Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

Decoding pathology: the role of computational pathology in research and diagnostics

Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.

Liver-Quant: Feature-based image analysis toolkit for automatic quantification of metabolic dysfunction-associated steatotic liver disease

BACKGROUND

Liver biopsy assessment by pathologists remains the gold standard for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD). Current automated image analysis tools for patient risk stratification are often proprietary or not applicable to whole slide images (WSIs). Here, we introduce "Liver-Quant," an open-source Python package for quantifying steatosis and fibrosis in liver WSIs.

METHOD

Liver-Quant leverages colour and morphological features to measure Steatosis Proportionate Area (SPA) and Collagen Proportionate Area (CPA). We evaluated the method using an internal dataset of 414 WSIs from adult patients (Leeds Teaching Hospitals NHS Trust, 2016-2022) and an external public dataset (109 WSIs). Semi-quantitative scores were extracted from pathological reports. The Spearman rank coefficient (ρ) assessed correlations between computed SPA/CPA and pathologist scores.

RESULTS

Steatosis quantification showed a substantial correlation (ρ = 0.92), while fibrosis quantification yielded a moderate correlation (ρ = 0.51). We further investigated the impact of three staining dyes (Van Gieson (VG), Picro Sirius Red (PSR), and Masson's Trichrome (MTC)) on fibrosis quantification (n = 18). Stain normalisation yielded excellent agreement in CPA measurements across all three stains. Without normalisation, PSR achieved the strongest correlation with human scores (ρ = 0.9) followed by VG (ρ = 0.8) and MTC (ρ = 0.59). Finally, we explored the impact of apparent magnification on SPA and CPA. High-resolution images (0.25 or 0.50 μm per pixel (MPP)) were necessary for accurate SPA measurement, while lower resolution (10 MPP) sufficed for CPA measurements.

CONCLUSIONS

Liver-Quant offers an open-source solution for rapid and precise MASLD quantification in WSIs applicable to multiple histological stains.

Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system

We developed an automatic method based on a convolutional neural network (CNN) that identifies metastatic lesions in whole slide images (WSI) of intraoperative frozen sections from sentinel lymph nodes in breast cancer. A total of 954 sentinel lymph node frozen sections, encompassing all types of breast cancer, were collected and examined at our institution between January 1, 2021, and September 27, 2022. Seventy-two cases from a total of 954 cases, including 50 macrometastases, 16 micrometastases, and 6 negatives, were selected and annotated for training a model, which was a self-developed platform (EasyPath) built using R 4.1.3 accompanied by Python 3.7 as the reticulate package. Another 105 metastasis-positive and 80 metastasis-negative cases from the remaining 882 cases were collected to validate and test the algorithm. Our algorithm successfully identified 103 cases (98 %) of metastases, including 85 cases of macrometastases and 18 cases of micrometastasis, with the inference time averaging 87.3 seconds per case. The algorithm correctly identified all of the macrometastases and 90 % of the micrometastases. The sensitivity for detecting micrometastases significantly outperformed that of the pathologists (p = 0.014, McNemar's test). Furthermore, we provide a workflow that deploys our algorithm into the daily practice of assessing intraoperative frozen sections. Our algorithm provides a robust backup for detecting metastases, particularly for high sensitivity for micrometastases, which will minimize errors in the pathological assessment of intraoperative frozen section of sentinel lymph nodes.

Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics

Objective

Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions. The pathological sections of radical specimens encompass a diverse range of histological information pertinent to the metastasis and survival of patients. In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients.

Methods

This study utilized pathological sections from 204 radical mastectomy specimens obtained between January 2013 and December 2014 at the Second Affiliated Hospital of the Medical College of Zhejiang University. The 204 pathological slices were scanned and transformed into whole slide imaging (WSI), with manual labeling of all tumor areas. The WSI was then partitioned into smaller tiles measuring 512 × 512 pixels. Three networks, namely Densely Connected Convolutional Network 121 (DenseNet121), Residual Network (ResNet50), and Inception_v3, were assessed. Subsequently, we combined patch-level predictions, probability histograms, and Term Frequency-Inverse Document Frequency (TF-IDF) features to create comprehensive participants representations. These features served as the foundational input for developing a machine learning algorithm for metastasis analysis and a Cox regression model for survival analysis.

