Mechanistic modeling of metastasis for prediction of overall survival in neuroblastoma

This code generates the results and figures reported in the paper:
Benzekry, S., Sentis, C., Coze, C., Tessonnier, L., & André, N. (2020). Development and Validation of a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis. JCO: Clinical Cancer Informatics, Vol 5, pp. 81-90. https://ascopubs.org/doi/full/10.1200/CCI.20.00092

Specifically, the code is composed of python and matlab scripts and jupyter notebooks (in python and R) and performs:

• Statistical analysis of prognosis factors of overall and progression-free survival (Kaplan-Meier, log-rank, Cox regression)

code/statistical_analysis.ipynb

with results exported in statistical_analysis/

• Simulation of a mechanistic model of metastasis

code/main_simulate.m

• Calibration of the model parameters from quantitative clinical data at diagnosis: primary tumor size, lactate dehydrogenase (LDH) and SIOPEN score from nuclear imaging

code/mechanistic.ipynb

with results exported in mechanistic/

• Assessment of the predictive power of patient-specific Cox regression-based models for overall survival

code/mechanistic.ipynb

and

code/cox_r_calibration_plot.ipynb

with results exported in cox_regression (clinical data alone) and mechanistic/cox_regression (clinical data + mathematical parameters).

The data used for the analysis is available in data/

The files generating figures and supplementary are available in manuscript/

Previous preprint: Descriptive and prognostic value of a computational model of metastasis in high-risk neuroblastoma Sebastien Benzekry, Coline Sentis, Carole Coze, Laetitia Tessonnier, Nicolas Andre medRxiv 2020.03.26.20042192; doi:https://doi.org/10.1101/2020.03.26.20042192