Multiinstitutional Evaluation of the Liver Surface Nodularity Score on CT for Staging Liver Fibrosis and Predicting Liver-Related Events in Patients With Hepatitis C

Elkassem, Asser Abou; Allen, Brian C.; Lirette, Seth T.; Cox, Kelly; Remer, Erick M.; Pickhardt, Perry J.; Lubner, Meghan G.; Sirlin, Claude B.; Dondlinger, Timothy; Schmainda, Michael; Jacobus, Robert B; Severino, Paige E.; Smith, Andrew D.

doi:10.2214/ajr.21.27062

Cited by 8 publications

(1 citation statement)

References 34 publications

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“…The next most important feature was a measure of “nodularity,” obtained by comparing the actual vs smoothed perimeter of the liver. This is frequently noted on visual inspection of the liver during laparotomy or autopsy, and in other radiologic studies, it has been shown to predict advanced fibrosis in patients with hepatitis C ( 28 ). Thus, in contrast to the “black box” criticism of typical CNN architectures, our approach simultaneously combines other solid radiologic indicators to better predict cirrhosis on nondedicated CT.…”

Section: Discussionmentioning

confidence: 95%

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans

Mazumder

Enchakalody

Zhang

et al. 2023

Clin Transl Gastroenterol

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Background: Undiagnosed cirrhosis remains a significant problem. In this study, we developed and tested an automated liver segmentation tool to predict the presence of cirrhosis in a population of patients with paired liver biopsy and CT scans. Methods: We utilized a cohort of 1590 CT scans within the Morphomics database to train an automated liver segmentation model using 3D-U-Net and Google’s DeeplLabv3+. Imaging features were then automatically calculated from an external test cohort of patients with chronic liver disease who had a paired liver biopsy and CT within 6 months of each other in Jan 2004-2012. Using gradient boosting decision trees, we developed multivariate models to predict presence of histologic cirrhosis and evaluated with five fold cross validated c-statistic. Results: Our cohort had 351 patients, 96 patients had cirrhosis. Of the total cohort, 72 were post liver transplant. Both FIB-4 and liver morphomics alone performed equally well with AUROC of 0.76 (95% CI 0.70-0.81) and 0.71 (95% CI 0.65-0.76), respectively (p = 0.2). However, the combination of liver morphomics with laboratory values or liver morphomics with laboratory and demographic data resulted in significant improved performance with AUROC of 0.84 (0.80-0.89) and 0.85 (0.81-0.90) compared to FIB-4 alone (p <0.001). In a subgroup analysis, we also examined performance in patients without liver transplantation and saw similar augmentation of FIB-4. Conclusion: This proof of principle study demonstrates that automatically extracted features within CT scans can be combined with classic electronic medical record data to improve prediction of cirrhosis in patients with liver disease. This tool may be utilized in both pre and post-transplant patients and has the potential to improve our ability to detect undiagnosed cirrhosis.

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Section: Discussionmentioning

confidence: 95%

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans

Mazumder

Enchakalody

Zhang

et al. 2023

Clin Transl Gastroenterol

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Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future

Huang,

Peng,

Kang

2024

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Liver fibrosis is a reparative response triggered by liver injury. Non‐invasive assessment and staging of liver fibrosis in patients with chronic liver disease are of paramount importance, as treatment strategies and prognoses depend significantly on the degree of fibrosis. Although liver fibrosis has traditionally been staged through invasive liver biopsy, this method is prone to sampling errors, particularly when biopsy sizes are inadequate. Consequently, there is an urgent clinical need for an alternative to biopsy, one that ensures precise, sensitive, and non‐invasive diagnosis and staging of liver fibrosis. Non‐invasive imaging assessments have assumed a pivotal role in clinical practice, enjoying growing popularity and acceptance due to their potential for diagnosing, staging, and monitoring liver fibrosis. In this comprehensive review, we first delved into the current landscape of non‐invasive imaging technologies, assessing their accuracy and the transformative impact they have had on the diagnosis and management of liver fibrosis in both clinical practice and animal models. Additionally, we provided an in‐depth exploration of recent advancements in ultrasound imaging, computed tomography imaging, magnetic resonance imaging, nuclear medicine imaging, radiomics, and artificial intelligence within the field of liver fibrosis research. We summarized the key concepts, advantages, limitations, and diagnostic performance of each technique. Finally, we discussed the challenges associated with clinical implementation and offer our perspective on advancing the field, hoping to provide alternative directions for the future research.

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Quantitative and qualitative liver CT: imaging feature association with histopathologically confirmed hepatic cirrhosis

et al. 2022

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Multiinstitutional Evaluation of the Liver Surface Nodularity Score on CT for Staging Liver Fibrosis and Predicting Liver-Related Events in Patients With Hepatitis C

Cited by 8 publications

References 34 publications

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans

Using Artificial Intelligence to Predict Cirrhosis From Computed Tomography Scans

Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future

Quantitative and qualitative liver CT: imaging feature association with histopathologically confirmed hepatic cirrhosis

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