imaging can be used for in vivo detection of early atherosclerosis. This imaging approach may harbor potential in the clinical setting for earlier diagnosis and treatment of atherosclerosis.

EVALUATION OF DOSE REDUCTION POTENTIALS OF A NOVEL SCATTER CORRECTION SOFTWARE FOR BEDSIDE CHEST X-RAY IMAGING

Bedside chest X-rays (CXR) for catheter position control may add up to a considerable radiation dose for patients in the intensive care unit (ICU). In this study, image quality and dose reduction potentials of a novel X-ray scatter correction software (SkyFlow, Philips Healthcare, Hamburg, Germany) were evaluated. CXRs of a 'LUNGMAN' (Kyoto Kagaku Co., LTD, Kyoto, Japan) thoracic phantom with a portacath system, a central venous line and a dialysis catheter were performed in an experimental set-up with multiple tube voltage and tube current settings without and with an antiscatter grid. Images with diagnostic exposure index (EI) 250-500 were evaluated for the difference in applied mAs with and without antiscatter grid. Three radiologists subjectively assessed the diagnostic image quality of grid and non-grid images. Compared with a non-grid image, usage of an antiscatter grid implied twice as high mAs in order to reach diagnostic EI. SkyFlow significantly improved the image quality of images acquired without grid. CXR with grid provided better image contrast than grid-less imaging with scatter correction.

Spatially resolved quantification of gadolinium(III)-based magnetic resonance agents in tissue by MALDI imaging mass spectrometry after in vivo MRI

Gadolinium(III)-based contrast agents improve the sensitivity and specificity of magnetic resonance imaging (MRI), especially when targeted contrast agents are applied. Because of nonlinear correlation between the contrast agent concentration in tissue and the MRI signal obtained in vivo, quantification of certain biological or pathophysiological processes by MRI remains a challenge. Up to now, no technology has been able to provide a spatially resolved quantification of MRI agents directly within the tissue, which would allow a more precise verification of in vivo imaging results. MALDI imaging mass spectrometry for spatially resolved in situ quantification of gadolinium(III) agents, in correlation to in vivo MRI, were evaluated. Enhanced kinetics of Gadofluorine M were determined dynamically over time in a mouse model of myocardial infarction. MALDI imaging was able to corroborate the in vivo imaging MRI signals and enabled in situ quantification of the gadolinium probe with high spatial resolution.