Prediction of Treatment Response for Combined Chemo- and Radiation Therapy for Non-Small Cell Lung Cancer Patients Using a Bio-Mathematical Model
By C. Geng, Harald Paganetti, C. Grassberger. 2017
112 citations recorded by the source corpus.
The goal of this work was to develop a mathematical model to predict Kaplan–Meier survival curves for chemotherapy combined with radiation in Non-Small Cell Lung Cancer patients for use in clinical trial design. The Gompertz model was used to describe tumor growth, radiation effect was simulated by the linear-quadratic model with an α/β-ratio of 10, and chemotherapy effect was based on the log-cell kill model. To account for repopulation during treatment, we considered two independent methods: 1) kickoff-repopulation using exponential growth with a decreased volume doubling time, or 2) Gompertz-repopulation using the gradually accelerating growth rate with tumor shrinkage. The input parameters were independently estimated by fitting to the SEER database for untreated tumors, RTOG-8808 for radiation only, and RTOG-9410 for sequential chemo-radiation. Applying the model, the benefit from concurrent chemo-radiation comparing to sequential for stage III patients was predicted to be a 6.6% and 6.2% improvement in overall survival for 3 and 5-years respectively, comparing well to the 5.3% and 4.5% observed in RTOG-9410. In summary, a mathematical model was developed to model tumor growth over extended periods of time, and can be used for the optimization of combined chemo-radiation scheduling and sequencing.
Subfields: Cancer Research, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “We perform experiments on a simulated dataset where we can control the amount of hidden confounding applied and a state-of-theart pharmacokinetic-pharmacodynamic (PK-PD) model of tumour growth (Geng et al., 2017) and show how the Time Series Deconfounder allows us to deconfound the estimation of tre”
- Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “The volume of tumour t days after diagnosis is modelled as follows: where K, ρ, β c , α r , β r , e t are sampled as described in Geng et al. (2017).”
- Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks (2018, methodology): “joint prescriptions of chemotherapy and radiotherapy [12]).”
- From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, methodology): “83 This PK/PD model has been used to obtain counterfactual outcomes and thus evaluate causal inference methods for estimating treatment effects over time.”