A Quantitative Magnetic Resonance Cholangiopancreatography Metric of Intrahepatic Biliary Dilatation Severity Detects High-Risk Primary Sclerosing Cholangitis

Accuracy, repeatability, reproducibility and reference ranges of primary sclerosing cholangitis specific biomarkers from quantitative MRCP

PURPOSE

To assess the repeatability and reproducibility of quantitative MRCP-derived metrics generated from MRCP + software, designed for assessing biliary tree health.

METHODS

Metric accuracy was assessed using a 3D-printed phantom containing 20 tubes with sinusoidally-varying diameters, simulating strictures and dilatations along ducts. Data from 80 participants (60 healthy volunteers and 20 with liver disease) was analysed in total. Repeatability and reproducibility of the quantitative metrics were assessed on Siemens, GE and Philips scanners at both 1.5T and 3T. All subjects were scanned on a Siemens Prisma 3T scanner which acted as the reference scanner. A subset of these participants also underwent scanning on the remaining scanners. Data from healthy volunteers was used to estimate the natural range of measured values (reference ranges). The reproducibility coefficient (RC) of 7 commonly reported quantitative metrics were compared between healthy controls and published values in primary sclerosing cholangitis (PSC) patients.

RESULTS

The phantom analysis confirmed measurement accuracy with absolute bias of 0.0-0.1 for strictures and 0.1-0.2 for dilatations across all scanners (95% limits of agreement within ± 1.0). In vivo, RCs for the quantitative MRCP-derived metrics across the scanners ranged from: 12.4-25.4 for total number of ducts; 4.9-7.9 for number of dilatations; 3.3-6.5 for number of strictures; 4.6-9.8 mm for total length of dilatations; 26.5-51.7 mm for total length of strictures; and 4.4-6.8 for number of ducts with a stricture or dilatation. Repeatability on the same scanner was generally better than comparisons across scanners. Six metrics demonstrated sufficient cross-scanner reproducibility to distinguish healthy volunteers from PSC patients.

CONCLUSION

The precision of quantitative MRCP-derived metrics were sufficient to differentiate PSC and healthy subjects and should be well suited for multi-centre trials and assessment of biliary tree health.

Diagnosis of Cholangiocarcinoma: The New Biological and Technological Horizons

The diagnosis of cholangiocarcinoma (CCA) remains challenging. Although new technologies have been developed and validated, their routine use in clinical practice is needed. Conventional cytology obtained during endoscopic retrograde cholangiopancreatography-guided brushings is the first-line technique for the diagnosis of CCA, but it has shown limited sensitivity when combined with endoscopic ultrasound-guided biopsy. Other diagnostic tools have been proposed for the diagnosis of CCA, with their respective advantages and limitations. Cholangioscopy with biopsy or cytology combined with FISH analysis, intraductal biliary ultrasound and confocal laser microscopy have made significant advances in the last decade. More recently, developments in the analytical "omics" sciences have allowed the mapping of the microbiota of patients with CCA, and liquid biopsy with proteomic and extracellular vesicle analysis has allowed the identification of new biomarkers that can be incorporated into the predictive diagnostics. Furthermore, in the preoperative setting, radiomics, radiogenomics and the integrated use of artificial intelligence may provide new useful foundations for integrated diagnosis and personalized therapy for hepatobiliary diseases. This review aims to evaluate the current diagnostic approaches and innovative translational research that can be integrated for the diagnosis of CCA.

Quantitative Magnetic Resonance Cholangiopancreatography Scoring and Its Predictive Value for Outcomes in Adults with Primary Sclerosing Cholangitis

