qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients

Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis

Background

Chronic liver disease diagnoses depend on liver biopsy histopathological assessment. However, due to the limitations associated with biopsy, there is growing interest in the use of quantitative digital pathology to support pathologists. We evaluated the performance of computational algorithms in the assessment of hepatic inflammation in an autoimmune hepatitis in which inflammation is a major component.

Methods

Whole-slide digital image analysis was used to quantitatively characterize the area of tissue covered by inflammation [Inflammation Density (ID)] and number of inflammatory foci per unit area [Focal Density (FD)] on tissue obtained from 50 patients with autoimmune hepatitis undergoing routine liver biopsy. Correlations between digital pathology outputs and traditional categorical histology scores, biochemical, and imaging markers were assessed. The ability of ID and FD to stratify between low-moderate (both portal and lobular inflammation ≤1) and moderate-severe disease activity was estimated using the area under the receiver operating characteristic curve (AUC).

Results

ID and FD scores increased significantly and linearly with both portal and lobular inflammation grading. Both ID and FD correlated moderately-to-strongly and significantly with histology (portal and lobular inflammation; 0.36≤R≤0.69) and biochemical markers (ALT, AST, GGT, IgG, and gamma globulins; 0.43≤R≤0.57). ID (AUC: 0.85) and FD (AUC: 0.79) had good performance for stratifying between low-moderate and moderate-severe inflammation.

Conclusion

Quantitative assessment of liver biopsy using quantitative digital pathology metrics correlates well with traditional pathology scores and key biochemical markers. Whole-slide quantification of disease can support stratification and identification of patients with more advanced inflammatory disease activity.

Development of a diagnostic support system for the fibrosis of nonalcoholic fatty liver disease using artificial intelligence and deep learning

Liver fibrosis is a pathological condition characterized by the abnormal proliferation of liver tissue, subsequently able to progress to cirrhosis or possibly hepatocellular carcinoma. The development of artificial intelligence and deep learning have begun to play a significant role in fibrosis detection. This study aimed to develop SMART AI-PATHO, a fully automated assessment method combining quantification of histopathological architectural features, to analyze steatosis and fibrosis in nonalcoholic fatty liver disease (NAFLD) core biopsies and employ Metavir fibrosis staging as standard references and fat assessment grading measurement for comparison with the pathologist interpretations. There were 146 participants enrolled in our study. The correlation of Metavir scoring system interpretation between pathologists and SMART AI-PATHO was significantly correlated (Agreement = 68%, Kappa = 0.59, p-value <0.001), which subgroup analysis of significant fibrosis (Metavir score F2-F4) and nonsignificant fibrosis (Metavir score F0-F1) demonstrated substantial correlated results (agreement = 80%, kappa = 0.61, p-value <0.001), corresponding with the correlation of advanced fibrosis (Metavir score F3-F4) and nonadvanced fibrosis groups (Metavir score F0-F2), (agreement = 89%, kappa = 0.74, p-value <0.001). SMART AI-PATHO, the first pivotal artificially intelligent diagnostic tool for the color-based NAFLD hepatic tissue staging in Thailand, demonstrated satisfactory performance as a pathologist to provide liver fibrosis scoring and steatosis grading. In the future, developing AI algorithms and reliable testing on a larger scale may increase accuracy and contribute to telemedicine consultations for general pathologists in clinical practice.

AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study

BACKGROUND AND AIMS

Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH.

METHODS

We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions.

RESULTS

About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region.

CONCLUSION

Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

3D-Printed SERS Chips for Highly Specific Detection of Denatured Type I and IV Collagens in Blood for Early Hepatic Fibrosis Diagnosis

Hepatic fibrosis, the insidious progression of chronic liver scarring leading to life-threatening cirrhosis and hepatocellular carcinoma, necessitates the urgent development of noninvasive and precise diagnostic methodologies. Denatured collagen emerges as a critical biomarker in the pathogenesis of hepatic fibrosis. Herein, we have for the first time developed 3D-printed collagen capture chips for highly specific surface-enhanced Raman scattering (SERS) detection of denatured type I and type IV collagen in blood, facilitating the early diagnosis of hepatic fibrosis. Employing a novel blend of denatured collagen-targeting peptide-modified silver nanoparticle probes (Ag@DCTP) and polyethylene glycol diacrylate (PEGDA), we engineered a robust ink for the 3D fabrication of these collagen capture chips. The chips are further equipped with specialized SERS peptide probes, Ag@ICTP@R1 (S-I) and Ag@IVCTP@R2 (S-IV), tailored for the targeted detection of type I and IV collagen, respectively. The SERS chip platform demonstrated exceptional specificity and sensitivity in capturing and detecting denatured type I and IV collagen, achieving detection limits of 3.5 ng/mL for type I and 3.2 ng/mL for type IV collagen within a 10-400 ng/mL range. When tested on serum samples from hepatic fibrosis mouse models across a spectrum of fibrosis stages (S0-S4), the chips consistently measured denatured type I collagen and detected a progressive increase in type IV collagen concentration, which correlated with the severity of fibrosis. This novel strategy establishes a benchmark for the multiplexed detection of collagen biomarkers, enhancing our capacity to assess the stages of hepatic fibrosis.

Liver fibrosis showed a two-phase regression rate during long-term anti-HBV therapy by three-time biopsies assessments

BACKGROUND

Evidence has proven that liver fibrosis or even cirrhosis can be reversed by anti-HBV treatment. However, the difference of fibrosis regression rates in short-term and long-term antiviral therapy remain unclear. Therefore, we aimed to identify the dynamic changes in fibrosis regression rate in patients with three-time liver biopsies during 5 years antiviral therapy.

METHODS

CHB patients with three times of liver biopsies (baseline, after 1.5-year and 5-year antiviral therapy) from a prospective cohort were enrolled. All patients were biopsy-proved Ishak stage ≥ 3 at baseline (n = 92). Fibrosis regression was defined as Ishak stage decreased ≥ 1 or predominantly regressive categorized by P-I-R score.

RESULTS

Totals of 65.2% (60/92) and 80.4% (74/92) patients attained fibrosis regression after 1.5-year and 5-year therapy, respectively. Median HBV DNA level declined from 6.5 log IU/ml (baseline) to 0 log IU/ml (1.5 years and 5 years, P < 0.001). The mean level of Ishak fibrosis stage in all patients decreased from stage 4.1 (baseline) to 3.7 (1.5 years) then 3.2 (5 years). Fibrosis regression rates were 0.27 stage/year between baseline to year 1.5 and 0.14 stage/year between year 1.5 and year 5. Furthermore, for patients who attained fibrosis regression after 5-year antiviral therapy, the two-phase regression rates were 0.39 stage/year (0 year-1.5 years) and 0.20 stage/year (1.5 years-5 years). This two-phase feature of regression rate was further confirmed by fully-quantification assessment of liver fibrosis based on SHG/TPEF.

