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

• Published in: Nature communications 2018-06, Vol.9 (1), p.2230-10, Article 2230
• Main Authors: Shen, Lujun, Zeng, Qi, Guo, Pi, Huang, Jingjun, Li, Chaofeng, Pan, Tao, Chang, Boyang, Wu, Nan, Yang, Lewei, Chen, Qifeng, Huang, Tao, Li, Wang, Wu, Peihong
• Format: Article
• Language: English
• Subjects: 631/67/1504/1610; 631/67/70; Adolescent; Adult; Aged; Aged, 80 and over; Bifurcations; Carcinoma, Hepatocellular - pathology; Carcinoma, Hepatocellular - therapy; Female; Hepatocellular carcinoma; Humanities and Social Sciences; Humans; Kaplan-Meier Estimate; Liver cancer; Liver Neoplasms - pathology; Liver Neoplasms - therapy; Male; Medical prognosis; Middle Aged; multidisciplinary; Outcome Assessment (Health Care) - methods; Outcome Assessment (Health Care) - statistics & numerical data; Patients; Prediction models; Prognosis; Proportional Hazards Models; Retrospective Studies; Science; Science (multidisciplinary); Survival; Test procedures; Time Factors; Time series; Young Adult
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-04633-7

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. Patients with hepatocellular carcinoma require regular follow-up. Here, using Cox-based feature selection to identify key prognostic features, the authors convert time-series follow-up data into a cascading survival map, and show that the approach improves dynamic prognosis prediction for patients.