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.

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.

Outcome prediction in metabolic dysfunction-associated steatotic liver disease using stain-free digital pathological assessment.

Computational quantification reduces observer-related variability in histological assessment of metabolic dysfunction-associated steatotic liver disease (MASLD). We undertook stain-free imaging using the SteatoSITE resource to generate tools directly predictive of clinical outcomes. Unstained liver biopsy sections (n = 452) were imaged using second-harmonic generation/two-photon excitation fluorescence (TPEF) microscopy, and all-cause mortality and hepatic decompensation indices constructed. The mortality index had greater predictive power for all-cause mortality (index >.14 vs. .31 vs.

Digital quantitation of bridging fibrosis and septa reveals changes in natural history and treatment not seen with conventional histology.

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) with bridging fibrosis is a critical stage in the evolution of fatty liver disease. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence (AI) provides sensitive and reproducible quantitation of liver fibrosis. This methodology was applied to gain an in-depth understanding of intra-stage fibrosis changes and septa analyses in a homogenous, well-characterised group with MASH F3 fibrosis. METHODS: Paired liver biopsies (baseline [BL] and end of treatment [EOT]) of 57 patients (placebo, n = 17 and tropifexor n = 40), with F3 fibrosis stage at BL according to the clinical research network (CRN) scoring, were included. Unstained sections were examined using SHG/TPEF microscopy with AI. Changes in liver fibrosis overall and in five areas of liver lobules were quantitatively assessed by qFibrosis. Progressive, regressive septa, and 12 septa parameters were quantitatively analysed. RESULTS: qFibrosis demonstrated fibrosis progression or regression in 14/17 (82%) patients receiving placebo, while the CRN scoring categorised 11/17 (65%) as 'no change'. Radar maps with qFibrosis readouts visualised quantitative fibrosis dynamics in different areas of liver lobules even in cases categorised as 'No Change'. Measurement of septa parameters objectively differentiated regressive and progressive septa (p < .001). Quantitative changes in individual septa parameters (BL to EOT) were observed both in the 'no change' and the 'regression' subgroups, as defined by the CRN scoring. CONCLUSION: SHG/TPEF microscopy with AI provides greater granularity and precision in assessing fibrosis dynamics in patients with bridging fibrosis, thus advancing knowledge development of fibrosis evolution in natural history and in clinical trials.

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.

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".

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.

AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response.

This study aimed to understand the dynamic changes in fibrosis and its relationship with the evaluation of post-treatment viral hepatitis using qFibrosis. A total of 158 paired pre- and post-treatment liver samples from patients with chronic hepatitis B (CHB; n = 100) and C (CHC; n = 58) were examined. qFibrosis was employed with artificial intelligence (AI) to analyze the fibrosis dynamics in the portal tract (PT), periportal (PP), midzonal, pericentral, and central vein (CV) regions. All patients with CHB achieved a virological response after 78 weeks of treatment, whereas patients with CHC achieved a sustained viral response after 24 weeks. For patients initially staged as F5/6 (Ishak system) at baseline, the post-treatment cases exhibited a significant reduction in the collagen proportionate area (CPA) (25-69%) and number of collagen strings (#string) (9-72%) across all regions. In contrast, those initially staged as F3/4 at baseline showed a similar CPA and #string trend at 24 weeks. For regression patients, 27 parameters (25 in the CV region) in patients staged as F3/4 and 15 parameters (three in the PT and 12 in the PP regions) in those staged as F5/6 showed significant differences between the CHB and CHC groups at baseline. Following successful antiviral treatment, the pre- and post-treatment liver samples provided quantitative evidence of the heterogeneity of fibrotic features. qFibrosis has the potential to provide new insights into the characteristics of fibrosis regression in both patients with CHB and CHC as early as 24 weeks after antiviral therapy.

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.

Machine learning liver histology scores correlate with portal hypertension assessments in nonalcoholic steatohepatitis cirrhosis.

BACKGROUND AND AIMS: In cirrhotic nonalcoholic steatohepatitis (NASH) clinical trials, primary efficacy endpoints have been hepatic venous pressure gradient (HVPG), liver histology and clinical liver outcomes. Important histologic features, such as septa thickness, nodules features and fibrosis area have not been included in the histologic assessment and may have important clinical relevance. We assessed these features with a machine learning (ML) model. METHODS: NASH patients with compensated cirrhosis and HVPG ≥6 mm Hg (n = 143) from the Belapectin phase 2b trial were studied. Liver biopsies, HVPG measurements and upper endoscopies were performed at baseline and at end of treatment (EOT). A second harmonic generation/two-photon excitation fluorescence provided an automated quantitative assessment of septa, nodules and fibrosis (SNOF). We created ML scores and tested their association with HVPG, clinically significant HVPG (≥10 mm Hg) and the presence of varices (SNOF-V). RESULTS: We derived 448 histologic variables (243 related to septa, 21 related to nodules and 184 related to fibrosis). The SNOF score (≥11.78) reliably distinguished CSPH at baseline and in the validation cohort (baseline + EOT) [AUC = 0.85 and 0.74, respectively]. The SNOF-V score (≥0.57) distinguished the presence of varices at baseline and in the same validation cohort [AUC = 0.86 and 0.73, respectively]. Finally, the SNOF-C score differentiated those who had >20% change in HVPG against those who did not, with an AUROC of 0.89. CONCLUSION: The ML algorithm accurately predicted HVPG, CSPH, the development of varices and HVPG changes in patients with NASH cirrhosis. The use of ML histology model in NASH cirrhosis trials may improve the assessment of key outcome changes.

