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1. Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Author

Jenny, Lee, Max, Westphal, Yasaman, Vali, Jerome, Boursier, Salvatorre, Petta, Rachel, Ostroff, Leigh, Alexander, Yu, Chen, Celine, Fournier, Andreas, Geier, Sven, Francque, Kristy, Wonder, Dina, Tiniako, Pierre, Bedossa, Mike, Allison, Georgios, Papatheodoridi, Helena, Cortez-Pinto, Raluca, Pai, Jean-Francois, Dufour, Diana Julie, Leeming, Stephen, Harrison, Jeremy, Cobbold, Adriaan G, Holleboom, Hannele, Yki-Järvinen, Javier, Crespo, Mattias, Ekstedt, Guruprasad P, Aithal, Elisabetta, Bugianesi, Manuel, Romero-Gomez, Richard, Torstenson, Morten, Karsdal, Carla, Yuni, Jörn M, Schattenberg, Detlef, Schuppan, Vlad, Ratziu, Clifford, Bra, Kevin, Duffin, Koos, Zwinderman, Michael, Pavlide, Quentin M, Anstee, Patrick M, Bossuyt, Anstee, Quentin M., Daly, Ann K., Govaere, Olivier, Cockell, Simon, Tiniakos, Dina, Bedossa, Pierre, Burt, Alastair, Oakley, Fiona, Cordell, Heather J., Day, Christopher P., Wonders, Kristy, Missier, Paolo, Mcteer, Matthew, Vale, Luke, Oluboyede, Yemi, Breckons, Matt, Bossuyt, Patrick M., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Nieuwdorp, Max, Holleboom, Adriaan G., Verheij, Joanne, Ratziu, Vlad, Clément, Karine, Patino-Navarrete, Rafael, Pais, Raluca, Paradis, Valerie, Schuppan, Detlef, Schattenberg, Jörn M., Surabattula, Rambabu, Myneni, Sudha, Straub, Beate K., Vidal-Puig, Toni, Vacca, Michele, Rodrigues-Cuenca, Sergio, Allison, Mike, Kamzolas, Ioanni, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J., Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, Mcglinchey, Aiden, Mato, Jose M., Millet, Óscar, Dufour, Jean-Françoi, Berzigotti, Annalisa, Masoodi, Mojgan, Pavlides, Michael, Harrison, Stephen, Neubauer, Stefan, Cobbold, Jeremy, Mozes, Ferenc, Akhtar, Salma, Olodo-Atitebi, Seliat, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Andersson, Anneli, Wigley, Ioan, Romero-Gómez, Manuel, Gómez-González, Emilio, Ampuero, Javier, Castell, Javier, Gallego-Durán, Rocío, Fernández, Isabel, Montero-Vallejo, Rocío, Karsdal, Morten, Guldager Kring Rasmussen, Daniel, Leeming, Diana Julie, Sinisi, Antonia, Musa, Kishwar, Sandt, Estelle, Tonini, Manuela, Bugianesi, Elisabetta, Rosso, Chiara, Armandi, Angelo, Marra, Fabio, Gastaldelli, Amalia, Svegliati, Gianluca, Boursier, Jérôme, Francque, Sven, Vonghia, Luisa, Driessen, Ann, Ekstedt, Mattia, Kechagias, Stergio, Yki-Järvinen, Hannele, Porthan, Kimmo, Arola, Johanna, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Rodrigues, Cecilia M. P., Valenti, Luca, Pelusi, Serena, Petta, Salvatore, Pennisi, Grazia, Miele, Luca, Geier, Andrea, Trautwein, Christian, Aithal, Guruprasad P., Francis, Susan, Hockings, Paul, Schneider, Moritz, Newsome, Philip, Hübscher, Stefan, Wenn, David, Rosenquist, Christian, Trylesinski, Aldo, Mayo, Rebeca, Alonso, Cristina, Duffin, Kevin, Perfield, James W., Chen, Yu, Yunis, Carla, Tuthill, Theresa, Harrington, Magdalena Alicia, Miller, Melissa, Chen, Yan, Mcleod, Euan Jame, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Skalshøi Kjær, Mette, Mørch Harder, Lea, Davidsen, Peter, Mikkelsen, Lars Frii, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Dougla, Shankar, Sudha, Torstenson, Richard, Fournier-Poizat, Céline, Llorca, Anne, Kalutkiewicz, Michael, Pepin, Kay, Ehman, Richard, Horan, Gerald, Ho, Gideon, Tai, Dean, Chng, Elaine, Patterson, Scott D., Billin, Andrew, Doward, Lynda, Twiss, Jame, Thakker, Paresh, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, Hytiroglou, Prodromos, Luca, Miele (ORCID:0000-0003-3464-0068), Jenny, Lee, Max, Westphal, Yasaman, Vali, Jerome, Boursier, Salvatorre, Petta, Rachel, Ostroff, Leigh, Alexander, Yu, Chen, Celine, Fournier, Andreas, Geier, Sven, Francque, Kristy, Wonder, Dina, Tiniako, Pierre, Bedossa, Mike, Allison, Georgios, Papatheodoridi, Helena, Cortez-Pinto, Raluca, Pai, Jean-Francois, Dufour, Diana Julie, Leeming, Stephen, Harrison, Jeremy, Cobbold, Adriaan G, Holleboom, Hannele, Yki-Järvinen, Javier, Crespo, Mattias, Ekstedt, Guruprasad P, Aithal, Elisabetta, Bugianesi, Manuel, Romero-Gomez, Richard, Torstenson, Morten, Karsdal, Carla, Yuni, Jörn M, Schattenberg, Detlef, Schuppan, Vlad, Ratziu, Clifford, Bra, Kevin, Duffin, Koos, Zwinderman, Michael, Pavlide, Quentin M, Anstee, Patrick M, Bossuyt, Anstee, Quentin M., Daly, Ann K., Govaere, Olivier, Cockell, Simon, Tiniakos, Dina, Bedossa, Pierre, Burt, Alastair, Oakley, Fiona, Cordell, Heather J., Day, Christopher P., Wonders, Kristy, Missier, Paolo, Mcteer, Matthew, Vale, Luke, Oluboyede, Yemi, Breckons, Matt, Bossuyt, Patrick M., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Nieuwdorp, Max, Holleboom, Adriaan G., Verheij, Joanne, Ratziu, Vlad, Clément, Karine, Patino-Navarrete, Rafael, Pais, Raluca, Paradis, Valerie, Schuppan, Detlef, Schattenberg, Jörn M., Surabattula, Rambabu, Myneni, Sudha, Straub, Beate K., Vidal-Puig, Toni, Vacca, Michele, Rodrigues-Cuenca, Sergio, Allison, Mike, Kamzolas, Ioanni, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J., Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, Mcglinchey, Aiden, Mato, Jose M., Millet, Óscar, Dufour, Jean-Françoi, Berzigotti, Annalisa, Masoodi, Mojgan, Pavlides, Michael, Harrison, Stephen, Neubauer, Stefan, Cobbold, Jeremy, Mozes, Ferenc, Akhtar, Salma, Olodo-Atitebi, Seliat, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Andersson, Anneli, Wigley, Ioan, Romero-Gómez, Manuel, Gómez-González, Emilio, Ampuero, Javier, Castell, Javier, Gallego-Durán, Rocío, Fernández, Isabel, Montero-Vallejo, Rocío, Karsdal, Morten, Guldager Kring Rasmussen, Daniel, Leeming, Diana Julie, Sinisi, Antonia, Musa, Kishwar, Sandt, Estelle, Tonini, Manuela, Bugianesi, Elisabetta, Rosso, Chiara, Armandi, Angelo, Marra, Fabio, Gastaldelli, Amalia, Svegliati, Gianluca, Boursier, Jérôme, Francque, Sven, Vonghia, Luisa, Driessen, Ann, Ekstedt, Mattia, Kechagias, Stergio, Yki-Järvinen, Hannele, Porthan, Kimmo, Arola, Johanna, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Rodrigues, Cecilia M. P., Valenti, Luca, Pelusi, Serena, Petta, Salvatore, Pennisi, Grazia, Miele, Luca, Geier, Andrea, Trautwein, Christian, Aithal, Guruprasad P., Francis, Susan, Hockings, Paul, Schneider, Moritz, Newsome, Philip, Hübscher, Stefan, Wenn, David, Rosenquist, Christian, Trylesinski, Aldo, Mayo, Rebeca, Alonso, Cristina, Duffin, Kevin, Perfield, James W., Chen, Yu, Yunis, Carla, Tuthill, Theresa, Harrington, Magdalena Alicia, Miller, Melissa, Chen, Yan, Mcleod, Euan Jame, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Skalshøi Kjær, Mette, Mørch Harder, Lea, Davidsen, Peter, Mikkelsen, Lars Frii, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Dougla, Shankar, Sudha, Torstenson, Richard, Fournier-Poizat, Céline, Llorca, Anne, Kalutkiewicz, Michael, Pepin, Kay, Ehman, Richard, Horan, Gerald, Ho, Gideon, Tai, Dean, Chng, Elaine, Patterson, Scott D., Billin, Andrew, Doward, Lynda, Twiss, Jame, Thakker, Paresh, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, Hytiroglou, Prodromos, and Luca, Miele (ORCID:0000-0003-3464-0068)

