Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types

Jiahui Zhang; Xiaoli Wang; Lixia Zhang; Linpeng Yao; Xing Xue; Siying Zhang; Xin Li; Yuanjun Chen; Peipei Pang; Dongdong Sun; Juan Xu; Yanjun Shi; Feng Chen

doi:10.21037/atm-19-4668

Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types

Ann Transl Med. 2020 Jul;8(13):820. doi: 10.21037/atm-19-4668.

Authors

Jiahui Zhang^{1

2}, Xiaoli Wang¹, Lixia Zhang¹, Linpeng Yao¹, Xing Xue¹, Siying Zhang¹, Xin Li³, Yuanjun Chen³, Peipei Pang³, Dongdong Sun⁴, Juan Xu⁴, Yanjun Shi⁵, Feng Chen¹

Affiliations

¹ Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
² Department of Radiology, Hangzhou Third Hospital, Hangzhou, China.
³ GE China Medical Life Sciences Division Core Image Senior Application Team, Guangzhou, China.
⁴ Medical Big Data, AliHealth, Hangzhou, China.
⁵ Department of Hepatobiliary and Pancreas Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Abstract

Background: Radiomics can be used to determine the prognosis of liver cancer, but it might vary among cancer types. This study aimed to explore the clinicopathological features, radiomics, and survival of patients with hepatocellular carcinoma (HCC), mass-type cholangiocarcinoma (MCC), and combined hepatocellular-cholangiocarcinoma (CHCC).

Methods: This was a retrospective cohort study of patients with primary liver cancer operated at the department of hepatobiliary surgery of the First Affiliated Hospital of Zhejiang University from 07/2013 to 11/2015. All patients underwent preoperative liver enhanced MRI scans and diffusion-weighted imaging (DWI). The radiomics characteristics of DWI and the enhanced equilibrium phase (EP) images were extracted. The mRMR (minimum redundancy maximum relevance) was applied to filter the parameters.

Results: There were 44 patients with MCC, 59 with HCC, and 33 with CHCC. Macrovascular invasion, tumor diameter, positive ferritin preoperatively, positive AFP preoperatively, positive CEA preoperatively, Correlation, Inverse Difference Moment, and Cluster Prominence in model A (DWI and clinicopathological parameters) were independently associated with overall survival (OS) (P<0.05). Lymphadenopathy, gender, positive ferritin preoperatively, positive AFP preoperatively, positive CEA preoperatively, Uniformity, and Cluster Prominence in model B (EP and clinicopathological parameters) were independently associated with OS (P<0.05). Macrovascular invasion, lymphadenopathy, gender, positive ferritin preoperatively, positive CEA preoperatively, Uniformity_EP, GLCMEnergy_DWI, and Cluster Prominence_EP in model C (image texture and clinicopathological parameters) were independently associated with OS (P<0.05). Those factors were used to construct three nomograms to predict OS.

Conclusions: Clinicopathological and radiomics features are independently associated with the OS of patients with primary liver cancer.

Keywords: Hepatocellular carcinoma (HCC); cholangiocarcinoma; magnetic resonance imaging (MRI); postoperative survival; radiomics.