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. 2023 Jul 1;78(1):258-271.
doi: 10.1097/HEP.0000000000000364. Epub 2023 Mar 31.

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

Jenny Lee  1 , Max Westphal  2 , Yasaman Vali  1 , Jerome Boursier  3 , Salvatorre Petta  4 , Rachel Ostroff  5 , Leigh Alexander  5 , Yu Chen  6 , Celine Fournier  7 , Andreas Geier  8 , Sven Francque  9 , Kristy Wonders  10 , Dina Tiniakos  10   11 , Pierre Bedossa  10 , Mike Allison  12 , Georgios Papatheodoridis  13 , Helena Cortez-Pinto  14 , Raluca Pais  15 , Jean-Francois Dufour  16 , Diana Julie Leeming  17 , Stephen Harrison  18 , Jeremy Cobbold  18 , Adriaan G Holleboom  19 , Hannele Yki-Järvinen  20 , Javier Crespo  21 , Mattias Ekstedt  22 , Guruprasad P Aithal  23 , Elisabetta Bugianesi  24 , Manuel Romero-Gomez  25 , Richard Torstenson  26 , Morten Karsdal  17 , Carla Yunis  27 , Jörn M Schattenberg  28 , Detlef Schuppan  29   30 , Vlad Ratziu  15 , Clifford Brass  31 , Kevin Duffin  6 , Koos Zwinderman  1 , Michael Pavlides  32 , Quentin M Anstee  9   33 , Patrick M Bossuyt  1 ; LITMUS investigators
Collaborators, Affiliations
Free article

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

Jenny Lee et al. Hepatology. .
Free article

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.

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