Abstract

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.

Published
2023

2. Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project): a comparative diagnostic accuracy study

Author

Yasaman Vali, Jenny Lee, Jerome Boursier, Salvatore Petta, Kristy Wonders, Dina Tiniakos, Pierre Bedossa, Andreas Geier, Sven Francque, Mike Allison, Georgios Papatheodoridis, Helena Cortez-Pinto, Raluca Pais, Jean-Francois Dufour, Diana Julie Leeming, Stephen A Harrison, Yu Chen, Jeremy F Cobbold, Michael Pavlides, Adriaan G Holleboom, Hannele Yki-Jarvinen, Javier Crespo, Morten Karsdal, Rachel Ostroff, Mohammad Hadi Zafarmand, Richard Torstenson, Kevin Duffin, Carla Yunis, Clifford Brass, Mattias Ekstedt, Guruprasad P Aithal, Jörn M Schattenberg, Elisabetta Bugianesi, Manuel Romero-Gomez, Vlad Ratziu, Quentin M Anstee, Patrick M Bossuyt, Epidemiology and Data Science, APH - Methodology, APH - Personalized Medicine, Graduate School, Gastroenterology and Hepatology, Vascular Medicine, ACS - Diabetes & metabolism, and AGEM - Amsterdam Gastroenterology Endocrinology Metabolism