Result

Our results show that the Inception_v3 model shows a particularly robust patch recognition ability for estrogen receptor (ER) recognition. Our pathological model shows high accuracy in predicting tumor regions. The train area under the curve (AUC) of the Inception_v3 model based on supervised learning is 0.975, which is higher than the model established by weakly supervised learning. But the AUC of the metastasis prediction in training and testing sets is higher than value based on supervised learning. Furthermore, the C-index of the survival prediction model is 0.710 in the testing sets, which is also better than the value by supervised learning.

Conclusion

Our study demonstrates the significant potential of deep learning models in predicting breast cancer metastasis and prognosis, with the pathomic model showing high accuracy in identifying tumor areas and ER status. The integration of clinical features and pathomics signature into a nomogram further provides a valuable tool for clinicians to make individualized treatment decisions.

Global trends in machine learning applied to clinical research in liver cancer: Bibliometric and visualization analysis (2001-2024)

This study explores the intersection of liver cancer and machine learning through bibliometric analysis. The aim is to identify highly cited papers in the field and examine the current research landscape, highlighting emerging trends and key areas of focus in liver cancer and machine learning. By analyzing citation patterns, this study sheds light on the evolving role of machine learning in liver cancer research and its potential for future advancements.

Development of a deep learning algorithm for Paneth cell density quantification for inflammatory bowel disease

BACKGROUND

Alterations in ileal Paneth cell (PC) density have been described in gut inflammatory diseases such as Crohn's disease (CD) and could be used as a biomarker for disease prognosis. However, quantifying PCs is time-intensive, a barrier for clinical workflow. Deep learning (DL) has transformed the development of robust and accurate tools for complex image evaluation. Our aim was to use DL to quantify PCs for use as a quantitative biomarker.

METHODS

A retrospective cohort of whole slide images (WSI) of ileal tissue samples from patients with/without inflammatory bowel disease (IBD) was used for the study. A pathologist-annotated training set of WSI were used to train a U-net two-stage DL model to quantify PC number, crypt number, and PC density. For validation, a cohort of 48 WSIs were manually quantified by study pathologists and compared to the DL algorithm, using root mean square error (RMSE) and the coefficient of determination (r2) as metrics. To test the value of PC quantification as a biomarker, resection specimens from patients with CD (n = 142) and without IBD (n = 48) patients were analysed with the DL model. Finally, we compared time to disease recurrence in patients with CD with low versus high DL-quantified PC density using Log-rank test.

FINDINGS

Initial one-stage DL model showed moderate accuracy in predicting PC density in cross-validation tests (RMSE = 1.880, r2 = 0.641), but adding a second stage significantly improved accuracy (RMSE = 0.802, r2 = 0.748). In the validation of the two-stage model compared to expert pathologists, the algorithm showed good performance up to RMSE = 1.148, r2 = 0.708. The retrospective cross-sectional cohort had mean ages of 62.1 years in the patients without IBD and 38.6 years for the patients with CD. In the non-IBD cohort, 43.75% of the patients were male, compared to 49.3% of the patients with CD. Analysis by the DL model showed significantly higher PC density in non-IBD controls compared to the patients with CD (4.04 versus 2.99 PC/crypt). Finally, the algorithm quantification of PCs density in patients with CD showed patients with the lowest 25% PC density (Quartile 1) have significantly shorter recurrence-free interval (p = 0.0399).

INTERPRETATION

The current model performance demonstrates the feasibility of developing a DL-based tool to measure PC density as a predictive biomarker for future clinical practice.

FUNDING

This study was funded by the National Institutes of Health (NIH).

Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin

Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.

Artificial intelligence-aided steatosis assessment in donor livers according to the Banff consensus recommendations

OBJECTIVES

Severe macrovesicular steatosis in donor livers is associated with primary graft dysfunction. The Banff Working Group on Liver Allograft Pathology has proposed recommendations for steatosis assessment of donor liver biopsy specimens with a consensus for defining "large droplet fat" (LDF) and a 3-step algorithmic approach.

