LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

Abstract Background Presurgical assessment of hepatocellular carcinoma (HCC) aggressiveness can benefit patients’ treatment options and prognosis. Purpose To develop an artificial intelligence (AI) tool, namely, LiSNet, in the task of scoring and interpreting HCC aggressiveness with computed tomography (CT) imaging. Methods A total of 358 patients with HCC undergoing curative liver resection were retrospectively included. Three subspecialists were recruited to pixel‐wise annotate and grade tumor aggressiveness based on CT imaging. LiSNet was trained and validated in 193 and 61 patients with a deep neural network to emulate the diagnostic acumen of subspecialists for staging HCC. The test set comprised 104 independent patients. We subsequently compared LiSNet with an experience‐based binary diagnosis scheme and human–AI partnership that combined binary diagnosis and LiSNet for assessing tumor aggressiveness. We also assessed the efficiency of LiSNet for predicting survival outcomes. Results At the pixel‐wise level, the agreement rate of LiSNet with subspecialists was 0.658 (95% confidence interval [CI]: 0.490–0.779), 0.595 (95% CI: 0.406–0.734), and 0.369 (95% CI: 0.134–0.566), for scoring HCC aggressiveness grades I, II, and III, respectively. Additionally, LiSNet was comparable to subspecialists for predicting histopathological microvascular invasion (area under the curve: LiSNet: 0.668 [95% CI: 0.559–0.776] versus subspecialists: 0.699 [95% CI: 0.591–0.806], p > 0.05). In a human–AI partnered diagnosis, combining LiSNet and experience‐based binary diagnosis can achieve the best predictive ability for microvascular invasion (area under the curve: 0.705 [95% CI: 0.589–0.820]). Furthermore, LiSNet was able to indicate overall survival after surgery. Conclusion The designed LiSNet tool warrants evaluation as an alternative tool for radiologists to conduct automatic staging of HCC aggressiveness at the pixel‐wise level with CT imaging. Its prognostic value might benefit patients’ treatment options and survival prediction.