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

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.