METHODS

We retrieved slides and initial pathology reports from potential liver donor biopsy specimens from 2010 to 2021. Following the Banff approach, we reevaluated LDF steatosis and employed a computer-assisted manual quantification protocol and artificial intelligence (AI) model for analysis.

RESULTS

In a total of 113 slides from 88 donors, no to mild (<33%) macrovesicular steatosis was reported in 88.5% (100/113) of slides; 8.8% (10/113) was reported as at least moderate steatosis (≥33%) initially. Subsequent pathology evaluation, following the Banff recommendation, revealed that all slides had LDF below 33%, a finding confirmed through computer-assisted manual quantification and an AI model. Correlation coefficients between pathologist and computer-assisted manual quantification, between computer-assisted manual quantification and the AI model, and between the AI model and pathologist were 0.94, 0.88, and 0.81, respectively (P < .0001 for all).

CONCLUSIONS

The 3-step approach proposed by the Banff Working Group on Liver Allograft Pathology may be followed when evaluating steatosis in donor livers. The AI model can provide a rapid and objective assessment of liver steatosis.

Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms

BACKGROUND & AIMS

The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment.

METHODS

We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC).

RESULTS

Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively.

CONCLUSIONS

Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes.

IMPACT AND IMPLICATIONS

We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.

Quantitative methods for optimizing patient outcomes in liver transplantation

Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

Artificial Intelligence Advances in Transplant Pathology

Transplant pathology plays a critical role in ensuring that transplanted organs function properly and the immune systems of the recipients do not reject them. To improve outcomes for transplant recipients, accurate diagnosis and timely treatment are essential. Recent advances in artificial intelligence (AI)-empowered digital pathology could help monitor allograft rejection and weaning of immunosuppressive drugs. To explore the role of AI in transplant pathology, we conducted a systematic search of electronic databases from January 2010 to April 2023. The PRISMA checklist was used as a guide for screening article titles, abstracts, and full texts, and we selected articles that met our inclusion criteria. Through this search, we identified 68 articles from multiple databases. After careful screening, only 14 articles were included based on title and abstract. Our review focuses on the AI approaches applied to four transplant organs: heart, lungs, liver, and kidneys. Specifically, we found that several deep learning-based AI models have been developed to analyze digital pathology slides of biopsy specimens from transplant organs. The use of AI models could improve clinicians' decision-making capabilities and reduce diagnostic variability. In conclusion, our review highlights the advancements and limitations of AI in transplant pathology. We believe that these AI technologies have the potential to significantly improve transplant outcomes and pave the way for future advancements in this field.

Artificial intelligence and frozen section histopathology: A systematic review

Frozen sections are a useful pathologic tool, but variable image quality may impede the use of artificial intelligence and machine learning in their interpretation. We aimed to identify the current research on machine learning models trained or tested on frozen section images. We searched PubMed and Web of Science for articles presenting new machine learning models published in any year. Eighteen papers met all inclusion criteria. All papers presented at least one novel model trained or tested on frozen section images. Overall, convolutional neural networks tended to have the best performance. When physicians were able to view the output of the model, they tended to perform better than either the model or physicians alone at the tested task. Models trained on frozen sections performed well when tested on other slide preparations, but models trained on only formalin-fixed tissue performed significantly worse across other modalities. This suggests not only that machine learning can be applied to frozen section image processing, but also use of frozen section images may increase model generalizability. Additionally, expert physicians working in concert with artificial intelligence may be the future of frozen section histopathology.

Carbon quantum dot fluorescent probe for labeling and imaging of stellate cell on liver frozen section below freezing point

