Authors
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
Published in
NPJ precision oncology. Jul 09, 2026. Epub Jul 09, 2026.
Abstract
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.
PMID:
42426202
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.
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