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<p>Early detection of focal liver lesions is critical for patient outcomes, but current imaging techniques have limitations. This study developed MULLET, an AI system using contrast-enhanced CT, and assessed its performance in assisting radiologists with FLL detection and characterization. A retrospective multicenter, multi-reader, multi-case trial was conducted, in which 10 radiologists independently interpreted 375 patients’ clinical images with and without MULLET assistance. This trial was registered on <a href="https://clinicaltrials.gov/">ClinicalTrials.gov</a> (ID: NCT06068413) with the registration name “A Retrospective, Multicenter, Multiple-viewer-multiple-case (MRMC) Clinical Trial to Evaluate the Safety and Efficacy of CT Image-assisted Detection Software for Focal Liver Lesions” in April 2023. Diagnostic performance was compared using area under the receiver operating characteristic curve (AUC) analysis. Without MULLET, the average AUC was 0.8188 (sensitivity: 74.45%, specificity: 87.77%). With MULLET, the average AUC significantly improved to 0.9268 (<i>p</i> &lt; 0.0001), sensitivity increased to 89.95% (<i>p</i> = 0.0003), and specificity rose to 93.57% (<i>p</i> = 0.0063). MULLET also showed improved performance across different FLL sizes and subtypes. Moreover, the average reading time for radiologists was reduced by 21.8% (<i>p</i> &lt; 0.0001). In conclusion, MULLET demonstrated promising performance in significantly improving radiologists’ sensitivity, accuracy, and efficiency in FLL detection and diagnosis.</p><p></p>

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Assessing the Impact of MULLET AI System on Focal Liver Lesion Detection: A Multicenter, Multi-reader, Multi-case Clinical Trial

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

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  • Zhongquan Sun,
  • Yining Chen,
  • Hongfan Ding,
  • Ying Chen,
  • Haoze Cao,
  • Xin Han,
  • Wanyi Chen,
  • Lei Wu,
  • Feng Fang,
  • Fan Wu,
  • Lianyue Yang,
  • Jiajun Bu,
  • Yuan Ding,
  • Weilin Wang

摘要

Early detection of focal liver lesions is critical for patient outcomes, but current imaging techniques have limitations. This study developed MULLET, an AI system using contrast-enhanced CT, and assessed its performance in assisting radiologists with FLL detection and characterization. A retrospective multicenter, multi-reader, multi-case trial was conducted, in which 10 radiologists independently interpreted 375 patients’ clinical images with and without MULLET assistance. This trial was registered on ClinicalTrials.gov (ID: NCT06068413) with the registration name “A Retrospective, Multicenter, Multiple-viewer-multiple-case (MRMC) Clinical Trial to Evaluate the Safety and Efficacy of CT Image-assisted Detection Software for Focal Liver Lesions” in April 2023. Diagnostic performance was compared using area under the receiver operating characteristic curve (AUC) analysis. Without MULLET, the average AUC was 0.8188 (sensitivity: 74.45%, specificity: 87.77%). With MULLET, the average AUC significantly improved to 0.9268 (p < 0.0001), sensitivity increased to 89.95% (p = 0.0003), and specificity rose to 93.57% (p = 0.0063). MULLET also showed improved performance across different FLL sizes and subtypes. Moreover, the average reading time for radiologists was reduced by 21.8% (p < 0.0001). In conclusion, MULLET demonstrated promising performance in significantly improving radiologists’ sensitivity, accuracy, and efficiency in FLL detection and diagnosis.