Thick Fibrous Septa on Liver Biopsy Specimens Predict the Development of Decompensation in Patients With Compensated Cirrhosis

Jain, Dhanpat; Sreenivasan, Prithvi; Inayat, Irteza; Deng, Yanhong; Ciarleglio, Maria; García–Tsao, Guadalupe

doi:10.1093/ajcp/aqab024

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Therefore, we have developed a machine learning model with 448 variables focusing on the key histological features of cirrhotic liver biopsies, including septa, previous literature demonstrating that septal thickness correlate with the severity of cirrhosis. 30 Also, the baseline machine learning HVPG score was not predictive of clinical liver events.…”

Section: Discussionmentioning

confidence: 95%

“…All these scores performed well and confirmed the value of including crucial histologic features of cirrhosis in histological assessment of liver biopsies. A recently published machine learning model which correlated well with CSPH in NASH cirrhosis patients included nodules of size that were weakly correlated with HVPG, and it did not consider septal thickness, 22 despite previous literature demonstrating that septal thickness correlate with the severity of cirrhosis 30 . Also, the baseline machine learning HVPG score was not predictive of clinical liver events.…”

Section: Discussionmentioning

confidence: 99%

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

Noureddin

Goodman

Tai

et al. 2023

Aliment Pharmacol Ther

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Summary 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.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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

Noureddin

Goodman

Tai

et al. 2023

Aliment Pharmacol Ther

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

Naoumov,

Kleiner,

Chng

et al. 2024

Liver International

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Background and AimsMetabolic 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.MethodsPaired 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.ResultsqFibrosis 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.ConclusionSHG/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.

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Compensated liver cirrhosis: Natural course and disease-modifying strategies

Kumar,

Prakash

2023

World J Methodol

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The documentation of psychological trauma is obviously a challenge to clinicians while they are diving deep into remote events related to their clients or patients. The potential role of psychological trauma in the early developmental stages, and even the existence of adverse childhood experiences, is important to prove, yet it is difficult to do so. A diverse range of methods have been applied, all of which presumably benchmark a big therapeutic step; however, these enthusiastic methods frequently do not last for long. While hypnosis supporters, Freudian and Neo-Freudian disciples can be acute enough to enhance and uncover suppressed memories, modern psychiatry relies mostly on diversely structured interviews. Functional magnetic resonance and its related subtleties might help, but the questions that remain unanswered are numerous and confusing. Connecting early experiences with long-term memory while identifying psychological trauma its importance for the individual’s growth trajectory; thus, it remains an intriguing issue.

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Thick Fibrous Septa on Liver Biopsy Specimens Predict the Development of Decompensation in Patients With Compensated Cirrhosis

Cited by 3 publications

References 22 publications

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

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

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

Compensated liver cirrhosis: Natural course and disease-modifying strategies

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