The targeted labeling imaging of stellate cells on liver frozen section by immunofluorescence is a very promising visualization technique to study the distribution of stellate cells in the liver. In this study, water soluble carbon quantum dots that can emit blue, green and yellow fluorescence are synthesized by the hydrothermal method, and their sizes are 3.2, 3.7, and 4.3 nm, respectively. The three carbon quantum dots have good fluorescence stability, and the quantum yields are 36.1%, 26.3% and 21%, respectively. When the mass fraction of KCl in the blue carbon quantum dot dispersion system is 13%, it still maintains the liquid state at -30 °C. The final fluorescent probe is obtained after the carbon quantum dots are coupled with the secondary antibody, spectral characterizations confirm that the conjugate probe still maintains protein immunoactivity and has good stability. Cell experiments prove that the probe has good biocompatibility, the rabbit anti-mouse Desmin antibody is used as the primary antibody, the results of cellular immunofluorescence imaging and flow cytometry show that the probe can specifically label hepatic stellate cell at -20 °C. The results of liver frozen section experiments show that hepatic stellate cell can be specifically targeted and labeled by the fluorescent probe. This labeling technology provides an important technical means for elucidating the structure and function of the liver at the cellular level, exploring the liver pathological change, and designing and developing drug.

Deep learning based image analysis of liver steatosis in mouse models

The incidence of non-alcoholic fatty liver disease (NAFLD) is a continuously growing health problem worldwide, along with obesity. Therefore, both novel methods to efficiently study the manifestation of NAFLD and to analyze drug efficacy in pre-clinical models are needed. In the present study, we developed a deep neural network -based model to quantify micro- and macrovesicular steatosis in the liver on hematoxylin-eosin stained whole slide images (WSIs), using the cloud-based platform, Aiforia Create (Aiforia Technologies, Helsinki, Finland). The training data included a total of 101 WSIs from dietary interventions of wild-type mice and from two genetically modified (GM) mouse models with steatosis. The algorithm was trained for the following: to detect liver parenchyma, to exclude the blood vessels and any artefacts generated during tissue processing and image acquisition, to recognize and differentiate the areas of micro- and macrovesicular steatosis, and to quantify the recognized tissue area. The results of the image analysis replicated well the evaluation by expert pathologists, and correlated well with the liver fat content measured by EcoMRI ex vivo, and the correlation with total liver triglycerides were notable. In conclusion, the developed deep learning-based model is a novel tool for studying liver steatosis in mouse models on paraffin sections, and thus, can facilitate reliable quantification of the amount of steatosis in large preclinical study cohorts.

Automated Segmentation and Morphological Characterization of Hepatic Steatosis and Correlation with Histopathology

Background/objectives

Prevalence of nonalcoholic fatty liver disease (NAFLD) has increased to 25% of the world population. Hepatic steatosis is a hallmark feature of NAFLD and is assessed histologically using visual and ordinal fat grading criteria (0-3) from the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network (CRN) scoring system. The purpose of this study is to automatically segment and extract morphological characteristics and distributions of fat droplets (FDs) on liver histology images and find associations with severity of steatosis.

Methods

A previously published human cohort of 68 NASH candidates was graded for steatosis by an experienced pathologist using the Fat CRN grading system. The automated segmentation algorithm quantified fat fraction (FF) and fat-affected hepatocyte ratio (FHR), extracted fat morphology by calculating radius and circularity of FDs, and examined FDs distribution and heterogeneity using nearest neighbor distance and regional isotropy.

Results

Regression analysis and Spearman correlation (ρ) yielded high correlations for radius (R2 = 0.86, ρ = 0.72), nearest neighbor distance (R2 = 0.82, ρ = -0.82), regional isotropy (R2 = 0.84, ρ = 0.74), and FHR (R2 = 0.90, ρ = 0.85), and low correlation for circularity (R2 = 0.48, ρ = -0.32) with FF and pathologist grades, respectively. FHR showed a better distinction between pathologist Fat CRN grades compared to conventional FF measurements, making it a potential surrogate measure for Fat CRN scores. Our results showed variation in distribution of morphological features and steatosis heterogeneity within the same patient's biopsy sample as well as between patients of similar FF.

Conclusions

The fat percentage measurements, specific morphological characteristics, and patterns of distribution quantified with the automated segmentation algorithm showed associations with steatosis severity; however, future studies are warranted to evaluate the clinical significance of these steatosis features in progression of NAFLD and NASH.

