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<p>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 (<i>n</i>&#xa0;=&#xa0;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 &#xa0;=&#xa0;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 (<i>n</i>&#xa0;=&#xa0;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.</p>

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A precision-constrained framework for evaluating noninvasive biomarkers in MASLD and beyond

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

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  • Guangyi Zhang,
  • Xiaohong Wang,
  • Arinc Ozturk,
  • Eugene Cheah,
  • Peng Guo,
  • Marian Martin,
  • Angela Shih,
  • Atul K. Bhan,
  • Nancy Obuchowski,
  • Amon Asgharpour,
  • Arun J. Sanyal,
  • Brian A. Telfer,
  • Theodore T. Pierce,
  • Anthony E. Samir

摘要

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-AUC0.5−0.7, 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-AUC0.5−0.7 for biomarker comparison, and provides a generalizable framework for evaluating screening biomarkers in clinical care and drug trials.