DIFFERENTIAL DIAGNOSIS OF MALIGNANT HEPATIC TUMORS USING CLINICAL INFORMATION AND MULTI-PHASE CONTRAST-ENHANCED CT

Abstract

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death in the world according to 2020 global cancer statistics [1]. A substantial number of malignant liver tumors are primary tumors, including HCC and ICC [2]. In clinical settings, the metastasis of tumors to the liver is also frequently encountered [3]. The treatment regimen for the different subtypes of hepatic tumors is all distinct [4], and multi-phase CECT has become the primary tool for diagnosis of hepatic tumors before surgery [5]. However, the differential diagnosis of malignant hepatic tumors is challenging, and misdiagnosis prior to surgery can mislead the treatment decision. An automated diagnostic model is desirable to be developed, which can assist doctors in hepatic tumors diagnosis, reduce observer variations and improve diagnostic efficiency. Few preliminary studies utilized deep learning to differentiate hepatic tumors [6,7,8,9], but they lacked detailed classification for malignant hepatic tumors, especially for ICC. Herein, we proposed a novel deep learning model, which was specifically customized for the differential diagnosis of malignant hepatic tumors based on patients’ preoperative multi-phase CECT and clinical features. All 723 patients enrolled in our study were pathologically confirmed with one of the following malignant hepatic tumors: HCC, ICC and metastatic liver cancer (Fig. 1A). The training and test sets were split, with 499 and 113 patients from center 1, respectively.