FibroSIGHT™ Plus
FIBROSIS ASSESSMENT WITH
AUTOMATED QUANTITATIVE ANALYSIS
Precisely measure MASH disease status and response to treatment
What is FibroSIGHT™ Plus?
FibroSIGHT™ Plus leverages our stain-free Second Harmonic Generation (SHG) and Two-Photon Excitation Fluorescence (TPEF) imaging technology to capture detailed tissue structure and collagen fibers with high consistency – eliminating the variability associated with conventional tissue staining.
At the core of FibroSIGHT Plus is qFibrosis®, HistoIndex’s proprietary AI-based algorithm, which automatically identifies and quantifies multiple fibrosis-related collagen parameters from SHG images. These are integrated into a continuous numerical index known as the qFibrosis® value, offering a detailed and objective measurement of fibrosis severity across the wide spectrum of fibrosis in MASH.
To support clinical decision-making, the qFibrosis value is further categorized into qualitative stages that align with the NASH-CRN fibrosis scoring system1, providing both clinical relevance and improved resolution for tracking disease progression or treatment response.
The Plus That Makes the Difference: How FibroSIGHT™ Plus Elevates Fibrosis Assessment
Standardized and
Objective Assessment
By removing reader subjectivity2, FibroSIGHT Plus delivers truly standardized fibrosis evaluation – reducing variability and increasing diagnostic confidence.
Continuous Fibrosis
Quantification
Unlike traditional scoring systems, FibroSIGHT Plus expresses fibrosis as a continuous variable rather than relying on categorical scoring systems – unlocking deeper insights into the disease severity.
Detects Subtle Fibrosis Changes
Track small but meaningful shifts in fibrosis over time3. This level of detail enables earlier detection of treatment effects or disease progression.
Precision for Personalized Care
Compared to current histology practices, by using more reliable, objective, and granular data, clinicians are able to make better-informed therapeutic decisions tailored to each patient.
How FibroSIGHT™ Plus Works
Capture Fibrosis with Advanced IMAGING Technology
FibroSIGHT Plus begins with a dual-channel imaging approach using Second Harmonic Generation (SHG) and Two-Photon Excitation Fluorescence (TPEF). These high-resolution, label-free techniques visualize liver biopsy samples without staining. TPEF captures anatomical structures like portal tracts (PT) and central veins (CV), enabling precise tissue zoning. The highly specific SHG channel provides accurate identification of collagen fibers, enabling identification and quantification of morphological and spatial metrics of fibrosis parameters.
QUANTIFYING Fibrosis with a Continuous qFibrosis value
Leveraging SHG images the qFibrosis algorithm automatically quantifies multiple collagen parameters and translates them into a continuous numerical index – the qFibrosis® value. This value captures both the severity and spatial distribution of fibrosis across the entire tissue sample. By offering greater granularity than traditional categorical scores, qFibrosis value enables clinicians to better characterize individual patients at diagnosis and to monitor subtle changes in their disease over time – such as early progression or regression – that may be missed with conventional methods.
Observe the power of qFibrosis in discriminating and subtly quantifying the severity of fibrosis of liver biopsy specimens:
TRANSLATING Scores into Clinically Interpretable Stages
The qFibrosis value is translated to ordinal stages that correspond to the NASH-CRN fibrosis stages to offer clinical interpretability. Through this multi-step, quantitative approach, FibroSIGHT Plus provides a reproducible and objective fibrosis assessment that enhances diagnostic accuracy and supports therapeutic decision making.
Hover over the red arrows on the gradient color bar below to observe the corresponding SHG image
Structured REPORT for Confident
Decision-Making
The final FibroSIGHT Plus report includes both the qFibrosis value and its corresponding stage in a clear, standardized format—making complex data easy to interpret. In addition, FibroSIGHT Plus identifies whether a sample falls into the at-risk MASH or cirrhotic MASH classification. Samples with a fibrosis stage of F2 or higher are classified as at-risk MASH⁴, while those with a fibrosis stage of F4 are categorized as cirrhotic MASH⁵. This added layer of interpretation supports clinical decision-making by highlighting patients who may require closer monitoring or intervention.
The report enables clinicians to quickly assess disease status and guide treatment decisions with confidence.
How FibroSIGHT™ Plus
Performs in Real-World Clinical Cohorts
The qFibrosis algorithm has been extensively validated, showing strong correlation with the NASH-CRN staging system and reinforcing its clinical relevance6.
For detecting at-risk fibrosis (F2–F4) vs. early-stage (F0/1), it achieved an AUROC of 0.88, with 85% sensitivity and 71% specificity. For identifying cirrhosis (F4), it reached an AUROC of 0.91 with 96% specificity, enabling confident diagnosis of advanced disease.
These results confirm qFibrosis as a reliable and accurate tool for fibrosis stratification in clinical and research settings.
Correlation of qFibrosis with pathologist staging with NASH-CRN staging system (rs = 0.81, p-value < 0.001)
Concordance of community pathologists (CP) and FibroSIGHT Plus to the reference fibrosis read.
An internal validation study of 120 MASH samples demonstrated that fully automated fibrosis assessment with FibroSIGHT Plus yielded approximately 10% more samples with concordance to reference fibrosis stages – defined by a panel of expert pathologists – compared to assessments by community pathologists using traditional Masson’s Trichrome staining, which represents the current standard of care.
This underscores the qFibrosis algorithm’s utility as a more consistent and reliable tool for fibrosis staging today.
References
1. Kleiner, D. E. et al. (2005). Hepatology, 41(6), 1313-1321.
2. Abdurrachim, D. et al. (2025). J Hepatol, 82(5), 898-908.
3. Sanyal, A.J., Jha, P. & Kleiner, D.E. Nat Rev Gastroenterol Hepatol 21, 57–69 (2024).
4. Noureddin, M. et al. (2024). Hepatology, 79(1), 135-148.
5. Heyens, L. J. et al. (2021). Front Med, 8, 615978.
6. Kendall, T. J., et al.(2024). Liver Int., 44(10), 2511-2516.