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

• DOI: 10.1038/s41467-018-04633-7
• Publication Date: 2018-06-04T09:34:52Z
• Authors: Lujun Shen; Qi Zeng; Pi Guo; Jingjun Huang; Chaofeng Li; Tao Pan; Boyang Chang; Nan Wu; Lewei Yang; Qifeng Chen; Tao Huang; Wang Li; Peihong Wu
• Keywords: Adult; Aged, 80 and over; Male; 0301 basic medicine; Carcinoma, Hepatocellular; Time Factors; Adolescent; Science; Q; Liver Neoplasms; Kaplan-Meier Estimate; Middle Aged; Prognosis; Article; Young Adult; 03 medical and health sciences; Outcome Assessment, Health Care; Humans; Female; Aged; Proportional Hazards Models; Retrospective Studies

AbstractPatients 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.