Computed tomography of the abdomen exhibits subtle and complex features of liver lesions, subjectively interpreted by physicians. We developed a deep learning-based localization and classification (DLLC) system for focal liver lesions (FLLs) in computed tomography imaging that could assist physicians in more robust clinical decision-making. We conducted a retrospective study (approval no. EMRP-109-058) on 1589 patients with 17335 slices with 3195 FLLs using data from January 2004 to December 2020. The training set included 1272 patients (male: 776, mean age 62±10.9), and the test set included 317 patients (male: 228, mean age 57±11.8). The slices were annotated by annotators with different experience levels, and the DLLC system was developed using generative adversarial networks for data augmentation. A comparative analysis was performed for the DLLC system versus physicians using external data. Our DLLC system demonstrated mean average precision at 0.81 for localization. The system's overall accuracy for multiclass classifications was 0.97 (95% confidence interval [CI]: 0.95-0.99). Considering FLLs≤3cm, the system achieved an accuracy of 0.83 (95% CI: 0.68-0.98), and for size>3cm, the accuracy was 0.87 (95% CI: 0.77-0.97) for localization. Furthermore, during classification, the accuracy was 0.95 (95% CI: 0.92-0.98) for FLLs≤3cm and 0.97 (95% CI: 0.94-1.00) for FLLs>3cm. This system can provide an accurate and non-invasive method for diagnosing liver conditions, making it a valuable tool for hepatologists and radiologists.
Read full abstract