Simultaneous labeling and multicolor fluorescence imaging of multiple immune cells on liver frozen section by polychromatic quantum dots below freezing points

A method for simultaneous labeling and multicolor fluorescence imaging of different hepatic immune cells below freezing point is established based on quantum dots. In the experiment, carbon quantum dots with emission wavelength of 435 nm, CdTe@CdS quantum dots at 542 nm and CdSe@ZnS quantum dots at 604 nm are synthesized respectively, it is found that when the mass fractions of KCl (as antifreeze) are 12 %, 14 %, and 12 %, respectively, the three quantum dot dispersion systems remain liquid state at -20 °C. After they are conjugated with the corresponding secondary antibodies, agarose gel electrophoresis, circular dichroism and capillary electrophoresis confirm the effectiveness of conjugation. By indirect immunofluorescence method, the above three quantum dot fluorescent probes are used to simultaneously and specifically target a variety of liver immune cells, and the multi-color simultaneous imaging of different liver immune cells is realized under the same excitation wavelength, it is found that hepatic macrophages are arranged radially in the liver, hepatic stellate cells present punctate distribution, and hepatic sinusoidal endothelial cells present circular distribution, which is consistent with the results of H&E staining and ultrathin section TEM. This study provides an important technical means for elucidating the structure and function of the liver.

Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology

Molecular tests are necessary to stratify cancer patients for targeted therapy. However, high cost and technical barriers limit the application of these tests, hindering optimal treatment. Recently, deep learning (DL) has been applied to predict molecular test results from digitized images of tissue slides. Furthermore, treatment response and prognosis can be predicted from tissue slides using DL. In this review, we summarized DL-based studies regarding the prediction of genetic mutation, microsatellite instability, tumor mutational burden, molecular subtypes, gene expression, treatment response, and prognosis directly from hematoxylin- and eosin-stained tissue slides. Although performance needs to be improved, these studies clearly demonstrated the feasibility of DL-based prediction of key molecular features in cancer tissues. With the accumulation of data and technical advances, the performance of the DL system could be improved in the near future. Therefore, we expect that DL could provide cost- and time-effective alternative tools for patient stratification in the era of precision oncology.

A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images

Access to lifesaving liver transplantation is limited by a severe organ shortage. One factor contributing to the shortage is the high rate of discard in livers with histologic steatosis. Livers with <30% macrosteatosis are generally considered safe for transplant. However, histologic assessment of steatosis by a pathologist remains subjective and is often limited by image quality. Here, we address this bottleneck by creating an automated digital algorithm for calculating histologic steatosis using only images of liver biopsy histology obtained with a smartphone.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.

Artificial intelligence to identify genetic alterations in conventional histopathology

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold-standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53 and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. This article is protected by copyright. All rights reserved.

A Clinical Tool to Guide Selection and Utilization of Marginal Donor Livers With Graft Steatosis in Liver Transplantation

Supplemental Digital Content is available in the text.

Background.

Donor liver biopsy (DLBx) in liver transplantation provides information on allograft quality; however, predicting outcomes from these allografts remains difficult.

Methods.

Between 2006 and 2015, 16 691 transplants with DLBx were identified from the Standard Transplant Analysis and Research database. Cox proportional hazard regression analyses identified donor and recipient characteristics associated with 30-d, 90-d, 1-y, and 3-y graft survival. A composite model, the Liver Transplant After Biopsy (LTAB) score, was created. The Mini-LTAB was then derived consisting of only donor age, macrosteatosis on DLBx, recipient model for end-stage liver disease score, and cold ischemic time. Risk groups were identified for each score and graft survival was evaluated. P values <0.05 were considered significant.

Results.

The LTAB model used 14 variables and 5 risk groups and identified low-, mild-, moderate-, high-, and severe-risk groups. Compared with moderate-risk recipients, severe-risk recipients had increased risk of graft loss at 30 d (hazard ratio, 3.270; 95% confidence interval, 2.568-4.120) and at 1 y (2.258; 1.928-2.544). The Mini-LTAB model identified low-, moderate-, and high-risk groups. Graft survival in Mini-LTAB high-risk transplants was significantly lower than moderate- or low-risk transplants at all time points.

Conclusions.

The LTAB and Mini-LTAB scores represent guiding principles and provide clinically useful tools for the successful selection and utilization of marginal allografts in liver transplantation.