Subjects
Hepatology, Gastroenterology, 610 Medicine & health
Abstract

BACKGROUND The reference standard for detecting non-alcoholic steatohepatitis (NASH) and staging fibrosis-liver biopsy-is invasive and resource intensive. Non-invasive biomarkers are urgently needed, but few studies have compared these biomarkers in a single cohort. As part of the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) project, we aimed to evaluate the diagnostic accuracy of 17 biomarkers and multimarker scores in detecting NASH and clinically significant fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) and identify their optimal cutoffs as screening tests in clinical trial recruitment. METHODS This was a comparative diagnostic accuracy study in people with biopsy-confirmed NAFLD from 13 countries across Europe, recruited between Jan 6, 2010, and Dec 29, 2017, from the LITMUS metacohort of the prospective European NAFLD Registry. Adults (aged ≥18 years) with paired liver biopsy and serum samples were eligible; those with excessive alcohol consumption or evidence of other chronic liver diseases were excluded. The diagnostic accuracy of the biomarkers was expressed as the area under the receiver operating characteristic curve (AUC) with liver histology as the reference standard and compared with the Fibrosis-4 index for liver fibrosis (FIB-4) in the same subgroup. Target conditions were the presence of NASH with clinically significant fibrosis (ie, at-risk NASH; NAFLD Activity Score ≥4 and F≥2) or the presence of advanced fibrosis (F≥3), analysed in all participants with complete data. We identified thres holds for each biomarker for reducing the number of biopsy-based screen failures when recruiting people with both NASH and clinically significant fibrosis for future trials. FINDINGS Of 1430 participants with NAFLD in the LITMUS metacohort with serum samples, 966 (403 women and 563 men) were included after all exclusion criteria had been applied. 335 (35%) of 966 participants had biopsy-confirmed NASH and clinically significant fibrosis and 271 (28%) had advanced fibrosis. For people with NASH and clinically significant fibrosis, no single biomarker or multimarker score significantly reached the predefined AUC 0·80 acceptability threshold (AUCs ranging from 0·61 [95% CI 0·54-0·67] for FibroScan controlled attenuation parameter to 0·81 [0·75-0·86] for SomaSignal), with accuracy mostly similar to FIB-4. Regarding detection of advanced fibrosis, SomaSignal (AUC 0·90 [95% CI 0·86-0·94]), ADAPT (0·85 [0·81-0·89]), and FibroScan liver stiffness measurement (0·83 [0·80-0·86]) reached acceptable accuracy. With 11 of 17 markers, histological screen failure rates could be reduced to 33% in trials if only people who were marker positive had a biopsy for evaluating eligibility. The best screening performance for NASH and clinically significant fibrosis was observed for SomaSignal (number needed to test [NNT] to find one true positive was four [95% CI 4-5]), then ADAPT (six [5-7]), MACK-3 (seven [6-8]), and PRO-C3 (nine [7-11]). INTERPRETATION None of the single markers or multimarker scores achieved the predefined acceptable AUC for replacing biopsy in detecting people with both NASH and clinically significant fibrosis. However, several biomarkers could be applied in a prescreening strategy in clinical trial recruitment. The performance of promising markers will be further evaluated in the ongoing prospective LITMUS study cohort. FUNDING The Innovative Medicines Initiative 2 Joint Undertaking.

Published
2023

4. Integrative hepatic transcriptome analysis identifies biomarkers of non-alcoholic steatohepatitis activity and fibrosis progression

Author

Olivier, Govaree, Simon, J Cockell, Dina, Tiniakos, Younes, Ramy, Michele, Vacca, Federico, Ravaioli, Jeremy, Palmer, Salvatore, Petta, Jerome, Boursier, Rosso, Chiara, Khaterine, Johnson, Christopher, P Day, Rebecca, Darlay, Heather, J Cordell, Fabio, Marra, Antonio, Vidal-Puig, Pierre, Bedossa, Jorn, Shattenberg, Karine, Clement, Mike, Allison, Bugianesi, Elisabetta, Vlad, Ratziu, Ann, Daly, and Quentin, M Anstee

Published
2019

6. Enteral stenting in 21 patients with malignant gastroduodenal obstruction

Author

Tjun Tang, Richard J. Dickinson, Phil Roberts, Mike Allison, and Irene Dunkley

Subjects
Adult, medicine.medical_specialty, Nausea, medicine.medical_treatment, Malignancy, Prosthesis, Enteral administration, 030218 nuclear medicine & medical imaging, Prosthesis Implantation, 03 medical and health sciences, 0302 clinical medicine, Stomach Neoplasms, medicine, Humans, Prospective Studies, Aged, Aged, 80 and over, Practice, business.industry, Stent, General Medicine, Middle Aged, medicine.disease, Survival Analysis, Symptomatic relief, Surgery, Pancreatic Neoplasms, Vomiting, Stents, Duodenal Obstruction, medicine.symptom, business, Complication, Intestinal Obstruction
Abstract

