Potential of using qFibrosis analysis to predict recurrent and survival outcome of patients with hepatocellular carcinoma after hepatic resection.

BACKGROUND: There remains a lack of studies addressing the stromal background and fibrosis features and its prognostic value in liver cancer. qFibrosis can identify, quantify and visualize the fibrosis features from biopsy samples. In this study, we aim to demonstrate the prognostic value of histological features by using qFibrosis analysis in liver cancer patients. METHODS: Liver specimen from 201 patients with hepatocellular carcinoma underwent curative resection were imaged and assessed using qFibrosis system, and generated a total of 33 and 156 collagen parameters from tumor part and non-tumor liver tissue, respectively. We used these collagen parameters on patients to build two combined indexes, RFS-index and OS-index, in order to differentiate patients with early recurrence and early death, respectively. The models were validated using leave-one-out method. RESULTS: Both combined indexes had significant prediction value of patients' outcome. The RFS-index of 0.52 well differentiates patients with early recurrence (p < 0.001), and the OS-index of 0.73 well differentiates patients with early death during follow-up (p = 0.02). CONCLUSIONS: Combined index calculated with qFibrosis from digital readout of fibrotic status of peri-tumor liver specimen in patients with HCC have prediction values for their disease and survival outcomes. These results demonstrated the potentials to transform histopathological features into quantifiable data that could be used to correlate with clinical outcome.

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.

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.

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.

Complexity of ballooned hepatocyte feature recognition: Defining a training atlas for artificial intelligence-based imaging in NAFLD.

BACKGROUND & AIMS: Histologically assessed hepatocyte ballooning is a key feature discriminating non-alcoholic steatohepatitis (NASH) from steatosis (NAFL). Reliable identification underpins patient inclusion in clinical trials and serves as a key regulatory-approved surrogate endpoint for drug efficacy. High inter/intra-observer variation in ballooning measured using the NASH CRN semi-quantitative score has been reported yet no actionable solutions have been proposed. METHODS: A focused evaluation of hepatocyte ballooning recognition was conducted. Digitized slides were evaluated by 9 internationally recognized expert liver pathologists on 2 separate occasions: each pathologist independently marked every ballooned hepatocyte and later provided an overall non-NASH NAFL/NASH assessment. Interobserver variation was assessed and a 'concordance atlas' of ballooned hepatocytes generated to train second harmonic generation/two-photon excitation fluorescence imaging-based artificial intelligence (AI). RESULTS: The Fleiss kappa statistic for overall interobserver agreement for presence/absence of ballooning was 0.197 (95% CI 0.094-0.300), rising to 0.362 (0.258-0.465) with a ≥5-cell threshold. However, the intraclass correlation coefficient for consistency was higher (0.718 [0.511-0.900]), indicating 'moderate' agreement on ballooning burden. 133 ballooned cells were identified using a ≥5/9 majority to train AI ballooning detection (AI-pathologist pairwise concordance 19-42%, comparable to inter-pathologist pairwise concordance of between 8-75%). AI quantified change in ballooned cell burden in response to therapy in a separate slide set. CONCLUSIONS: The substantial divergence in hepatocyte ballooning identified amongst expert hepatopathologists suggests that ballooning is a spectrum, too subjective for its presence or complete absence to be unequivocally determined as a trial endpoint. A concordance atlas may be used to train AI assistive technologies to reproducibly quantify ballooned hepatocytes that standardize assessment of therapeutic efficacy. This atlas serves as a reference standard for ongoing work to refine how ballooning is classified by both pathologists and AI. LAY SUMMARY: For the first time, we show that, even amongst expert hepatopathologists, there is poor agreement regarding the number of ballooned hepatocytes seen on the same digitized histology images. This has important implications as the presence of ballooning is needed to establish the diagnosis of non-alcoholic steatohepatitis (NASH), and its unequivocal absence is one of the key requirements to show 'NASH resolution' to support drug efficacy in clinical trials. Artificial intelligence-based approaches may provide a more reliable way to assess the range of injury recorded as "hepatocyte ballooning".

Differential remodeling of extracellular matrices by breast cancer initiating cells.