CONCLUSION

During the 5 years of long-term antiviral treatment, liver fibrosis rapidly regresses in the first 1.5 years before slowing down in the following 3.5 years.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Nomograms integrating the collagen signature and systemic immune-inflammation index for predicting prognosis in rectal cancer patients

BACKGROUND

This study aimed to develop and validate a model based on the collagen signature and systemic immune-inflammation index to predict prognosis in rectal cancer patients who underwent neoadjuvant treatment.

METHODS

Patients with rectal cancer who had residual disease after neoadjuvant treatment at two Chinese institutions between 2010 and 2018 were selected, one used as a training cohort and the other as a validation cohort. In total, 142 fully quantitative collagen features were extracted using multiphoton imaging, and a collagen signature was generated by least absolute shrinkage and selection operator Cox regression. Nomograms were developed by multivariable Cox regression. The performance of the nomograms was assessed via calibration, discrimination and clinical usefulness. The outcomes of interest were overall survival and disease-free survival calculated at 1, 2 and 3 years.

RESULTS

Of 559 eligible patients, 421 were selected (238 for the training cohort and 183 for the validation cohort). The eight-collagen-features collagen signature was built and multivariable Cox analysis demonstrated that it was an independent prognostic factor of prognosis along with the systemic immune-inflammation index, lymph node status after neoadjuvant treatment stage and tumour regression grade. Then, two nomograms that included the four predictors were computed for disease-free survival and overall survival. The nomograms showed satisfactory discrimination and calibration with a C-index of 0.792 for disease-free survival and 0.788 for overall survival in the training cohort and 0.793 for disease-free survival and 0.802 for overall survival in the validation cohort. Decision curve analysis revealed that the nomograms could add more net benefit than the traditional clinical-pathological variables.

CONCLUSIONS

The study found that the collagen signature, systemic immune-inflammation index and nomograms were significantly associated with prognosis.

Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions

The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.

Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study

BACKGROUND

Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM.

METHODS

Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts.

RESULTS

By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations.

CONCLUSIONS

This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.

Automated quantitative assay of fibrosis characteristics in tuberculosis granulomas

Introduction

Granulomas, the pathological hallmark of Mycobacterium tuberculosis (Mtb) infection, are formed by different cell populations. Across various stages of tuberculosis conditions, most granulomas are classical caseous granulomas. They are composed of a necrotic center surrounded by multilayers of histocytes, with the outermost layer encircled by fibrosis. Although fibrosis characterizes the architecture of granulomas, little is known about the detailed parameters of fibrosis during this process.

Methods

In this study, samples were collected from patients with tuberculosis (spanning 16 organ types), and Mtb-infected marmosets and fibrotic collagen were characterized by second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy using a stain-free, fully automated analysis program.

Results

Histopathological examination revealed that most granulomas share common features, including necrosis, solitary and compact structure, and especially the presence of multinuclear giant cells. Masson's trichrome staining showed that different granuloma types have varying degrees of fibrosis. SHG imaging uncovered a higher proportion (4%~13%) of aggregated collagens than of disseminated type collagens (2%~5%) in granulomas from matched tissues. Furthermore, most of the aggregated collagen presented as short and thick clusters (200~620 µm), unlike the long and thick (200~300 µm) disseminated collagens within the matched tissues. Matrix metalloproteinase-9, which is involved in fibrosis and granuloma formation, was strongly expressed in the granulomas in different tissues.

Discussion

Our data illustrated that different tuberculosis granulomas have some degree of fibrosis in which collagen strings are short and thick. Moreover, this study revealed that the SHG imaging program could contribute to uncovering the fibrosis characteristics of tuberculosis granulomas.

Digital pathology for nonalcoholic steatohepatitis assessment

Histological assessment of nonalcoholic fatty liver disease (NAFLD) has anchored knowledge development about the phenotypes of the condition, their natural history and their clinical course. This fact has led to the use of histological assessment as a reference standard for the evaluation of efficacy of drug interventions for nonalcoholic steatohepatitis (NASH) - the more histologically active form of NAFLD. However, certain limitations of conventional histological assessment systems pose challenges in drug development. These limitations have spurred intense scientific and commercial development of machine learning and digital approaches towards the assessment of liver histology in patients with NAFLD. This research field remains an area in rapid evolution. In this Perspective article, we summarize the current conventional assessment of NASH and its limitations, the use of specific digital approaches for histological assessment, and their application to the study of NASH and its response to therapy. Although this is not a comprehensive review, the leading tools currently used to assess therapeutic efficacy in drug development are specifically discussed. The potential translation of these approaches to support routine clinical assessment of NAFLD and an agenda for future research are also discussed.

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.

Guidelines for the Prevention and Treatment of Chronic Hepatitis B (version 2022)

To facilitate the achieving of the goal of "eliminating viral hepatitis as a major public health threat by 2030" set by the World Health Organization, the Chinese Society of Hepatology together with the Chinese Society of Infectious Diseases (both are branches of the Chinese Medical Association) organized a panel of experts and updated the guidelines for prevention and treatment of chronic hepatitis B in China (version 2022). With the support of available evidence, this revision of the guidelines focuses on active prevention, large scale testing, and expansion of therapeutic indication of chronic hepatitis B with the aim of reducing the hepatitis B related disease burden.

Matrisome gene-based subclassification of patients with liver fibrosis identifies clinical and molecular heterogeneities

BACKGROUND AIMS

Excessive deposition and crosslinking of extracellular matrix increases liver density and stiffness, promotes fibrogenesis, and increases resistance to fibrinolysis. An emerging therapeutic opportunity in liver fibrosis is to target the composition of the extracellular matrix or block pathogenic communication with surrounding cells. However, the type and extent of extracellular changes triggering liver fibrosis depend on the underlying etiology. Our aim was to unveil matrisome genes not dependent on etiology, which are clinically relevant to liver fibrosis.

APPROACH RESULTS

We used transcriptomic profiles from liver fibrosis cases of different etiologies to identify and validate liver fibrosis-specific matrisome genes (LFMGs) and their clinical and biological relevance. Dysregulation patterns and cellular landscapes of LFMGs were further explored in mouse models of liver fibrosis progression and regression by bulk and single-cell RNA sequencing. We identified 35 LFMGs, independent of etiology, representing an LFMG signature defining liver fibrosis. Expression of the LFMG signature depended on histological severity and was reduced in regressive livers. Patients with liver fibrosis, even with identical pathological scores, could be subclassified into LFMG Low and LFMG High , with distinguishable clinical, cellular, and molecular features. Single-cell RNA sequencing revealed that microfibrillar-associated protein 4 + activated HSC increased in LFMG High patients and were primarily responsible for the LFMG signature expression and dysregulation.