Background: Primary sclerosing cholangitis (PSC) is an immune-mediated disease that has an unfavorable prognosis and needs a liver transplant (LT). The aim of this paper was to show the usefulness of the Majoie classification on magnetic resonance cholangiopancreatography (MRCP) images in assessing the prognosis in adult patients with PSC. Methods: Our work presents a retrospective monocentric study performed on 64 adult patients with PSC of the large bile ducts. Two radiologists evaluated the MRCP of diagnosis and calculated MRCP scores using the Majoie classification. Liver-related outcome (LT or liver-related death) was marked as a primary endpoint. Results: Univariate analysis showed that patients with more severe lesions (sum score of intrahepatic and extrahepatic ducts > 3) had a lower age at diagnosis, of 37.2 years, complicated with liver cirrhosis (53.1% of patients) and recurrent cholangitis (28.1%) p < 0.05, without significant differences in mortality, association with IBD or LT. Concordance analysis between MRCP prognostic scores and progression to a PSC-related event showed a moderate relationship (c-statistic 0.662), and a good AUROC was observed for the UKPSC score (0.893) and the MRS (0.936). Conclusions: In the study, we observed a good correlation between the imaging scores based on the Majoie classification and the evolution of the patients. These scores were outperformed by the UKPSC, MRS, and PREsTo clinical models. Their utility was best in predicting recurrent cholangitis.

Quantitative MRCP and metrics of bile duct disease over time in patients with primary sclerosing cholangitis: A prospective study

BACKGROUND

Imaging markers of biliary disease in primary sclerosing cholangitis (PSC) have potential for use in clinical and trial disease monitoring. Herein, we evaluate how quantitative magnetic resonance cholangiopancreatography (MRCP) metrics change over time, as per the natural history of disease.

METHODS

Individuals with PSC were prospectively scanned using non-contrast MRCP. Quantitative metrics were calculated using MRCP+ post-processing software to assess duct diameters and dilated and strictured regions. Additionally, a hepatopancreatobiliary radiologist (blinded to clinical details, biochemistry and quantitative biliary metrics) reported each scan, including ductal disease assessment according to the modified Amsterdam Cholangiographic Score (MAS).

RESULTS

At baseline, 14 quantitative MRCP+ metrics were found to be significantly different in patients with PSC (N = 55) compared to those with primary biliary cholangitis (N = 55), autoimmune hepatitis (N = 57) and healthy controls (N = 18). In PSC specifically, baseline metrics quantifying the number of strictures and the number and length of bile ducts correlated with the MAS, transient elastography and serum ALP values (p < 0.01 for all correlations). Over a median 371-day follow-up (range: 364-462), 29 patients with PSC underwent repeat MRCP, of whom 15 exhibited quantitative changes in MRCP+ metrics. Compared to baseline, quantitative MRCP+ identified an increasing number of strictures over time (p < 0.05). Comparatively, no significant differences in biochemistry, elastography or the MAS were observed between timepoints. Quantitative MRCP+ metrics remained stable in non-PSC liver disease.

CONCLUSION

Quantitative MRCP+ identifies changes in ductal disease over time in PSC, despite stability in biochemistry, liver stiffness and radiologist-derived cholangiographic assessment (trial registration: ISRCTN39463479).

Biliary Duct Dilatation: AJR Expert Panel Narrative Review

Biliary duct dilatation is a common incidental finding in practice, but it is unlikely to indicate biliary obstruction in the absence of clinical symptoms or elevated levels on liver function tests (LFTs). However, the clinical presentation may be nonspecific, and LFTs may either be unavailable or difficult to interpret. The goal of this AJR Expert Panel Narrative Review is to highlight a series of topics fundamental to the management of biliary duct dilatation, providing consensus recommendations in a question-and-answer format. We start by covering a basic approach to interpreting LFT results, the strengths and weaknesses of the biliary imaging modalities, and how and where to measure the extrahepatic bile duct. Next, we define the criteria for biliary duct dilatation, including patients with prior cholecystectomy and advanced age, and discuss when and whether biliary duct dilatation can be attributed to papillary stenosis or sphincter of Oddi dysfunction. Subsequently, we discuss two conditions in which the duct is pathologically dilated but not obstructed: congenital cystic dilatation (i.e., choledochal cyst) and intraductal papillary neoplasm of the bile duct. Finally, we provide guidance regarding when to recommend obtaining additional imaging or testing, such as endoscopic ultrasound or ERCP, and include a discussion of future directions in biliary imaging.