Cancer initiating cells (CICs) have been the focus of recent anti-cancer therapies, exhibiting strong invasion capability via potentially enhanced ability to remodel extracellular matrices (ECM). We have identified CICs in a human breast cancer cell line, MX-1, and developed a xenograft model in SCID mice. We investigated the CICs' matrix-remodeling effects using Second Harmonic Generation (SHG) microscopy to identify potential phenotypic signatures of the CIC-rich tumors. The isolated CICs exhibit higher proliferation, drug efflux and drug resistant properties in vitro; were more tumorigenic than non-CICs, resulting in more and larger tumors in the xenograft model. The CIC-rich tumors have less collagen in the tumor interior than in the CIC-poor tumors supporting the idea that the CICs can remodel the collagen more effectively. The collagen fibers were preferentially aligned perpendicular to the CIC-rich tumor boundary while parallel to the CIC-poor tumor boundary suggesting more invasive behavior of the CIC-rich tumors. These findings would provide potential translational values in quantifying and monitoring CIC-rich tumors in future anti-cancer therapies. CIC-rich tumors remodel the collagen matrix more than CIC-poor tumors.

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

BACKGROUND & AIMS: There is increasing need for accurate assessment of liver fibrosis/cirrhosis. We aimed to develop qFibrosis, a fully-automated assessment method combining quantification of histopathological architectural features, to address unmet needs in core biopsy evaluation of fibrosis in chronic hepatitis B (CHB) patients. METHODS: qFibrosis was established as a combined index based on 87 parameters of architectural features. Images acquired from 25 Thioacetamide-treated rat samples and 162 CHB core biopsies were used to train and test qFibrosis and to demonstrate its reproducibility. qFibrosis scoring was analyzed employing Metavir and Ishak fibrosis staging as standard references, and collagen proportionate area (CPA) measurement for comparison. RESULTS: qFibrosis faithfully and reliably recapitulates Metavir fibrosis scores, as it can identify differences between all stages in both animal samples (p<0.001) and human biopsies (p<0.05). It is robust to sampling size, allowing for discrimination of different stages in samples of different sizes (area under the curve (AUC): 0.93-0.99 for animal samples: 1-16 mm(2); AUC: 0.84-0.97 for biopsies: 10-44 mm in length). qFibrosis can significantly predict staging underestimation in suboptimal biopsies (<15 mm) and under- and over-scoring by different pathologists (p<0.001). qFibrosis can also differentiate between Ishak stages 5 and 6 (AUC: 0.73, p=0.008), suggesting the possibility of monitoring intra-stage cirrhosis changes. Best of all, qFibrosis demonstrates superior performance to CPA on all counts. CONCLUSIONS: qFibrosis can improve fibrosis scoring accuracy and throughput, thus allowing for reproducible and reliable analysis of efficacies of anti-fibrotic therapies in clinical research and practice.

Correlation of MR elastography with morphometric quantification of liver fibrosis (Fibro-C-Index) in chronic hepatitis B.

PURPOSE: We evaluated the correlation of MR Elastography (MRE) with morphometric assessment of liver fibrosis in chronic hepatitis B (CHB). METHODS: Thirty-two patients with CHB underwent both MRE and a liver biopsy within a 6-month interval. MRE was performed using standard MRE sequence on a 1.5 Tesla clinical scanner. The liver stiffness (LS) was measured on automatically generated stiffness maps. Morphometric quantification of fibrosis of liver biopsies was performed using a semi-automated image analysis program and expressed as percentage area (Fibro-C-Index). Correlations between MRE, Fibro-C-Index, and histologic fibrosis stages were evaluated. Receiver operating curve (ROC) analysis of MRE and Fibro-C-index for differentiating fibrosis (≥F1), significant fibrosis (≥F2), advanced fibrosis (≥F3), and cirrhosis (F4) was performed. RESULTS: MRE showed excellent correlation with both Fibro-C-Index (r = 0.78, 95% confidence interval [CI], 0.59-0.88, P < 0.001) and histologic staging (rho = 0.87, 95% CI, 0.72-0.94, P < 0.0001). Significant differences in MRE (P = 0.0001) and Fibro-C-Index (P = 0.003) among different stages of liver fibrosis was found. MRE and Fibro-C-Index had similar accuracies for differentiating fibrosis stages: ≥F1 (0.87 versus 0.81, P = 0.6), ≥F2 (0.95 versus 0.94, P = 0.78), ≥F3 (0.98 versus 0.96, P = 0.76), and F4 (1.00 versus 0.92, P = 0.10). CONCLUSION: MRE is an excellent noninvasive indicator of liver fibrosis burden in CHB.