CONCLUSIONS

The microfibrillar-associated protein 4 + -activated HSC-derived LFMG signature classifies patients with liver fibrosis with distinct clinical and biological characteristics. Our findings unveil hidden information from liver biopsies undetectable using traditional histologic assessments.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer

Objectives

The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.

Methods

A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method.

Results

The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.

Conclusions

The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD

BACKGROUND

Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment.

AIM

To investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD.

METHODS

AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts.

RESULTS

AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts.

CONCLUSION

AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.

Practical Guide, Challenges, and Pitfalls in Liver Fibrosis Staging

Liver fibrosis staging has many challenges, including the large number of proposed staging systems, the heterogeneity of the histopathologic changes of many primary liver diseases, and the potential for slight differences in histologic interpretation to significantly affect clinical management. This review focuses first on fibrosis regression. Following this, each of the major categories of liver disease is discussed in regard to (1) appropriate fibrosis staging systems, (2) emerging concepts, (3) current clinical indications for liver biopsy, (4) clinical decisions determined by fibrosis stage, and (5) histologic challenges and pitfalls related to staging.

Development and validation of a collagen signature to predict the prognosis of patients with stage II/III colorectal cancer

The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CS_TME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of "TNM stage + CS_TME" with that of TNM stage alone. Results indicated that the CS_TME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180-3.962, p < 0.0001), and the integration of the TNM stage and CS_TME had a better prognostic value than that of the TNM stage alone (AUC_(TNM+CSTME) = 0.772, AUC _TNM = 0.687, p < 0.0001). This study provided an application of "seed and soil" strategy for prognosis prediction and individualized therapy.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagen^DL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagen^DL classifier was significantly associated with DFS and OS (P < 0.001). The collagen^DL nomogram, integrating the collagen^DL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagen^DL classifier, rather than low-collagen^DL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagen^DL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Quantitative image-based collagen structural features predict the reversibility of hepatitis C virus-induced liver fibrosis post antiviral therapies

The novel targeted therapeutics for hepatitis C virus (HCV) in last decade solved most of the clinical needs for this disease. However, despite antiviral therapies resulting in sustained virologic response (SVR), a challenge remains where the stage of liver fibrosis in some patients remains unchanged or even worsens, with a higher risk of cirrhosis, known as the irreversible group. In this study, we provided novel tissue level collagen structural insight into early prediction of irreversible cases via image based computational analysis with a paired data cohort (of pre- and post-SVR) following direct-acting-antiviral (DAA)-based treatment. Two Photon Excitation and Second Harmonic Generation microscopy was used to image paired biopsies from 57 HCV patients and a fully automated digital collagen profiling platform was developed. In total, 41 digital image-based features were profiled where four key features were discovered to be strongly associated with fibrosis reversibility. The data was validated for prognostic value by prototyping predictive models based on two selected features: Collagen Area Ratio and Collagen Fiber Straightness. We concluded that collagen aggregation pattern and collagen thickness are strong indicators of liver fibrosis reversibility. These findings provide the potential implications of collagen structural features from DAA-based treatment and paves the way for a more comprehensive early prediction of reversibility using pre-SVR biopsy samples to enhance timely medical interventions and therapeutic strategies. Our findings on DAA-based treatment further contribute to the understanding of underline governing mechanism and knowledge base of structural morphology in which the future non-invasive prediction solution can be built upon.

Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label-free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different.

Artificial intelligence and deep learning: new tools for histopathological diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis

Nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) is associated with metabolic syndrome and is rapidly increasing globally with the increased prevalence of obesity. Although noninvasive diagnosis of NAFLD/NASH has progressed, pathological evaluation of liver biopsy specimens remains the gold standard for diagnosing NAFLD/NASH. However, the pathological diagnosis of NAFLD/NASH relies on the subjective judgment of the pathologist, resulting in non-negligible interobserver variations. Artificial intelligence (AI) is an emerging tool in pathology to assist diagnoses with high objectivity and accuracy. An increasing number of studies have reported the usefulness of AI in the pathological diagnosis of NAFLD/NASH, and our group has already used it in animal experiments. In this minireview, we first outline the histopathological characteristics of NAFLD/NASH and the basics of AI. Subsequently, we introduce previous research on AI-based pathological diagnosis of NAFLD/NASH.

Non-alcoholic fatty liver disease: the pathologist's perspective

Non-alcoholic fatty liver disease (NAFLD) is a spectrum of diseases characterized by fatty accumulation in hepatocytes, ranging from steatosis, non-alcoholic steatohepatitis, to cirrhosis. While histopathological evaluation of liver biopsies plays a central role in the diagnosis of NAFLD, limitations such as the problem of interobserver variability still exist and active research is underway to improve the diagnostic utility of liver biopsies. In this article, we provide a comprehensive overview of the histopathological features of NAFLD, the current grading and staging systems, and discuss the present and future roles of liver biopsies in the diagnosis and prognostication of NAFLD.

Second-Harmonic Generated Quantifiable Fibrosis Parameters Provide Signatures for Disease Progression and Regression in Nonalcoholic Fatty Liver Disease

Introduction:

The current ordinal fibrosis staging system for nonalcoholic steatohepatitis (NASH) has a limited dynamic range. The goal of this study was to determine if second-harmonic generated (SHG) quantifiable collagen fibrillar properties (qFP) and their derived qFibrosis score capture changes in disease progression and regression in a murine model of NASH, in which disease progression can be induced by a high fat sugar water (HFSW) diet and regression by reversal to chow diet (CD).

Methods:

DIAMOND mice were fed a CD or HFSW diet for 40 to 52 weeks. Regression related changes were studied in mice with diet reversal for 4 weeks after 48 to 60 weeks of a HFSW diet.

Results:

As expected, mice on HFSW developed steatohepatitis with stage 2 to 3 fibrosis between weeks 40 and 44. Both the collagen proportionate area and the qFibrosis score based on 15 SHG-quantified collagen fibrillar properties in humans were significantly higher in mice on HFSW for 40 to 44 weeks compared to CD fed mice. These changes were greatest in the sinusoids (Zone 2) with further increase in septal and portal fibrosis related scores between weeks 44 and 48. Diet reversal led to decrease in qFibrosis, septal thickness, and cellularity with greatest changes in Zone 2. Specific qFPs associated with progression only, regression only, or both processes were identified and categorized based on direction of fibrosis change.

Conclusion:

Complementing recent human studies, these findings support the concept that changes of disease progression and regression can be assessed using SHG-based image quantification of fibrosis related parameters.

Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH

BACKGROUND & AIMS

Liver fibrosis is a key prognostic determinant for clinical outcomes in non-alcoholic steatohepatitis (NASH). Current scoring systems have limitations, especially in assessing fibrosis regression. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses provides standardized evaluation of NASH features, especially liver fibrosis and collagen fiber quantitation on a continuous scale. This approach was applied to gain in-depth understanding of fibrosis dynamics after treatment with tropifexor (TXR), a non-bile acid farnesoid X receptor agonist in patients participating in the FLIGHT-FXR study (NCT02855164).

METHOD

Unstained sections from 198 liver biopsies (paired: baseline and end-of-treatment) from 99 patients with NASH (fibrosis stage F2 or F3) who received placebo (n = 34), TXR 140 μg (n = 37), or TXR 200 μg (n = 28) for 48 weeks were examined. Liver fibrosis (qFibrosis®), hepatic fat (qSteatosis®), and ballooned hepatocytes (qBallooning®) were quantitated using SHG/TPEF microscopy. Changes in septa morphology, collagen fiber parameters, and zonal distribution within liver lobules were also quantitatively assessed.

RESULTS

Digital analyses revealed treatment-associated reductions in overall liver fibrosis (qFibrosis®), unlike conventional microscopy, as well as marked regression in perisinusoidal fibrosis in patients who had either F2 or F3 fibrosis at baseline. Concomitant zonal quantitation of fibrosis and steatosis revealed that patients with greater qSteatosis reduction also have the greatest reduction in perisinusoidal fibrosis. Regressive changes in septa morphology and reduction in septa parameters were observed almost exclusively in F3 patients, who were adjudged as 'unchanged' with conventional scoring.

CONCLUSION

Fibrosis regression following hepatic fat reduction occurs initially in the perisinusoidal regions, around areas of steatosis reduction. Digital pathology provides new insights into treatment-induced fibrosis regression in NASH, which are not captured by current staging systems.

LAY SUMMARY

The degree of liver fibrosis (tissue scarring) in non-alcoholic steatohepatitis (NASH) is the main predictor of negative clinical outcomes. Accurate assessment of the quantity and architecture of liver fibrosis is fundamental for patient enrolment in NASH clinical trials and for determining treatment efficacy. Using digital microscopy with artificial intelligence analyses, the present study demonstrates that this novel approach has greater sensitivity in demonstrating treatment-induced reversal of fibrosis in the liver than current systems. Furthermore, additional details are obtained regarding the pathogenesis of NASH disease and the effects of therapy.

Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images

Liver fibrosis staging is clinically important for liver disease progression prediction. As the portal tract fibrotic quantity and size in a liver biopsy correlate with the fibrosis stage, an accurate analysis of portal tract regions is clinically critical. Manual annotations of portal tract regions, however, are time-consuming and subject to large inter- and intra-observer variability. To address such a challenge, we develop a Multiple Up-sampling and Spatial Attention guided UNet model (MUSA-UNet) to segment liver portal tract regions in whole-slide images of liver tissue slides. To enhance the segmentation performance, we propose to use depth-wise separable convolution, the spatial attention mechanism, the residual connection, and multiple up-sampling paths in the developed model. This study includes 53 histopathology whole slide images from patients who received liver transplantation. In total, 6,012 patches derived from 30 images are used for our deep learning model training and validation. The remaining 23 whole slide images are utilized for the model testing. The average liver portal tract segmentation performance of the developed MUSA-UNet is 0.94 (Precision), 0.85 (Recall), 0.89 (F1 Score), 0.89 (Accuracy), 0.80 (Jaccard Index), and 0.91 (Fowlkes-Mallows Index), respectively. The clinical Scheuer fibrosis stage presents a strong correlation with the resulting average portal tract fibrotic area (R = 0.681, p<0.001) and portal tract percentage (R = 0.335, p = 0.02) computed from the MUSA-UNet segmentation results. In conclusion, our developed deep learning model MUSA-UNet can accurately segment portal tract regions from whole-slide images of liver tissue biopsies, presenting its promising potential to assist liver disease diagnosis in a computational manner.

In Vivo Comparison of Synthetic Macroporous Filamentous and Sponge-like Skin Substitute Matrices Reveals Morphometric Features of the Foreign Body Reaction According to 3D Biomaterial Designs

Synthetic macroporous biomaterials are widely used in the field of skin tissue engineering to mimic membrane functions of the native dermis. Biomaterial designs can be subclassified with respect to their shape in fibrous designs, namely fibers, meshes or fleeces, respectively, and porous designs, such as sponges and foams. However, synthetic matrices often have limitations regarding unfavorable foreign body responses (FBRs). Severe FBRs can result in unfavorable disintegration and rejection of an implant, whereas mild FBRs can lead to an acceptable integration of a biomaterial. In this context, comparative in vivo studies of different three-dimensional (3D) matrix designs are rare. Especially, the differences regarding FBRs between synthetically derived filamentous fleeces and sponge-like constructs are unknown. In the present study, the FBRs on two 3D matrix designs were explored after 25 days of subcutaneous implantation in a porcine model. Cellular reactions were quantified histopathologically to investigate in which way the FBR is influenced by the biomaterial architecture. Our results show that FBR metrics (polymorph-nucleated cells and fibrotic reactions) were significantly affected according to the matrix designs. Our findings contribute to a better understanding of the 3D matrix tissue interactions and can be useful for future developments of synthetically derived skin substitute biomaterials.

Efficacy and Safety of a Botanical Formula Fuzheng Huayu for Hepatic Fibrosis in Patients with CHC: Results of a Phase 2 Clinical Trial

Background. Hepatitis C virus (HCV) is a common cause of progressive hepatic fibrosis, cirrhosis, and hepatocellular carcinoma worldwide. Despite the availability of effective direct-acting antivirals, patients often have significant hepatic fibrosis at the time of diagnosis due to delay in diagnosis and comorbidities which promote fibrogenesis. Thus, antifibrotic agents represent an attractive adjunctive therapy. Fuzheng Huayu (FZHY), a traditional Chinese medicine botanical formulation, has been used as an antifibrotic agent in chronic HBV infection. Our aim was to assess FZHY in patients with HCV infection and active viremia. Method. We randomized 118 patients with active viremia from 8 liver centers in the U.S. to receive oral FZHY (n = 59) or placebo (n = 59) for 48 weeks. Efficacy was assessed by histopathologic changes at the end of therapy. A subset of biopsies was further analyzed using qFibrosis to detect subtle changes in fibrosis in different zones of the hepatic lobules. Results. FZHY was well tolerated and safe. Patients with baseline Ishak fibrosis stages F3 and F4 had better response rates to FZHY than patients with baseline F0–F2 ( $p=0.03$ ). qFibrosis zonal analysis showed significant improvement in fibrosis in all zones in patients with regression of the fibrosis stage. Conclusions. FZHY produced antifibrotic effects in patients with baseline Ishak F3 and F4 fibrosis stages. Reduction in fibrosis severity was zonal and correlated with the severity of inflammation. Based on its tolerability, safety, and efficacy, FZHY should be further investigated as a therapy in chronic liver diseases because of its dual anti-inflammatory and antiibrotic properties. Lay Summary. This is the first US-based, multicenter and placebo-controlled clinical trial that shows statistically significant reduction in fibrosis in patients with active HCV using an antifibrotic botanical formula. This has important implications as there is an immediate need for effective antifibrotic agents in treating many chronic diseases including NASH that lead to scarring of the liver. With artificial intelligence-based methodology, qFibrosis, we may provide a more reliable way to assess the FZHY as a therapy in chronic liver diseases because of its dual anti-inflammatory and antifibrotic properties.

