Non-invasive assessment of liver quality in transplantation based on thermal imaging analysis

Incorporating artificial intelligence in portable infrared thermal imaging for the diagnosis and staging of nonalcoholic fatty liver disease

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the most prevalent chronic liver diseases worldwide. Thermal imaging combined with advanced image-processing and machine learning analysis accurately classified disease status in a study on mice; this study aimed to develop this tool for humans. This prospective study included 46 patients who underwent liver biopsy. Liver thermal imaging was performed on the same day as liver biopsy. We developed an image-processing algorithm that measured the relative spatial thermal variation across the skin covering the liver. The texture parameters obtained from the thermal images were input into the machine learning algorithm. Patients were diagnosed with MASLD and stratified according to nonalcoholic fatty liver disease activity score (NAS) and fibrosis stage using the METAVIR score. Twenty-one of 46 patients were diagnosed with MASLD. Using thermal imaging followed by processing, detection accuracy for patients with NAS >4 was 0.72.

Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study

BACKGROUND

Model for end-stage liver disease (MELD) is currently used for liver transplantation (LT) allocation, however, it is not a sufficient criterion.

OBJECTIVE

This current study aims to perform a hybrid neural network analysis of different data, make a decision tree and finally design a decision support system for improving LT prioritization.

MATERIAL AND METHODS

In this cohort follow-up-based study, baseline characteristics of 1947 adult patients, who were candidates for LT in Shiraz Organ Transplant Center, Iran, were assessed and followed for two years and those who died before LT due to the end-stage liver disease were considered as dead cases, while others considered as alive cases. A well-organized checklist was filled for each patient. Analysis of the data was performed using artificial neural networks (ANN) and support vector machines (SVM). Finally, a decision tree was illustrated and a user friendly decision support system was designed to assist physicians in LT prioritization.

RESULTS

Between all MELD types, MELD-Na was a stronger determinant of LT candidates' survival. Both ANN and SVM showed that besides MELD-Na, age and ALP (alkaline phosphatase) are the most important factors, resulting in death in LT candidates. It was cleared that MELD-Na <23, age <53 and ALP <257 IU/L were the best predictors of survival in LT candidates. An applicable decision support system was designed in this study using the above three factors.

CONCLUSION

Therefore, Meld-Na, age and ALP should be used for LT allocation. The presented decision support system in this study will be helpful in LT prioritization by LT allocators.

Assessment of Donor Liver Pathology Predicts Survival After Liver Transplantation: A Retrospective Cohort Study

BACKGROUND

The aims of this study were to investigate the pathologic manifestation of pretransplant biopsy and to provide an accurate assessment method for liver graft of China Donation after Citizen's Death (CDCD).

METHODS

A retrospective analysis was performed based on clinical and biopsy data of 96 CDCD liver transplantations completed between January 2012 and December 2017. The pretransplant pathologic sections were semiquantitatively scored according to Banff Schema recommendations on liver allograft pathology. Graft overall survival (OS) and early allograft dysfunction (EAD) rates were observed.

RESULTS

The histologic analysis of the 96 CDCD liver graft biopsy specimens was summarized, including portal area neutrophilic infiltrate, macrovesicular steatosis, microvesicular steatosis, and hepatocellular swelling. Among these pathologic characteristics, only portal area neutrophilic infiltrate ≥20% was an independent risk factor for graft survival, although it has limited effect on the recipient's short-term prognosis.

CONCLUSIONS

We found that portal area neutrophilic infiltrate ≥20% was an independent risk factors for long-term graft survival. According to this criterion, we can identify liver transplant recipients at risk for poor prognosis and make timely interventions.

Modeling of pre-transplantation liver viability with spatial-temporal smooth variable selection

BACKGROUND AND OBJECTIVE

Liver viability assessment plays a critical role in liver transplantation, and the accuracy of the assessment directly determines the success of the transplantation surgery and patient's outcomes. With various factors that affect liver viability, including pre-existing medical conditions of donors, the procurement process, and preservation conditions, liver viability assessment is typically subjective, invasive or inconsistent in results among different surgeons and pathologists. Motivated by these challenges, we aimed to create a non-invasive statistical model utilizing spatial-temporal infrared image (IR) data to predict the binary liver viability (acceptable/unacceptable) during the preservation.

METHODS

The spatial-temporal features of liver surface temperature, monitored by IR thermography, are significantly correlated with the liver viability. A spatial-temporal smooth variable selection (STSVS) method is proposed to define the smoothness of model parameters corresponding to different liver surface regions at different times.

RESULTS

A case study, using porcine livers, has been performed to validate the efficacy of the STSVS method. The comparison results show that STSVS has the better overall prediction performance compared to the past state-of-the-art predictive models, including generalized linear model (GLM), support vector machine (SVM), LASSO, and Fused LASSO. Moreover, the significant predictors identified by the STSVS method indicate the importance of edges of lobes in predicting liver viability during the pre-transplantation preservation.

CONCLUSIONS

The proposed method has the best performance in predicting liver viability. This 'real-time' prediction method may increase the utilization of donors' livers without damaging tissues and time-consuming, yet imprecise feature assessment.

Early Graft Dysfunction Following Kidney Transplantation: Can Thermographic Imaging Play a Predictive Role?

The shortage of organs and the growing need for them over recent years have led to the adoption of less stringent donor acceptance criteria, resulting in the approval of marginal organs for transplant, especially from elderly donors. This implies a higher risk of graft dysfunction, a higher frequency of immunological and vascular complications, and shorter graft survival. Several strategies have been implemented in clinical practice to assess graft quality and suitability for transplantation. We have started to test the prospective intraoperative use of thermo-vision cameras during graft reperfusion. Images were acquired using the FLIR One Pro thermo-vision camera for android devices. We hypothesized that thermal images would give a better perspective about the quality of arterial perfusion and graft revascularization of the renal cortex. Thermo-vision cameras provide an easy-to-use, noninvasive, cost-effective tool for the global assessment of kidney graft cortical microcirculation in the immediate post-reperfusion period, providing additional data on the immediate viability and function of a graft.

Automated thermal imaging for the detection of fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests remains challenging. Here, we sought to develop a novel, cost-effective screening tool for NAFLD based on thermal imaging. We used a commercially available and non-invasive thermal camera and developed a new image processing algorithm to automatically predict disease status in a small animal model of fatty liver disease. To induce liver steatosis and inflammation, we fed C57/black female mice (8 weeks old) a methionine-choline deficient diet (MCD diet) for 6 weeks. We evaluated structural and functional liver changes by serial ultrasound studies, histopathological analysis, blood tests for liver enzymes and lipids, and measured liver inflammatory cell infiltration by flow cytometry. We developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. Thermal parameters including temperature variance, homogeneity levels and other textural features were fed as input to a t-SNE dimensionality reduction algorithm followed by k-means clustering. During weeks 3,4, and 5 of the experiment, our algorithm demonstrated a 100% detection rate and classified all mice correctly according to their disease status. Direct thermal imaging of the liver confirmed the presence of changes in surface thermography in diseased livers. We conclude that non-invasive thermal imaging combined with advanced image processing and machine learning-based analysis successfully correlates surface thermography with liver steatosis and inflammation in mice. Future development of this screening tool may improve our ability to study, diagnose and treat liver disease.

Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms

An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.