Translational tumor control probability modeling for NSCLC: A two-dimensional maximum-likelihood framework.

backgroundTumor control probability (TCP) modeling for early-stage non-small cell lung cancer (NSCLC) is usually performed by one-dimensional (1D) fitting that assumes error-free dose and assigns all uncertainty to the local control (LC) axis. Neglecting dose-axis uncertainties leads to a classical errors-in-variables (EIV) problem that can bias both the dose required for 50% TCP ( PURPOSE: To develop a mechanistic TCP framework that (i) mitigates EIV bias by explicitly modeling uncertainties in both dose and LC, (ii) decomposes TCP steepness into contributions from clonogenic heterogeneity and inter-patient radiosensitivity, and (iii) validates the translational consistency.

methodsCell-survival data from A549 NSCLC cells were fitted with three radiobiological models-saturable repair and repairability (SRR), universal survival curve (USC), and linear-quadratic (LQ)-using maximum-likelihood estimation. Clinical TCP modeling used 34 early-stage NSCLC studies (41 data points, 3-year LC). Prescriptions were converted to EQD2 using each model's formalism, and TCP curves were fitted by a two-dimensional (2D) maximum-likelihood framework. The LC-axis uncertainty was constructed from binomial sampling uncertainty and an intrinsic scatter, the latter co-estimated with the effective dose-axis uncertainty. TCP steepness was measured by the inter-patient variability

resultsIn cell-survival fitting, the SRR and USC models accurately reproduced A549 survival (AIC =

conclusionsThe proposed 2D maximum-likelihood framework mitigates EIV bias and links clinical TCP to mechanistic cell-survival models. In early-stage NSCLC, inter-patient radiosensitivity dominates TCP steepness while clonogenic cell-number variability contributes negligibly. This framework provides a robust basis for future TCP modeling and dose-prescription studies.