PSHG-TISS: A collection of polarization-resolved second harmonic generation microscopy images of fixed tissues

Second harmonic generation (SHG) microscopy is acknowledged as an established imaging technique capable to provide information on the collagen architecture in tissues that is highly valuable for the diagnostics of various pathologies. The polarization-resolved extension of SHG (PSHG) microscopy, together with associated image processing methods, retrieves extensive image sets under different input polarization settings, which are not fully exploited in clinical settings. To facilitate this, we introduce PSHG-TISS, a collection of PSHG images, accompanied by additional computationally generated images which can be used to complement the subjective qualitative analysis of SHG images. These latter have been calculated using the single-axis molecule model for collagen and provide 2D representations of different specific PSHG parameters known to account for the collagen structure and distribution. PSHG-TISS can aid refining existing PSHG image analysis methods, while also supporting the development of novel image processing and analysis methods capable to extract meaningful quantitative data from the raw PSHG image sets. PSHG-TISS can facilitate the breadth and widespread of PSHG applications in tissue analysis and diagnostics.

Measurement(s)	Type I Collagen
Technology Type(s)	multi-photon laser scanning microscopy
Factor Type(s)	second order susceptibility tensor elements
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	laboratory environment
Sample Characteristic - Location	Romania

Developing a New qFIBS Model Assessing Histological Features in Pediatric Patients With Non-alcoholic Steatohepatitis

Background

The evolution of pediatric non-alcoholic fatty liver disease (NAFLD) to non-alcoholic steatohepatitis (NASH) is associated with unique histological features. Pathological evaluation of liver specimen is often hindered by observer variability and diagnostic consensus is not always attainable. We investigated whether the qFIBS technique derived from adult NASH could be applied to pediatric NASH.

Materials and Methods

102 pediatric patients (<18 years old) with liver biopsy-proven NASH were included. The liver biopsies were serially sectioned for hematoxylin-eosin and Masson trichrome staining for histological scoring, and for second harmonic generation (SHG) imaging. qFIBS-automated measure of fibrosis, inflammation, hepatocyte ballooning, and steatosis was estabilshed by using the NASH CRN scoring system as the reference standard.

Results

qFIBS showed the best correlation with steatosis (r = 0.84, P < 0.001); with ability to distinguish different grades of steatosis (AUROCs 0.90 and 0.98, sensitivity 0.71 and 0.93, and specificity 0.90 and 0.90). qFIBS correlation with fibrosis (r = 0.72, P < 0.001) was good with high AUROC values [qFibrosis (AUC) > 0.85 (0.85–0.95)] and ability to distinguish different stages of fibrosis. qFIBS showed weak correlation with ballooning (r = 0.38, P = 0.028) and inflammation (r = 0.46, P = 0.005); however, it could distinguish different grades of ballooning (AUROCs 0.73, sensitivity 0.36, and specificity 0.92) and inflammation (AUROCs 0.77, sensitivity 0.83, and specificity 0.53).

Conclusion

It was demonstrated that when qFIBS derived from adult NASH was performed on pediatric NASH, it could best distinguish the various histological grades of steatosis and fibrosis.

Breakthroughs in therapies for NASH and remaining challenges

Initially a condition that received limited recognition and whose clinical impact was controversial, non-alcoholic steatohepatitis (NASH) has become a leading cause of chronic liver disease. Although there are no approved therapies, major breakthroughs, which will be reviewed here, have paved the way for future therapeutic successes. The unmet medical need in NASH is no longer disputed, and progress in the understanding of its pathogenesis has resulted in the identification of many pharmacological targets. Key surrogate outcomes for therapeutic trials are now accepted by regulatory agencies, thus creating a path for drug registration. A set of non-invasive measurements enabled early-stage trials to be conducted expeditiously, thus providing early indications on the biological and possibly clinical actions of therapeutic candidates. This generated efficacy results for a number of highly promising compounds that are now in late-stage development. Intense research aimed at further improving the assessment of histological endpoints and in developing non-invasive predictive biomarkers is underway. This will help improve the design and feasibility of successful trials, ultimately providing patients with therapeutic options that can change the course of the disease.

Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy

Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.

Association of the Collagen Signature with Pathological Complete Response in Rectal Cancer Patients

Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.

Collagen Score in the Tumor Microenvironment Predicts the Prognosis of Rectal Cancer Patients after Neoadjuvant Chemoradiotherapy

BACKGROUND AND PURPOSE

Little is known about the relationship between collagen and the prognosis of patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). This study aimed to quantitatively analyze collagen alterations, establish a collagen score (CS) in the tumor microenvironment, and evaluate and validate the relationship of the CS with prognosis in these patients.

MATERIALS AND METHODS

A total of 365 primary patients diagnosed with LARC after nCRT between 2011 and 2018 were retrospectively reviewed (training cohort: 210; independent validation cohort: 155). Multiple collagen features of two fields in the tumor microenvironment, the core of the tumor (CT) and the invasive margin (IM), were derived from multiphoton imaging, and the CS_IM-CT was generated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis.

RESULTS

The CS_IM-CT was created based on 3 features: collagen area, number of collagen fibers and a Gabor textural feature. In the training cohort, the CS_IM-CT predicted 3-year disease-free survival (DFS) with an area under the receiver operating characteristic curve (AUROC) of 0.765 (0.675-0.854) and an overall survival (OS) with AUROC of 0.822 (0.734-0.909). Additionally, the CS_IM-CT was significantly associated with DFS and OS in the two cohorts. A nomogram with the CS_IM-CT was developed and showed good prognostic value predicting a 3-year DFS with an AUROC of 0.826 (0.748-0.905) and an OS with AUROC of 0.882 (0.803-0.960).

CONCLUSIONS

The CS_IM-CT is an effective prognostic marker in patients with LARC after nCRT, and the nomogram with the CS_IM-CT can be used to accurately predict the individual prognosis of these patients.

