An Artificial Intelligence Pipeline for Diagnosing Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus

Abstract

Background and purpose: Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. The preoperative diagnosis of BDTT is usually based on identification of dilated bile ducts (DBDs) on medical images (eg., CT and MRI images). However, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high misdiagnosis rate in practice. The aim of the present study was to develop an artificial intelligence (AI) pipeline for diagnosing HCC patients with BDTT using medical images. Methods: The proposed AI pipeline included two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed to have BDTT if the proportion of images with at least one identified DBD exceeds some threshold value. Four-fold cross validation was used to evaluate the performance of the proposed AI pipeline. Results: The proposed AI pipeline was applied on a real dataset consisting of CT images collected from 34 HCC patients (16 with BDTT and 18 without BDTT). The average true positive rate for identifying DBDs per patient was 0.92, while the patient-level true positive rate for diagnosing BDTT was 0.94. The AUC value of patient-level diagnosis of BDTT was 0.92 (95% CI: 0.83, 1.00), compared with 0.71 (95% CI: 0.52, 0.89) by random forest. Conclusions: This study first proposes an AI pipeline to identify DBDs and diagnose BDTT, and the high accuracies demonstrate that it is successful in the diagnosis of BDTT.