Placement of an enteral stent is emerging as an effective alternative to surgery for symptomatic relief in patients with malignant gastric outflow and duodenal obstruction. We report experience in a series of patients who had the Wallstent enteral prosthesis inserted. 21 consecutive patients with inoperable obstruction, median age 75 years, had twenty-three procedures for insertion of the Wallstent under fluoroscopic guidance. Stent implantation was successful in 18 patients (86%), all of whom gained relief from nausea and vomiting. There were no short-term complications. Median survival after the procedure was four months (longest ten months), and no deaths were related to insertion. The efficacy and lack of complications in this series support use of the endoscopically placed stent for palliative management of malignant upper gastrointestinal obstruction.

Published
2003

8. Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: A systematic review and meta-analysis

Author

Emmanuel Anandraj Selvaraj, Ferenc Emil Mózes, Arjun Narayan Ajmer Jayaswal, Mohammad Hadi Zafarmand, Yasaman Vali, Jenny A. Lee, Christina Kim Levick, Liam Arnold Joseph Young, Naaventhan Palaniyappan, Chang-Hai Liu, Guruprasad Padur Aithal, Manuel Romero-Gómez, M. Julia Brosnan, Theresa A. Tuthill, Quentin M. Anstee, Stefan Neubauer, Stephen A. Harrison, Patrick M. Bossuyt, Michael Pavlides, Quentin Anstee, Ann Daly, Katherine Johnson, Olivier Govaere, Simon Cockell, Dina Tiniakos, Pierre Bedossa, Fiona Oakley, Heather Cordell, Chris Day, Kristy Wonders, Patrick Bossuyt, Hadi Zafarmand, Jenny Lee, Vlad Ratziu, Karine Clement, Raluca Pais, Detlef Schuppan, Jörn Schattenberg, Toni Vidal-Puig, Michele Vacca, Sergio Rodrigues-Cuenca, Mike Allison, Ioannis Kamzolas, Evangelia Petsalaki, Matej Oresic, Tuulia Hyötyläinen, Aiden McGlinchey, Jose M. Mato, Oscar Millet, Jean-François Dufour, Annalisa Berzigotti, Stephen Harrison, Jeremy Cobbold, Ferenc Mozes, Salma Akhtar, Rajarshi Banerjee, Matt Kelly, Elizabeth Shumbayawonda, Andrea Dennis, Charlotte Erpicum, Emilio Gómez-González, Javier Ampuero, Javier Castell, Rocío Gallego-Durán, Isabel Fernández, Rocío Montero-Vallejo, Morten Karsdal, Elisabeth Erhardtsen, Daniel Rasmussen, Diana Julie Leeming, Mette Juul Fisker, Antonia Sinisi, Kishwar Musa, Fay Betsou, Estelle Sandt, Manuela Tonini, Elisabetta Bugianesi, Chiara Rosso, Angelo Armandi, Fabio Marra, Amalia Gastaldelli, Gianluca Svegliati, Jérôme Boursier, Sven Francque, Luisa Vonghia, Mattias Ekstedt, Stergios Kechagias, Hannele Yki-Jarvinen, Panu Luukkonen, Saskia van Mil, George Papatheodoridis, Helena