Guidelines for Prevention and Treatment of Chronic Hepatitis B

To achieve the goal of the World Health Organization to eliminate viral hepatitis as a major public health threat by 2030, the Chinese Society of Infectious Diseases and the Chinese Society of Hepatology convened an expert panel in 2019 to update the guidelines for the prevention and treatment of chronic hepatitis B (CHB). The current guidelines cover recent advances in basic, clinical, and preventive studies of CHB infection and consider the actual situation in China. These guidelines are intended to provide support for the prevention, diagnosis, and treatment of CHB.

Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis

Simple Summary

Hepatocellular carcinoma (HCC) is the third most commonly diagnosed cancer in the world, and surgical resection is the commonly used curative management of early-stage disease. However, the recurrence rate is high after resection, and liver fibrosis has been thought to increase the risk of recurrence. Conventional histological staging of fibrosis is highly subjective to observer variations. To overcome this limitation, we used a fully quantitative fibrosis assessment tool, qFibrosis (utilizing second harmonic generation and two-photon excitation fluorescence microscopy), with multi-dimensional artificial intelligence analysis to establish a fully-quantitative, accurate fibrotic score called a “combined index”, which can predict early recurrence of HCC after curative intent resection. Therefore, we can pay more attention on the patients with high risk of early recurrence.

Abstract

Background: Liver fibrosis is thought to be associated with early recurrence of hepatocellular carcinoma (HCC) after resection. To recognize HCC patients with higher risk of early recurrence, we used a second harmonic generation and two-photon excitation fluorescence (SHG/TPEF) microscopy to create a fully quantitative fibrosis score which is able to predict early recurrence. Methods: The study included 81 HCC patients receiving curative intent hepatectomy. Detailed fibrotic features of resected hepatic tissues were obtained by SHG/TPEF microscopy, and we used multi-dimensional artificial intelligence analysis to create a recurrence prediction model “combined index” according to the morphological collagen features of each patient’s non-tumor hepatic tissues. Results: Our results showed that the “combined index” can better predict early recurrence (area under the curve = 0.917, sensitivity = 81.8%, specificity = 90.5%), compared to alpha fetoprotein level (area under the curve = 0.595, sensitivity = 68.2%, specificity = 47.6%). Using a Cox proportional hazards analysis, a higher “combined index” is also a poor prognostic factor of disease-free survival and overall survival. Conclusions: By integrating multi-dimensional artificial intelligence and SHG/TPEF microscopy, we may locate patients with a higher risk of recurrence, follow these patients more carefully, and conduct further management if needed.

Reliable computational quantification of liver fibrosis is compromised by inherent staining variation

Biopsy remains the gold-standard measure for staging liver disease, both to inform prognosis and to assess the response to a given treatment. Semiquantitative scores such as the Ishak fibrosis score are used for evaluation. These scores are utilised in clinical trials, with the US Food and Drug Administration mandating particular scores as inclusion criteria for participants and using the change in score as evidence of treatment efficacy. There is an urgent need for improved, quantitative assessment of liver biopsies to detect small incremental changes in liver architecture over the course of a clinical trial. Artificial intelligence (AI) methods have been proposed as a way to increase the amount of information extracted from a biopsy and to potentially remove bias introduced by manual scoring. We have trained and evaluated an AI tool for measuring the amount of scarring in sections of picrosirius red-stained liver. The AI methodology was compared with both manual scoring and widely available colour space thresholding. Four sequential sections from each case were stained on two separate occasions by two independent clinical laboratories using routine protocols to study the effect of inter- and intra-laboratory staining variation on these tools. Finally, we compared these methods to second harmonic generation (SHG) imaging, a stain-free quantitative measure of collagen. Although AI methods provided a modest improvement over simpler computer-assisted measures, staining variation both within and between laboratories had a dramatic effect on quantitation, with manual assignment of scar proportion being the most consistent. Manual assessment also most strongly correlated with collagen measured by SHG. In conclusion, results suggest that computational measures of liver scarring from stained sections are compromised by inter- and intra-laboratory staining. Stain-free quantitative measurement using SHG avoids staining-related variation and may prove more accurate in detecting small changes in scarring that may occur in therapeutic trials.

Semiautomated quantification of the fibrous tissue response to complex three-dimensional filamentous scaffolds using digital image analysis

Fibrosis represents a relevant response to the implantation of biomaterials, which occurs not only at the tissue-material interface (fibrotic encapsulation) but also within the void fraction of complex three-dimensional (3D) biomaterial constructions (fibrotic ingrowth). Usual evaluation of the biocompatibility mostly depicts fibrosis at the interface of the biomaterial using semiquantitative scores. Here, the relations between encapsulation and infiltrating fibrotic growth are poorly represented. Virtual pathology and digital image analysis provide new strategies to assess fibrosis in a more differentiated way. In this study, we adopted a method previously used to quantify fibrosis in visceral organs to the quantification of fibrosis to 3D biomaterials. In a proof-of-concept study, we transferred the "Collagen Proportionate Area" (CPA) analysis from hepatology to the field of biomaterials. As one task of an experimental animal study, we used CPA analysis to quantify the fibrotic ingrowth into a filamentous scaffold after subcutaneous implantation. We were able to demonstrate that the application of the CPA analysis is well suited as an additional fibrosis evaluation strategy for new biomaterial constructions. The CPA method can contribute to a better understanding of the fibrotic interactions between 3D scaffolds and the host tissue responses.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Comparing hepatic steatosis distribution patterns between non-alcoholic fatty liver disease and fatty liver disease with chronic hepatitis B by second-harmonic generation/two-photon excited fluorescence method

INTRODUCTION AND OBJECTIVES

Hepatitis B virus (HBV) might be an etiological factor modulating fat distribution in steatotic livers. We aim to compare hepatic steatosis distribution patterns between NAFLD and FL&CHB patients with second-harmonic generation (SHG)/two-photon excited fluorescence (TPEF) method.

PATIENTS AND METHODS

42 patients with NAFLD, 46 with FL&CHB and 55 without steatosis were enrolled in the study. Overall and regional steatosis in liver sections were quantified by SHG/TPEF method. The accuracy of which was validated by pathologist evaluation and magnetic resonance spectroscopy (MRS). Difference in degree of overall and regional steatosis between NAFLD and FL&CHB groups was analyzed by Mann-Whitney U test. Multivariable linear regression analysis was used to model factors contributing to steatosis distribution.

RESULTS

The hepatic steatosis measured by SHG/TPEF method was highly correlated with pathologist grading (r=0.83, p<0.001) and MRS measurement (r=0.82, p<0.001). The level of overall steatosis in FL&CHB group is significantly lower than that in NAFLD group (p<0.001). In NAFLD group, periportal region has significantly lower steatosis percentage than lobule region and overall region (p<0.001); while in FL&CHB group there is no difference among regions. The ratio of steatosis at periportal region to lobule region is significantly higher in FL&CHB group than that in NAFLD group (p<0.05). Multivariable linear regression analysis shows that HBV infection is the major contributing factor (β=0.322, p<0.01).

