Dynamically prognosticating patients with hepatocellular carcinoma through survival paths mapping based on time-series data

Lujun Shen, Qi Zeng, Pi Guo, Jingjun Huang, Chaofeng Li, Tao Pan, Boyang Chang, Nan Wu, Lewei Yang, Qifeng Chen, Tao Huang, Wang Li () and Peihong Wu ()
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Lujun Shen: Sun Yat-sen University Cancer Center
Qi Zeng: Fifth Affiliated Hospital of Sun Yat-sen University
Pi Guo: Shantou University Medical College
Jingjun Huang: Second Affiliated Hospital of Guangzhou Medical University
Chaofeng Li: Collaborative Innovation Center for Cancer Medicine
Tao Pan: Third Affiliated Hospital of Sun Yat-sen University
Boyang Chang: Sun Yat-sen University Cancer Center
Nan Wu: Memorial University of Newfoundland
Lewei Yang: Fifth Affiliated Hospital of Sun Yat-sen University
Qifeng Chen: Sun Yat-sen University Cancer Center
Tao Huang: Sun Yat-sen University Cancer Center
Wang Li: Sun Yat-sen University Cancer Center
Peihong Wu: Sun Yat-sen University Cancer Center

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract Patients with hepatocellular carcinoma (HCC) always require routine surveillance and repeated treatment, which leads to accumulation of huge amount of clinical data. A predictive model utilizes the time-series data to facilitate dynamic prognosis prediction and treatment planning is warranted. Here we introduced an analytical approach, which converts the time-series data into a cascading survival map, in which each survival path bifurcates at fixed time interval depending on selected prognostic features by the Cox-based feature selection. We apply this approach in an intermediate-scale database of patients with BCLC stage B HCC and get a survival map consisting of 13 different survival paths, which is demonstrated to have superior or equal value than conventional staging systems in dynamic prognosis prediction from 3 to 12 months after initial diagnosis in derivation, internal testing, and multicentric testing cohorts. This methodology/model could facilitate dynamic prognosis prediction and treatment planning for patients with HCC in the future.

Date: 2018
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DOI: 10.1038/s41467-018-04633-7

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