CdTe@CdS quantum dots for labeling and imaging of macrophages in liver frozen section below freezing point

CdTe@CdS core-shell quantum dots with different particle sizes are synthesized by aqueous method, and the coating of CdS shell layer improves the quantum yield (36%→59%) and fluorescence stability (37%→77%) of...

Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

BACKGROUND

The global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis.

METHODS

A systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated.

RESULTS

Twenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91-0.99), 0.98 (95% CI: 0.89-1.00), 0.98 (95% CI: 0.93-1.00), and 0.95 (95% CI: 0.88-0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66-0.82), 0.82 (95% CI: 0.74-0.88), 0.75 (95% CI: 0.60-0.86), and 0.82 (0.74-0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75-0.85), 0.69 (95%CI: 0.53-0.82) for identifying NASH, as well as 0.99-1.00 and 0.76-1.00 for diagnosing liver fibrosis stage F1-F4, respectively.

CONCLUSION

AI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation.

PROTOCOL REGISTRATION

PROSPERO (CRD42021230391).

Labeling of liver cells with CdSe/ZnS quantum dot-based fluorescence probe below freezing point

In this paper, CdSe/ZnS quantum dots (QDs) with particle size of 5.5 ~ 9.3 nm were synthesized, and the fluorescence emission ranged from 545 ~ 616 nm. When the volume fraction of ethanol was 30%, the water-soluble QD dispersion system remained liquid under -20 °C freezing conditions, the fluorescence intensity increased with a decrease in temperature, and the quantum yield reached 79% at -20 °C. The endothelial cell adhesion molecule CD31 antibody (anti-CD31) was used as the primary antibody, QDs were coupled with IgG as the secondary antibody (QD-Ab), and effective labeling of hepatic sinusoid endothelial cells was achieved at -20 °C. Fluorescence imaging and flow cytometry analysis showed that the labeling efficiency was as high as 97%, indicating that QDs have an important application prospect in microscopic section tomography of the liver.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

Liver Transplantation With Grafts From Super Obese Donors

There are limited data on liver transplant (LT) outcomes with grafts from super obese donors. The present study aims to evaluate a unique cohort of recipients following LT using grafts from donors with body mass index (BMI) ≥50.

Methods

Patients receiving grafts from donors with BMI ≥50 and BMI <50 from 2010 to 2019 were identified. A 1:2 case-control match was conducted to compare outcomes between the groups. Survival was analyzed using the Kaplan-Meier curves.

Results

Six hundred sixty-five adult LTs were performed in the study period. Eighteen patients receiving a graft from a donor with BMI ≥50 were identified and matched to 36 patients receiving a graft from a donor with BMI <50. Grafts from male donors were significantly lower in the donor BMI ≥50 group when compared with the donor BMI <50 group (16.7% versus 66.7%, P = 0.001). Liver biopsy was performed in 77.8% of grafts in the donor BMI ≥50 group, whereas only in 38.8% of the grafts in the donor BMI <50 group (P = 0.007). Recipients in the donor BMI ≥50 group had a significantly higher diagnosis rate of hepatocellular carcinoma pretransplant versus the donor BMI <50 group (38.9% versus 8.3%, respectively; P = 0.006). Major complications within 30 d did not differ statistically between groups. Biliary complications within the first 30 d were equal among groups (16.7%). Subanalysis comparing the super obese donor group versus the nonobese donor group showed no differences in terms of postoperative complications, readmission rate, graft rejection, or major complications including the need for reoperation, retransplantation, or mortality. Graft and patient survival at 1-, 3-, and 5-y graft were similar between the donor BMI ≥50 group versus donor BMI <50 group (94%/89%/89% versus 88%/88%/88%, P = 0.89, and 94%/94%/94% versus 88%/88%/88%, P = 0.48, respectively).

Conclusions

LT with carefully selected grafts from super obese donors can be safely performed with outcomes comparable with non-super obese donor livers. Therefore, these types of grafts could represent a safe means to expand the donor pool.

Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation

Purpose of Review

Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients.

Recent Findings

Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival.

Summary

AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.

State of machine and deep learning in histopathological applications in digestive diseases

Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.

Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.