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A "Patient-Like" Orthotopic Syngeneic Mouse Model of Hepatocellular Carcinoma Metastasis
Published on: October 24, 2015
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Dynamically prognosticating patients with hepatocellular carcinoma through survival paths mapping based on
Lujun Shen1,2, Qi Zeng3, Pi Guo4
1Department of Minimally Invasive Interventional Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, Guangdong, China.
Nature Communications
|June 10, 2018
Summary
A new model predicts hepatocellular carcinoma (HCC) survival using time-series data, creating a survival map for better dynamic prognosis and treatment planning in HCC patients.
Area of Science:
- Oncology
- Medical Informatics
- Biostatistics
Background:
- Hepatocellular carcinoma (HCC) management generates extensive clinical data.
- Dynamic prognosis prediction and treatment planning for HCC require advanced analytical models.
Purpose of the Study:
- To develop a novel analytical approach for dynamic prognosis prediction in HCC.
- To create a predictive model utilizing time-series clinical data for HCC patients.
Main Methods:
- Converted time-series patient data into a cascading survival map.
- Employed Cox-based feature selection to identify prognostic factors.
- Applied the model to a database of BCLC stage B HCC patients.
Main Results:
- Generated a survival map with 13 distinct survival paths.
- Demonstrated superior or equal predictive value compared to conventional staging systems.
- Validated performance across derivation, internal testing, and multicentric testing cohorts for 3-12 month predictions.
Conclusions:
- The cascading survival map methodology offers improved dynamic prognosis prediction for HCC.
- This approach can aid in personalized treatment planning for HCC patients.
- The model shows potential for future clinical application in HCC management.
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