Interobserver agreement and prognostic value of image-based scoring systems in patients with primary sclerosing cholangitis

BACKGROUND

Primary sclerosing cholangitis (PSC) is a cholestatic liver disease that progresses to cirrhosis and liver failure. The Anali and Amsterdam scores are based upon imaging features on MRI and ERCP, respectively.

AIMS

We aimed to compare the interobserver variability and performances of these scores.

METHODS

Patients with PSC with at least 1 MRCP were included. Images were independently scored by 2 experts. Agreement and prognostic performance with a primary end point of hepatic decompensation was assessed.

RESULTS

Fifty-nine patients were included (67.8% male, 86.4% IBD). Interobserver agreement for the Anali and Amsterdam scores were moderate (k = 0.49; 95% CI 0.35-0.64 and k = 0.43; 95% CI 0.30-0.56, respectively). Among the Anali components, dysmorphy (caudate/right lobe ratio > 0.9) had fair agreement (k = 0.37; 95% CI 0.14-0.60) and portal hypertension (k = 0.64, 95% CI 0.32-0.89) and intrahepatic dilation (k = 0.70; 95% CI 0.53-0.87) had substantial agreement. The Amsterdam extrahepatic and intrahepatic scores had fair agreement (k = 0.38; 95% CI 0.23-0.52) and moderate agreement (k = 0.50; 95% CI 0.34-0.67), respectively. Anali score (HR 5.90, 95% CI 1.64-21.21), total bilirubin (HR = 3.23; 95% Cl 1.06-9.91), and age (HR = 1.05; 95% CI 1.00-1.11) were independent predictors of hepatic decompensation. Mayo risk score and Anali score had good discriminative ability with c-statistics of 0.78 (CI 0.59-0.96) and 0.76 (CI 0.56-0.91). Anali score remained an independent predictor after adjusting for Mayo risk score.

CONCLUSION

Anali score adds additional predictive value for hepatic decompensation in patients with PSC.

Prognostic models and autoimmune liver diseases

Autoimmune liver diseases (AILDs) are complex diseases with unknown causes and immune-mediated pathophysiology. In primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) disease modifying drugs are available which improve patient quality and quantity of life. In primary sclerosing cholangitis (PSC) no medical therapy is available and the only accepted treatment is liver transplantation (LT). PBC, PSC and AIH possess features that describe the archetype of patients within each disorder. On the other hand, the classical disorders are not homogeneous, and patients within each diagnosis may present with a range of clinical, biochemical, serological, and histological findings. Singularly, they are considered rare diseases, but together, they account for approximately 20% of LTs in Europe and USA. Management of these patients is complex, as AILDs are relatively uncommon in clinical practice with challenges in developing expertise, disease presentation can be sneaky, clinical phenotypes and disease course are heterogeneous. Prognostic models are key tools for clinicians to assess patients' risk and to provide personalized care to patients. Aim of this review is to discuss challenges of the management of AILDs and how the available prognostic models can help. We will discuss the prognostic models developed in AILDs, with a special focus on the prognostic models that can support the clinical management of patients with AILDs: in PBC models based on ursodeoxycholic acid (UDCA) response and markers of liver fibrosis; in PSC several markers including biochemistry, disease stage and radiological semiquantitative markers; and finally in AIH, markers of disease stage and disease activity.

A composite score using quantitative magnetic resonance cholangiopancreatography predicts clinical outcomes in primary sclerosing cholangitis

Background & Aims

Magnetic resonance cholangiopancreatography (MRCP) for evaluation of biliary disease currently relies on subjective assessment with limited prognostic value because of the lack of quantitative metrics. Artificial intelligence-enabled quantitative MRCP (MRCP+) is a novel technique that segments biliary anatomy and provides quantitative biliary tree metrics. This study investigated the utility of MRCP+ as a prognostic tool for the prediction of clinical outcomes in primary sclerosing cholangitis (PSC).

Methods

MRCP images of patients with PSC were post-processed using MRCP+ software. The duration between the MRCP and clinical event (liver transplantation or death) was calculated. Survival analysis and stepwise Cox regression were performed to investigate the optimal combination of MRCP+ metrics for the prediction of clinical outcomes. The resulting risk score was validated in a separate validation cohort and compared with an existing prognostic score (Mayo risk score).