CONCLUSIONS

SHG/TPEF method is an accurate and objective method in hepatic steatosis quantification. By quantifying steatosis in different histological regions, we found steatosis distribution patterns are different between FL&CHB and NAFLD patients.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients

BACKGROUND

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

METHODS

In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.

RESULTS

The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.

CONCLUSIONS

The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Association of the collagen score with anastomotic leakage in rectal cancer patients after neoadjuvant chemoradiotherapy

BACKGROUND

Collagen changes in the extracellular matrix caused by neoadjuvant chemoradiotherapy are a potential mechanism of anastomotic leakage. We aimed to construct a fully quantitative collagen score to describe collagen structure changes in the extracellular matrix and then develop and validate a prediction model to identify patients who are at a high risk of postoperative anastomotic leakage.

METHODS

This is a retrospective study in which 372 patients were enrolled, and their baseline clinicopathological characteristics were collected. Anastomotic distal and proximal "doughnut" specimens underwent second harmonic generation imaging, and collagen features were extracted. A LASSO regression was used to select significant predictors, and the collagen score was constructed. A prediction model based on collagen score was developed and internally and externally validated.

RESULTS

The primary cohort included 214 consecutive patients, and the anastomotic leakage rate was 8.9%. The validation cohort comprised 158 consecutive patients, and the anastomotic leakage rate was 10.1%. The collagen score was significantly related to anastomotic leakage in both cohorts (P < .001). Multivariate analysis revealed that tumor location, preoperative albumin, and collagen score were independent predictors of anastomotic leakage. These 3 predictors were incorporated into the prediction model, and a nomogram was established. The model showed good discrimination in the primary (area under the curve: 0.954) and validation (area under the curve: 0.928) cohorts. Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

The collagen score is associated with anastomotic leakage, and the collagen nomogram based on the collagen score is useful for individualized prediction of anastomotic leakage in rectal cancer patients with neoadjuvant chemoradiotherapy after surgery.

Regression of liver fibrosis: evidence and challenges

It has been reported that liver fibrosis could be reversed after eliminating liver injuries. This article systematically summarizes the evidence of fibrosis regression based on histology, liver stiffness, and serum biomarkers, and discusses several clinically relevant challenges. Evidence from liver biopsy has been regarded as the gold standard in the assessment of fibrosis regression. Semi-quantitative staging and grading systems are traditionally and routinely used to define regression. Recently, the predominantly regressive, indeterminate, and predominantly progressive score was proposed, based on the regressive features from "hepatic repair complex", to provide additional information regarding the quality of fibrosis. For non-invasive assessment, although liver stiffness and serum biomarkers could be applied to reflect the dynamic changes of liver fibrosis, other confounding factors such as liver inflammation have to be considered. In conclusion, both histology and non-invasive methods can provide evidence regarding fibrosis regression. The predictive value of fibrosis regression in long-term prognosis warrants further investigation.

Association of the Collagen Signature in the Tumor Microenvironment With Recurrence and Survival of Patients With T4N0M0 Colon Cancer

BACKGROUND

The current clinicopathological risk factors do not accurately predict disease recurrence in patients with T4N0M0 colon cancer. We hypothesized that the collagen signature combined with clinicopathological risk factors (new model) had a better prognostic value than clinicopathological risk factors alone (clinicopathological model).

OBJECTIVE

This study aimed to establish a collagen signature in the tumor microenvironment and to validate its role in predicting the recurrence of T4N0M0 colon cancer.

DESIGN

This was a retrospective study.

SETTINGS

This study took place at a tertiary medical center.

PATIENTS

Patients with T4N0M0 colon cancer who underwent surgery at our center between 2009 and 2015 (n = 416) were included.

INTERVENTION

A total of 142 collagen features were analyzed in the tumor microenvironment in specimens of colon cancer by using second-harmonic generation imaging. A collagen signature was constructed using a least-absolute shrinkage and selection operator Cox regression model.

MAIN OUTCOME MEASURES

The primary outcomes measured were disease-free survival and overall survival.

RESULTS

The training and testing cohorts consisted of 291 and 125 randomly assigned samples, with recurrence rates of 19.9% and 22.4%. A 3-feature-based collagen signature predicted the recurrence risk at 1, 3, and 5 years, with the area under the receiver-operating characteristic curves of 0.808, 0.832, and 0.791 in the training cohort and 0.836, 0.807, and 0.794 in the testing cohort. Multivariate analysis revealed that the collagen signature could independently predict the disease-free survival (HR = 7.17, p < 0.001) and overall survival rates (HR = 5.03, p < 0.001). The new model had a better prognostic value than the clinicopathological model, which included 4 clinicopathological risk factors: obstruction or perforation, lymphovascular invasion, tumor budding, and no chemotherapy.

LIMITATIONS

This study was limited by its retrospective design.

CONCLUSIONS

The collagen signature in the tumor microenvironment may be a new prognostic marker that can effectively predict the recurrence and survival of patients with T4N0M0 colon cancer. See Video Abstract at http://links.lww.com/DCR/B503. ASOCIACIÓN DE LA RÚBRICA DE COLÁGENO EN EL MICROAMBIENTE TUMORAL CON LA RECIDIVA Y LA SOBREVIDA DE PACIENTES CON CÁNCER DE COLON T4N0M0: Los factores de riesgo clínico-patológicos actuales no predicen con precisión la recurrencia de la enfermedad en pacientes con cáncer de colon estadío T4N0M0. Presumimos que la rúbrica de colágeno combinada con factores de riesgo clínico-patológicos (nuevo modelo) tendrían un mejor valor pronóstico que los factores de riesgo clínico-patológicos solos (modelo clínico-patológico).El establecer una rúbrica de colágeno en el microambiente tumoral y validar su papel en la predicción de la recidiva del cáncer de colon T4N0M0.Estudio retrospectivo.Investigación llevada a cabo en un centro médico terciario.Se incluyeron pacientes con cáncer de colon T4N0M0 operados en nuestro centro entre 2009 y 2015 (n = 416).Se analizaron un total de 142 características de colágeno en el microambiente tumoral en muestras de cáncer de colon utilizando imágenes de segunda generación armónica. Se construyó una rúbrica de colágeno utilizando un modelo de regresión LASSO Cox.Sobrevida libre de enfermedad y sobrevida global.Las cohortes de entrenamiento y prueba consistieron en 291 y 125 muestras asignadas al azar, con tasas de recurrencia del 19,9% y 22,4%, respectivamente. La rúbrica del colágeno basada en 3 características predijo el riesgo de recurrencia a 1, 3 y 5 años, con el área bajo las curvas características operativas del receptor de 0,808, 0,832 y 0,791 en la cohorte de entrenamiento y 0,836, 0,807 y 0,794 en la cohorte de prueba, respectivamente. El análisis multivariado reveló que la firma de colágeno podría predecir de forma independiente la supervivencia libre de enfermedad (HR = 7,17, p <0,001) y las tasas de sobrevida general (HR = 5,03, p <0,001). El nuevo modelo tuvo un mejor valor pronóstico que el modelo clínico-patológico, que incluyó cuatro factores de riesgo clínico-patológicos: obstrucción o perforación, invasión linfovascular, gemación tumoral y ausencia de quimioterapia.Este estudio estuvo limitado por su diseño retrospectivo.La rúbrica de colágeno en el microambiente tumoral puede ser un nuevo marcador pronóstico para predecir eficazmente la recurrencia y la subrevida de los pacientes con cáncer de colon T4N0M0. Consulte Video Resumen en http://links.lww.com/DCR/B503. (Traducción-Dr. Xavier Delgadillo).

