The purpose of this study was to determine whether a relation does exist between clinicopathologic features and the prognosis of hepatocellular carcinoma (HCC) with respect to serum alpha-fetoprotein (AFP) levels at diagnosis. We reviewed the clinical data of 309 pathologically proven HCC cases divided into three groups: group 1 with normal AFP (<20 IU/mL), group 2 with moderately elevated AFP (20-399 IU/mL) and group 3 with markedly elevated AFP (> or =400 IU/mL). Of these, there were 76 (24.6%), 78 (25.2%), and 155 patients (50.2%) in groups 1, 2, and 3, respectively. We found that HCC patients with high AFP tended to have greater tumor size, bilobar involvement, massive or diffuse types, and portal vein thrombosis. Nonetheless, we could not establish a correlation between increased AFP and Okuda's stages, degree of tumor differentiation, or extrahepatic metastasis. The median survival rates in groups 1 (6 months) and 2 (7 months) were significantly longer than that of group 3 (3 months). On multivariate logistic regression analysis, positive hepatitis B surface antigen (HBsAg) status and bilobar tumor involvement represented the independent factors for predicting high AFP values. We concluded that AFP is useful not only for diagnosis, but also as a prognostic indicator in patients with HCC . However, it cannot be considered a sensitive tumor marker, particularly during the early stages in HBsAg-negative patients.

Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3–89.6), 83.9% (95%CI: 80.3–87.4), and 97.1% (95%CI: 96.5–97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7–78.3), 84.9% (95%CI: 81.6–88.2), and 97.1% (95%CI: 96.5–97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3–82.8), 97.8% (95%CI: 96.7–98.9), and 96.5% (95%CI: 95.0–97.9) on the internal test set; and 81.5% (95%CI: 74.2–88.8), 94.4% (95%CI: 92.8–96.0), and 97.4% (95%CI: 96.2–98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted.

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