Assessment of liver steatosis and fibrosis in rats using integrated coherent anti-Stokes Raman scattering and multiphoton imaging technique.

We report the implementation of a unique integrated coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), and two-photon excitation fluorescence (TPEF) microscopy imaging technique developed for label-free monitoring of the progression of liver steatosis and fibrosis generated in a bile duct ligation (BDL) rat model. Among the 21 adult rats used in this study, 18 rats were performed with BDL surgery and sacrificed each week from weeks 1 to 6 (n = 3 per week), respectively; whereas 3 rats as control were sacrificed at week 0. Colocalized imaging of the aggregated hepatic fats, collagen fibrils, and hepatocyte morphologies in liver tissue is realized by using the integrated CARS, SHG, and TPEF technique. The results show that there are significant accumulations of hepatic lipid droplets and collagen fibrils associated with severe hepatocyte necrosis in BDL rat liver as compared to a normal liver tissue. The volume of normal hepatocytes keeps decreasing and the fiber collagen content in BDL rat liver follows a growing trend until week 6; whereas the hepatic fat content reaches a maximum in week 4 and then appears to stop growing in week 6, indicating that liver steatosis and fibrosis induced in a BDL rat liver model may develop at different rates. This work demonstrates that the integrated CARS and multiphoton microscopy imaging technique has the potential to provide an effective means for early diagnosis and detection of liver steatosis and fibrosis without labeling.

Automated scoring of liver fibrosis through combined features from different collagen groups.

Liver biopsy remains the gold standard in monitoring progression of liver fibrosis associated with an abnormal increase in collagen. Descriptive scoring systems are still being widely used to grade biopsy samples. In this study, we propose a new set of features by clustering collagen fibers into three groups first based on their localization and connectivity properties, and then by extracting morphological features of collagen fibers. The new feature set is compared to the earlier features used in classification of liver fibrosis, which were based on the total amount of collagen fibers. Our results show that new features lead to more accurate grading of liver fibrosis.

Toward surface quantification of liver fibrosis progression.

Monitoring liver fibrosis progression by liver biopsy is important for certain treatment decisions, but repeated biopsy is invasive. We envision redefinition or elimination of liver biopsy with surface scanning of the liver with minimally invasive optical methods. This would be possible only if the information contained on or near liver surfaces accurately reflects the liver fibrosis progression in the liver interior. In our study, we acquired the second-harmonic generation and two-photon excitation fluorescence microscopy images of liver tissues from bile duct-ligated rat model of liver fibrosis. We extracted morphology-based features, such as total collagen, collagen in bile duct areas, bile duct proliferation, and areas occupied by remnant hepatocytes, and defined the capsule and subcapsular regions on the liver surface based on image analysis of features. We discovered a strong correlation between the liver fibrosis progression on the anterior surface and interior in both liver lobes, where biopsy is typically obtained. The posterior surface exhibits less correlation with the rest of the liver. Therefore, scanning the anterior liver surface would obtain similar information to that obtained from biopsy for monitoring liver fibrosis progression.

Pulse-modulated second harmonic imaging microscope quantitatively demonstrates marked increase of collagen in tumor after chemotherapy.

Pulse-modulated second harmonic imaging microscopes (PM-SHIMs) exhibit improved signal-to-noise ratio (SNR) over conventional SHIMs on sensitive imaging and quantification of weak collagen signals inside tissues. We quantify the spatial distribution of sparse collagen inside a xenograft model of human acute myeloid leukemia (AML) tumor specimens treated with a new drug against receptor tyrosine kinase (ABT-869), and observe a significant increase in collagen area percentage, collagen fiber length, fiber width, and fiber number after chemotherapy. This finding reveals new insights into tumor responses to chemotherapy and suggests caution in developing new drugs and therapeutic regimens against cancers.

Fibro-C-Index: comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy.

We develop a standardized, fully automated, quantification system for liver fibrosis assessment using second harmonic generation microscopy and a morphology-based quantification algorithm. Liver fibrosis is associated with an abnormal increase in collagen as a result of chronic liver diseases. Histopathological scoring is the most commonly used method for liver fibrosis assessment, where a liver biopsy is stained and scored by experienced pathologists. Due to the intrinsic limited sensitivity and operator-dependent variations, there exist high inter- and intraobserver discrepancies. We validate our quantification system, Fibro-C-Index, with a comprehensive animal study and demonstrate its potential application in clinical diagnosis to reduce inter- and intraobserver discrepancies.