Cortez-Pinto, Luca Valenti, Salvatore Petta, Luca Miele, Andreas Geier, Christian Trautwein, Guru Aithal, Paul Hockings, Philip Newsome, David Wenn, Cecília Maria Pereira Rodrigues, Pierre Chaumat, Rémy Hanf, Aldo Trylesinski, Pablo Ortiz, Kevin Duffin, Julia Brosnan, Theresa Tuthill, Euan McLeod, Judith Ertle, Ramy Younes, Rachel Ostroff, Leigh Alexander, Mette Skalshøi Kjær, Lars Friis Mikkelsen, Maria-Magdalena Balp, Clifford Brass, Lori Jennings, Miljen Martic, Juergen Loeffler, Guido Hanauer, Sudha Shankar, Céline Fournier, Kay Pepin, Richard Ehman, Joel Myers, Gideon Ho, Richard Torstenson, Rob Myers, Lynda Doward, LITMUS Investigators, Innovative Medicines Initiative, European Commission, European Federation of Pharmaceutical Industries and Associations, Epidemiology and Data Science, APH - Aging & Later Life, APH - Methodology, ARD - Amsterdam Reproduction and Development, Graduate School, APH - Personalized Medicine, Selvaraj E.A., Mozes F.E., Jayaswal A.N.A., Zafarmand M.H., Vali Y., Lee J.A., Levick C.K., Young L.A.J., Palaniyappan N., Liu C.-H., Aithal G.P., Romero-Gomez M., Brosnan M.J., Tuthill T.A., Anstee Q.M., Neubauer S., Harrison S.A., Bossuyt P.M., Pavlides M., Daly A., Johnson K., Govaere O., Cockell S., Tiniakos D., Bedossa P., Oakley F., Cordell H., Day C., Wonders K., Bossuyt P., Zafarmand H., Lee J., Ratziu V., Clement K., Pais R., Schuppan D., Schattenberg J., Vidal-Puig T., Vacca M., Rodrigues-Cuenca S., Allison M., Kamzolas I., Petsalaki E., Oresic M., Hyotylainen T., McGlinchey A., Mato J.M., Millet O., Dufour J.-F., Berzigotti A., Harrison S., Cobbold J., Mozes F., Akhtar S., Banerjee R., Kelly M., Shumbayawonda E., Dennis A., Erpicum C., Gomez-Gonzalez E., Ampuero J., Castell J., Gallego-Duran R., Fernandez I., Montero-Vallejo R., Karsdal M., Erhardtsen E., Rasmussen D., Leeming D.J., Fisker M.J., Sinisi A., Musa K., Betsou F., Sandt E., Tonini M., Bugianesi E., Rosso C., Armandi A., Marra F., Gastaldelli A., Svegliati G., Boursier J., Francque S., Vonghia L., Ekstedt M., Kechagias S., Yki-Jarvinen H., Luukkonen P., van Mil S., Papatheodoridis G., Cortez-Pinto H., Valenti L., Petta S., Miele L., Geier A., Trautwein C., Aithal G., Hockings P., Newsome P., Wenn D., Pereira Rodrigues C.M., Chaumat P., Hanf R., Trylesinski A., Ortiz P., Duffin K., Brosnan J., Tuthill T., McLeod E., Ertle J., Younes R., Ostroff R., Alexander L., Kjaer M.S., Mikkelsen L.F., Balp M.-M., Brass C., Jennings L., Martic M., Loeffler J., Hanauer G., Shankar S., Fournier C., Pepin K., Ehman R., Myers J., Ho G., Torstenson R., Myers R., and Doward L.