Results

In this retrospective study, 102 patients were included in a training cohort and a separate 50 patients formed a validation cohort. Between the two cohorts, 34 patients developed clinical outcomes over a median duration of 3 years (23 liver transplantations and 11 deaths). The proportion of bile ducts with diameter 3-5 mm, total bilirubin, and aspartate aminotransferase were independently associated with transplant-free survival. Combined as a risk score, the overall discriminative performance of the MRCP+ risk score (M+BA) was excellent; area under the receiver operator curve 0.86 (95% CI: 0.77, 0.95) at predicting clinical outcomes in the validation cohort with a hazard ratio 5.8 (95% CI: 1.5, 22.1). This was superior to the Mayo risk score.

Conclusions

A composite score combining MRCP+ with total bilirubin and aspartate aminotransferase (M+BA) identified PSC patients at high risk of liver transplantation or death. Prospective studies are warranted to evaluate the clinical utility of this novel prognostic tool.

Impact and Implications

Primary sclerosis cholangitis (PSC) is a disease of the biliary tree where inflammation and fibrosis cause areas of narrowing (strictures) and expansion (dilatations) within the biliary ducts leading to liver failure and/or cancer (cholangiocarcinoma). In this study, we demonstrate that quantitative assessment of the biliary tree can better identify patients with PSC who are at high risk of either death or liver transplantation than a current blood-based risk score (Mayo risk score).

Imaging of the biliary tract

PURPOSE OF REVIEW

Magnetic resonance cholangiopancreatography (MRCP) has become the reference examination for the exploration of the biliary tract and has replaced endoscopic cholangiography for the analysis of the biliary tract because of its equivalent performance and its noninvasive character.

RECENT FINDINGS

Based on the International Primary Sclerosing Cholangitis (PSC) Study Group recommendations for MR imaging in PSC, two protocols can be distinguished for the imaging of biliary tract: a basic protocol and a more complete protocol. It is essential to know the main pitfalls in order not to wrongly describe biliary anomalies. In addition to the excellent performance of MR imaging with MRCP in analyzing the anatomy and the anomalies of the biliary tree, complementary techniques have recently been developed. Several MR prognostic factors have been described. New hepato-specific contrast agents are now available for assessment of the general and segmental liver function. MR Elastography and Diffusion-weighted MR sequences are accurate to evaluate the degree of hepatic fibrosis. Finally, images obtained in MRCP can be postprocessed by a software that will analyze and model the biliary tree in order to quantitatively evaluate the biliary system.

SUMMARY

Magnetic resonance imaging with its recent developments becomes by now an essential tool for the evaluation of biliary diseases.

Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis

BACKGROUND

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can lead to cirrhosis and hepatic decompensation. However, predicting future outcomes in patients with PSC is challenging. Our aim was to extract magnetic resonance imaging (MRI) features that predict the development of hepatic decompensation by applying algebraic topology-based machine learning (ML).

METHODS

We conducted a retrospective multicenter study among adults with large duct PSC who underwent MRI. A topological data analysis-inspired nonlinear framework was used to predict the risk of hepatic decompensation, which was motivated by algebraic topology theory-based ML. The topological representations (persistence images) were employed as input for classification to predict who developed early hepatic decompensation within one year after their baseline MRI.

RESULTS

We reviewed 590 patients; 298 were excluded due to poor image quality or inadequate liver coverage, leaving 292 potentially eligible subjects, of which 169 subjects were included in the study. We trained our model using contrast-enhanced delayed phase T1-weighted images on a single center derivation cohort consisting of 54 patients (hepatic decompensation, n = 21; no hepatic decompensation, n = 33) and a multicenter independent validation cohort of 115 individuals (hepatic decompensation, n = 31; no hepatic decompensation, n = 84). When our model was applied in the independent validation cohort, it remained predictive of early hepatic decompensation (area under the receiver operating characteristic curve = 0.84).

CONCLUSIONS

Algebraic topology-based ML is a methodological approach that can predict outcomes in patients with PSC and has the potential for application in other chronic liver diseases.

Artificial intelligence for precision medicine in autoimmune liver disease

Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.