Abstract:

Objective: To evaluate whether the differences within intra-observer and inter-observer can be reduced by the assisted segmentation based on artificial intelligence algorithm fordetection of liver metastasis from gastric cancer. Methods: Thirty two patients with liver metastasis from gastric cancer were retrospectively analyzed. Two radiologists blindly applied the Liver Lesion Analysis Tool (Siemens Healthineers, not for commercial use) for performing repeated measurement of the longest diameter and three-dimensional volume of liver lesions in CT images in a semi-automatic mode and by the manual mode. The measurements were repeated by one of the two radiologist after two weeks. The Bland-Altman method was used to evaluate the intra-observer and inter-observer differences. The intra-group correlation coefficient(ICC) was used to estimate the difference in variability. Results: In intra-observer analysis, the 95% consistency interval of the manual and semi-automatic longest diameter measurements were -31.70% -34.55% and -28.04% -27.89%, respectively. The 95% consistency interval ininter-observer of manual and semi-automatic longest diameter measurements were -74.26% -38.85% and -59.54% -40.98%, respectively. The 95% consistency interval inintra-observer of the manual and semi-automatic volume measurements were -148.10% -102.70% and -75.92% -63.79%, respectively, and in inter-observer were -127.40% -111.50% and -87.66% -43.77%, respectively. The ICC of the manual longest diameter measurement and the volume measurement variability inintra-observer were 0.914 and 0.950, respectively (P<0.001), and those in semi-automatic mode were 0.967 and 0.970, respectively (P<0.001). The ICC of the longest diameter and the volume measurement variability in inter-observer were 0.884 and 0.939, respectively (P<0.001), and those in semi-automatic were 0.928 and 0.974, respectively (P<0.001). The time required for semi-automatic mode was significantly shorter than that required for manual mode[（25.78±4.23） s vs （4.53±2.82） s, P<0.01]. Conclusions: The semi-automatic segmentation of liver metastasis using artificial intelligence-assisted automatic segmentation software is reproducible and can improve the efficiency of segmentation of liver metastases. The AI-assisted liver analysis tool has the potential to be a quantitative tool for clinical follow-up and treatment evaluation.

Key words: Gastric cancer, Liver metastases, Longest diameter measurement, Volume measurement, Computed tomography, Reproducibility