A precision-constrained framework for evaluating noninvasive biomarkers in MASLD and beyond.
Authors
Affiliations (7)
Affiliations (7)
- Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Faculty of Neurology, The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health and Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.
- Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. [email protected].
Abstract
In patients with Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD), accurate and low-cost non-invasive risk stratification remains a major unmet need. We developed a clinical records-based neural network integrating patient history, routine laboratory tests, and ultrasound imaging features. Here we show that in an internal test set (n = 209), the model achieved receiver operating characteristic area under the curve (ROC-AUC) values of 0.85 vs 0.82 (F ≥2), 0.90 vs 0.86 (F ≥3), 0.96 vs 0.89 (F = 4) compared with Fibrosis-4 (FIB-4). To address limitations of ROC-AUC, we applied RP-AUC<sub>0.5-0.7</sub>, a recall-precision metric focused on clinically relevant precision range, showing improved performance over FIB-4. External validation (n = 194) shows reduced liver biopsy failure rate from 86.6% to 50.0% for at-risk metabolic dysfunction-associated steatohepatitis (MASH) prediction. Our work presents a low-cost neural network improving FIB-4, introduces RP-AUC<sub>0.5-0.7</sub> for biomarker comparison, and provides a generalizable framework for evaluating screening biomarkers in clinical care and drug trials.