Tropifexor for nonalcoholic steatohepatitis: an adaptive, randomized, placebo-controlled phase 2a/b trial.

The multimodal activities of farnesoid X receptor (FXR) agonists make this class an attractive option to treat nonalcoholic steatohepatitis. The safety and efficacy of tropifexor, an FXR agonist, in a randomized, multicenter, double-blind, three-part adaptive design, phase 2 study, in patients with nonalcoholic steatohepatitis were therefore assessed. In Parts A + B, 198 patients were randomized to receive tropifexor (10-90 µg) or placebo for 12 weeks. In Part C, 152 patients were randomized to receive tropifexor 140 µg, tropifexor 200 µg or placebo (1:1:1) for 48 weeks. The primary endpoints were safety and tolerability to end-of-study, and dose response on alanine aminotransferase (ALT), aspartate aminotransferase (AST) and hepatic fat fraction (HFF) at week 12. Pruritus was the most common adverse event in all groups, with a higher frequency in the 140- and 200-µg tropifexor groups. Decreases from baseline in ALT and HFF were greater with tropifexor versus placebo at week 12, with a relative decrease in least squares mean from baseline observed with all tropifexor doses for ALT (tropifexor 10-90-µg dose groups ranged from -10.7 to -16.5 U l-1 versus placebo (-7.8 U l-1) and tropifexor 140- and 200-µg groups were -18.0 U l-1 and -23.0 U l-1, respectively, versus placebo (-8.3 U l-1)) and % HFF (tropifexor 10-90-µg dose groups ranged from -7.48% to -15.04% versus placebo (-6.19%) and tropifexor 140- and 200-µg groups were -19.07% and -39.41%, respectively, versus placebo (-10.77%)). Decreases in ALT and HFF were sustained up to week 48; however, similar trends in AST with tropifexor at week 12 were not observed. As with other FXR agonists, dose-related pruritus was frequently observed. Clinicaltrials.gov registration: NCT02855164.

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.

A microfluidic 3D hepatocyte chip for drug toxicity testing.

We have developed a microfluidic 3D hepatocyte chip (3D HepaTox Chip) for in vitro drug toxicity testing to predict in vivo drug hepatotoxicity. The 3D HepaTox Chip is based on multiplexed microfluidic channels where a 3D microenvironment is engineered in each channel to maintain the hepatocytes' synthetic and metabolic functions. The multiplexed channels allow for simultaneous administration of multiple drug doses to functional primary hepatocytes while an incorporated concentration gradient generator enables the in vitro dose-dependent drug responses to predict in vivo hepatotoxicity. The IC(50) values of 5 model drugs derived from the dose-dependent on-chip testing correlate well with the reported in vivo LD(50) values. The 3D HepaTox Chip can be integrated with on-chip sensors and actuators as the next generation cell-based on-chip drug testing platform.

Nonlinear optical microscopy: use of second harmonic generation and two-photon microscopy for automated quantitative liver fibrosis studies.

Liver fibrosis is associated with an abnormal increase in an extracellular matrix in chronic liver diseases. Quantitative characterization of fibrillar collagen in intact tissue is essential for both fibrosis studies and clinical applications. Commonly used methods, histological staining followed by either semiquantitative or computerized image analysis, have limited sensitivity, accuracy, and operator-dependent variations. The fibrillar collagen in sinusoids of normal livers could be observed through second-harmonic generation (SHG) microscopy. The two-photon excited fluorescence (TPEF) images, recorded simultaneously with SHG, clearly revealed the hepatocyte morphology. We have systematically optimized the parameters for the quantitative SHG/TPEF imaging of liver tissue and developed fully automated image analysis algorithms to extract the information of collagen changes and cell necrosis. Subtle changes in the distribution and amount of collagen and cell morphology are quantitatively characterized in SHG/TPEF images. By comparing to traditional staining, such as Masson's trichrome and Sirius red, SHG/TPEF is a sensitive quantitative tool for automated collagen characterization in liver tissue. Our system allows for enhanced detection and quantification of sinusoidal collagen fibers in fibrosis research and clinical diagnostics.

Illumination and fluorescence collection volumes for fiber optic probes in tissue.

Optical fibers can deliver light to, and collect it from, regions deep in tissue. However, reported illumination and fluorescence collection volumes adjacent to the fiber tip have been inconsistent, and systematic data on this topic are not available. Illumination and fluorescence collection profiles were characterized with high spatial resolution for different optical fibers in tissue and various fluids using two-photon flash photolysis and excitation. We confirm that illumination and fluorescence collection volumes for optical fibers are near identical. Collection volume is determined by the core dimensions and numerical aperture (NA) of the fiber and the scattering properties of the medium. For a multimode optical fiber with 100 microm core diam and NA=0.22, 80% of the total fluorescence is collected from a depth of 170 microm in tissue and 465 microm in nonscattering fluid. A semiempirical mathematical description of photon flux adjacent to the fiber tip was also developed and validated. This was used to quantify the extent of temporal blurring associated with propagation of a wavefront of altered fluorescence emission across the region addressed by fiber optic probes. We provide information that will facilitate the design of optical probes for tissue imaging or therapeutic applications.

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.

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.