Second-harmonic generation (SHG) microscopy and hepatic venous pressure gradient-based validation of a novel histological staging system for alcoholic hepatitis

Alcoholic hepatitis (AH) lacks specific histological staging. A novel fibrosis staging that encompasses perisinusoidal fibrosis and cirrhosis sub-stages, substantiated by Hepatic venous pressure gradient (HVPG) and automated fibrosis quantification, is imperative. To correlate novel histological staging system of AH with second-harmonic generation microscopy (SHG)-based q-fibrosis, HVPG, and activated hepatic stellate cells (HSCs). Liver biopsies of AH (n = 175) were staged semi-quantitatively as F0, F1, F2, F3A and F3B and Laennec substages of cirrhosis 4A, 4B and 4C. Stages were correlated with SHG q-fibrosis parameters, HVPG and HSCs. Mean age 41.2 ± 9.4 years, 96.6% males, bilirubin 20.58 ± 8.0 mg/dl and Maddrey's discriminant function 78.9 ± 36.7 displayed advanced fibrosis in 98.6%. With increasing histological stages, an increase in q-fibrosis indices and mean HVPG (p < 0.0001) were recorded; stage 4C showed the most significant difference from other stages (p < 0.000). Stages 3A and 3B were comparable with the stages 4A and 4B, respectively, for q-fibrosis (p = 1) and HVPG (p = 1). HSCs (> 30%) were significantly higher in stage 3 (75%) compared with 4 (49%) and 2 (59%), p = 0.018. Overall agreement for histological staging was excellent for all stages (0.82). SHG quantified fibrosis and HVPG corroborates the novel histological staging of AH. Expansive PCF matches with collagen content and clinical severity to early sub-stages of cirrhosis. This highlights the need for an accurate quantification and inclusion of PCF as a separate stage. SHG-based quantification can be a useful adjunct to histological fibrosis staging systems.

Development and validation of a collagen signature-based nomogram for preoperatively predicting lymph node metastasis and prognosis in colorectal cancer

Background

Current preoperative evaluation approaches cannot provide adequate information for the prediction of lymph node (LN) metastasis in colorectal cancer (CRC). Collagen alterations in the tumor microenvironment affect the progression of tumor cells. To more accurately assess the LN status of CRC preoperatively, we developed and validated a collagen signature-based nomogram for predicting LN metastasis in CRC.

Methods

In total, 342 consecutive CRC patients were assigned to the training and validation cohorts. A total of 148 fully quantitative collagen features were extracted based on preoperative biopsies using multiphoton imaging, and the least absolute shrinkage and selection operator method was utilized to construct the collagen signature. A collagen signature-based nomogram was developed by multivariable logistic regression in the training cohort. Nomogram performance was evaluated for its discrimination, calibration, and clinical usefulness and then validated in the validation cohort. The prognostic values of the nomogram were also evaluated.

Results

A seven-feature-based collagen signature was built. We found that the collagen signature showed a significant association with LN metastasis in CRC. Additionally, a nomogram incorporating preoperative tumor differentiation, computed tomography-reported T stage and LN status, carcinoembryonic antigen level, carbohydrate antigen 19-9 level and collagen signature was developed. This nomogram had good discrimination and calibration, with AUROCs of 0.826 and 0.846 in the training and validation cohorts, respectively, and had a sensitivity of 86.5%, a specificity of 68.2%, an accuracy of 76.9%, a negative predictive value of 84.9%, and a positive predictive value of 71.2% for all patients. Compared to the clinicopathological model, which consisted of the clinicopathological risk factors for LN metastasis, the collagen signature-based nomogram demonstrated a significantly improved ability to discriminate LN status. Moreover, a nomogram-predicted high-risk subgroup had remarkably reduced survival compared with that of the low-risk subgroup.

Conclusions

The collagen signature in the tumor microenvironment of preoperative biopsies is an independent predictor for LN metastasis in CRC, and the collagen signature-based nomogram is helpful for tailored treatment and prognostic predictions in CRC preoperatively.

Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram

Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.

Improved second harmonic generation and two-photon excitation fluorescence microscopy-based quantitative assessments of liver fibrosis through auto-correction and optimal sampling

Background

Second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy is commonly used for the quantitative assessment of liver fibrosis; however, the accuracy is susceptible to sampling error and count error due to disturbances induced by some forms of collagen in liver specimens. In this study, we sought to improve the accuracy of quantitative assessments by removing the effects of this disturbing collagen and optimizing the sampling protocol.

Methods

Large liver resection samples from 111 patients with chronic hepatitis B were scanned using SHG/TPEF microscopy with multiple adjacent images. During the quantitative assessment, we then removed SHG signals associated with three types of extraneous physiological collagen: large patches of collagen near the boundary of the capsule, collagen around tubular structures, and collagen associated with distorted vessel walls. The optimal sampling protocol was identified by comparing scans from regions of interest of various sizes (3×3 tiles and 5×5 tiles) with full scans of the same tissue.

Results

The proposed auto-correction algorithm detected 88 of 97 (90.7%) disturbing collagen on the images from the validation set. Removing these signals of disturbing collagen improved the correlation between Metavir stage and quantification of all 41 proposed collagen features. Through optimal sampling, five scans of 5×5 tiles or ten scans of 3×3 tiles were sufficient to minimize the mean error rate to around 2% of collagen percentage quantification and to achieve similar correlations around 0.27 with Metavir stage as using full tissue scans.

Conclusions

Our results demonstrate that the quantitative assessments of liver fibrosis can be greatly enhanced in terms of accuracy and efficiency through optimal sampling and the automated removal of disturbing collagen signals. These types of image processing could be integrated in next-generation SHG/TPEF microscopic systems.