Fully automated multi-sequence detection and alignment of focal liver lesions in dynamic contrast-enhanced MRI.

OBJECTIVES: To validate an artificial intelligence (AI) method for fully automated detection and alignment of focal liver lesions (FLLs) in multi-sequence dynamic contrast-enhanced MRI (DCE-MRI). MATERIALS AND METHODS: Retrospective patient data from three hospitals were included from February 2020 to August 2022. A multi-reader, multi-case analysis was conducted, using the AI-assisted senior radiologists' detection results as the reference. The performance of AI, radiologists, and AI-assisted radiologists in detecting FLLs was analyzed at the lesion and patient levels. The senior radiologists validated the AI detection results for the same lesion across the nine different DCE-MRI sequences. The subgroup analyses evaluated detection sensitivity based on lesion size (< 20 mm vs ≥ 20 mm) and lesion number (1, 2-5, and ≥ 6 lesions). RESULTS: A total of 477 patients (median age 59 years, IQR 48-68 years) were included. The AI correctly detected 1532 FLLs with sensitivities of 0.990 (95% CI: 0.984-0.994) and 0.997 (0.985-1.000) at the lesion and patient levels, respectively. Radiologists showed detection sensitivities of 0.607 (0.581-0.631) and 0.920 (0.886-0.943), respectively. The AI-assisted radiologists significantly improved detection sensitivity from 0.607 (0.581-0.631) to 0.718 (0.695-0.740) at the lesion level (p < 0.001) and achieved an accuracy of 0.904 (0.875-0.930) at the patient level. Across nine DCE-MRI sequences, 1395/1532 (91.1%) correctly detected lesions were correctly aligned. The AI performed well, with detection sensitivity consistently exceeding 0.982 in all subgroups of lesion size and number. CONCLUSION: AI enables fully automated detection and alignment of FLLs in DCE-MRI across nine MRI sequences. KEY POINTS: Question Current manual reading of DCE-MRI to detect FLLs is time-consuming and error-prone. Findings The AI system outperformed radiologists in detecting FLLs and improved the sensitivity of radiologists while maintaining precise cross-sequence alignment. Clinical relevance AI can assist radiologists in improving FLLs detection on DCE-MRI, demonstrating high alignment capability across nine sequences. Since AI has exhibited strong robustness in detecting and displaying FLLs, it may serve as a valuable tool for radiologists in reading DCE-MRI.