Subjects
0301 basic medicine, FIBROSIS NONINVASIVE ASSESSMENT, Cirrhosis, Transient elastography, deMILI, 0302 clinical medicine, Medicine, BARIATRIC SURGERY CANDIDATES, Non-alcoholic steatohepatitis, medicine.diagnostic_test, NONALCOHOLIC STEATOHEPATITIS, Fatty liver, Magnetic Resonance Imaging, 3. Good health, Area Under Curve, Liver biopsy, Elasticity Imaging Techniques, NASH-MRI, 030211 gastroenterology & hepatology, Bio-markers, Radiology, Elastography, Diffusion-weighted imaging, Life Sciences & Biomedicine, Adult, PREDICTS ADVANCED FIBROSIS, medicine.medical_specialty, Biomarkers, deMILI, Diffusion-weighted imaging, Magnetic resonance elastography, NASH-MRI, Non-alcoholic fatty liver disease, Non-alcoholic steatohepatitis, Shear wave elastography, Transient elastography, Adult,Area Under Curve, Elasticity Imaging Techniques, Humans, Magnetic Resonance Imaging, Non-alcoholic Fatty Liver Disease, ROC Curve, fibro-MRI, Iron-corrected T1, Liver fibrosis, Liver fibrosis, CONTROLLED ATTENUATION PARAMETER, STIFFNESS MEASUREMENT, 03 medical and health sciences, Iron-corrected T1, Humans, FATTY LIVER-DISEASE, Science & Technology, Hepatology, Gastroenterology & Hepatology, business.industry, RADIATION FORCE IMPULSE, Magnetic resonance imaging, medicine.disease, CONTROLLED TRANSIENT ELASTOGRAPHY, Magnetic resonance elastography, 030104 developmental biology, ROC Curve, Shear wave elastography, XL PROBE, Human medicine, fibro-MRI, Steatohepatitis, business, Biomarkers, Non-alcoholic fatty liver disease
Abstract

[Background and Aims] Vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2-dimensional shear wave elastography (2DSWE), magnetic resonance elastography (MRE), and magnetic resonance imaging (MRI) have been proposed as non-invasive tests for patients with non-alcoholic fatty liver disease (NAFLD). This study evaluated their diagnostic accuracy for liver fibrosis and non-alcoholic steatohepatitis (NASH)., [Methods] PubMED/MEDLINE, EMBASE and the Cochrane Library were searched for studies examining the diagnostic accuracy of these index tests, against histology as the reference standard, in adult patients with NAFLD. Two authors independently screened and assessed methodological quality of studies and extracted data. Summary estimates of sensitivity, specificity and area under the curve (sAUC) were calculated for fibrosis stages and NASH, using a random effects bivariate logit-normal model., [Results] We included 82 studies (14,609 patients). Meta-analysis for diagnosing fibrosis stages was possible in 53 VCTE, 11 MRE, 12 pSWE and 4 2DSWE studies, and for diagnosing NASH in 4 MRE studies. sAUC for diagnosis of significant fibrosis were: 0.83 for VCTE, 0.91 for MRE, 0.86 for pSWE and 0.75 for 2DSWE. sAUC for diagnosis of advanced fibrosis were: 0.85 for VCTE, 0.92 for MRE, 0.89 for pSWE and 0.72 for 2DSWE. sAUC for diagnosis of cirrhosis were: 0.89 for VCTE, 0.90 for MRE, 0.90 for pSWE and 0.88 for 2DSWE. MRE had sAUC of 0.83 for diagnosis of NASH. Three (4%) studies reported intention-to-diagnose analyses and 15 (18%) studies reported diagnostic accuracy against pre-specified cut-offs., [Conclusions] When elastography index tests are acquired successfully, they have acceptable diagnostic accuracy for advanced fibrosis and cirrhosis. The potential clinical impact of these index tests cannot be assessed fully as intention-to-diagnose analyses and validation of pre-specified thresholds are lacking., [Lay summary] Non-invasive tests that measure liver stiffness or use magnetic resonance imaging (MRI) have been suggested as alternatives to liver biopsy for assessing the severity of liver scarring (fibrosis) and fatty inflammation (steatohepatitis) in patients with non-alcoholic fatty liver disease (NAFLD). In this study, we summarise the results of previously published studies on how accurately these non-invasive tests can diagnose liver fibrosis and inflammation, using liver biopsy as the reference. We found that some techniques that measure liver stiffness had a good performance for the diagnosis of severe liver scarring., This work has been undertaken as part of the LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis) project. The LITMUS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 777377. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Europen Federation of Pharmaceutical Industries and Associations (efpia.eu).

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